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What Is Conjoint Analysis & How Can You Use It?

• 18 Dec 2020

For a business to run effectively, its leadership needs a firm understanding of the value its products or services bring to consumers. This understanding allows for a more informed strategy across the board—from long-term planning to pricing and sales.

In today’s business environment, most products and services include multiple features and functions by default. So, how do businesses go about learning which ones their customers value most? Is it possible to assign a specific value to each feature a product offers?

This is where conjoint analysis becomes an essential tool.

Here’s an overview of conjoint analysis, why it’s important, and steps you can take to analyze your products or services.

Access your free e-book today.

What Is Conjoint Analysis?

Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It’s based on the principle that any product can be broken down into a set of attributes that ultimately impact users’ perceived value of an item or service.

Conjoint analysis is typically conducted via a specialized survey that asks consumers to rank the importance of the specific features in question. Analyzing the results allows the firm to then assign a value to each one.

Learn about conjoint analysis in the video below, and subscribe to our YouTube channel for more explainer content!

Types of Conjoint Analysis

Conjoint analysis can take various forms. Some of the most common include:

• Choice-Based Conjoint (CBC) Analysis: This is one of the most common forms of conjoint analysis and is used to identify how a respondent values combinations of features.
• Adaptive Conjoint Analysis (ACA): This form of analysis customizes each respondent's survey experience based on their answers to early questions. It’s often leveraged in studies where several features or attributes are being evaluated to streamline the process and extract the most valuable insights from each respondent.
• Full-Profile Conjoint Analysis: This form of analysis presents the respondent with a series of full product descriptions and asks them to select the one they’d be most inclined to buy.
• MaxDiff Conjoint Analysis: This form of analysis presents multiple options to the respondent, which they’re asked to organize on a scale of “best” to “worst” (or “most likely to buy” to “least likely to buy”).

The type of conjoint analysis a company uses is determined by the goals driving its analysis (i.e., what does it hope to learn?) and, potentially, the type of product or service being evaluated. It’s possible to combine multiple conjoint analysis types into “hybrid models” to take advantage of the benefits of each.

What Is Conjoint Analysis Used For?

The insights a company gleans from conjoint analysis of its product features can be leveraged in several ways. Most often, conjoint analysis impacts pricing strategy, sales and marketing efforts, and research and development plans.

Conjoint Analysis in Pricing

Conjoint analysis works by asking users to directly compare different features to determine how they value each one. When a company understands how its customers value its products or services’ features, it can use the information to develop its pricing strategy.

For example, a software company hoping to take advantage of network effects to scale its business might pursue a “freemium” model wherein its users access its product at no charge. If the company determines through conjoint analysis that its users highly value one feature above the others, it might choose to place that feature behind a paywall.

As such, conjoint analysis is an excellent means of understanding what product attributes determine a customer’s willingness to pay . It’s a method of learning what features a customer is willing to pay for and whether they’d be willing to pay more.

Conjoint Analysis in Sales & Marketing

Conjoint analysis can inform more than just a company’s pricing strategy; it can also inform how it markets and sells its offerings. When a company knows which features its customers value most, it can lean into them in its advertisements, marketing copy, and promotions.

On the other hand, a company may find that its customers aren’t uniform in assigning value to different features. In such a case, conjoint analysis can be a powerful means of segmenting customers based on their interests and how they value features—allowing for more targeted communication.

For example, an online store selling chocolate may find through conjoint analysis that its customers primarily value two features: Quality and the fact that a portion of each sale goes toward funding environmental sustainability efforts. The company can then use that information to send different messaging and appeal to each segment's specific value.

Conjoint Analysis in Research & Development

Conjoint analysis can also inform a company’s research and development pipeline. The insights gleaned can help determine which new features are added to its products or services, along with whether there’s enough market demand for an entirely new product.

For example, consider a smartphone manufacturer that conducts a conjoint analysis and discovers its customers value larger screens over all other features. With this information, the company might logically conclude that the best use of its product development budget and resources would be to develop larger screens. If, however, future analyses reveal that customer value has shifted to a different feature—for example, audio quality—the company may use that information to pivot its product development plans.

Additionally, a company may use conjoint analysis to narrow down its product or service’s features. Returning to the smartphone example: There’s only so much space within a smartphone for components. How a phone manufacturer’s customers value different features can inform which components make it into the end product—and which are cut.

One example is Apple’s 2016 decision to remove the headphone jack from the iPhone to free up space for other components. It’s reasonable to assume this decision was reached after analysis revealed that customers valued other features above a headphone jack.

Leveraging Conjoint Analysis for Your Business

Conjoint analysis is an incredibly useful tool you can leverage at your company. By using it to understand which product or service features your customers value over others, you can make more informed decisions about pricing, product development, and sales and marketing activities.

Are you interested in learning more about how customers perceive and realize value from the products they buy, and how you can use that information to better inform your business? Explore Economics for Managers — one of our online strategy courses —and download our free e-book on how to formulate a successful business strategy.

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What is Conjoint Analysis?

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences, which is useful when a company wants to:

• Select product features.
• Assess consumers’ sensitivity to price changes.
• Forecast its volumes and market share.
• Predict adoption of new products or services.

Conjoint analysis is frequently used across different industries for all types of products, such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel. It is applicable in various instances that centre around discovering what type of product consumers are likely to buy and what consumers value the most (and least) about a product. As such, it is a familiar tool for marketers, product managers, and pricing specialists.

Businesses of all sizes can benefit from conjoint analysis, including even local grocery stores and restaurants — and its scope is not just limited to consumer contexts, for example, charities can use conjoint analysis’ techniques to find out donor preferences, while HR departments can use it to build optimal compensation packages .

How does conjoint analysis work?

Conjoint analysis works by breaking a product or service down into its components ( attributes and levels ) and testing different combinations of these components to identify consumer preferences .

For example, consider a conjoint study on smartphones. The smartphone is broken down into four attributes which are each assigned different possible variations to create levels:

Each choice task then presents a respondent with different possible smartphones, each created by combining different levels for each attribute:

Going further than simply asking respondents what they like in a product, or what features they find most important, conjoint analysis employs a more realistic approach: asking each respondent to choose between potential product concepts (or alternatives) formed through the combination of attributes and levels. These combinations are carefully assembled into choice sets (or questions). Each respondent is typically presented with 8 to 12 questions . The process of assembling attributes and levels into product concepts and then into choice sets is called experimental design and requires extensive statistical and mathematical analysis (done automatically by Conjointly or manually by researchers).

Using survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a “ preference score ” (AKA “partworth utility” or “utility score”). The below example shows preference scores for attributes and levels of a mobile phone plan.

Preference scores are used to build simulators that forecast market shares for a set of different products offered to the market. By using the simulator to model (i.e. simulate ) respondents’ decisions, we can identify the specific features and pricing that balance value to the customer with cost to the company and forecast potential demand in a competitive market situation. The below example shows how different data amounts in a mobile plan will affect a company’s market share.

Consider you are launching a new product and wish to address several research questions. Through the below example, we demonstrate how various outputs from your Conjointly survey report can be used to gain insights.

• It is also possible to perform clustering based on raw conjoint utilities .

Why do conjoint analysis with Conjointly?

Conjointly automates the often complicated experimental design process using state-of-the-art methodology. This gives you control over specific settings , such as the number of concepts per choice set and the number of choice sets per respondent when you set up a conjoint analysis experiment. Respondents then complete the choice tasks within the conjoint survey – this typically requires a few hundred responses but may vary depending on the complexity of the study.

Once we’ve gathered the recommended sample size of respondents, Conjointly produces a survey report which contains several in-depth outputs. The outputs of Brand Specific Conjoint , Generic Conjoint , and Brand-Price Trade-Off include estimates of respondents’ preferences, overall sample profile, segmentation and interactive simulations. Conjointly estimates and charts preference shares, revenue projections, and price elasticity using simulators.

There are many types/flavours of conjoint analysis , classified by response type, questioning approach, design type, and adaptivity of the design. All flavours of conjoint analysis have the same basics but not all are as effective as others. That’s why Conjointly offers two key conjoint designs, called generic and brand-specific, and uses the most tested, developed, and theoretically sound response type – choice-based conjoint analysis (CBC). CBC’s predictive power far surpasses its alternatives , such as SIMALTO and self-explicated conjoint, making it the ideal choice for your next experiment.

Don’t have a large marketing budget or the scope to conduct conjoint analysis? That’s OK: Conjointly does full conjoint analysis for you, affordably . Unlike desktop software tools, Conjointly does not require you to deep dive into the advanced methodology of conjoint analysis. Your business can rely on the full functionality of the software to deliver high-quality analysis and powerfully accurate results. Conjointly embodies an agile approach that puts you in control of the research process without the need.

Conjointly is made unique by the following characteristics:

We are the home of conjoint analysis. Conjointly offers complete set of outputs and features through an accessible interface.

Quick to set up. Setting up your experiment is fast and hassle-free with a simple wizard, which helps you choose appropriate settings and suggests your minimum sample size. You won’t need to customise or test any survey – the system does that for you. Conjointly can send participants invites on your behalf or generate a shareable link for you.

Easy on respondents. Experiment participants only need a few minutes to complete a survey and can answer questions with ease on their mobile phone, tablet, or computer.

Smart analytics done for you. Behind the scenes, Conjointly uses state-of-the-art analytics to crunch the numbers, and check validity of reporting. Outputs are ready for any application of conjoint analysis (pricing, feature selection, product testing, new market entry, cannibalisation analysis, etc.) in any industry (telecommunications, SaaS, FMCG, automotive, financial services, HR, etc.).

Our market research experts are always ready to support your studies. Schedule a consultation if you need any assistance.

What is the difference between conjoint and discrete choice experiments?

Conjoint analysis is a survey-based quantitative research technique of presenting respondents with several options (each described in terms of feature and price levels) and measuring their response to these options.

When the measured response is their choice between these options (rather than ranking or rating each of these options), it is called choice-based conjoint (which is the most commonly-used type of discrete choice experiments).

Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Discrete choice analysis can be done on historical data (e.g. sales data) or from experiments (including survey-based experiments).

Choice-based conjoint is an example of discrete choice experimentation.

History of conjoint analysis

Conjoint analysis has its roots in academic research from the 1960s and has been used commercially since the 1970s. In 1964, two mathematicians, Duncan Luce and John Tukey published a rather indigestible (by modern standards) article called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’ . In abstract terms, they sketched the idea of “measuring the intrinsic goodness of certain characteristics of objects by measuring the goodness of an object as a whole”.

The article did not mention data collection, products, features, prices, or other elements that we associate with conjoint analysis today, but it spurred academic interest in the topic and perhaps gave rise to the name “conjoint”. It not only kick-started the topic but also set the tone for future developments in the area. Over time, it has become technical to the point of inaccessibility to most people, led by American academics with a strong emphasis on the statistical workings of survey research.

Green and Srinivasin (1978) agree that the theory of conjoint measurement was developed in Luce and Tukey’s paper but that “the first detailed, consumer-orientated” approach was Green and Rao’s (1971) ‘Conjoint Measurement for Quantifying Judgmental Data’ . In 1974, Professor Paul E. Green penned ‘On the Design of Choice Experiments Involving Multifactor Alternatives’ , cementing the impact of conjoint analysis in market research.

Over the next few decades, conjoint analysis became an increasingly popular method across the globe with notable studies in the 1980s and 90s highlighting its growing adoption and development during this time (Wittink & Cattin 1989; Wittink, Vriens, and Burhenne 1994 cited in Green, Kreiger & Wind 2001) .

Conjoint surveys are continuously developing on a range of software platforms, through which many different flavours of conjoint analysis can be enjoyed. Today, conjoint analysis thrives as a widespread tool built on a robust methodology and is used by market researchers daily as an indispensable tool for understanding consumer trade-offs.

Example outputs of Generic Conjoint on ice-cream

This is a simple conjoint analysis report for a Generic Conjoint test on ice-cream. You can also take this survey yourself . We tested three features:

• Flavour (Fudge, Vanilla, Strawberry, and Mango)
• Size (from 120g to 200g)
• Price (from $1.95 to $3.50)

We collected over 1,500 good quality responses in this test (even though this report would be robust enough with a hundred complete answers). It turns out that variation of price was a more important driver of people’s decision-making than differences in both flavour and size of the cone combined:

Unsurprisingly, people preferred larger and cheaper cones. Fudge and vanilla were the two top flavours:

But when we look at confidence intervals, we notice that we are much less certain about average preferences for flavours than for size or price:

It is probably because if we simulate preference shares for four concepts with varied flavours but fixed price and size, we observe that the distribution of people who pick different options is not extremely skewed towards one flavour:

But when we do simulation analysis with different price points, we clearly see that more people prefer to pay a lower price. Even though some still stick with a higher price, probably due to price-quality inference.

Another useful output of the study is marginal willingness to pay , which shows the equivalent amount of money for upgrade from the less preferred to the more preferred features:

If you want to pick the topmost preferred combination of product features, you can take a look at the following ranking as well:

It looks like a large dollop of modestly-priced Frosty Vanilla is the winner today.

A simple conjoint analysis example in Excel

To further your understanding, you can download a conjoint analysis example in Excel , also available on Google Sheets (which you can copy to edit). This example covers:

• Inputs for a conjoint study
• Questions presented to respondents
• Calculations of preference scores (relative preferences and importance scores of attributes)

This example is limited to:

• Ten choice-based responses (in real conjoint tests, we collect ~12 choices from 100 to 2,000 respondents);
• Four attributes with two levels each (in real conjoint tests, we can have up to a dozen attributes and up to several dozen levels);
• A multiple linear regression (in real conjoint tests, we use hierarchical Bayesian multinomial logit );
• A fractional factorial design .

The best way to learn more about conjoint analysis is to set up your own study, which you can do when you sign up . You can also read about:

• Alternatives to conjoint (such as MaxDiff and Claims Test )
• Common mistakes and practical tips for setting up conjoint studies
• Key takeaways from our Conjoint Analysis 101 webinar

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Home Market Research

Conjoint Analysis: Definition, Example, Types, and Model

Have you ever bought a house? As one of the most complex purchase decisions you can make, you must consider many preferences. Everything from the location and price to interest rate and quality of local schools can play a factor in your home-buying decision. You can use conjoint analysis to make data-driven decisions that will help you meet customer needs and develop your organization.

LEARN ABOUT: Level of Analysis

Less complicated purchases feature a similar process of choosing a good or service that meets your needs. You just may not be aware you’re making those decisions.

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Subconsciously, one person might be more price-sensitive while another is more feature-focused. Understanding which elements consumers consider essential and trivial is the core purpose of conjoint analysis.

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What is conjoint analysis?

Why is it important for researchers, why use conjoint analysis in surveys, when to use it, how to use conjoint analysis, types of conjoint analysis, conjoint analysis: key terms, when is a good time to run a discrete choice-based conjoint study, advantages of conjoint analysis, conjoint analysis example, conjoint algorithm: how it is works, level-up conjoint analysis insights, conjoint analysis marketing example, how to conduct conjoint analysis using questionpro.

Conjoint analysis is defined as a survey-based advanced market research analysis method that attempts to understand how people make complex choices. We make choices that require trade-offs every day — so often that we may not even realize it. Even simple decisions like choosing a laundry detergent or deciding to book a flight are mental conjoint studies that contain multiple elements that lead us to our choice.

Conjoint analysis is one of the most effective models for extracting consumer preferences during the purchasing process . This data is then turned into a quantitative measurement using statistical analysis. It evaluates products or services in a way no other method can.

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Researchers consider conjoint analysis as the best survey method for determining customer values. It consists of creating, distributing, and analyzing surveys among customers to model their purchasing decision based on response analysis.

QuestionPro can automatically compute and analyze numerical values to explain consumer behavior . Our software analyzes responses to see how much value is placed on price, features, geographic location, and other factors. The software then correlates this data to consumer profiles. A software-driven regression analysis of data obtained from real customers makes an accurate report instead of a hypothesis. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

LEARN ABOUT: Consumer Surveys

Reliable, accurate data gives your business the best chance to produce a product or service that meets all your customer’s needs and wants.

Currently, choice-based conjoint analysis is the most popular form of conjoint. Participants are shown a series of options and asked to select the one they would most likely buy. Other types of conjoint include asking participants to rate or rank products. Choosing a product to buy usually yields more accurate results than ranking systems.

We recommend you take a look at this free resource: Conjoint analysis survey template

The survey question shows each participant several choices of products or features. The answers they give allow our software to work out the underlying values. For example, the program can work out its preferred size and how much it would pay for its favorite brand. Once we have the choice data, there is a range of analytic options. The critical tools for analysis include What-if modeling, forecasting, segmentation, and applying cost-benefit analysis.

LEARN ABOUT:   Statistical Analysis Methods

Traditional rating surveys can’t place a value on the different attributes that make up a product. On the other hand, conjoint analysis can sift through respondents’ choices to determine the reasoning for those choices. Analyzing the data gives you the ability to peek into your target audience’s minds and see what they value most in goods or services and acts as a market simulator.

Many businesses shy away from the conjoint analysis because of its seemingly sophisticated design and methodology. But the truth is, you can use this method efficiently, thanks to user-friendly survey software like QuestionPro. Here is a breakdown of conjoint in simple terms, along with a conjoint analysis marketing example.

Over the past 50 years, Conjoint analysis has evolved into a method that market researchers and statisticians implement to predict the kinds of decisions consumers will make about products by using questions in a survey.

The central idea is that consumers evaluate different characteristics of a product and decide which are more relevant to them for any purchase decision. An online conjoint survey’s primary aim is to set distinct values to the alternatives that the buyers may consider when making a purchase decision. Equipped with this knowledge, marketers can target the features of products or services that are highly important and design messages more likely to strike a chord with target buyers.

You can also find best alternatives of Conjoint.ly for your business.

The discrete choice conjoint analysis presents a set of possible decisions to consumers via a survey and asks them to decide which one they would pick. Each concept is composed of a set of attributes (e.g., color, size, price) detailed by a set of levels.

GATHER RESEARCH INSIGHTS

Conjoint models predict respondent preference. For instance, we could have a conjoint study on laptops. The laptop can come in three colors (white, silver, and gold), three screen sizes (11”, 13”, and 15”), and three prices ($200, $400, and $600). This would give 3 x 3 x 3 possible product combinations. In this example, there are three attributes (color, size, and price) with three levels per attribute.

A set of concepts or tasks, based on the defined attributes, are presented to respondents. Respondents make choices as to which product they would purchase in real life. It is important to note that there are a lot of variations of conjoint techniques. QuestionPro’s conjoint analysis software uses choice-based analysis, which most accurately simulates the purchase process of consumers.

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There are two main types of conjoint analysis: Choice-based Conjoint (CBC) Analysis and Adaptive Conjoint Analysis (ACA).

Discrete choice-based conjoint (CBC) analysis:

This type of conjoint study is the most popular because it asks consumers to imitate the real market’s purchasing behavior: which products they would choose, given specific criteria on price and features.

For example, each product or service has a specific set of fictional characters. Some of these characters might be similar to each other or will differ. For instance, you can present your respondents with the following choice:

The devices are almost identical, but device 2 has triple cameras with better configuration, and Device 1 has a higher battery power than Device 2. You would know how vital the trade-off between the number of cameras and battery capacity is by analyzing the responses.

Using the discrete choice model, QuestionPro offers three design types to conduct conjoint analysis:

• Random: This design displays random samples of the possible attributes. For each respondent, the survey software uniquely combines the characteristics. You can run a conjoint concept simulator to know what the choices that the tool will present when you deploy your survey.
• D-Optimal: A flawlessly designed experiment helps researchers estimate parameters without minimum variance and bias. A D-optimal design runs a few tests to investigate or optimize the subject under study. The algorithm helps to create a design that is optimal for the sample size and tasks per respondent.
• Import design: You can also import designs in SPSS format. For example, QuestionPro lets you import fractional factorial orthogonal designs to make use of in surveys.

Adaptive conjoint analysis (ACA):

Researchers use this type of conjoint analysis often in scenarios where the number of attributes/features exceeds what can be done in a choice-based scenario. ACA is great for product design and product segmentation research but not for determining the ideal price.

For example, the adaptive conjoint analysis is a graded-pair comparison task wherein the survey respondents are asked to assess their relative preferences between a set of attributes, and each pair is then evaluated on a predefined point scale.

QuestionPro uses CBC, or Discrete Choice Conjoint Analysis, a great option if the price is one of the most critical factors for you or your customers. The method’s key benefit is that it provides a picture of the market’s willingness to make tradeoffs between various features. The result is an answer to what constitutes an “ideal” product or service.

It is a statistical analysis plan used in market research to gain a better understanding of how people make complex decisions. The following are some key terms of it:

• Attributes (Features): The product features are evaluated by the analysis. Examples of characteristics of Laptops: Brand, Size, Color, and Battery Life.
• Levels:  The specifications of each attribute. Examples of standards for Laptops include Brands: Samsung, Dell, Apple, and Asus.
• Task: The number of times the respondent must make a choice. The example shows the first of the five functions as indicated by “Step 1 of 5.” 5.”
• Concept or Profile : The hypothetical product or offering. This is a set of attributes with different levels that are displayed at each task count. There are usually at least two to choose from.
• Relative importance : “attribute importance,” which depicts which of the various attributes of a product/service is more or less important when making a purchasing decision. Example of Laptop Relative Importance: Brand 35%, Price 30%, Size 15%, Battery Life 15%, and Color 5%.
• Part-Worths/Utility values : Part-Worths, or utility values, is how much weight an attribute level carries with a respondent. The individual factors that lead to a product’s overall value to consumers are part-worths. Example part-worths for Laptops Brands: Samsung – 0.11, Dell 0.10, Apple 0.17, and Asus -0.16.
• Profiles : Discover the ultimate product with the highest utility value. At a glance, QuestionPro lets you compare all the possible combinations of product profiles ranked by utility value to build the product or service that the market wants.
• Market share simulation : One of the most unique and fascinating aspects of conjoint analysis is the conjoint simulator. This gives you the ability to “predict” the consumer’s choice for new products and concepts that may not exist. Measure the gain or loss in market share based on changes to existing products in the given market.
• Brand Premium : How much more will help a customer pay for a Samsung versus an LG television? Assigning price as an attribute and tying that to a brand attribute returns a model for a $ per utility distribution. This is leveraged to compute the actual dollar amount relative to any characteristic. When the analysis is done relative to the brand, so you get to put a price on your brand.
• Price elasticity and demand curve : Price elasticity relates to the aggregate demand for a product and the demand curve’s shape. We calculate it by plotting the demand (frequency count/total response) at different price levels. ADD_THIS_TEXT

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We’re asked this question a lot. So much so that we’ve coined the term Conjoint O’ Clock. If you find yourself needing to get into your customers’ minds to understand why they buy, ask yourself what you hope to get from your insights. It’s time for Conjoint O’Clock if you are trying to:

• Launch a new product or service in the market.
• Repackage existing products or services to the market.
• Understand your customers and what they value in your products.
• Gain actionable insight to increase your brand’s competitive edge .
• Place a price on your brand versus competing brands.
• Revamp your pricing structure.

LEARN ABOUT: Pricing Research

There are multiple advantages to using conjoint analysis in your surveys:

• It helps researchers estimate the tradeoffs that consumers make on a psychological level when they evaluate numerous attributes simultaneously.
• Researchers can measure consumer preferences at an individual level.
• It gives researchers insights into real or hidden drivers that may not be too apparent.
• Conjoint analysis can study the consumers and attributes deeper and create a needs-based segmentation.

For example, assume a scenario where a product marketer needs to measure individual product features’ impact on the estimated market share or sales revenue.

In this conjoint study example, we’ll assume the product is a mobile phone. The competitors are Apple, Samsung, and Google. The organization needs to understand how different customers value attributes, such as brand, price, screen size, and screen resolution. Armed with this information, they can create their product range to match consumer preferences.

Conjoint analysis assigns values to these product attributes and levels by creating realistic choices and asking people to evaluate them.

LEARN ABOUT: Average Order Value

It enables businesses to mathematically analyze consumer or client behavior and make decisions based on real insights from customer data. This allows them to develop better business strategies that provide a competitive edge. To fulfill customer wishes profitably requires companies to fully understand which aspects of their product and service are most valued.

We use a logic model coupled with a Nelder-Mead Simplex algorithm. It helps to calculate the utility values or part-worths. This algorithm’s benefit allows QuestionPro to offer a cohesive and comprehensive survey experience all within one platform.

We understand that most businesses don’t need the intricate details of our mathematical analysis. However, we want to provide you with the transparency you need to use conjoint survey results. Have confidence in your results by reviewing the algorithm below.

• Let there be R respondents, with individuals r = 1 … R
• Let each respondent see T tasks, with t = 1 … T
• Let each task t have C concepts, with c = 1 … C
• If we have A attributes, a = 1 to A, with each attribute having La levels, l = 1 to La, then the part-worth for a particular attribute/level is w’(a,l). In this exercise, we will be solving this (jagged array) of part worths.
• We can simplify this to a one-dimensional array w(s), where the elements are {w′(1, 1), w′(1, 2)…w′(1, L1), w′(2, 1)…w′(A, LA)} with w having S elements.
• A specific concept x can be shown as a one-dimensional array x(s), where x(s)=1 if the specific attribute is available, and 0 otherwise.
• Let X rtc  represent the specific concept of the c th  concept in the t th  task for the r th respondent. Thus, the experiment design is represented by the four-dimensional matrix X with size RxTxCxS.
• If respondent r chooses concept c in task t then let Y rtc =1; otherwise 0.
• The value Ux of a definite idea is the total of the part-worths for those elements available in the conception, i.e. the scalar product of x and w.

Multinomial logit model

For a simple choice between two concepts, with utilities U1 and U2, the multinomial logit (MNL) model predicts that concept 1 will be chosen.

Modeled Choice Probability

Let the choice probability (using MNL model) of choosing the cth concept in the tth task for the r th respondent be:

Log-Likelihood Measure

Solving for Part-Worths using Maximum Likelihood

We solve for the part-worth vector by finding the vector w that gives the maximum value for LL. Note that we are solving for S variables.

• This is a multi-dimensional, nonlinear continuous maximization research problem , and it is essential to have a standard solver library. We use the Nelder-Mead Simplex Algorithm.
• The Log-Likelihood function should be implemented as a function LL(w, Y, X) and then optimized to find the vector w that gives us a maximum. The responses Y, and the design.

X is specified and constant for a specific development. The starting values for w can be set to the origin 0. The final part-worth values, w, are re-scaled so that the part-worths for any attribute have a mean of zero. This is done by subtracting the mean of the part-worths for all levels of each quality.

Although conjoint analysis requires more involvement in survey design and analysis, the additional planning effort is often worth it. With a few extra steps, you get an authentic look into your most significant customer preferences when choosing a product.

Price, for example, is vital to most folks shopping for a laptop. But how much more is the majority willing to pay for longer battery life for their laptop if it means a heavier and bulkier design? How much less in value is a smaller screen size compared to a slightly larger one? Using conjoint surveys, you’ll discover these details before making a considerable investment in product development.

Conjoint is just a piece of the insights pie. Capture the full story with a cohesive pricing, consumer preference, branding, or go-to-market strategy using other question types and delivery methodologies to stretch the project to its full potential. With QuestionPro, you can build and deliver comprehensive surveys that combine conjoint analysis results with insights from additional questions or custom profiling information included in the survey.

Gather research insights

Click on the Add New Question link and select the Conjoint (Discrete Choice) Option from under Advanced Question Types. This will open the wizard-based conjoint question template to create tasks by entering attributes (features) and levels for each of the features.

For example, an organization produces televisions and they are a competitor of Samsung, LG, or Vizio. The organization needs to understand how different customers value specific attributes such as the size, brand, and price of a television. Armed with this information, they can create their very own product range and offering that meets a market need and generates revenue.

Step 2: Enter the features and levels.

Enter the features and levels. Set up the task counts and concepts per task and assign feature types: Price, Brand, or Other. Using television brands as an example, consider the following:

• Features for televisions: Price, Size, Brand.
• Price:$800, $1,200, $1,500
• Size: 36”, 45”, 52”
• Brand: Sony, LG, Vizio

Step 3: Select Design Type to either of the three design types: Random, D-Optimal, and Import.

Step 4: Add additional setting options, including fixed tasks and prohibited concepts.

Step 5: Preview, review text data, and distribute the survey.

In this example, the survey would look like this:

Where can I view Reports for the conjoint questions?

Step 1:  Go To  Login »  Surveys »  Analytics »  Choice Modelling »  Conjoint Analysis

Step 2: Here, you can view the online reports.

Step 3: You can download the data in Excel/CSV or HTML format.

The QuestionPro conjoint analysis offering includes the following tools:

• Conjoint Task Creation Wizard: Wizard-based interface to create Conjoint Tasks based on merely entering features(attributes), like price and levels, like $100 or $200, for each feature.
• Conjoint Design Parameters: Tweak your design by choosing the number of tasks, the number of profiles per task, and the “Not-Applicable” option.
• Utility Calculation: Automatically calculates utilities.
• Relative Importance: Automatically calculates the relative importance of attributes (based on utilities).
• Cross/Segmentation and Filtering: Filter the data based on criteria and then run Relative Importance calculations.

LEARN ABOUT: 12 Best Tools for Researchers

Conjoint analysis is an effective market research technique that helps businesses better understand their customer’s preferences and make educated decisions about product creation, pricing, and marketing strategies.

LEARN ABOUT: Market research vs marketing research

The conjoint analysis provides significant insights into how customers assess different aspects when making purchase decisions by breaking down complex purchasing decisions into smaller components and examining them systematically. 

There are several types of conjoint analysis models accessible, each with its own set of advantages and disadvantages. Choosing the best model is determined by the study objectives and the specific characteristics of the market under consideration.

Conjoint analysis is a valuable tool for any company wanting to obtain a better knowledge of its customers and keep ahead of the competition in today’s ever-changing market. If you are thinking about conducting conjoint analysis, QuestionPro is there for you. 

QuestionPro provides a comprehensive set of features and tools to assist businesses in conducting conjoint analysis efficiently and effectively, making it a valuable tool for market research professionals. Contact QuestionPro right away!

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What is a conjoint analysis conjoint types & when to use them.

11 min read Conjoint analysis is a popular market research approach for measuring the value that consumers place on individual and packages of features of a product.

Conjoint analysis explained

Conjoint analysis can be defined as a popular survey-based statistical technique used in market research. It is the optimal approach for measuring the value that consumers place on features of a product or service. This commonly used approach combines real-life scenarios and statistical techniques with the modeling of actual market decisions.

Product testing and employee benefits packages are examples of where conjoint analysis is commonly used. Conjoint surveys will show respondents a series of packages where feature variables are different to better understand which features drive purchase decisions.

Note: For an in-depth guide to conjoint analysis, download our free eBook:   12 Business Decisions you can Optimize with Conjoint Analysis

Menu-based conjoint analysis

Menu-based conjoint analysis is an analysis technique that is fast gaining momentum in the marketing world. One reason is that menu-based conjoint analysis allows each respondent to package their own product or service.

Conjoint studies can help you determine pricing, product features, product configurations, bundling packages, or all of the above. Conjoint is helpful because it simulates real-world buying situations that ask respondents to trade one option for another.

For example, in a survey, the respondent is shown a list of features with associated prices. The respondent then chooses what they want in their ideal product while keeping price as a factor in their decision. For the person conducting the market research , key information can be gained by analyzing what was selected and what was left out. If feature A for $100 was included in the menu question but feature B for $100 was not, it can be assumed that this respondent prefers feature A over feature B.

The outcome of menu-based conjoint analysis is that we can identify the trade-offs consumers are willing to make. We can discover trends indicating must-have features versus luxury features.

Add in the fact that menu-based conjoint analysis is a more engaging and interactive process for the survey taker, and one can see why menu-based conjoint analysis is becoming an increasingly popular way to evaluate the utility of features.

The advanced functionality of Qualtrics allows for the perfect conjoint survey – built with the exact look and feel needed to provide a reliable, easy to understand experience for the respondent. This means better quality data for you.

  There are numerous conjoint methodologies available from Qualtrics.

• Full-Profile Conjoint Analysis
• Choice-Based/Discrete-Choice Conjoint Analysis
• Adaptive Conjoint Analysis
• Max-Diff Conjoint Analysis

To provide a sense of these options, the following discussion provides an overview of conjoint analysis methods.

Two-attribute tradeoff analysis

Perhaps the earliest conjoint data collection method involved presented a series of attribute-by-attribute (two attributes at a time) tradeoff tables where respondents ranked their preferences for the different combinations of the attribute levels. For example, if two attributes each had three levels, the table would have nine cells and the respondents would rank their tradeoff preferences from 1 to 9.

The two-factor-at-a-time approach makes few cognitive demands of the respondent and is simple to follow but it is both time-consuming and tedious. Moreover, respondents often lose their place in the table or develop some stylized pattern just to get the job done. Most importantly, however, the task is unrealistic in that real alternatives do not present themselves for evaluation two attributes at a time.

Full-profile conjoint analysis

Full-profile conjoint analysis takes the approach of displaying a large number of full product descriptions to the respondent. The evaluation of these packages yields large amounts of information for each customer/respondent. Full-profile conjoint analysis has been a popular approach to measure attribute utilities. In the full-profile conjoint task, different product descriptions (or even different actual products) are developed and presented to the respondent for acceptability or preference evaluations.

Each product profile represents a part of a fractional factorial experimental design that evenly matches the occurrence of each attribute with all other attributes. By controlling the attribute pairings, the researcher can correlate attributes with profile preferences and estimate the respondent’s utility for each level of each attribute tested. In the rating task, the respondent gives their preference or likelihood of purchase. While many features and levels may be studied, this type of conjoint is best used where a moderate number of profiles are presented, thereby minimizing respondent fatigue. The advanced functionality of Qualtrics employs experimental designs to reduce the number of evaluation requests within the survey. The output and analysis accumulated from full-profile conjoint surveys is similar to that of other conjoint models.

Adaptive conjoint analysis

Adaptive conjoint analysis varies the choice sets presented to respondents based on their preference. This adaption targets the respondent’s most preferred feature and levels, thereby making the conjoint exercise more efficient, wasting no questions on levels with little or no appeal. Every package shown is more competitive and will yield ‘smarter’ data.

Adaptive conjoint analysis is often more engaging to the survey-taker and thus can produce more relevant data. It reduces the survey length without diminishing the power of the conjoint analysis metrics or simulations. There are multiple ways to adapt the conjoint scenarios to the respondent. Most commonly the design is based on the most important feature levels. As each package is presented for evaluation, the survey accounts for the choice and then makes the next question more efficient. A combination of full profile and feature evaluation methods can be utilized and is referred to as Hybrid Conjoint Analysis.

Choice-based conjoint

The Choice-based conjoint analysis (CBC) (also known as discrete-choice conjoint analysis) is the most common form of conjoint analysis. Choice-based conjoint requires the respondent to choose their most preferred full-profile concept. This choice is made repeatedly from sets of 3–5 full profile concepts.

This choice activity is thought to simulate an actual buying situation, thereby mimicking actual shopping behavior. The importance and preference for the attribute features and levels can be mathematically deduced from the trade-offs made when selecting one (or none) of the available choices. Choice-based conjoint designs are contingent on the number of features and levels. Often, that number is large and an experimental design is implemented to avoid respondent fatigue. Qualtrics provides extreme flexibility in utilizing experimental designs within the conjoint survey.

The output of a Choice-based conjoint analysis provides excellent estimates of the importance of the features, especially in regards to pricing. Results can estimate the value of each level and the combinations that make up optimal products. Simulators report the preference and value of a selected package and the expected choice share (surrogate for market share).

Self-explicated conjoint analysis

Self-explicated conjoint analysis offers a simple but surprisingly robust approach that is easy to implement and does not require the development of full-profile concepts. Self-explicated conjoint analysis is a hybrid approach that focuses on the evaluation of various attributes of a product. This conjoint analysis model asks explicitly about the preference for each feature level rather than the preference for a bundle of features.

Although the approach is different, the outcome is still the same in that it produces high-quality estimates of preference utilities.

• First, like ACA, factors and levels are presented to respondents for elimination if they are not acceptable in products under any condition
• For each feature, the respondent selects the levels they most and least prefer
• Next, the remaining levels of each feature are rated in relation to the most preferred and least preferred levels
• Finally, we measure how important the overall feature is in their preference. The relative importance of the most preferred level of each attribute is measured using a constant sum scale (allocate 100 points between the most desirable levels of each attribute).
• The attribute level desirability scores are then weighted by the attribute importance to provide utility values for each attribute level.

Self-explicated conjoint analysis does not require the statistical analysis or the heuristic logic required in many other conjoint approaches. This approach has been shown to provide results equal or superior to full-profile approaches, and places fewer demands on the respondent. There are some limitations to self-explicated conjoint analysis, including an inability to trade off price with other attribute bundles. In this situation, the respondent always prefers the lowest price, and other conjoint analysis models are more appropriate.

Max-diff conjoint analysis

Max-Diff conjoint analysis presents an assortment of packages to be selected under best/most preferred and worst/least preferred scenarios. Respondents can quickly indicate the best and worst items in a list, but often struggle to decipher their feelings for the ‘middle ground’. Max-Diff is often an easier task to undertake because consumers are well trained at making comparative judgments.

Max-Diff conjoint analysis is an ideal methodology when the decision task is to evaluate product choice. An experimental design is employed to balance and properly represent the sets of items. There are several approaches that can be taken with analyzing Max-Diff studies including: Hierarchical Bayes conjoint modeling to derive utility score estimations, best/worst counting analysis and TURF analysis.

Hierarchical Bayes analysis (HB)

Hierarchical Bayes Analysis (HB) is similarly used to estimate attribute level utilities from choice data. HB is particularly useful in situations where the data collection task is so large that the respondent cannot reasonably provide preference evaluations for all attribute levels. As part of the procedure to estimate attribute level utilities for each individual, hierarchical Bayes focuses individual respondent measurement on highly variable attributes and uses the sample’s attribute level averages when attribute-level variability is smaller. This approach again allows more attributes and levels to be estimated with smaller amounts of data collected from each individual respondent.

Conjoint is a highly effective analysis technique

Conjoint analysis methodology has withstood intense scrutiny from both academics and professional researchers for more than 30 years. It is widely used in consumer products, durable goods, pharmaceutical, transportation, and service industries, and ought to be a staple in your research toolkit.

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An introduction to conjoint analysis

Last updated

1 April 2024

Reviewed by

Customers have different preferences that play a role in their purchase decisions. For businesses, meeting these different needs can be challenging. However, conjoint analysis can help make data-driven decisions that optimize products and services, making them more appealing to customers. 

Market analysis template

Save time, highlight crucial insights, and drive strategic decision-making

• What is conjoint analysis?

Conjoint analysis is a survey-based statistical analysis method to understand how customers value products and services and why they make certain choices when buying. 

A product or service comprises multiple conjoined attributes or features, and this is what conjoint analysis focuses on. A conjoint analysis breaks down a product or service into its attributes and tests the different components to reveal customer preferences. 

• Why is it important for researchers?

Conjoint analysis is an essential component of market research because:

It helps measure the value the consumer places on each product attribute.

It predicts a combination of features that will have the most value to customers. 

It helps segment customers according to their perceived preferences. This helps with tailoring market campaigns to the right target customers. 

It enables researchers to get customer feedback about an upcoming product. 

• Uses of conjoint analysis

Conjoint analysis is primarily used to make informed decisions relating to:

Buyer decisions

Customer preferences

Market sales

New product pricing

Selection of the best service or product feature

Market campaign validation

• Why use conjoint analysis in surveys?

Conjoint analysis pinpoints what customers value the most, thus revealing their preferences, what they’re prepared to “trade off”, and why.  

• Two types of conjoint analysis 

Two types of conjoint analysis are:

Discrete choice-based conjoint (CBC) analysis

CBC is the most common form of conjoint analysis that asks customers to mimic their buying habits. It asks respondents to choose between a set of product or service concepts. For instance, the choice-based conjoint analysis format presents questions such as "Would you rather?". 

The advantage of discrete choice-based conjoint is that it reflects a realistic scenario of choosing between products rather than directly questioning respondents about each attribute's significance. 

Adaptive conjoint analysis (ACA)

This flexible approach adopts a questionnaire procedure that tailors questions to address personal preferences. The adaptive conjoint analysis targets the respondent's most preferred attribute, thus making the analysis more efficient. 

• When to use it? 

Businesses use conjoint analysis for the following:

Conjoint analysis in pricing

Businesses can use conjoint analysis to ask customers to compare different product features to determine how they value them. It’s an excellent way to learn what features customers are willing to pay for. 

When business owners fully understand what customers value, they can determine the price they’re willing to pay for their products or services. 

Conjoint analysis in sales & marketing

With conjoint analysis, businesses discover customer preferences, allowing them to create marketing campaigns that will target their preferences and increase sales. 

Also, findings of a conjoint analysis could help determine whether there’s enough market for a new product or service.

Conjoint analysis in research & development

With conjoint analysis, product developers can define customer needs and bring the right product or service idea to life. 

In addition, at the beginning of product development , a conjoint analysis will help reveal the concepts that aren’t valued by customers, allowing businesses to eliminate them at the early stages. This saves time and valuable resources and minimizes the risk of a failed product launch. 

• How to do a conjoint analysis

The steps of performing a conjoint analysis are as follows:

Step 1: Define the study problem

Defining the problem establishes the purpose of the experiment. Whether you want to understand your customers better, find a perfect pricing strategy, or predict the market share, problem definition will define the scope of the study. 

In this step, the business owner must consider the target audience and craft specific, meaningful questions. 

Step 2: Break down the product or service into attributes

The next step is to determine the list of attributes of your product or service. Attributes should have varying levels in real life, be clearly defined, and be expected to influence customer preferences and exhibit strong correlations. 

For instance, if you sell cars, the attributes could be engine capacity, trim level, fuel efficiency, color, pricing, warranty, and design. Again, remember to use short descriptions to avoid misunderstandings. 

Step 3: Choose the conjoint analysis methodology

The next step is to organize the questionnaire according to the type of conjoint analysis preferred. 

Choosing CBC is effective when you want respondents to select a preference from a set of choices. ACA is appropriate when you want more accurate information on an individual level. 

Step 4: Deploy the questionnaires to your target respondents 

The questionnaire should have varying features so that the researcher can observe the attributes driving the choice. If the ACA method is used, ask the respondents to rank the attributes based on their needs. 

When the rankings are complete, the researchers get a clear picture of which feature(s) are highly rated by respondents and which aren’t.

Step 5: Data collection and analysis

This step involves collecting data accordingly and using it for decision-making . The rating given by respondents is a raw set of data. The business owner then assigns weights to each category. 

Finally, you can determine the attribute that ranks as the most important, and this will give you information about what customers value the most in your product or service. 

• Five advantages of conjoint analysis

The advantages of using conjoint analysis include the following:

Researchers can determine customer preferences at an individual level.

It reveals the hidden drivers of why customers make certain choices.

It’s a perfect tool for experimenting with attributes such as price before launching a new product or service. 

Conjoint analysis is highly flexible and can be used to develop almost every product or service.

It’s a versatile method that realistically reflects an everyday purchase decision.

• Conjoint analysis examples

The following are two real-world examples of conjoint analysis: 

Example one: A manufacturer seeking to launch a new laptop

When launching a new laptop, manufacturers must know what customers value the most to ascertain what feature draws them to their offerings. Therefore, businesses must conduct a conjoint analysis. The manufacturer will develop a questionnaire that will gather insights from the respondents. 

The attributes that define the laptop are:

The operating system is either Microsoft Windows, Linux, or MacOS. 

The processing speeds

Storage space: is it a 500GB hard drive or 1TB?

Battery life

Screen size

With the help of conjoint analysis, the manufacturer puts a value on each attribute and tailors the product to what’s valued most by a customer. Findings of customer preferences allow the manufacturer to design the "best" laptop technically possible.  

Example two: A restaurant owner seeking to attract a broad customer base 

The restaurant owner may want to differentiate themselves from the competition and attract a wider customer base . They will conduct a conjoint analysis based on what people value the most to understand customer choices. 

People go to restaurants for several reasons, including:

Quality of food

Meal purposes (business, tourist, family, etc.)

Type of food served (seafood, Chinese food, etc.)

The restaurant owner will carry out a conjoint analysis based on the above criteria. The survey response will reveal what customers value the most and allow the restaurant owner to maximize the highly valued feature.

What is an attribute in conjoint analysis?

It’s a product characteristic such as price, size, brand, or color. 

What are attribute levels?

Attribute levels are the values that each characteristic can take. For instance, the attribute shape can have small, medium, large, or extra-large levels. 

How do you identify an attribute?

When defining an attribute, use a language that a customer understands. You can also use images to minimize confusion.

How many people do you need for conjoint analysis?

The sample size for a conjoint analysis depends on the target market. If the target market is relatively small, use a small sample size and vice versa. A general rule of thumb is to use sample sizes that range from 150 to 1,200 respondents. 

What are the real-life applications of conjoint analysis?

You can use conjoint analysis to test the appeal of new products such as soft drinks, footwear, or home appliances. 

How do you calculate market share in conjoint analysis?

You can determine market share by taking a business's sales over a period and dividing it by the industry's total revenue over the same period.

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Conjoint Analysis: A Research Method to Study Patients’ Preferences and Personalize Care

Basem al-omari.

1 Department of Epidemiology and Population Health, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates; [email protected] (J.F.); [email protected] (M.E.)

2 KU Research and Data Intelligence Support Center (RDISC) AW 8474000331, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates

Joviana Farhat

Mai ershaid, associated data.

Not applicable.

This article aims to describe the conjoint analysis (CA) method and its application in healthcare settings, and to provide researchers with a brief guide to conduct a conjoint study. CA is a method for eliciting patients’ preferences that offers choices similar to those in the real world and allows researchers to quantify these preferences. To identify literature related to conjoint analysis, a comprehensive search of PubMed (MEDLINE), EMBASE, Web of Science, and Google Scholar was conducted without language or date restrictions. To identify the trend of publications and citations in conjoint analysis, an online search of all databases indexed in the Web of Science Core Collection was conducted on the 8th of December 2021 without time restriction. Searching key terms covered a wide range of synonyms related to conjoint analysis. The search field was limited to the title, and no language or date limitations were applied. The number of published documents related to CA was nearly 900 during the year 2021 and the total number of citations for CA documents was approximately 20,000 citations, which certainly shows that the popularity of CA is increasing, especially in the healthcare sciences services discipline, which is in the top five fields publishing CA documents. However, there are some limitations regarding the appropriate sample size, quality assessment tool, and external validity of CA.

1. Introduction

The popularity of conjoint analysis (CA) in health outcomes research has been increasing in recent years [ 1 , 2 ]. Yet, the untraditional concept of this research method is still unclear for many healthcare researchers and clinicians in terms of the design complexity and the absence of confirmed sample size [ 1 , 3 , 4 ]. Throughout clinical practice, healthcare professionals have been driving their efforts towards a patient-oriented profession to improve patient adherence to medications, prognosis, and quality of life [ 5 ]. Over the years, approaches that are referred to as stated and revealed preference methods have been executed to assess patients’ preferences in relation to drug pharmacodynamics, pharmacokinetics, and financial characteristics [ 5 , 6 ]. The stated preferences method relies on what people state while evaluating alternative hypothetical situations (hypothetical decision) [ 7 , 8 ]. Contrarily, the revealed preferences method relies on the observations of the actual choices made by people to measure preferences (actual decision) [ 7 , 8 , 9 ]. Since stated choice does not always reveal the actual preference [ 8 ], behavioral scientists developed alternative techniques that involve studying choice behavior rather than just stated choices [ 10 ]. Therefore, the application of alternative methods in clinical practice is linked to the patients’ own perspective of selecting the best cost-effective treatment, while considering their social and psychological situation instead of relying mostly on disease symptoms [ 6 , 11 , 12 ].

One of the main methods of examining patients’ preferences is the CA, which was developed to scrutinize preferences within the decision-making process [ 4 , 13 ]. CA is a stated preference method that measures how respondents state that they will react in a certain situation [ 14 , 15 ]. Conjoint measurement was first developed in the 1960s by the American mathematical psychologist Duncan Luce and the statistician John Tukey [ 16 ]. In the early 1970s, Green and Rao introduced conjoint measurement to marketing research in order to understand and predict buyer behavior [ 17 ] and thereafter it was most widely used in marketing research [ 18 ]. Although the CA technique was developed in the 1960s, it was not until the 1990s that it was used to elicit patients’ views in the healthcare field [ 19 ]. Since then, its popularity and social impact have been growing gradually through its frequent usage in health services rating based research studies [ 1 , 3 ].

The term CA generally belongs to the description of the variety of quantitative methods used to analyze preferences [ 19 , 20 ]. The denomination “conjoint” refers to the idea that several factors can be “considered jointly” [ 21 ]. Therefore, CA permits people to choose between different hypothetical products or treatments scenarios rather than evaluating their characteristics separately. CA presents people with ideas that closely resemble the decisions made in real life when choosing between alternatives [ 22 ]. For example, if a patient is requested to select the preferred surgical procedure from several alternatives for the treatment of kidney stones, they may consider a specific procedure superior to others. CA elicits patients’ preferences for the selected surgical procedure by evaluating multiple factors associated with each offered procedure. These factors may include adverse events, associated benefits, recovery time, and cost.

When people are making treatment decisions, they base their choice on several characteristics of this treatment. CA assumes that each one of these characteristics has a specific importance to people and they are making trade-offs between these characteristics [ 4 , 23 ]. It is also suggested that people give well-ordered preferences when evaluating options together rather than in isolation [ 17 ]. Therefore, unlike traditional questionnaires, CA poses several hypothetical scenarios and asks patients to rate, rank, or choose their preferred scenario [ 24 ]. Accordingly, the importance of CA is highlighted by being a multivariate technique used specifically to understand how respondents develop preferences for products or services [ 25 ]. In most cases, individuals could make up their minds about a particular treatment characteristic, but they might change their preference when this characteristic is combined to form a treatment scenario. For example, a patient’s decision to choose between different pain-relieving medications could be based on several characteristics of these medications, which may include a pain-relieving effect, frequency of administration, and side effects. This patient may state that she/he would like the medication that provides maximum pain relief, is taken once a day, and has no side effects. Yet, she/he may change their decision when the reality states that the medications with maximum benefits hold risks of side effects and may have to be taken more than once a day. In this situation, the patient must trade off benefits, frequency, and side effects and make some compromises. Hence, CA suggests that presenting patients with several scenarios in a conjoint task could resemble the decision made when selecting medication in real life.

CA methods use three main approaches and tools to elicit well-ordered preferences: ranking, rating, or discrete choices [ 19 ]. When conducting a CA questionnaire, researchers can either utilize a pre-developed questionnaire design or develop their own customized one. For example, Ratcliffe and colleagues developed their conjoint questionnaire using a computer software package to produce a fractional factorial design [ 26 ]. Others built narratives describing different options and asked participants to rate these options [ 27 ] or created hypothetical scenarios and asked participants to choose between them [ 22 ]. In recent years, pre-developed designs such as adaptive conjoint analysis (ACA) or adaptive choice-based conjoint (ACBC) by “Sawtooth software” (a provider of CA software packages) are becoming popular [ 4 , 28 , 29 ]. These designs provide researchers with questionnaire templates and an analysis platform. Consequently, researchers can customize the template to suit their requirements and can build up the questionnaire using the attributes and levels specific to their study. Then, a built-in statistical software such as hierarchical basin (HB) can be used to analyze the data.

This article aims to provide an overview of the CA method and analyze the growth of its application over the past 70 years. It also narratively discusses the literature of the CA method’s process and validity, its use in healthcare settings, and its strengths and limitations.

A comprehensive literature search was conducted. Following Gasparyan and colleagues’ recommendations [ 30 ], PubMed (MEDLINE), EMBASE, Web of Science, and Google Scholar were electronically searched without language or date restrictions. Keywords related to “conjoint analysis”, “discrete choice”, “choice experiment”, “rating conjoint”, and “ranking conjoint” were used to search the literature. Additionally, the lead author has significant experience in the field, and the opinions expressed in this article are also based on personal experience of writing, editing, and commenting on reviewed articles.

The Web of Science Core Collection (WoSCC) was utilized to identify the trend of publications and citations over the past 70 years. WoSCC is a database providing access to billions of cited references dating back to 1900 in the areas of life sciences, social sciences, arts, and humanities [ 31 ], and is an emerging source of citation index [ 32 , 33 , 34 , 35 ]. Bibliometric studies, which are used to systematize and summarize the growing body of publications [ 36 ] and focus on a topic’s popularity at a given point in time [ 37 ], mainly use WoSCC. Therefore, an online search was conducted utilizing all databases indexed in the WoSCC to identify the publications and citations trend in CA. The retrieved database was searched on the 8th of December 2021. The database was accessed through the electronic library portal of Khalifa University, United Arab Emirates. The Boolean search query method was applied. The searching key terms covered a wide range of synonyms which included “conjoint analysis” OR “conjoint measurement” OR “conjoint studies” OR “conjoint choice experiment” OR “discrete choice conjoint experiment” OR “discrete choice experiment” OR “pairwise choices” OR “Best-Worst Scaling” OR “Best Worst Scaling” OR “MaxDiff Scaling” OR “Maximum Difference Scaling” OR “ranking conjoint” OR “rating conjoint” OR “adaptive conjoint analysis” OR “adaptive choice based conjoint” OR “choice based analysis” OR “full profile conjoint” OR “choice based conjoint” OR “choice set” OR “relative preference weight” OR “hypothetical scenario” OR “stated preference”. The search field was limited to the title, and no language or date limitations were applied.

3. Conjoint Analysis Trend over the Past 70 Years

The WoSCC search identified a total of 9614 documents related to CA, which were published between 1950 and the 8th of December 2021. The result of the search demonstrated a significant increase in the production and citation of published papers related to CA over the years to reach nearly 900 documents and 20,000 citations in 2020 and 2021 (see Figure 1 ). The gradual increase in citations and research production indicates the expanded popularity of CA methods. Furthermore, it is an indication of the improvement of the reporting of conjoint experiments that are conducted for commercial purposes. Between 1981 and 1985, it was estimated that approximately 400 commercial conjoint analysis applications were carried out each year [ 38 ]. Yet, only a few documents were published each year during the 1980s. This indicates that the primary purpose of using CA during that period was commercial and academic application and reporting have only become popular during the last 20 years. Furthermore, many advances in CA methods were documented during the 1980s and 1990s [ 39 ] along with observations of greater interest in CA usage throughout the healthcare field during the 1990s [ 19 ].

The trend of CA documents published between 1950 and 2021.

The results of the citations analysis indicated that the business and economics field has the highest number of publications of CA. This is expected, as CA originated from this area of research, more specifically for marketing research. It is not surprising that the healthcare field of research was one of the top five areas publishing papers on CA topics. This indicates the growing interest in the CA method by healthcare researchers (see Table 1 ). In terms of the type of documents, the highest number of published documents were research articles (n = 7047; 73.3%), then meeting abstracts (n = 1624; 16.9%). Furthermore, the highest contributing countries to CA research were the USA (n = 2321; 24.1%), People’s Republic of China (n = 1549; 16.1%), and England (n = 899; 9.4%).

Top 10 research areas publishing CA documents.

Note: The number of published papers in Table 1 adds up to more than the total analyzed documents (n = 9614). The reason for this is that several documents are classified by the databases under several research areas; for example, some documents would be classified under psychology and healthcare sciences services at the same time.

4. The Conjoint Analysis Study Process

During the 1990s, the initial focus of researchers was to assess patient preferences and satisfaction regarding the treatment outcome only [ 25 ]. This was evidenced by the large number of health studies assessing patients’ quality-adjusted life years (QALYs) and healthy-years equivalents [ 24 ]. By the year 2000, the use of preferences methods gradually increased in the healthcare setting. Ryan and Farrar aimed to familiarize and engage patients with their treatment plan in cooperation with their physicians by allowing them to exhibit their preferences. This not only considered patients’ treatment response but also treatment characteristics, surgical options, as well as physicians’ care and attitude [ 16 ]. Ryan and Farrar stratified a multistep plan in order to practice a standardized CA study and achieve a precise assessment of patients’ preferred choices through five main stages stated as follows.

4.1. Identifying the Relevant Attributes

Attributes are known to be the factors, features, or characteristics which are believed to influence people’s preferences for a particular product or treatment [ 40 , 41 ]. Identifying attributes must be supported by evidence that suggests the potential range of preferences and values that people may hold [ 42 ]. Attributes must also be balanced between what is important to the respondent and what is relevant to decision-makers [ 42 ]. Selecting and defining the attributes can be achieved through reviewing the literature, healthcare experts’ group discussions, and interviewing individual subjects from the patients and public involvement (PPI) groups [ 41 ]. In some cases, a predefined policy question may be already available. Defining attributes is the most fundamental and critical aspect of designing a good CA study [ 43 ]. Therefore, attributes must be written in terminology that is easy for patients to understand. This was achieved when the wording and terminology of the attributes were based on the research users’ group (RUG) recommendations and suggestions [ 23 ]. Table 2 shows examples of different attributes for pain-relieving medication.

An example of attributes and levels for pain-relieving medication.

4.2. Assigning Levels

In CA, each attribute is defined by a series of levels [ 28 ]. Therefore, assigning levels to attributes will follow up from identifying attributes and is considered important. Levels represent the different alternatives for each attribute and must be reasonable and capable of being traded off against each other [ 44 ]. Bridges and colleagues suggested that researchers should avoid the use of ranges to define attributes (such as a copayment from USD 5–10) because this requires the respondent to subjectively interpret the levels, which will affect the results, and they should also be cautious of choosing too many levels [ 42 ]. Furthermore, levels of unrealistic and extreme values should be avoided as they will not be acceptable to respondents [ 23 ]. Table 2 shows examples of levels for pain-relieving medication attributes.

4.3. Choosing Scenarios

Once the attributes and levels are identified, the levels of all attributes are combined to form all possible scenarios. CA tasks are the mechanism by which possible profiles are presented to respondents for preference elicitation [ 42 ]. The higher the number of attributes and levels, the higher the number of possible scenarios. Therefore, researchers can very rarely use all produced scenarios. Instead, CA studies utilize the orthogonal fractional factorial experimental designs to construct a set of hypothetical scenarios [ 45 ]. If the scenarios are described with respect to all of the attributes being studied, this is referred to as a full-profile choice experiment [ 46 ]. For example, if the treatment studied has six important attributes, a full-profile scenario would describe the treatment of all six attributes. This means that each scenario would have all six levels; one from each attribute. When a study includes a large number of attributes, it becomes complex for participants to process these scenarios. Instead, the concept of partial-profile choice experiments has been proposed to estimate preferences for a large set of attributes [ 47 ]. In the partial-profile choice experiment, each scenario includes a subset of the total number of attributes being studied [ 18 , 48 ]. All attributes are randomly rotated into the different scenarios, so across all scenarios in the experiment, each respondent typically considers all attributes and levels [ 18 , 48 , 49 ]. For example, if the treatment to be studied has 12 attributes, a partial profile choice experiment describes the treatment on a few attributes in each scenario, i.e., each scenario would possibly have six characteristics of the treatment. The main issue with the partial-profile choice experiment is that the data are spread quite thinly because each task has many attribute omissions [ 50 , 51 ]. This task assumes that respondents can ignore omitted attributes and base their choice solely on the partial information presented in each task [ 51 ].

4.4. Establishing Preference

People’s preferences of the developed scenarios can be established using a rating, ranking, or choice-based approach. The ranking approach in Table 3 asks respondents to list the scenarios in order of preference [ 19 ], whereas the rating approach in Table 4 requests respondents to assign a score to each scenario, e.g., 0–10 or 0–100, usually presented on a visual analog scale [ 52 ]. Accordingly, the rating/ranking CA approach usually requires a low cognitive load and can be easily implemented, but they are direct scaling methods that impose the lack of clear trade-off between preferences [ 53 ]. The choice-based approach in Table 5 asks respondents to choose their preferred scenario out of a couple or few scenarios [ 54 ]. The choice-based approach presents multiple attributes susceptible to simultaneous assessment [ 55 ], then allows patients to pick the best available option from a set of scenarios which in turn permits patients to make clear trade-offs between different levels [ 56 ]. However, with the choice-based approach, there is a lack of ability to justify the reasons behind the choices made [ 57 ]. Rating, ranking, and choice-based CA approaches can be presented to respondents in various ways using different techniques. So, these conjoint methods could be designed through hard copies of pen and paper questionnaires or via computerized software programs.

Examples of the ranking approach.

Examples of the rating approach.

Examples of the choice-based approach.

In Table 5 , each vertical column represents a group of characteristics specific to medication A, B, and C, while each row illustrates the levels of a particular attribute in each of the medication scenarios. The participants are asked to select their preferred scenario and therefore, they have to trade-off attributes and levels against each other.

4.5. Analyzing Data

In CA methods, regression techniques are used to estimate the relative importance of the attributes and the utilities (part-worths) of the levels [ 19 , 23 ]. Moreover, the maximum amount of money that the patients are willing to pay for service or treatment is known as the willingness to pay (WTP) [ 44 , 58 ]. The part-worths are interval data within each attribute that represent the utilities of the levels within that attribute. Generally, part-worths are scaled to an arbitrary additive, while the relative importance is percentages data that are given to each attribute. The higher the percentage the more important is that attribute to the respondents and the relative importance of all attributes add up to 100%.

5. Conjoint Analysis in Healthcare

Several traditional quantitative and qualitative methods have been used to study patients’ preferences. However, these studies were limited in number [ 59 ]. These methods have been used to accentuate patients’ preference in general but did not identify the trade-off that patients make to one of the treatment factors against another [ 60 ]. Unlike traditional questionnaires, the CA method can be used to study preferences [ 13 ] and quantify the trade-off that patients do between the different treatment factors [ 61 ].

Nowadays, there has been a rapid increase in the use of CA to quantify preferences for various healthcare services and treatment options [ 62 ]. For example, in clinics where the majority of cases are non-urgent, CA was found to be a useful tool for allowing patients to discuss their needs and choose medication, health service, and diagnostic tests that suit them the most [ 63 , 64 ]. In turn, CA constituted a supportive tool for clinicians to better understand patients’ preferences and individualize their treatment plans [ 24 , 65 , 66 ]. Hence, the CA use in practice was suggested as a tool to effectively strengthen the communication between patients and healthcare professionals and to engage both parties in the shared decision-making process [ 12 , 62 ]. A scoping review of the studies using CA recommends the use of CA to identify patient preferences for mental health services, which could improve the quality of care and increase the acceptability and uptake of services among patients [ 67 ].

In the hospital setting, implementation of CA constituted a feasible and useful tool in several clinical areas such as investigating hospital stakeholders’ decision-making in the adoption of evidence-based interventions [ 68 ], eliciting patients preferences regarding diagnostics and screening [ 69 , 70 ], improving decision-making regarding patients’ treatment [ 71 , 72 ], understanding patients’ perceived needs and expectations [ 73 ], and determining the clinical factors that physicians prioritize regarding patients’ treatment [ 74 ]. Furthermore, the CA method was very useful during the recent pandemic, as it has been used to understand how people prioritize when deciding whether to present to the emergency department during the coronavirus disease (COVID-19) pandemic for care unrelated to COVID-19 [ 75 ]. This understanding of patients’ priorities helps healthcare professionals to structure an appropriate patient safety assessment which led to remarkably reduced chances of deaths, departments’ crowdedness, and spread of infections [ 75 ]. Moreover, CA was seen to efficiently assess the cost-effectiveness value of alternatives when patients are requested to select their preferred treatment option taking into consideration their financial situation [ 76 ]. Thus, CA is a practical method for generating treatments’ marketing decisions in the pharmaceutical industry, which highly relies on patients’ preferences about a specific drug.

Throughout the discussed CA-based studies, we can discern that CA is a major asset for valuing patients’ important contributions in the decision-making process by assessing patients’ preferences in accordance with treatment selection, patient care, offered health services, and cost comparisons. Research-based literature verified that CA can be a practical method of estimating utility for any combination of attributes, including combinations representing goods or services which may not currently be available [ 77 ]. Thus, when studying patients’ preferences for treatment, CA allows patients to choose their preferred treatment option based on the treatments’ characteristics in isolation of treatments’ names or market brands. Therefore, the precise performance of CA can offer valuable approximation in relation to the relative importance of different aspects of care, the trade-offs between these aspects, as well as the total satisfaction or utility that respondents obtain from healthcare services [ 19 ].

6. Validity of Conjoint Analysis Data

Recently, it has been suggested that the value of CA is not only related to its elevated frequency of implementation but also to the unique ability of this method to generate individuals’ preferences realistically enough to match various decision-making processes faced by individuals in the real world [ 78 ]. Assessing the validity of CA means examining the ability of this data collection tool to accurately measure what it is supposed to measure [ 79 , 80 ]. There are many types of validity and some of these types may overlap, and researchers may argue the names of different types of validity; for example, face validity is often confused with content validity [ 81 ]. Over 20 years ago, very little was known whether CA works in predicting significant real-world actions [ 82 ]. However, within the last couple of decades, researchers have been studying and investigating the validity of CA. Despite the high expansion of CA usage in market and healthcare research, CA validity studies are still limited. A large validity study including over 2000 commercial CA research indicated that there was no validity gain for CA over time [ 83 ]. This could be due to the variation in the CA tools, the expansion of recruitment methods to online and social media, and the differences in the estimation parameters used for each study. In general, the validity for all CA tools can be measured in two ways:

• External validity is the ability of the CA tool to predict what people would choose in real life. This can be achieved by asking the question “did people choose what CA predicted?”. For example, in a conjoint study estimating the market share for an American multinational telecommunications corporation, various trial simulations were implemented hypothesizing that several product features had to be changed in order to attain desired sales (8% of the total market share) [ 56 ]. Four years after launching this product, the actual share was just under 8% [ 56 ], concluding that CA contributes towards the identification of people-desired choices and the estimation of the actual preference behavior. Investigating external validity for CA methods is a challenging task that requires the researcher to follow the participants to examine if they did what the CA tool predicted in terms of buying a product, taking a treatment, attending a particular doctor’s clinic, etc.
• Internal consistency validity is the main validity criterion that has been studied in recent years for strengthening the reliability and applicability of CA. To test the internal validity, the holdouts’ choices are used [ 84 ]. The holdouts are choices that are similar to those selected by the participants in real life but are “held out” of the conjoint approximation by not being part of the final estimation. The internal validity of the conjoint task is examined by comparing how well conjoint utilities predict choices from the holdout tasks. Therefore, the holdout tasks are not used in the estimation of part-worths, but they are presumed to represent respondent choices in the real world [ 85 ]. In a review evaluating CA as a method of estimating consumers’ preferences, Green and Srinivasan reported that several studies have demonstrated the consistency of conjoint models in terms of reproducing current market conditions [ 39 ]. Furthermore, a study offering four topical antibiotics to treat acne confirmed CA consistency and validity when patients’ preferences assessment, the simulated product rankings, and the results of the traditional questionnaire were matched [ 86 ].

Generally, as the use of CA in the healthcare setting increased, some validity studies were performed to approach more patients’ preferences and expectations. These were not focused only on the patients’ benefit–risk trade-offs, but also on evaluating the patients’ WTP for treatments or services [ 55 ].

7. Strengths and Limitations of Conjoint Analysis

Over the years, CA design enabled researchers to elicit and quantify patients’ preferences for treatments and services using a smaller number of scenarios extracted from a larger pool of choices [ 1 , 19 , 72 , 87 , 88 , 89 ]. CA is well known for providing easy experimentation for aspects such as price and features before launching a new product, treatment, or health service [ 90 ]. It is suggested that when people are deciding between multi-attributes alternatives, they apply an unconscious scoring mechanism or system of their preference weight; CA is capable of uncovering this system [ 18 ]. This is achieved by providing respondents with the opportunity to make trade-offs between the specific features of competing items to reach final realistic decisions [ 18 , 91 ]. These trade-offs are based on the value that people place on each attribute.

Some of the limitations of the CA methods are due to the lack of validated quality assessment tools for CA studies and lack of consensus on appropriate sample sizes [ 1 , 3 , 4 ]. Furthermore, one of the inherent limitations of CA is that respondents are evaluating hypothetical scenarios, which might be different from what they do in real life [ 59 ]. It is suggested that the CA questionnaire fatigues respondents as it takes more time to complete than traditional questionnaires and it requires more focus and concentration [ 92 ]. It is also suggested that many patients are not well exposed to research surveys [ 57 , 93 ]. Therefore, reconsidering the number of questions and alternatives presented to participants during data collection is vital to avoid unnecessary respondents’ fatigue [ 1 , 4 ].

8. Strengths and Limitations of This Study

To the best of our knowledge, this is the first article to report the trend of CA publications and citations over the past 70 years and the increase in its popularity based on the amount of published literature. This article takes a well-defined, rich, and clear approach to the discussion of CA. It provides a summary of the very large and wealthy literature describing CA methods. The narrative nature of this article is based on a comprehensive search of the literature and utilized several databases. However, the narrative nature of the discussion could be subjective and open to different interpretations. Therefore, we recommend that the results of this article must be interpreted in line with its limitations. The results in relation to the CA trend over the past 70 years are not based on a comprehensive bibliometric analysis in terms of visualization. Nonetheless, it is based on a comprehensive search of WoSCC databases to identify the growth in CA publications.

9. Conclusions

The popularity of CA in healthcare has been increasing and its use in this setting is gradually competing with its use in business and marketing research. CA is a useful method for eliciting patients’ preferences and WTP. However, there are some limitations in the available CA literature, specifically regarding the appropriate sample size, quality assessment tool, and the validity of CA. This highlights the need for researchers from different fields that use CA methods to come together and develop tools to address these limitations.

Acknowledgments

The authors would like to thank Khalifa University of Science, Technology, and Research for funding and supporting this project.

Author Contributions

Conceptualization, B.A.-O.; methodology, B.A.-O.; software, B.A.-O.; validation, B.A.-O., J.F. and M.E.; formal analysis, B.A.-O.; investigation, B.A.-O., J.F. and M.E.; resources, B.A.-O., J.F. and M.E.; data curation, B.A.-O.; writing—original draft preparation, B.A.-O., J.F. and M.E.; writing—review and editing, B.A.-O., J.F. and M.E.; visualization, B.A.-O.; supervision, B.A.-O.; project administration, B.A.-O., J.F. and M.E. All authors have read and agreed to the published version of the manuscript.

Khalifa University of Science, Technology, and Research, Fund number: 8474000267/FSU-2020-32.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Conjoint Analysis: Definition, Types, Benefits, Examples

Conjoint Analysis is a sophisticated statistical technique used in market research to decipher how consumers value different attributes of a product or service. By presenting respondents with a series of choices that include various combinations of product features, Conjoint Analysis helps identify the most influential attributes that drive consumer preferences and purchasing decisions. This method can be considered a more specialised form of Key Drivers Analysis , which identifies the most impactful factors influencing outcomes, and Latent Class Analysis , which uncovers hidden subgroups within a dataset.

This method breaks down consumer preferences by asking them to evaluate different product configurations or product features. For example, consumers might be presented with various combinations of features for a new smartphone, such as battery life, camera quality, and price. By analysing these choices, businesses can uncover the relative importance of each attribute and understand which combinations are most appealing to different consumer groups. This process of tailoring the model to different groups is akin to the segmentation techniques used in Segmentation Analysis , where consumer groups are categorised based on shared characteristics.

Conjoint Analysis is invaluable in product development and pricing strategy. It provides actionable insights into which features should be prioritised and how much consumers are willing to pay for them. This ensures that new products are designed to meet consumer needs and are priced appropriately to maximise market acceptance and profitability. For instance, if the analysis reveals that customers place a high value on battery life over camera quality, a smartphone manufacturer might focus on enhancing battery performance in their next model.

In real-world applications, Conjoint Analysis has been used by companies across various industries to optimise their offerings. For example, in the automotive industry, manufacturers use it to balance features like fuel efficiency, safety, and design. In the tech industry, companies leverage it to fine-tune features and pricing of hardware and software. This method helps businesses stay competitive by aligning their products more closely with consumer desires.

History of conjoint analysis

Conjoint Analysis has a rich history that traces back to the early 1970s when it was introduced as a method to better understand consumer preferences and decision-making processes. The technique was developed by marketing researchers Paul Green and Vithala Rao, who sought to create a more sophisticated approach to market research than traditional survey methods.

Initially, Conjoint Analysis was used primarily in academic settings to study consumer behaviour and preferences. However, its practical applications quickly became apparent, and it soon gained traction in various industries. The early methods involved presenting respondents with a set of hypothetical products and asking them to rank or rate their preferences. These responses were then analysed to determine the relative importance of different attributes.

Over the years, advancements in computer technology and statistical methods have significantly enhanced the complexity and capabilities of Conjoint Analysis. Modern techniques allow for more detailed and accurate analyses, including the ability to handle larger datasets and more attributes. Software advancements have also made it easier to design and administer Conjoint Analysis surveys, broadening its accessibility and application. Techniques from Survey Weighting are often incorporated into Conjoint Analysis to correct biases in the data and ensure accuracy. These techniques also facilitate specialised conjoint methods, such as the constant sum patient allocation exercise, where choice exercises are framed in terms of, for example, the percentage of patients prescribed a treatment.

The evolution of Conjoint Analysis has led to the development of various methodologies, including Discrete Choice-Based Conjoint (CBC), MaxDiff, and Best-Worst Scaling , each offering unique advantages for specific research needs. These advancements have solidified Conjoint Analysis as a vital tool in marketing research, product development, and pricing strategy, helping businesses make data-driven decisions that align with consumer preferences.

Although Conjoint Analysis is the popular term for this family of techniques it is also referred to in academic circles and literature as Discrete Choice Modelling. 

Types of Conjoint Analysis

At The Stats People, we specialise in several types of Conjoint Analysis, each designed to address specific research needs and provide actionable insights. Here’s an overview of our preferred methodologies and a critique of some common alternatives.

Choice Based Conjoint Analysis (CBCA)

Choice-Based Conjoint Analysis (CBCA) which we sometimes refer to in the Stats People as full Discrete Choice Modelling (DCM)  is a widely used method that presents respondents with a series of choice tasks . Each task includes a set of fully described product profiles, and respondents are asked to choose their preferred option. This approach closely mimics real-world purchasing decisions, providing robust data on consumer preferences and trade-offs. Choice-Based Conjoint Analysis is particularly effective in scenarios where predicting market or preference shares for the full product is more important than understanding the relative hierarchy of which attributes are driving the choice. The method’s realism and flexibility make it ideal for diverse applications, from product design to pricing strategy.

MaxDiff (Maximum Difference Scaling)

MaxDiff, or Maximum Difference Scaling, is a simple but powerful trade-off technique . It asks respondents to identify the most and least important alternatives from sets of simple options. These options can be product benefits, standalone 

Single-level features (sometimes referred to as simple attributes), or any other set of stimulus options we want to trade off. This scale free method is highly effective for determining the relative importance of each option. MaxDiff is particularly useful in product development and feature prioritisation.

Best-Worst Case 2

Best-Worst Case 2 (BWC2) is a half-way house between a full-DCM Choice Based Conjoint Analysis and a MaxDiff . Just like in a simplest trade-off model, respondents are presented with multiple sets of features and are asked to choose the best and worst in each set, however, unlike MaxDiff, BWC2 allows a full attribute grid, just like CBC would, with attributes and levels. We usually deploy BWC2 when the main goal is to understand what attributes and attribute levels are  driving the choice, rather than to estimate product share. Unlike a CBC it allows the attributes’ utilities for all attributes and levels to be estimated and compared on a single scale. This method provides a deeper understanding of consumer decision-making processes and helps refine product features to align more closely with market demands.

Critique of Other Approaches

While other methods like Adaptive Choice-Based Conjoint (ACBC), Menu-Based Conjoint (MBC), and Adaptive Conjoint Analysis (ACA) are popular, we prefer our tailored methodologies. ACBC and ACA, for instance, adapt the survey based on previous responses, which can introduce complexity and respondent fatigue and requires the use of specialist survey hosting software which isn’t a requirement for CBC, MaxDiff and BWC2. 

MBC allows respondents to select combinations of features from a menu, which can be useful but often complicates the analysis due to the vast number of possible combinations. We find that our chosen methods provide clearer, more actionable insights without the added complexity.

Benefits of Conjoint Analysis

Conjoint Analysis offers several key benefits that can significantly enhance business strategy, product development, and market positioning. Here’s how:

1. Understanding Consumer Preferences

Conjoint Analysis provides deep insights into consumer preferences by breaking down how customers value different product attributes. This understanding helps businesses tailor their offerings to better meet customer needs and desires. For example, a company can determine whether specific groups of consumers prioritise price, performance, or specific features, enabling them to design products that resonate more effectively with that group. This insight is also crucial in Segmentation Analysis , as understanding consumer preferences helps in categorising and targeting specific market segments.

2. Competitive Analysis

By revealing what attributes consumers value most, Conjoint Analysis helps businesses understand their competitive position . Companies can compare their offerings against competitors’ and identify areas where they have a competitive edge or need improvement. This insight allows for strategic adjustments that can enhance market share and overall competitiveness.

3. Optimising Product Value and Pricing Strategy

Conjoint Analysis is instrumental in determining the optimal pricing strategy for a product . By understanding the trade-offs consumers are willing to make, businesses can set prices that reflect the perceived value of different features. This leads to better pricing decisions that maximise revenue without sacrificing customer satisfaction.

4. Market Research Insights

The detailed data obtained from Conjoint Analysis in marketing research can inform broader market research efforts. It helps in segmenting the market based on preferences, allowing for more targeted marketing strategies. These insights can guide advertising campaigns, product launches, and other marketing activities to ensure they are aligned with consumer expectations and behaviours.

5. Product Development and Innovation

Insights from Conjoint Analysis guide product development by highlighting which features are most important to consumers . This allows businesses to focus their innovation efforts on areas that will deliver the most value. By prioritising features that consumers care about, companies can create products that are more likely to succeed in the market.

6. Improved Decision-Making

Conjoint Analysis supports data-driven decision-making by providing clear, quantifiable insights into consumer preferences. This helps businesses make informed choices about product features, pricing, and market positioning, reducing the risk of costly mistakes and increasing the likelihood of success.

7. Enhanced Customer Satisfaction

By aligning products and services with consumer preferences, businesses can significantly enhance customer satisfaction. When customers feel that a product meets their needs and preferences, they are more likely to be satisfied and loyal, leading to repeat business and positive word-of-mouth.

Case Studies (Examples of successful Conjoint Analysis studies)

Conjoint Analysis has been effectively employed by The Stats People to help various clients enhance their product development and pricing strategies. Here are two notable case studies that illustrate its successful application and provide a conjoint analysis example:

1. Choice Modelling for Medical Research Agency

The Stats People were engaged by a leading medical research agency to perform a complex Choice Modelling study on trade-offs between branded and biosimilar ophthalmology products across multiple countries. This study required customised approaches for each market, incorporating various attributes like discounts and existing prescribing practices.

Project Execution:

The project began with detailed planning to define the requirements for each hypothetical scenario and product. The Stats People then developed a customised simulator integrating all parameters and included a dashboard interface. This required extensive data handling and iterative adjustments to meet client specifications.

Challenges:

The key challenge was adapting to ongoing client requests that did not fit the conventional Choice Modelling framework. The team had to find practical solutions to incorporate these new requirements into the existing model.

The final deliverable was a highly customised Excel-based simulator with a dashboard add-in, successfully meeting all the client’s objectives and providing a powerful tool for analysing market trade-offs.

2. Best-Worst Case 2 (BWC2) for Remuneration Benefits Analysis

The Stats People were enlisted by a company to analyse employee preferences for various remuneration and benefits packages using the Best-Worst Case 2 (BWC2) method. This approach was chosen to understand which elements of compensation were most valued by employees and how the company could optimise its offerings to enhance satisfaction and retention.

The project started with identifying key attributes of the remuneration and benefits packages, such as salary increments, bonuses, health insurance, and flexible working hours. The BWC2 method was then employed to present these attributes in sets, asking employees to select the most and least preferred options in each set. This approach allowed The Stats People to gather detailed insights into the relative importance of each benefit component.

A significant challenge was ensuring that the BWC2 model accurately reflected the diverse preferences of the employee base. The team had to manage and interpret a large volume of complex data, requiring careful calibration of the model to ensure that it provided clear and actionable insights.

The analysis resulted in a highly detailed understanding of employee preferences. Flexible working hours and health insurance emerged as top priorities, while bonuses and salary increments were less critical. These findings enabled the company to restructure its benefits package to align more closely with employee values, leading to increased job satisfaction and reduced turnover.

These case studies highlight how conjoint analysis can provide valuable insights that drive strategic decisions, leading to enhanced customer satisfaction, better product development, and optimised conjoint analysis pricing strategies.

Conjoint Analysis FAQ’s

1. what is conjoint analysis, 2. how does conjoint analysis pricing aid in developing a pricing strategy, 3. what are the types of conjoint analysis, 4. why is conjoint analysis important in product development, 5. how does conjoint analysis differ from other market research methods.

Statistical consultancy is our core business and our clients see us as the “go-to” team for high-quality consulting and analysis.

We regularly consult with leading agencies and their clients on the sampling and weighting of complex private and national statistic surveys.

Survey solutions is at the core of our business and for those looking for a one-stop-shop we provide the whole package.

Feels like we are a good match? Let’s talk and see how we can help you.

The Plain-English Guide to Conjoint Analysis

Published: February 23, 2022

Sometimes, commercials really get me.

T-Mobile 's Super Bowl commercial this year is a prime example — "What's for Dinner?" demonstrates the infuriating process of choosing what to do for dinner for a young couple, and it's gold .

The reason T-Mobile's ad was so relatable is because of their market research. They looked at what their target audiences wanted — including their thought processes, what informs their decisions, and the trade-offs they're willing to make for their products.

To accomplish all of these important factors in one go, many companies use conjoint analysis.

What is Conjoint Analysis?

Conjoint analysis is a market research tactic that attempts to understand how people make decisions. A common approach, the conjoint analysis combines realistic hypothetical situations to measure buying decisions and consumer preferences.

Think about buying a new phone. Attributes you might consider are color, size, and model. The reason phone companies include these specs in their marketing is due to research such as conjoint analysis.

Would consumers purchase this product or service if brought to market? That's the question conjoint analysis strives to answer. It's a quantitative measure in marketing research, meaning it measures numbers rather than open-ended questions. Questions on the phone company survey would include price points, color preference, and camera quality.

Surveys intended for conjoint analysis are formatted to reflect the buyer's journey.

For instance, notice in this example for televisions, the specs are the options and the consumer picks what best reflects their lifestyle:

This direct method of giving consumers multiple profiles to then analyze is how conjoint analysis got its name. These answers are helpful when determining how to market a new product.

If answers on the phone company survey proved that their target audience of adults ages 18-25 wanted a green phone from $400-600 and a camera with portrait mode, advertisements can cater directly to that.

The conjoint analysis shows what consumers are willing to give up in order to get what they need. For instance, some might be willing to pay a little more money for a larger model of a phone if their preference is larger text.

Types of Conjoint Analysis

Choice-based conjoint (CBC) and Adaptive Conjoint Analysis (ACA) are the two main types of conjoint analysis.

Choice-based is the most common form because it asks consumers to mimic their buying habits. ACA is helpful for product design, offering more questions about specs of a product.

Choice-based conjoint analysis questions are usually presented in a "Would you rather?" format. For example, "Would you rather take a ride-share service to a location 10 minutes away for $13 or walk 30 minutes for free?" The marketer for the ride-share service could use answers from this question to think of the upsides to show off in different campaigns.

ACA leans towards a Likert-scale format (most likely to least likely) for its attribute-based questions. Respondents can base their preference on specs by showing how likely they are to buy a product with slight differences — for example, similar cars with different doors and manufacturers.

How To Do A Conjoint Analysis

To create a conjoint analysis, you'll first need to define a list of attributes about your product. Attributes are usually four to five items that describe your product or service. Consider color, size, price, and market-specific attributes, such as lenses if you're selling cameras.

Additionally, try to keep in mind your ideal respondents. Who do you want to answer your survey? A group of adult men? A group of working mothers? Identify your respondent base and ask specific questions catered to that target market.

The next step is to organize your questionnaire depending on the type of conjoint analysis you want to conduct. For instance, to run an adaptive conjoint analysis, you will present questions with a Likert-scale.

You can use a conjoint analysis tool to create and modify your survey. Then, you can distribute your questionnaire through multiple channels, including email, SMS, and social media.

For more ways to introduce product marketing into your company, check out our ultimate guide here .

Examples of Conjoint Analysis

Sawtooth Software offers a great example of conjoint analysis for a phone company:

The analysis puts three different phone services next to each other. The horizontal column of the model identifies which service is offering a certain program, described by the vertical values. The bottom row shows a percent value of consumers' preferences.

QuestionPro offers this fun, interactive conjoint analysis template about retirement home options. The survey gives you a scenario and asks your course of action. For instance, it asks if you would sign a rental agreement for retirement home housing immediately, and considers specs like rent, meals, size, etc.

Conjoint analysis isn't limited to existing products. They're also very helpful for figuring out if a brand-new product is worth developing. For instance, if surveys show that audiences would be into the idea of an app that chooses clothes for consumers, that could be a new venture for clothing companies in the future.

Looking to create a conjoint analysis of your own? Check out our top four conjoint analysis tools below.

Conjoint Analysis Tools

1. qualtrics.

Image Source

Qualtrics is an easy-to-use survey tool that offers comprehensive product insights. You can create, modify, distribute, and analyze a conjoint analysis in one place. All it takes is four steps — define your attributes, build and modify your questions in the survey editor, distribute the survey, and analyze the results. 

What We Like: Qualtrics goes beyond product insights — this powerful software also captures customer, brand, and employee experience insights.

Pro Tip: Leverage email to invite respondents to take your survey. With Qualtrics, you can embed a survey question directly in your email survey invite. 

2. Cojoint.ly

Conjoint.ly offers a complete toolbox for product and pricing research — including a Product Description test, an A/B test, and a Price Sensitivity test. You can also source your own respondents for your survey or buy quality-assured respondents from Conjoint.ly.

What We Like: Users can simply choose a tool that best fits their research question. These tools are organized under four main categories: pricing research, features and claims, range optimization, and concept testing.

Pro Tip: If you want to "try before you buy," you can use Conjoint.ly's Quick Feedback tool. For a small price, you get around 50 respondents to provide feedback within a 6-hour window.

3. 1000minds

1000minds offers an adaptive conjoint analysis tool. Meaning, each time a choice is made, it adapts by formulating a new question to ask based on all previous choices. This makes the survey feel more like a conversation.

What We Like: We're impressed by the scalability of 1000minds. The tool allows you to include as many participants as you like, potentially in the thousands.

Pro Tip: You can use their conjoint analysis templates or build your own model from scratch. 

4. Q Research Software

Q is analysis software that is specifically designed by market researchers. Its conjoint analysis tool is ideal for choice-based analyses. Users can create experimental designs, analyze the data, and generate reports. 

What We Like: Q cuts through the grunt work with automation — including cleaning and formatting data, updating surveys, and producing reports.

Pro Tip: With just a few clicks, you can export any reports or visualizations from Q to PowerPoint and Excel.

A conjoint analysis requires a solid survey design and analysis, but the extra effort is often worth it. By going the extra mile, you can access insights into your audience's preferences and buying decisions — which is invaluable when determining how to market a new product or service.

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What is Conjoint Analysis -  Examples & Use Cases

What Is Conjoint Analysis?

Conjoint analysis is a marketing research method that leverages statistical analyses and mathematical models to quantify survey respondents' preferences for product features, determine what attributes impact product demand, and can also predict how the market will respond to new products pre-launch.

These research methods use specialized surveys to ask respondents to pick between bundles, and the importance of specific product features is then analytically derived from the results. Once the survey results are collected, the organization can determine how valuable each product feature is and if they should implement them before launch.

For example, a TV manufacturer might conduct a survey where they give the respondents bundles with different features to pick from. Each bundle might include screen type, size, brand, and price. Respondents will choose which bundles are more appealing to them, and the TV manufacturer can determine how much each respondent values certain features.

Conjoint analysis is becoming increasingly popular among market researchers and other organizations because it provides valuable insights that are difficult to obtain elsewhere.

Why Should Your Business Use Conjoint Analysis? 

Conjoint analysis helps organizations optimize features and pricing by giving them the means to quantify and study consumer preferences. Using conjoint analysis, organizations can measure the importance of different factors by having customers choose between realistic product packages. Organizations can deduce the importance of various factors, such as pricing and product features, by modeling utilities after the data derived from conjoint exercises. When using conjoint analysis in tandem with market segmentation , you can easily narrow down which customers prefer what features or services.  

If you ask consumers directly what is important to them, the answer is usually everything.

A Conjoint Analysis Example 

For example, most respondents will say that price is very important when buying a smartphone. However, high-price brands like Apple have a very strong market share. Factually, price is less important than product features like the brand, the features, etc. Optimizing features and pricing requires more accurate quantification of consumer preferences. Respondents are not asked to state the importance of different factors. Instead, they are asked to pick between realistic options (products with features and pric es). The importance is derived from the choices they made. This is why conjoint analysis is often used over other methods.

For market research agencies, purchasing survey software is an important decision. For example, many agencies evaluate survey software on three dimensions: functionality, price per complete, and quality of service. Conducting a conjoint analysis across these three dimensions provides insight into tradeoffs between the three.

At IntelliSurvey, we pride ourselves in our ability to immerse respondents in the conjoint exercise. Our video walkthrough of a survey conjoint with an agency’s lead generation activities is a great example

What Makes an Effective Conjoint Analysis?

Effective conjoint analysis models feature natural relationships between respondents and product attributes. Conjoint exercises should appear “au naturel” to customers to yield the most accurate data possible. One way to accomplish this is to tailor conjoint models specifically for critical customer segments; various segments will weigh attributes differently, and taking a one-size-fits-all approach may invalidate the data.

Incorporate Natural Relationships Between Respondents and Attributes

The best conjoint analysis models incorporate natural relationships between respondents and product attributes. Effective conjoint analysis models feature surveys that closely resemble the final decision point; for example, if you conduct a conjoint analysis for phone plans, you might want to replicate a large phone carrier’s bundle price range.

If imperfectly contextualized, the presentation might not reveal natural attribute preferences. Over time, attention to detail on the look and feel has decreased for practical reasons; organizations run more conjoints, and directional results are often enough. However, the best conjoint results leverage UI elements close to the final product to obtain more detailed results.

Tailor Conjoint Exercises for Critical Customer Segments

Where applicable, organizations should tailor conjoint exercises for critical customer segments. For example, an airline may create different conjoint models for frequent and infrequent flyers. Regular airline customers may receive a free, first-class upgrade, so measuring first-class against economy will have to incorporate more factors than just pricing preferences. 

For many organizations, it can be challenging to analyze these complexities and adapt their exercises to provide options for critical customers, but it is crucial for gaining valuable insights across all customer segments.

Streamline Presentation for Mobile Devices

Roughly half of the online survey traffic is mobile, meaning voluminous choice options will not properly fit on a smartphone or similar device’s single screen. In these exercises, scrolling can become too intense for respondents to focus on and cause them to disengage.

It is possible to limit conjoint exercises to desktop users, but this creates a selection bias in survey responses. In many cases, acquiring data from younger respondents becomes challenging. Conjoint presentations have been streamlined, often with intro pages to give details to keep the task manageable on a mobile device. 

It is critical for organizations to review and test conjoint exercises on an online device before launching to ensure proper data quality.

Exploring Types of Conjoint: 4 Examples of Conjoint Analysis

There are four different types of conjoint analysis methods that organizations employ during market research: choice-based conjoint (CBC) analysis, adaptive choice-based (ACB) conjoint analysis, adaptive conjoint analysis (ACA), and menu-based conjoint (MBC) analysis.

1. Choice-Based Conjoint (CBC)

Choice-based conjoint (CBC) analysis is one of the most common forms of this research method. CBC records how a respondent values different combinations of features within a product or service. 

This conjoint analysis method asks survey respondents to review a set of product concepts with potential features and allows them to select their favorite combination. Using these results, researchers can predict the market share for different scenarios, depending on the product they roll out.

2. Adaptive Choice-Based (ACB) Conjoint

Adaptive choice-based (ACB) conjoint analysis models are newer and more advanced than other conjoint models. Adaptive models are most effective when the attribute list grows because it delves deeper into the respondents' preferences. In return, this conjoint analysis method provides a thoroughly engaging experience for the respondent. ACB conjoint analysis models are more detailed and require more time to conduct than a CBC study.

3. Adaptive Conjoint Analysis (ACA)

Adaptive conjoint analysis (ACA) is a conjoint analysis research method from the 1980s that provides each respondent with a personalized experience. This conjoint analysis method gives each respondent a different survey experience based on their answers to previous questions. Although many organizations do not use ACA today, it is helpful where organizations must evaluate numerous product features or attributes simultaneously to hasten the process.

4. Menu-Based Conjoint (MBC)

Menu-based conjoint (MBC) analysis models are advanced tools that assist in analyzing menu choice experiments with multiple checks. While this conjoint analysis model provides organizations with better opportunities for modeling complex consumer preferences, it requires a high level of expertise. Many organizations will defer this to experienced professionals with the necessary platforms to make this conjoint model scalable and viable.

When Is Conjoint Analysis Used?

Conjoint analysis methods help organizations conduct market simulations, predict demand, and estimate price sensitivities for potential products. Many researchers also leverage data from conjoint analyses to organize respondents based on what product attributes and features they prefer. In short, conjoint analysis is used to help companies determine which features users value the most and assists them primarily in the three following areas: pricing, marketing efforts, and research and development.

Conjoint Analysis Helps Determine Pricing

Conjoint analysis helps businesses determine pricing for different products and services. Using conjoint analysis research methods, organizations can compare various product features to determine how consumers value each. For example, attributes that users appreciate more in service might wind up in a more expensive subscription package, while lesser-valued features might become part of a trial version.

Conjoint Analysis Informs Marketing Strategies

Conjoint analysis methods help organizations create marketing strategies by informing them of which factors are most valuable to various target audiences. Organizations can leverage respondents’ answers about which features are highly favored to determine where they should allocate their marketing resources. Conjoint analysis can also help marketers segment and target different audiences based on what features different demographics value the most.

Conjoint Analysis Assists in Research Development

Insights obtained from conjoint analyses can assist an organization in researching and developing new products and services. For example, if a competitor's product offers more features, they can deploy a survey to their users with sample packages that include those features. If users favor one sample over the others, the organization can move forward with implementing those features.

Modeling is a critical part of running a conjoint project and has evolved over the years. Conjoint experts back in the day were much more attentive to the design and presentation of the choice scenes themselves. We regularly heard concerns about whether respondents would be able to interact with the scenarios in an intuitive way. If imperfectly contextualized, the presentation might not reveal natural attribute preferences. Thus, IntelliSurvey often undertook great efforts to model conjoints that would appear “au naturel” for respondents - in market freezer doors, new car stickers, six-packs, and more.

As conjoint has gotten more "packaged", one can see much more "plug and chug" thinking about the exercises. Presumably, for many exercises, this is just fine. When the “au naturel” target interaction involves respondents picking one plan from three presented in three columns.  Such exercises are quite straightforward for data collectors to deploy, sometimes requiring just a few hours.

How IntelliSurvey Can Help

We're always jazzed to see more complex models, and presentation schematics. We're excited to work with friends old and new from the high-end modeling community, and introduce interested parties to new modeling methods. The world of bespoke still exists, and may be better than the cookbook at reliably modeling the utilities and relationships critical to your business success.

IntelliSurvey has a variety of products to help companies conduct surveys and multi-market studies. IntelliSurvey has been deploying conjoint analyses for more than 20 years and excels at creating models that are intuitive and organic to survey respondents. We help organizations optimize their features and pricing based on customer preferences, contact us for more information.

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A Guide to Conjoint Analysis

Conjoint analysis explained + survey template.

Definition: Conjoint analysis is a research technique used to quantify how people value the individual features of a product or service. A conjoint survey question shows respondents a set of concepts, asking them to choose or rank the most appealing ones. When the results are displayed, each feature is scored, giving you actionable data. This data can help determine optimal product features, price sensitivity, and even market share.

Why Is It Important? Conjoint analysis goes beyond a standard rating question. It forces respondents to pick what product concepts they like best, helping identify what your audience truly values.

Interactive Conjoint Example Question

Here is an example of Conjoint analysis: A store wanting to release a new ice cream offering needs to determine the optimal flavor, size, and price. Each flavor would become a level for the flavor attribute, while each price point would become a level for the price attribute. Customers would pick from various combinations of these levels, helping identify preferences.  

This sample question has five total attributes, displayed over three sets, with three attributes shown per set.

If we offered a new menu item for ice cream, which of the following options would be most appealing to you? Please make one choice per set. If no options look appealing, choose "None."

This is an interactive example of choice based conjoint

When to Use Conjoint Analysis

Without conjoint analysis it would be impossible to ask about product prices along with flavor and size; a separate rating question for each flavor and size combination is needed. Conjoint analysis solves these problems with a straightforward survey question. When respondents evaluate this question, concept features are compared against one another, and a researcher can identify preferences.

Conjoint analysis is useful in two specific scenarios, marketing research and pricing analysis.

Marketing Research

Conjoint analysis is used in marketing research to identify what features of a product or service are most appealing to a customer base. This research can be conducted on existing products to improve advertising engagement or identify areas of improvement to increase sales. Conjoint analysis could also be used to conduct preliminary research for product feasibility.

A conjoint study will usually include demographic questions such as gender. A marketing executive can then segment the survey data by gender, revealing hidden insights used to bolster marketing strategy.

Pricing Research

Conjoint analysis is useful in pricing research because it forces customers to decide using trade-offs, helping to identify optimal prices for various levels. The ice cream example we use in this document has a $5 USD price with the highest utility, which is paired with a "medium" size. Without a conjoint study, it would have been logical to assume the "large" size should be sold for $5. Because of the trade-offs, the optimal size and price combination was found.

If the restaurant chain used multiple rating questions instead of conjoint, respondents would likely rate multiple flavors as good, and likely choose the lowest price. Using that method, it would be hard to gather reliable data.

How to Conduct a Conjoint Analysis Study

Often, preliminary data needs to be collected before running your conjoint study. An initial survey would include a MaxDiff or a Van Westendorp question to determine important product features or an acceptable price range. The preliminary survey acts as a baseline to reduce the number of conjoint concepts. A smaller number of concepts reduces survey fatigue and increases the quality of responses.

You also want to organize any custom data that you can be used in the survey. Suppose you want to segment your research by country (USA vs European customers). In that case, you need to make sure that internal data is valid, complete, and accessible by your team before running the conjoint study. If custom data is unavailable, you can add additional questions to the survey before the conjoint question.

With the preliminary survey data in hand and custom data organized, you can now create your conjoint analysis study. You can upload the product attributes and levels, include custom data, and you can add follow-up questions to ensure a successful project.

Conjoint Analysis Terminology

Conjoint analysis is an advanced research technique that uses a variety of unique terminology. To help you get a complete understanding, here is a list of commonly used conjoint terminology:

The high-level product features that respondents will evaluate are called attributes. Attributes are the first column in the above example question. That example has the following features: flavor, size, and price. If you studied a new car offering, you might have features such as color, make, model, MPG, and tire type. There is a limit of 20 attributes on the SurveyKing platform.

The items listed within an attribute are called levels. In the example, the "Flavor" attribute has levels of "Chocolate," "Vanilla," "Cookie Dough," and "Strawberry." When you create the conjoint survey, you define an attribute and the levels that go with each attribute. There is a limit of 15 levels on the SurveyKing platform.

Combining all your attributes and levels, which creates a hypothetical product, is called a concept. In the above example, concepts are the columns that respondents choose. Concepts are sometimes referred to as "cards" in statistical software. There is a limit of 7 concepts on the SurveyKing platform.

Also referred to as a task, a set contains multiple concepts or product offerings. Respondents will choose one concept per set and then be shown a new set of concepts. There is a limit of 20 sets on the SurveyKing platform.

Part-Worths/Utilities

This term is the most crucial in conjoint analysis. It defines how a respondent values each attribute level. When all the utilities for all respondents are analyzed, a researcher can determine an overall product value. Utilities are the output of a regression equation.

Utilities have no scale compared to other conjoint projects you run. They only matter in the context of the current question you are looking at.

Sometimes utilities are called "part-worths" or "part-worth utilities." We use the term "utility."

Types of Conjoint Analysis

Choice-based conjoint.

This is the most common form of conjoint. The example question above is a choice-based conjoint question. Respondents pick the most appealing concept for each set. Each set contains a random set of concepts that are evenly distributed. This type of conjoint best simulates buyer behavior since each set contains hypothetical products (concepts). When respondents choose a complete profile, a researcher can calculate preferences from the tradeoffs made. (e.g even though "Strawberry" isn't a preferred flavor, if the price were low enough, it would still provide consumer utility")

As with most conjoint studies, preliminary research is essential to reduce the number of attributes and levels to choose from. With fewer attributes and levels, the number of concepts is reduced, which lowers survey fatigue. A MaxDiff or ranking survey can be used to find the top four ice cream flavors.

Currently this is the only type of conjoint offered by SurveyKing.

Best/Worst Conjoint

Sometimes referred to as MaxDiff conjoint. Similar to choice-based conjoint, this method shows respondents a set of concepts. In each set, respondents are asked to pick the most/least (or best/worst) concepts. This approach is used when a product or service has features that cause both positive and negative reactions. An example could be studying how parents select daycare. The number of full-time faculty would draw a positive reaction. The percentage of fellow students that are economically disadvantaged could produce a negative reaction.

This is a future addition to the SurveyKing platform.

Adaptive Conjoint

This method is also similar to choice-based conjoint. Respondents pick the most appealing concept for each set, except with this method, the next set of concepts are not random but are tailored based on the previous answers. This method is more engaging to respondents and can help fine-tune the data.

Full-profile conjoint analysis

This method displays many concepts and asks respondents to rate each one based on the likelihood of purchase. This method is outdated and was primarily used prior to the introduction of survey tools that offer choice-based conjoint. Asking to rate lots of concepts at once is error-prone, quickly causes fatigue, and yields low-quality data.

Rating or Ranking Conjoint

Ranking and rating conjoint was the method used for full-profile conjoint analysis. As software has progressed, it is now possible to conduct rating or ranking conjoint similar to a choice-based conjoint. Respondents are shown a set of concepts and asked to rank or rate each concept. They could rank by entering a value for each concept, which sums to 100 for each set, or they could enter a number based on a scale. This method is also sometimes referred to as "Continuous Sum Conjoint".

Ranking conjoint is a future addition to the SurveyKing platform.

Menu-Based Conjoint

Menu-based conjoint is a new conjoint method. This method gives respondents the ability to pick multiple levels from a menu. For example, a car manufacturer could ask respondents to choose a base model and price, just like choice-based conjoint. But then they could also ask to check a box for each additional feature desired such as "Alloy Wheels for $1,500", "Sunroof for $1,000", or "Parking Assist for $1,500".

This method is much more advanced in terms of front-end programming and back-end statistics than choice-based conjoint. Often custom solutions need to be built for a company wishing to create this type of project.

Creating a Conjoint Survey

Any survey that contains a conjoint question is referred to as a conjoint survey. SurveyKing currently only offers choice-based conjoint. Here are the steps needed to create your own conjoint survey:

• Navigate to the "Builder" page of your survey
• Click on the "conjoint" element box, drag it into your questionnaire, or click the "Insert question" dropdown to add a conjoint question at the end of a specific page.
• To add a new attribute, click "Add attribute" within the conjoint builder. The builder will show levels for the attribute to the right of each attribute.
• Choose how many sets and concepts you want to display.
• Select any options to customize the question further.

Conjoint Survey Options

• "None" choice - This option will add one additional card, or column, per set that says "None" This option is marked by default. This setting reflects the real world, where consumers can choose not to buy a product. You should exclude this setting from projects where customers are forced to pick an option, such as a government service.
• Reset choices - With this option, respondents can start back at the beginning. The respondent will clear all answers for the question, and the first set will be displayed when the "reset" button is clicked. We recommend reserving this option for specific circumstances, as it could lead to second-guessing and low-quality data.

How Many Attributes, Levels, Concepts, & Sets are Needed?

An ideal conjoint question will have roughly 5 attributes (rows), 4 concepts per set (columns), and approximately 5 - 10 sets. This will help ensure respondents are not fatigued. A detailed breakdown is below:

• Attributes - Roughly 5 attributes with no more than 10 total levels per attribute. Having fewer levels per attribute ensures the survey will show various concepts more often.
• Concepts - Roughly 4 concepts to show each set. Too many concepts per set, and you risk respondents not making effective choices. The total amount of concepts available is calculated by multiplying the number of levels in each attribute. In the example above, we had four flavors, three sizes, and two prices. Total concepts available would be equal to 4 * 3 * 2 = 24. Ideally, this number should be no larger than 50. The more total concepts, the harder it becomes to draw meaningful conclusions.
• Total Sets - Showing no more than 10 total sets to respondents to avoid survey fatigue. Generally, 3-5 are best.

How Many Responses are Needed?

We recommend collecting at least 100 responses for each segment being researched. For example, if you wanted to research both males and females, you would want to collect 100 responses for both.

Conjoint Analysis Scoring & Results

Conjoint analysis uses regression to calculate how different attributes and levels are valued.

Because conjoint uses categorical data (a name like ice cream flavor) instead of continuous data (a number like a temperature), a particular type of regression is used called logistic regression . Just like any regression equation, the result of this regression calculates coefficients. These coefficients are referred to as "utilities".

Utility is not a standard unit of measure. It can be thought of as "happiness". If a lot of respondents choose concepts containing "Cookie Dough" and only a few choose concepts with "Vanilla.", even without doing the math, you can imagine that the coefficient for "Cookie Dough" would be higher than the coefficient for "Vanilla."

Let's say the coefficient for "Cookie Dough" is 5 and the coefficient for "Vanilla" is 1. We could interpret this as saying "A Cookie dough flavor of ice cream will add 5 units of happiness to a consumer, while vanilla would add only 1 unit of happiness." We would also factor in the utilities for serving size, and price, to come up with the product (or list of products) that would provide customers with the most value or "happiness".

To illustrate this concept, we ran the above ice cream example with 20 respondents. Below is the analysis of those responses. This analysis includes the utilities for each level in addition to the relative importance of each attribute.

Sample Survey Data - Summary Table

Attribute	Importance	Level	Utility
Flavor	61%	Chocolate Vanilla Cookie Dough Strawberry Small Medium Large $2 USD $5 USD Walking Through the Analysis The utilities in the last column are the output of regression analysis. Next to each number is a small bar chart for visual representation. Remember, utilities are not an actual unit of measurement and could be thought of as happiness. If we look at the above table, the "Cookie Dough" flavor has a utility of 14, and the "Vanilla" flavor has a utility value of 7. We could interpret this as "Cookie Dough has double the happiness of Vanilla." The importance column is the weighted difference in utilities ranges for the product levels. You can see that flavor has the level with the largest difference of roughly 7. The larger the utility differences for an attribute, the more important they are to consumers. To get a significant difference, as we see with cookie dough, many respondents choose concepts with that flavor. We know the other levels are evenly distributed, meaning that cookie dough was a significant driving factor in decision-making regardless of size or price. Here's how you would calculate the importance: Take the largest number for each level, and sum: 14.11+4.03+5.06 = 23.02 Divide each of the highest levels by this number. The calculation for flavor importance is 14.11 / 23.02 = 61% Statistical Details SurveyKing uses ChoiceModelR , a package in the R statistical program to compute conjoint utilities. ChoiceModelR calculates a coefficient using logistic regression with the maximum likelihood for each attribute level by each respondent. When the analysis is complete, utilities for each level are averaged. The output of our example can be found in this Excel file . We color-coded the Excel file for each attribute level. Row 22 has an average subtotal, which the average utility for a specific level. The regression equations use effects coding to ensure each attribute in total sums to 0. Because of this, you will notice the excel file contains negative utilities. We shift each number by a constant to eliminate negatives and put the baseline to 0. The dark blue flavor columns were adjusted by 5.43 before the results being loaded into our dashboard. Having a 0 baseline makes the data easier to interpret. Data used to populate ChoiceModelR: Data Matrix - See this Excel file , which is the input for the ice cream example. The first row of each card set contains the card number chosen (column G). The first card selected was 4. This is because the "none" option was selected. When the "none" is the chosen option, the highest index + 1 is the card selection. This is the input required for ChoiceModelR. Other programs use an output similar to this file . You'll see it's the same setup, except column G has a "1" if the card is selected or "0" if not selected. An additional row is added for the none column. R - The total number of iterations of the Markov chain Monte Carlo (MCMC chain) to be performed. Default value: 4000. Use - The number of iterations to be used in parameter estimation. Default value: 2000. Keep - The thinning parameter defining the number of random draws to save. Default value: 5. wgt - the choice-set weight parameter; possible values are 1 to 10. This parameter only needs to be specified if estimating a model using a share dependent variable. Default value: 1. xcoding - A number that specifies the way in which each attribute will be coded. We code each attribute as categorical, which is the value 0. Prices could technically be labeled as continuous, but for ease of calculations and consistency, we code all variables are categorical. Time Spent Per Set The time spent on each conjoint set is also included in the results. This data is useful to eliminate low-quality responses. Responses that answered a set too fast (under 2 seconds) should generally be eliminated from the results. Analyzing Concept Profiles A powerful benefit of conjoint analysis is quantifying how each concept would fare in the market. We can easily see the product with the most utility would be Cookie Dough, Medium, for $5 USD. But what about the top three products? Or the bottom three products? In the ice cream example, there were 24 hypothetical products. Unique to the SurveyKing platform is the ability to scroll through each concept in ranked order, to see what profiles faired the best or worst (or offer the most utility). The reporting section will automatically include the table shown below: Rank Flavor Size Price Total Utility 1 Cookie Dough Medium $5 USD 488.02 2 Cookie Dough Large $5 USD 436.91 3 Cookie Dough Small $5 USD 403.10 4 Cookie Dough Medium $2 USD 381.51 5 Vanilla Medium $5 USD 341.31 6 Cookie Dough Large $2 USD 330.40 7 Cookie Dough Small $2 USD 296.58 8 Vanilla Large $5 USD 290.20 9 Vanilla Small $5 USD 256.39 10 Vanilla Medium $2 USD 234.80 11 Chocolate Medium $5 USD 200.82 12 Strawberry Medium $5 USD 191.44 13 Vanilla Large $2 USD 183.68 14 Vanilla Small $2 USD 149.87 15 Chocolate Large $5 USD 149.71 16 Strawberry Large $5 USD 140.33 17 Chocolate Small $5 USD 115.90 18 Strawberry Small $5 USD 106.51 19 Chocolate Medium $2 USD 94.31 20 Strawberry Medium $2 USD 84.93 21 Chocolate Large $2 USD 43.20 22 Strawberry Large $2 USD 33.81 23 Chocolate Small $2 USD 9.38 24 Strawberry Small $2 USD - To get these figures from the Excel output file, you could create a table with all possible combinations, and use sumproduct to calculate to total utility. Here is an example . Conjoint Analysis by Question Segments Sometimes it's important to analyze different segments, such as gender. To do this, add a multiple-choice question to your survey for each segment you wish to study. In the reporting section, you can choose "Conjoint Segment Report." From here, select the appropriate question, and the report will output a data table for each answer. Using the ice cream example, you may notice "Males" prefer "Cookie Dough," while "Females" prefer "Vanilla." These are additional data points to fine-tune your marketing efforts. Here is an interactive example of a conjoint comparison report unique to the SurveyKing platform. The first question asks for gender and the second question asks for a preferred ice cream concept. You can see males prefer "Cookie dough" with a utility of 23.06, while females prefer "Vanilla" with a utility of 25.63. Each gender segment lists flavor as the most important attribute. The report also includes a segmented ranking of concepts. Analyzing Concept Market Share dfsgyfg dfgfsgsfg Sample Survey Data Conjoint analysis tips. Keep descriptions simple - For both attributes and levels, keep the descriptions as short as possible. This will make picking choices easier and reduce survey fatigue. Images - Because of limited space, we recommend using images inside of each level sparingly. When images are used, we recommend that each image be custom-made for this project with a size no larger than 150px X 150px. Additional descriptions - Let's say you are researching a new phone. If you have a weight level of 7oz and 11oz, people won't be able to gauge that difference. You would want to say (ideally in the question text), "Use the iPhone 7 as a baseline weight, that weight would be considered average" Then the size product labels would be "Light," "Average," "Heavier." Be aware of incorrect conjoint content - There is a popular online video that explains conjoint analysis in Excel. The video uses "Dummy Variables" to compute the regression. This would be incorrect for two reasons. Excel cannot do logistic regression without any addons. Also, removing dummy variables is unnecessary if logistic regression is done correctly. The video codes a three-level attribute with 1's and 0's, which results in collinearity. Logistic regression assigns categorical data to a unique number. Like in our example, a four-level attribute would have the numbers 1, 2, 3, or 4, depending on what concepts were displayed. Home Blog When To Use A Conjoint Analysis When To Use A Conjoint Analysis Wordpress + bootstrap. To customize the contents of this header banner and other elements of your site, go to Dashboard > Appearance > Customize Category: "> What Is A Conjoint Analysis A conjoint analysis is a market research approach that helps you understand how people make decisions. With conjoint analyses, you see how people make trade-offs in real time, giving you much needed intel on what features to emphasize, and which ones to deprioritize. In essence, it replicates the real-time decisions individuals make during any purchase decision. Customers are faced with many products or services in any given category. And, each of these likely comes with different features, benefits, and price points. By understanding how customers prioritize these elements, you can develop options that increase their purchase likelihood. Business Questions Answered By Performing A Conjoint Analysis How do you know if a conjoint analysis is right for you? When talking to prospective clients, we keep our ears open for a few key business questions. These are….  What features should we release?  What benefits are most compelling?  How sensitive are customers to price changes?  Do any features, benefits, or price points alienate customers?  What is the best mix of features, benefits, and price to optimize purchase interest? If you find yourself asking these questions, especially when planning for a new product release , chances are a conjoint is worth considering. What You Learn When Performing A Conjoint Analysis Let’s now breakdown exactly what kind of information you’ll learn when you run a conjoint analysis. For the sake of making things tangible, we’ll pretend we have a financial services company considering launching a new payment product. We need to know how important certain features are to customer preference. Additionally, we want to understand sensitivities to price and feature changes. Preference Importance A key learning you’ll get from running a conjoint analysis is something called preference importance. Preference importance lets you see how important individual features are to customers’ purchase decisions. A high importance means that feature has a very strong influence on purchase decisions. In contrast, a low importance means it has very weak influence. In the case of our pretend financial services company, we see that Fee holds a 45% importance preference. This high importance value tells us that customers really care about the product’s fee when deciding whether or not to buy. Meanwhile, Credit Score holds a 5% importance. This tells us that it has some, but very little, impact on our customer purchase decision. In this example, looking at preference importance shows us the the premium customers put on Fees over any other decisions element. Nevertheless, our mocked up data also shows us that we can improve consideration with changes to Payment Period and, to a lesser extent, Rewards. And, it tells us that changing Credit Score won’t have a material impact. Preference Share Another learning you’ll get from a conjoint analysis is the preference share within a given feature. Preference share shows how much a given level is preferred over other levels. A high share means that a particular level is very strongly preferred. In contrast, a low share means it not at all preferred. Let’s look at this again in the context of our financial services company. This time, we’re going to home in on the Fees feature. Specifically, we’ll see how much customers prefer paying one fee amount versus others. In this example, customers have a very strong preference for low fees. This is why a fee of .5% has a 65% share while a fee of 1.5% has a 5% share. This outcome isn’t very surprising. Customers always want to pay less. However, when performed for other features (e.g. payment periods), we may see that customers are more balanced in their preferences. Relative Utility Lastly, running a conjoint analysis lets us understand the relative utility of levels within each feature. Said more simply, it lets us identify how sensitive customers are to changes in feature levels. And, it shows when changing certain levels really alienates customers. Once again, let’s look at this for our financial services company. In this example, a .5% fee has a higher relative utility than a 1% fee. However, both of these options have a positive utility. That is, going from .5% to 1% still results in positive customer interest, albeit the fee hike does decrease interest a bit. However, when the fee goes to 1.5%, the story changes. There is now a negative utility. This means we are alienating customers. Relative utility lets us see how elastic or inelastic customers are to feature changes. In the case of Fees, we’re seeing a lot of elasticity. This means that customer demand changes a lot when fees increase or decrease. Meanwhile, customers are less elastic when it comes to Payment Day. We can infer this because the graph shows far narrower shifts in relative utility as payment days change. This represents a valuable insight for businesses looking for ways to improve a product’s economics while increasing customer demand. It tells them which features they can change the most and still keep customer interest high. How To Perform A Conjoint Analysis Without getting too deep into the weeds, let’s walk through the standard approach for performing a conjoint analysis. Develop A List Of Features & Feature Levels First things first, you need to determine the specific features you want to test. In the case of our financial services company, this would be things like payment periods, fees, rewards, etc. Then, you need to figure out what levels within those features you want to test. For instance, you may want to look at 10-day, 15-day, and 20-day payment periods. Or 1% versus 2% versus 3% fees. Build A Digital Survey To Capture Input Using a digital survey tool, we program these different features and feature levels to capture the data needed for subsequent analysis. When taking the survey, respondents see unique product bundles that mix and match different feature levels. For instance, one product may show a 1% fee and a 30-day payment period. Another may show a 3% fee and a 5-day payment period. Respondents select their preferred options. The bundle options then refresh so that respondents see a new set of bundle options. Once again, they select their favorite bundle. This process repeats multiple times across all respondents. Analyze The Results Once the data is in, we can dig into learning the preference importance, preference share, and relative utility of different features and feature levels. Additionally, if sample sizes permit, we also look at findings by unique segments. This tells us if individual customer groups have different preferences and elasticities. Alternatives To A Conjoint Analysis A conjoint analysis is extremely powerful. But, sometimes it’s overkill. When organizations are in the early days of developing a product or service, you may not even know what features to include. As a result, it’s far too early to dive into testing feature importance, let alone the relative utility of feature levels. Instead, you’re likely better off doing product concept tests . This “dip your toe in the water” approach offers initial validation before you get too entrenched in a product idea. Or, you may have a list of high-level features but aren’t sure which ones truly matter. In this scenario, you’re not ready to test feature levels. You just need to understand the relative value of unique features. If you find yourself in this instance, consider a MaxDiff study instead. This approach gives you clear insight into what features customers really want, and which ones they’re comfortable leaving behind. Most Popular Posts Research Services Case Studies Original Research In the News Get the latest research trends and best practices in the PlanBeyond monthly newsletter. Conjoint Analysis Definition, Types, and Examples Conjoint analysis is a market research technique used to understand how consumers value different features of a product or service. It involves presenting respondents with a series of hypothetical scenarios and asking them to choose their preferred option. Table of contents What is Conjoint Analysis? What are the types of conjoint analysis. Why is Conjoint Analysis important for Researchers? Benefits of Conjoint Analysis Drawbacks of conjoint analysis, conjoint analysis examples, tips for using conjoint analysis. Have you ever wondered how companies determine the perfect combination of product features that will appeal to their customers? One popular technique used by market researchers is called “conjoint analysis.” This method involves presenting survey respondents with different product configurations and asking them to rate or rank their preferences. By analyzing the data, researchers can identify which product attributes are most important to consumers and how they interact with one another. In this article, we’ll dive into the definition, types, and examples of conjoint analysis to help you better understand this valuable research tool. Conjoint analysis is a statistical technique used in surveys to understand how people make decisions and evaluate products or services based on their attributes. It involves presenting participants with a series of hypothetical scenarios that vary in the attributes of the product or service being evaluated. By analyzing the choices participants make in these scenarios, researchers can determine the relative importance of different attributes and how they affect overall preference. Conjoint analysis can provide valuable insights into consumer behavior and help businesses make informed decisions about product development, pricing, and marketing. It is commonly used in market research, product design, and customer satisfaction studies. Hot-Button Conjoint Analysis This type focuses on the emotional response of respondents to features and aspects of products or services. It can provide valuable insights into the correlation between emotional responses and purchase decisions. Pairwise Comparisons Choice-based analysis is a survey-based method used in market research, new product design, government policy-making, and the social sciences to understand people’s preferences and shape products and policies accordingly. It is based on the 1000minds PAPRIKA technique , which uses questions based on choosing between pairs of alternatives to determine people’s utilities (weights). Grid Analysis Grid analysis is a type of market research technique that helps to evaluate the attractiveness of different product or service features. It can help companies determine which features are most important and make sure they include them in their products. Grid analysis can also be useful in helping to identify which features consumers are willing to pay a premium for and which ones they aren’t as interested in. This can be helpful in developing pricing strategies and product design. Rating Scale Analysis Rating scale analysis of conjoint data is a type of analysis used to assess consumer preferences and make decisions about product features and marketing strategy. It is different from other forms of conjoint analysis, such as choice-based conjoint analysis, as it does not directly link to behavioral theory. It is limited in the number of attributes that can be included in the study, but it provides an effective way to understand consumer preferences and make decisions about product features and marketing strategy. Tree Analysis Tree analysis is a type of conjoint analysis often used in market research to understand the customer’s preferences for different product attributes. This type is different from other analyses in that it uses a hierarchical structure to organize and rank customer preferences. For example, a tree analysis could differentiate between a brand preference, such as “HP” vs. “Dell” versus the actual product attributes, such as processor type, hard disk size and amount of memory. MaxDiff Conjoint Analysis MaxDiff analysis is a type of market research methodology used to determine the relative values of combinations of features by asking customers to rate them from best to worst. It is similar to other forms of conjoint analysis, such as Choice-Based Conjoint (CBC) Analysis, Adaptive Conjoint Analysis (ACA), and Full-Profile Analysis, but differs in that it presents a smaller set of product profiles for evaluation. This makes the task easier for respondents, and MaxDiff can also be used with other research techniques to provide more detailed insights into customer preferences. Multi-Way Analysis A multi-way analysis is used to measure the reactions to a range of product attributes by creating a matrix of choices. Unlike traditional analysis which only presents a single attribute or feature to the respondent at a time, multi-way analysis presents multiple attributes or features to the respondent for consideration in a single-choice task. This allows the researcher to understand how different combinations of attributes affect the respondent’s preference. Multi-way analysis can also be used in combination with other forms of conjoint analysis such as choice-based conjoint (CBC), adaptive conjoint analysis (ACA), full-profile conjoint analysis, MaxDiff conjoint analysis, and hierarchical Bayesian Analysis (HB). Choice Modeling Choice modeling is a type of analysis that looks at the choices that customers make when they are presented with several options. It is used to understand the trade-offs that consumers make when evaluating different attributes of a product, and can be used to uncover hidden drivers that may not be apparent to respondents. It also mimics realistic choices or shopping tasks and can be used to develop needs-based segmentation in some cases. Why is Conjoint Analysis Important for Researchers? Conjoint analysis is one of the most important tools for researchers as it helps them to gain insights into a consumer’s preferences and decision-making processes on an individual level. It allows for a deeper study of the consumers and attributes involved to create a needs-based segmentation, providing user-based affirmation of what is most valued in the product or service. This helps researchers to understand the trade-offs that consumers make when they evaluate multiple attributes simultaneously, giving them insight into the real and hidden drivers that may not be readily apparent. Furthermore, researchers are able to measure consumer preferences and analyze data to gain statistically relevant insights representative of a larger group. As a result, conjoint analysis has become the gold standard for preference research and is used by many businesses in different industries across the globe. By using surveys, businesses can measure the value that different features have for consumers. This information can be used to create products and services that better meet the needs of customers. By understanding what customers value most, businesses can create offerings that increase satisfaction. Conjoint analysis is a technique that can be used to find the best combination of product features by surveying customers. First, determine the features you would like to examine, and select the target customers to survey. Then, reach out to customers with a survey that presents them with different combinations of features and asks them to rank them based on their preference. After the surveys are returned, analyze the results to determine the optimal feature set for your needs. This analysis can be used to estimate the market share of new products by gathering data from customers on their preferences for different product alternatives and attributes. This data is then used to create a choice model which estimates the likelihood of each product being chosen by potential customers. Conjoint analysis is a tool that can help businesses identify which product features are most valuable to customers. By conducting a conjoint analysis survey, businesses can determine which features are the most important to their customers and develop a marketing strategy that is most successful. Conjoint analysis can be used to evaluate the effectiveness of advertising campaigns by determining what consumers are willing to pay for certain features and attributes. By analyzing the data collected from a conjoint study, marketers can gain a better understanding of what consumers are willing to buy, which allows them to refine their advertising strategies. Conjoints require careful consideration of multiple attributes and levels, which can lead to a complex design. As the number of attributes and levels increases, so does the complexity of the design, making it difficult to manage and analyze. Conjoints often involve asking respondents to evaluate a large number of product profiles, which can lead to respondent fatigue. This can result in lower response rates and lower-quality data as respondents may not be fully engaged in the survey. The results of conjoint analysis are specific to the attributes and levels included in the design. This means that the results may not be generalizable to other products or markets, limiting the usefulness of the analysis. Conjoints assume that respondents make decisions based on a rational evaluation of the attributes and levels presented to them. However, in reality, decision-making is often more complex, and emotional and psychological factors can also play a role. Here are four examples of how conjoint analysis can be used in real-world scenarios: Hotel Room Preferences – A hotel chain wanted to know which room features were most important to their guests, such as the size of the room, the view, and the amenities. Using this analysis, they presented survey respondents with different room configurations and asked them to rate their preferences. The analysis revealed that a spacious room and a good view were the most important factors for guests. Fast Food Menu Optimization – A fast food chain was looking to optimize their menu by determining which items and prices would be most appealing to customers. Using conjoint analysis, they presented survey respondents with different menu options and asked them to rank them. The analysis revealed which items were the most popular and at what price points they were most appealing. Car Purchase Decisions – An automotive manufacturer wanted to understand which car features were most important to consumers when making a purchase decision. Using this analysis, they presented survey respondents with different car configurations and asked them to rate their preferences. The analysis revealed that safety features, fuel efficiency, and performance were the most important factors for consumers. Smartphone Preferences – A smartphone manufacturer was planning to launch a new device and wanted to understand which features would be most appealing to consumers. They presented survey respondents with different phone configurations and asked them to rank them by their preference. The analysis revealed that the most important factors for consumers were screen size, battery life, and camera quality. With this information, the manufacturer was able to optimize their new phone’s features and pricing strategy to better meet customer preferences. Know the purpose of the analysis and the questions you are trying to answer. Identify the factors that are important to customers and the attributes of your product or service that you want to measure. Test and refine the design of the questions to ensure they accurately measure the preferences of customers. Create scenarios that best reflect what customers would experience in the real world. Analyze the data collected and interpret the results to get the most out of your conjoint analysis. Leverage the results to create models that help you make better, more informed decisions. Consider partnering with a professional data analysis firm for additional insight. In conclusion, conjoint analysis is a powerful tool for understanding consumer behavior and preferences. It provides a systematic way to evaluate and compare different attributes of products or services and their impact on overall preference. There are several types of conjoint analysis, including full-profile, adaptive, and choice-based, each with their own strengths and weaknesses. Examples of applications include new product development, pricing research, and customer satisfaction studies. By using conjoint analysis, businesses can gain insights into what factors drive consumer decision-making, and use that knowledge to make informed decisions about product development, pricing, and marketing strategies. FAQ on Conjoint Analysis What is conjoint analysis and how does it work. Conjoint analysis is a market research technique used to determine how consumers value different features of a product or service. It works by presenting participants with a series of hypothetical product or service profiles that vary in terms of their attributes (such as price, quality, and design), and asking them to choose their preferred option from each set. What are the advantages of using conjoint analysis in market research? Conjoint analysis can provide valuable insights into how consumers make decisions and what factors influence their choices. It can also help businesses understand how to price their products or services, design new products or services, and target specific consumer segments. How do you design a conjoint analysis study? To design a conjoint analysis study, you need to first identify the attributes that are most relevant to your product or service. You then need to create a set of product or service profiles that vary in terms of these attributes, using a statistical technique called fractional factorial design. Finally, you need to recruit participants and present them with the profiles, asking them to choose their preferred option from each set. What are some limitations of conjoint analysis? Conjoint analysis relies on participants' ability to accurately evaluate and compare different product or service profiles. If the profiles are too complex or if participants are not familiar with the attributes being tested, the results may not be reliable. Additionally, conjoint analysis assumes that participants make decisions based solely on the attributes presented, which may not be true in real-world situations. Related pages Logistic regression. Learn how logistic regression, also known as the Logit model, works and its benefits for precise probability modeling and decision-making in data analysis. TURF Analysis Learn how TURF Analysis can optimize your product range and media plans. Unlock strategies to maximize market reach and improve ROI. Regression Analysis Explore the power of Regression Analysis to forecast trends, assess risks, and make data-driven decisions. . Key Driver Analysis Explore Key Driver Analysis (KDA): the game-changing statistical tool that identifies what really drives customer satisfaction and loyalty. Discover how the Kano Model guides market research by categorizing customer needs. Optimize product features to boost satisfaction & ROI. Van Westendorp Price Sensitivity Meter Comprehensive guide to the Van Westendorp pricing model: ✓ Definition ✓ Implementation ✓ Graph ✓ Interpretation ► Get informed Discover the t-test, a statistical method to compare group means, and learn how to calculate it to make data-driven decisions. MaxDiff Scaling Discover MaxDiff Scaling, a powerful technique to measure relative preferences, with real-world examples and guidance on effective usage. Implicit Association Test Uncover hidden biases with the Implicit Association Test. Delve into your subconscious preferences in a revealing psychological experiment. Gabor-Granger Analysis Learn to determine the optimal price with our Gabor-Granger analysis guide covering the basics, benefits, drawbacks, and tips. 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Table of Contents:  How does conjoint analysis work?  When to use conjoint analysis  Different types of conjoint analysis  How to create a conjoint analysis survey, with example questions How does conjoint analysis help interpret preferences . Alternatives to conjoint analysis How to run a conjoint analysis study with quantilope  How does conjoint analysis work?   Conjoint analysis is a technique where respondents are presented with a set of product or service concepts and asked to choose their preferred one. Within each description are multiple features (attributes) of that product/service, and options that can be compared on a like-for-like basis. For example, if you were researching toothpaste you might present some of the following options for 100ml tubes with different price points, flavors, and benefit claims: Colgate - $3.70 - spearmint - plaque removal Crest - $3.25 - fresh mint - whitening Sensodyne - $4.20 - cool mint - gum health Respondents are asked to make trade-offs between multiple products. However, the number of attributes included within each product description should be limited - ideally no more than six or seven - so that decision-making isn’t too complicated for the respondent; especially because in a real-life shopping scenario, consumers generally would not compare any more than this number of attributes when making a purchase decision. Back to Table of Contents Conjoint analysis is invaluable for any research into the impact of different product features on consumers’ purchase intent. If asked directly, in either quantitative or qualitative research, consumers will often say that all attributes are equally important, or won’t be able to say exactly which are more motivating than others in their purchase decision. A conjoint’s market simulation approach forces respondents to make trade-offs in the same way they might when making real-world decisions. Even if consumers are unaware of which attributes sway their decision, a conjoint analysis will reveal them. Some common business questions that can be answered with conjoint include:  What product/service configuration maximizes potential market share and/or revenue? What price point(s) are ideal for a given configuration? Can we increase price without negatively impacting share? What additional value can be offered to offset a pricing increase? How is share impacted if competitors change their pricing strategy or value props? The consumer preferences extracted from conjoint can be fed into sales, marketing, and advertising strategies in any business. Product design, product development, product management, branding issues, package design, pricing research , and market segmentation exercises all benefit from conjoint analysis. Back to Table of Contents Different types of conjoint analyses  All conjoint studies compare total ‘packages’ of different products/services (the unique combination of attributes that each has), as well as the variations on those different attributes (called ‘attribute levels’ - for example within the attribute of ‘flavor’, the levels might include spearmint, fresh mint, and cool mint). How each attribute and level affect a respondent’s choice is calculated into a numerical value called a preference score (also known as a utility score). This can then be used to model ideal product scenarios, combining motivating attributes with optimum price points to see how they would affect projected market share and/or revenue. Within this method there are different types of conjoint analyses; three examples are given below. 1. Choice-based conjoint analysis Also known as discrete choice conjoint analysis, CBC is the most-used form of the method. One of its main benefits is that it reflects a realistic scenario of choosing between products rather than questioning respondents directly about the importance of each attribute. Respondents are shown sets of 3-5 concepts at a time and asked to choose their favorite, then the importance of each attribute is inferred from their choices. This is a powerful way to understand which features are most important to include in a new product and how to price it. From this information, brands can derive optimal product configurations. 2. Adaptive conjoint analysis Adaptive conjoint analysis is a flexible approach to CBC, adapting as a survey progresses so that the choice sets that each respondent sees depend on the preferences they have expressed up to that point. Tailoring the questions to each individual streamlines the approach to CBC, as it doesn’t waste time showing product concepts that the respondent would not find appealing, thus cutting down the length of the survey. Respondents can also find this type of conjoint experience more engaging when they see the survey is reacting to their personal preferences. 3. Self-explicated conjoint analysis This version of conjoint analysis takes the focus off the package of attributes as a whole and instead zooms in on the attributes and attribute levels. Respondents are able to eliminate attributes they wouldn’t consider at all, as well as choose their favorite and least favorite attribute levels. The remaining levels are then rated against the most/least favorite level. The importance of the favorite attribute in the context of the product as a whole is calculated, and a utility score is given for each attribute and attribute level. This CBC method doesn't demand the same level of statistical analysis as other types of conjoints, but it isn’t best suited to pricing research as price can’t be fairly compared to other attributes. In product research Suppose a fast food chain wants to introduce a new burger to its menu. Some of its business questions before launching this new menu item might be around ingredients, calorie content, taste, and price. The new burger has a number of potential options that it could offer to the market but the fast food business needs to be sure that the options it chooses will have optimal uptake among their diners. Using conjoint analysis, the fast food chain could test the following combinations of product features to respondents, asking them to identify their preferred burger: Option 1:         Plant-based, spicy, 650 calories, $5.25 Option 2:        Prime beef, spicy, 790 calories, $4.29 Option 3:        Prime beef, with cheese, 820 calories, $4.99 Option 4:        Plant-based, with cheese, 900 calories, $6.19 Rather than asking respondents about ingredients, taste, calories, and price separately, conjoint analysis presents the features within a realistic product context and analyzes the results to reveal which features are the most powerful in driving purchase. In service research Similarly, service propositions can be presented with a variety of feature combinations for respondents to choose from. For example, a TV & broadband service set of options might look like this: Option 1:        Fast broadband, basic TV bundle plus sports channels, $50 per month Option 2:       Superfast broadband, basic TV bundle, and no sports channels, $90 per month Option 3:       Superfast broadband, TV with movies and sports channels, $120 per month Option 4.       Fast broadband, TV with movies but no sports channels, $100 per month Conjoint analysis will reveal which parts of the service respondents attach the most importance to and which they will pay more for. Back to Table of Contents Consumers are faced with product decisions every day and there's a lot of different aspects that go into choosing a certain product over another - but often, we don't know what those are. Many standard usage and attitude questions don't quite get into the nitty-gritty details of consumer decision making that businesses need to succeed. Conjoint analysis (a type of advanced methodology) helps researchers unravel consumers' complex decision-making by effectively breaking down product offerings into attributes and levels. By breaking down the overwhelming amount of products (and their features) into distinct attributes, businesses can hone in on what specific features of a product actually impact final decision making. Researchers can present respondents with different attributes in various combinations to understand the overall preference (and therefore, value) of each individual feature.   Through conjoint analysis, researchers can quantify trade-offs consumers are willing to make between different attributes, providing insights into their underlying preferences. Knowing this, a business is then in a position to use the most preferred features during the final product design phase, to develop a solid pricing strategy, to craft an influential marketing campaign, and ultimately, to enjoy a successful final product launch.  Alternatives to conjoint analysis   As mentioned above, the alternative to using a conjoint analysis for understanding decision making might be left to standard usage and attitude questions such as 'Which product do you prefer' or 'How much do you like this product'?; a s you might guess, these outputs aren't going to give you nearly the same actionable insights as a conjoint analysis will.  While conjoint analysis is one of the best methodologies a brand can use to understand feature importance, there are some alternatives for those who don't have access to conjoint or who prefer to go another route: MaxDiff (Maximum Difference Scaling): MaxDiff is similar to conjoint analysis in that it forces respondents to make tradeoffs, but the difference is that it focuses on the most and least preferred features individually, rather than evaluating the features as part of set combinations (which is more realistic to a true shoppers' experience).  Van Westendorp Price Sensitivity Meter (PSM): For brands looking to make a decision around pricing specifically, Van Westendorp is a great method to use to understand how price sensitive your consumers are. Respondents are asked a series of questions to determine acceptable price ranges for a given product (i.e at which price point the product is considered too cheap, too expensive, a bargain, or justified). The intersection of all these price ranges helps identify an optimal price range. But remember, pricing is just one aspect that a brand could test in a conjoint analysis, creating a much more cohesive understanding of a product rather than looking at price alone.  Key Driver Analysis (KDA): The end goal for researchers running a conjoint analysis is really to learn what's driving consumer purchases. Another advanced methodology that's effective in uncovering this kind of information is, as it's aptly named, the Key Driver Analysis (KDA). A KDA identifies the overall factors or variables that have a significant impact on a particular outcome - such as product purchase intent in this case. This is a useful method when looking to understand the overall impact a variable might have on consumers' decisions, while a c onjoint analysis is better at understanding and optimizing individual features.  How to run a conjoint analysis study with quantilope quantilope’s advanced, automated conjoint analysis method identifies the relative importance of product attributes and attribute levels in a category to help you create a product that offers the optimal configuration of those attributes. The market simulator will predict how market share would change as a result of lowering or increasing the price, or by tweaking any other attributes that make up the product. It can also show how preferences vary by customer segment. All you have to do is decide which attributes you would like respondents to trade off against each other. Once you have your set of attributes, you simply need to drag the pre-programmed method into your survey before it goes live . Upon survey data capture, quantilope’s AI-driven tools analyze the data using statistical techniques. Check out quantilope’s conjoint analysis approach to pricing research for an example of how this method can be applied for this particular use case. This brief demo video also shows how straightforward it is to set up a conjoint study on quantilope's platform, as well as how simple it is to interact with findings and create the optimal product profile - in this case, an energy drink. The video demonstrates how altering the price can affect market share, and how changing other aspects such as ingredients and packaging can affect potential uptake. To learn more about how your brand can leverage quantilope's conjoint analysis for your own brand needs, get in touch with us below:  Request a demo! Latest articles. 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Unfortunately, approximately 95% of new product launches fail because they do not meet customers' requirements, expectations, or needs. Therefore, it's crucial to conduct research to answer these questions and avoid potential failures. Fortunately, these questions can be answered during the development phase with the help of a conjoint analysis. In this article we'll explore how you can leverage the power of the conjoint analysis enabling the prediction of potential consumers' behavior in advance by presenting realistic purchase scenarios to identify gaps in existing product competition. What is the conjoint analysis? The term conjoint comes from a combination of "considered" and "jointly," which also defines the conjoint analysis. It involves considering various product features (attributes) together and weighing them against other variants. The Conjoint analysis originated in psychology and was developed by Robert Luce and John Tukey in 1964. Since then, it has primarily been used in market research and product development to determine what attributes consumers want and perceive as particularly important during the development stage. Attributes can include functions, designs, or features such as weight, size, and price. However, because consumers tend to want as many attributes as possible for the lowest cost, conjoint analysis takes a different approach from methods such as the MaxDiff Method . The distinctive feature of the conjoint method is the combination of different attributes instead of independent comparison. This makes it useful for high-priced products like automobiles, hardware such as laptops or smartphones, luxury goods, as well as everyday products or during the conception phase. The concept of the analysis is simple. Consumers are shown different products that differ in the combination of features. This creates a realistic experience that closely mimics an everyday purchase decision. For example, in a conjoint analysis to determine consumer preferences for types of chocolate, the filling attribute might be divided into levels such as vanilla cream, strawberries & cream, and kiwi ganache. In this sample conjoint analysis, the aim is to determine which types of chocolate  consumers prefer and what price they are willing to pay for each type. The respective attributes are leveled, i.e. they are displayed in a certain form. For the chocolate  example the filling attribute is divided into the levels vanilla cream, strawberries & cream and, kiwi ganache. Using this approach, a ranking can be created that shows which attributes are most important and which characteristics are most attractive. This evaluation can then be used to decide on the most appealing and profitable combination for both consumers and the company. What is the difference between the conjoint method and Discrete Choice Model? While there are some similarities between the Conjoint analysis and the Discrete Choice Model (DCM), there are also some notable differences. Both models are preference-structured and designed to uncover the factors that influence consumption choices . However, the key difference lies in how respondents view the product profiles and their attributes. In a Conjoint analysis , respondents view the product profiles in smaller groups, while in a DCM , they see all the products simultaneously. This makes the DCM a bit more realistic in predicting buyer behavior than the Conjoint analysis. However, it can also be overwhelming for respondents as they are presented with a large number of options. One of the advantages of the Conjoint analysis is that it provides more information about the attributes' relativity and importance to each other, as well as their contribution to the final buying decision. This is not possible with the DCM. Moreover, the Conjoint analysis is an excellent tool for predicting behavior before the product is launched, which is less likely when using a DCM. The Choice-Based conjoint method The Choice-Based conjoint analysis (CBC) is the most popular form of conjoint analysis and for good reason. Unlike other forms, CBC analysis asks consumers to make decisions between product variants and accept trade-offs , resulting in a more detailed and realistic analysis. This approach reflects the fact that we make numerous decisions daily where we weigh different attributes against each other. In CBC analysis, all previously defined attributes are combined evenly to create a statistically valid ranking at the end of the analysis. Although there are other types of conjoint analysis, such as the Adaptive Choice conjoint and the Menu-Based conjoint analysis, they are not as flexible as the CBC method and cannot be used as widely. At Appinio, we specialize in CBC analysis and can help you gain valuable insights into consumer preferences and behavior. Use cases for Conjoint Analysis The conjoint analysis is a versatile market research method suitable for a variety of use cases. Three common applications of conjoint analysis are: Concept testing Conjoint analysis is useful for testing product concepts in the early stages of development. By identifying consumer preferences and potential flaws early on, resources can be saved and the risk of a failed product launch can be minimized. Diversification and product range expansion The conjoint method is also helpful for testing new product variants, such as different sizes, flavors, or colors, and for optimizing the product range. Price determination The conjoint analysis can be used to determine the optimal price for a product or service. It can be used as a stand-alone method or in combination with other price analysis techniques like Van Westendorp price analysis . By testing different concepts for their willingness to pay, businesses can make informed pricing decisions. Research experts answer: When exactly should a Conjoint Analysis be conducted? Conduct a Conjoint Analysis when expanding into new markets, launching new products, or optimizing existing ones. It provides detailed insights into customer preferences and trade-offs, ensuring your offerings meet market demands. Conjoint Analysis' best practices When conducting a conjoint analysis, it is important to follow best practices in order to ensure accurate results. Here are some tips to keep in mind: Use short and concise descriptions of product features to avoid misunderstandings that could distort the analysis. Use pictures to help respondents distinguish between different variants and imagine the products being tested. Use descriptive comparisons for attributes rather than abstract levels such as "light" or "heavy". Concrete comparisons, such as "as heavy as a similar product," are more appropriate. To make implementation of these tips easier, consider using the Appinio Conjoint Analysis Tool. This tool provides the necessary setting options for a successful conjoint analysis. Book a demo and our experts will support all your research needs. Get a free demo and see the Appinio platform in action! Setting up a conjoint analysis (with Appinio) Conducting a Conjoint analysis with Appinio couldn't be easier. Step 1: Get the survey ready Register on the Appinio platform . Define the 3-4 most important product features (e.g. price, design) to be tested. Contact one of our market research experts. They will guide you with formulating the definition of the product features right up until your survey goes live. Step 2: Send your survey live Our research consultants will do a final check before your survey goes live. See the answers coming in! Our panel responds as soon as the survey is live. Step 3: Analyze your data Go to the Appinio interactive dashboard and start analyzing the data you collected. The results of the conjoint survey are calculated and visualised in bar graphs and tables by our research consultants to show the utilities and importance of each factor. Accordingly, the results can be used immediately for decision-making. Export your results to Excel, PPT or CSV at any time. What are the advantages and disadvantages of a conjoint analysis? Conjoint analysis offers several advantages and disadvantages that should be considered when implementing this research method. Conjoint analysis can help determine which product features are necessary and which ones consumers would be willing to forgo. The analysis can measure subconscious decisions, thanks to the many different combinations of attributes and levels that can be included. The research design is highly flexible and can be adapted to fit almost any product or concept. The method is incredibly versatile, covering a wide range of studies such as price willingness , design tests , or product attributes . Disadvantages As with any research method, there are also potential disadvantages to consider when using conjoint analysis. For example: Respondents may choose luxury variants since they are not actually spending any money and therefore have no sense of making a real purchasing decision. This can lead to a discrepancy between survey results and actual market behavior. Conclusion for Conjoint Analysis Conjoint analysis is a powerful market research tool that offers a multitude of advantages and can be used for a wide range of use cases, particularly in the areas of product development and marketing. Its flexibility and ability to realistically reflect everyday purchase decisions make it an essential tool for businesses looking to develop and launch successful products. With conjoint analysis, several combinations and variants can be tested without consumers having to choose their favorites from a list of attributes, allowing for a more accurate analysis of consumer preferences. Overall, conjoint analysis is an effective way to make informed decisions about product development and marketing strategies, ultimately helping businesses to succeed in a competitive market. Conjoint Analysis explained What are the types of conjoint analysis? What are the basic steps in a conjoint analysis? What are the main goals of a conjoint analysis? Is the conjoint analysis quantitative or qualitative? What industries use conjoint analysis? Ready for your conjoint analysis study with Appinio? In our dashboard, you will find a sample tracking and questionnaire templates that you can customize and get the insights you need to bring your brand to the next level. Get free access to the platform! Join the loop 💌 Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox. Get the latest market research news straight to your inbox! 💌 Wait, there's more 22.08.2024 | 32min read What is Voice of the Customer (VoC)? Program, Examples 20.08.2024 | 30min read What is Employee Experience (EX) and How to Improve It? 19.08.2024 | 14min read Revolutionizing Brand Health with Mental Availability: Key Takeaways What Is Conjoint Analysis? Discover why conjoint analysis is one of the best market research techniques and learn which type is best for you and your business. Learn how to use conjoint analysis for market research and content strategy. When you’re developing a marketing strategy, one of the first and most important steps is to do your research. In order to actually sell your product or service, you need to fully understand your target audience when they’re in an actual buying situation: What gets their attention? Why would they choose you over a competitor? How do they make their purchase decisions? One of the most effective ways to answer each of these questions is with conjoint analysis. Defining conjoint analysis Conjoint analysis is a survey-based statistical analysis technique used during market research that quantifies the value customers place on attributes of a product or service. Already that may sound complicated, so let’s break it down a little more. Conjoint analysis is a statistical technique that uses a survey to determine consumer preferences before they make purchase decisions. It asks each respondent a series of questions—also known as choice tasks—in which they select between a few packaged options based on relative importance and what they deem most valuable. Each package presents different product features, called attributes, and each attribute shows multiple types, or levels. Here are some examples of attributes: Product material Time for completed service Distance or location Company/brand features Next, here are examples of each attribute level: Product material: leather, polyester, denim Price: $12.99, $15.49, $19.29 Time for completed service: 7 minutes, 15 minutes, 25 minutes Distance/location: 2-minute walk, 5-minute walk, 10-minute walk Company/brand features: Women-owned, dermatologist-approved, sustainably made As they answer each question, respondents determine which trade-off feels like the best deal for them. Finally, companies use the data from that survey to determine a utility score, revealing which attribute(s) respondents find most valuable. Survey data can also be used to measure the consumers’ overall preference scores, which state a consumer's likelihood to purchase a packaged option based on preference. Types of conjoint analysis The reason why this method is called conjoint analysis is because survey respondents have to choose a conjoined product package with different attributes and levels. When developing a conjoint analysis survey, companies can use a variety of statistical techniques. Each method asks the respondent a different series of questions that businesses can leverage for different insights. Choice-based conjoint analysis (aka discrete choice conjoint analysis) This is the most prevalent type of conjoint analysis that market researchers use. Choice-based conjoint analysis (CBC)—or discrete choice conjoint analysis—simulates the market and demonstrates how respondents value certain attribute levels. Since this method is also most commonly used to explain how conjoint analysis works, let’s go over an example. Say you’ve started a car cleaning service and you want to develop a survey that asks customers what sort of cleaning package they’d prefer. You break down your service into attributes and levels. The attributes could be price, services provided, time spent cleaning, and a manual vs automated car wash. Then you include different levels of pricing, services, and time. Once your survey is complete and you analyze your conjoint data, you may learn that more respondents would prefer a car service that’s quick and inexpensive. Or you may learn that respondents would rather spend the extra $10 for a wax and tire polish—despite the service taking much longer. Adaptive conjoint analysis (ACA) Adaptive conjoint analysis (ACA) is similar to CBC analysis. The difference though, is that each question updates in real time and adapts to each respondent's choices. Adaptive conjoint analysis is ideal for when respondents need to evaluate more attributes than in a choice-based survey. This way, companies can get a full perspective of what their customers value and are looking for by presenting combinations of attributes and levels that companies may not have thought of before. ACA is also a more efficient way of surveying respondents because each follow-up question becomes more curated to each input answer, which makes the survey feel more relevant and pertinent to the respondent’s values and desires. Full-profile conjoint analysis This conjoint analysis technique requires a full description of each product in a choice task. Other market research techniques usually limit the number of attributes, but with full-profile conjoint analysis, the respondent is able to see a thorough description with every attribute. Respondents then select which product they’d purchase with maximum likelihood. Menu-based conjoint analysis Typically, a conjoint survey doesn’t ask respondents outright what they’d like to pay for a product or what features they’d like to see with it. Menu-based conjoint analysis surveys differ because they enable the respondent to package a product by themselves. This allows companies to see how potential customers may value certain combinations of attributes and levels. Of course, everyone would like to have the best-quality product that’s inexpensive, comes with all sorts of benefits and features, or takes the least amount of time to finish or arrive. However, menu-based conjoint analysis surveys prompt respondents to categorize each predetermined attribute and level so they can customize a packaged product that they feel would deliver the most value. Why use conjoint analysis for market research? Companies often conduct conjoint analysis surveys because they are one of the best survey methods for determining customer values and preferences during the buying process. Let’s go over the business benefits of conjoint analysis and why it’s so effective. Highlight consumer preferences When companies know which product features are the most valuable to consumers, they can highlight them in their advertisements. Say, for example, you learn that one respondent group values your brand’s environmental mission and another group values the quality of your materials. With data from your conjoint study, you can target some consumers with ads that highlight your stance on climate change while other ads target consumers that are looking for a brand with high-quality materials. Companies can also use a conjoint analysis experiment to determine which new product features to add or take away based on survey data, utility scores, and preference scores. If you learn that most respondents preferred an old feature compared to a potential new one, you could save time, money, and resources that would be spent launching new products with multiple features that your customers wouldn’t prefer as much. Mimic real-life trade-offs People make trade-offs every day. However, not all trade-offs are created equal because different people have different priorities. For example, some people trade sleeping in so they can go to the gym and grab breakfast to go before starting work; others may prefer to sleep in more and prepare a quick breakfast at home before work. Conjoint analysis mimics this kind of daily trade-off. For instance, when it comes to purchase decisions, consumers often trade: Higher or lower price for quality Timeliness of a service for the amount of available services Imported items for locally made goods As mentioned, conjoint analysis doesn’t necessarily ask respondents what they specifically prefer in a product or service. Instead, it demonstrates a realistic context by asking respondents to choose which packaged option they prefer, ultimately revealing which attribute level respondents are willing to trade for another. Develop insightful product and pricing research Conjoint analysis methods allow companies to gain insights on how much a consumer monetarily values their product or service. By developing conjoint surveys that focus primarily on product and pricing research, companies can understand how much consumers are willing to pay. Simulate competitive markets Businesses can develop surveys that employ a brand price trade-off approach, wherein they learn if consumers have a bias toward a competitor solely based on a name brand. This allows companies to simulate a competitive market situation, allowing them to see whether or not customers would prefer them over another brand and why. Predict marketing trends Instead of hoping that a new product, feature, or service will land well with new consumers, conjoint analysis can help companies make more informed decisions with their marketing strategies. Companies often use conjoint analysis to forecast potential demand, predict marketing trends, or determine product acceptance before they launch by noticing trends and quickly acting on relevant data. 5 steps to creating a conjoint analysis survey Let’s go over the 5 steps to creating an effective marketing research campaign with a conjoint analysis survey. Step 1: Define attributes, levels, and a pricing structure When you start developing a conjoint survey, make sure you can succinctly define each attribute and level, and that you have enough to present a few packaged options to a respondent. Break down your product or service into attributes of interest and define each level that you want to evaluate. Take a look back at our choice-based conjoint analysis example for ideas of where to start. Also keep in mind what your price range is so you can position each price level as slightly above and slightly below your range. This ensures that respondents don’t all reply that they’d want a price that’s much lower than you’d like. Note that the only way to move forward from here is if you have succinct, well-defined attributes, levels, and pricing. If these aren’t defined, you likely won’t get a lot of actionable insights. Step 2: Create a conjoint survey There are tons of websites that provide conjoint analysis software, and some even allow users to create a survey for free. You can also create a survey on platforms like SurveyMonkey or with any of Mailchimp’s free survey tools . Before you start, think about which conjoint analysis method you’d like to use. Here’s a quick breakdown on how to use each type of conjoint analysis we covered: Choice-based conjoint (CBC) analysis/discrete choice conjoint analysis: Asks respondents to choose which packaged option they would most likely purchase. Adaptive conjoint analysis (ACA): Ideal for a large number of attributes; adapts to each respondent's choices and develops new questions in real time. Full-profile conjoint analysis: Presents respondents with multiple full product descriptions, prompting them to choose the product they’re most inclined to purchase. Menu-based conjoint analysis: Enables respondents to package a product or service themselves based on their preferences. Step 3: Invite survey respondents You invite survey respondents by sending an email to subscribed customers or potential consumers. Always let your invitees know that their opinion matters and that your survey is for consumer preference research that will help you better understand them. You can even provide a small incentive for participating in your survey, such as a discount or the chance to win a gift. However, before you send out survey invitations, make sure you have reliable resources to collect their response data. Most survey platforms offer ways to manage invitations and collect insights. However, some offer more helpful analysis insights than others, so make sure to choose your survey platform wisely. Step 4: Analyze survey data Once the responses to your market research survey are ready, your selected conjoint software or survey tool will analyze the data and provide insights on each utility and preference scores. Each score will outline which product or service respondents are most likely to purchase, which features are most desired, and which attributes have the most impact during the buying process. Step 5: Deploy conjoint data in marketing Finally, you have your data, preference scores, and other valuable analysis insights. However, remember that it’s okay to not get everything you need after one survey. You may need to do a few rounds of surveys for different audiences or at different times. It’s possible you may have to adjust your survey completely if you find that another conjoint analysis technique may be more helpful. Nonetheless, your first survey can help you start implementing your newfound understanding into marketing strategies, campaigns, and advertisements. Also, no matter what you learn, just know that it can take weeks or months to implement your findings into a marketing strategy. Always set realistic expectations and stick to a schedule in order to stay on top of your marketing goals. Understand the consumer with conjoint analysis Conjoint analysis is an ideal way for businesses to learn more about trending preferences and pivot upon learning what consumers like or don’t like. Deploying a conjoint analysis study not only enables businesses to effectively provide what consumers are looking for, but it can save businesses time, money, and resources that might get squandered by just guessing what is currently trending. Even if your findings surprise you and your team, a conjoint analysis method can help you strengthen your marketing strategy and get you closer to your customer base—which is really valuable in the long run. What is conjoint analysis? The complete guide Conjoint analysis is used to build market models and forecasts to answer questions such as "Should we build in more features, or change our prices?" or "Which of these changes will hurt our competitors most?", or "What is the optimum price to charge?" that allow the business to optimise product and service design to customer needs. To explore or play with conjoint analysis, try our interactive Conjoint Demonstration , our simple conjoint in Excel to see how conjoint analysis works numerically, or use our free, full-featured Conjoint Explorer to design and test your own conjoint experiments. Conjoint overview Every customer making choices between products and services is faced with trade-offs ( see our conjoint demonstration ). Is high quality more important than a low price and quick delivery for instance? Or is good service more important than design and looks? For businesses, understanding precisely how customers value different elements of a product or service allows product development to be optimised to give the best balance of features or quality, for the prices the customer is willing to pay. At a market level, conjoint analysis can be used to identify the best product range for different segments or market needs, by determining which features, value and price, across a set of products, would maximise customer value and market returns. Conjoint analysis is also known as Discrete Choice Estimation, or stated preference research and is one of a range of trade-off based research techniques. An established and powerful means of estimating customer value With on-going development and improvements since it was invented in the 1970s conjoint analysis has become a core tool for product planning and pricing research. By understanding precisely how people make decisions and what they value in your products and services, you can work out the sweetspot or optimum level of features and services that balance value to the customer against cost to the company and forecast potential demand or market share in a competitive market situation. It is, however, a sophisticated technique and expertise is required to ensure the design and outputs will achieve the business objectives. Conjoint principles - attributes and levels For example a computer may be described in terms of attributes such as processor type, hard disk size and amount of memory. Each of these attributes is broken down into levels - for instance levels of the attribute for memory size might be 1GB, 2GB, 3GB and 4GB. Play with attribute and levels in our Conjoint Explorer to see how designs can be created. From attributes and levels to product profiles and choice tasks Go Hands-on with our Conjoint Explorer These attributes and levels can be used to define different products by choosing different levels for different products so the first stage in conjoint analysis is to create a set of product profiles (possible combinations of attributes and levels) to produce a set of options from which customers or respondents are then asked to choose - know as choice sets or choice tasks. Obviously, the number of potential profiles increases rapidly for every new attribute added as the number of possible combinations increases, so there are statistical techniques and design methods to simplify both the number of profiles to be tested and the way in which preferences are tested so that the maximum amount of choice information can be collected from the smallest set of choice tasks. Choosing the right type or flavour of conjoint analysis The precise approach to creating 'choice tasks' depends on the which type or flavours of conjoint analysis is most appropriate to use. The most common approach is choice-based conjoint (CBC), but other flavours exist. Students often get taught full-profile conjoint using ratings or cards, for more attributes adaptive designs get used, such as adaptive conjoint analysis (ACA), menu-based conjoint, or adaptive choice based conjoint (ACBC). Economists might look at Stated Preference or Discrete Choice Methods. Conjoint analysis might not be the right option. Other approaches such as MaxDiff, Simalto or hierarchy of needs studies, each have different ways to manage the balance between the number of attributes that can be included and the relative complexity of the choices that need to be shown in order to get good quality data. Statistical design and analysis A conjoint analysis study relies on appropriate statistical design in order to be able to estimate the utility models. Once all the choice tasks have been completed, analysis involves modelling what drove customers choices or preferences from the product profiles offered. The statistical output then quantifies both what is driving the preference from the attributes and levels shown - known as utilities or part-worths and importance scores. These utilities give an measurement of value for each level, of each attribute, in terms of its contribution to the choices that were made and so shows the relative value of one level against another. Market models - forecasting market potential The statistical output gives a detailed quantified picture of how customers make decisions, and a set of data that can be used to build market models which can predict preferences or estimate market share in new market conditions in order to forecast the impact of product or service changes on the market. For businesses this allows them to see where and how they can gain the greatest improvements over their competitors, where they can add value for the customer, how price impacts on decisions and so forecast demand and revenue. Not surprisingly conjoint analysis has become a key tool in building and developing market strategies . By combining these market models with internal project costings, companies can evaluate decisions in terms of Return on Investment (ROI) before going to market. For example determining what resources to put into New Product Development and in what areas. Choice-based conjoint or discrete choice modelling also form the basis of much pricing research and powerful needs-based segmentation . "We were looking for an agency that could understand our solutions and complex customer base in order to transfer this understanding into a comprehensive customer survey. dobney.com quickly gained deep insight into the specificities of our business and designed an excellent, state-of-the-art conjoint survey. They delivered professional and individual service of a quality we had never experienced before. It was great working with dobney.com and the findings derived from the survey are invaluable for us." Marketing Manager, Leica Microsystems Alternatives to conjoint - from maxdiff to configurators and e-commerce mock-ups. Conjoint analysis is relatively complex as it requires an understanding of how to use and create attributes and levels, what flavour to use, how to make the product profiles, what choice task to offer and then how to analyse the data and build the market model. It is possible to use off-the-shelf software which will provide guidance and help, but it can be also make it easy to make mistakes or generate poor designs. And conjoint analysis doesn't always fit, particularly if there are many levels, or a deeper more emotional drive to decision making. So, depending on the product or service, it is possible that off-the-shelf approaches aren't always suitable and other methods are needed. Fortunately there are a number of related approaches used as alternatives to conjoint analysis , such as MaxDiff, configurators or Simalto (also known as trade-off grids). MaxDiff is more about measuring the value from a list of items, than generating complete products, but it uses many of the same features and analytics as conjoint. Simalto, like conjoint analysis, breaks products down into attributes and levels, but then presents them as a grid of options to respondents. A range of other research techniques including menu building (building a configured product from a range of selected options), and search and filter studies in the form of e-commerce style mock-ups where respondents hunt for their most preferred products can also be used in conjunction with or as alternatives to conjoint analysis. Demonstrations and further reading To see the mathematical workings, we have a fully worked up simple conjoint analysis worked example in Excel to show how conjoint analysis functions mathematically to estimate part-worths or utilities from design to analysis. Or just play with our Pizza demonstration which shows how utility estimations arise from choices about pizza preferences. See our interactive instant conjoint analysis demonstration showing how customer value can be calculated from choices. See how conjoint market models and simulators work to enable better ROI decisions based on customer values. Explore conjoint analysis in-depth with our online Conjoint Explorer which allows you to design and test your own attributes and levels and choice tasks, and see results. Conjoint analysis design principles Conjoint analysis types or flavours For help and advice on using conjoint analysis for market modelling, or to carry out conjoint analysis research email [email protected] Conjoint Analysis overview Conjoint demonstration Attributes and levels Flavours of conjoint analysis Conjoint models Conjoint in Excel MaxDiff plus Uses of conjoint types International conjoint Conjoint analysis alternatives Conjoint Explorer Access level: public 244 IMAGES What Are The Steps of Conjoint Analysis? Conjoint Analysis: Definition, Example, Types and Model Conjoint Analysis: Definition, Example, Types and Model Conjoint Analysis and Its Types Getting familiar with conjoint analysis What is Conjoint Analysis? (with examples) COMMENTS What Is Conjoint Analysis & How Can You Use It? Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It's based on the principle that any product can be broken down into a set of attributes that ultimately impact users' perceived value of an item or service. Conjoint analysis Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on ... What is Conjoint Analysis? (with examples) Conjoint analysis is a popular method of product and pricing research that uncovers consumers' preferences, which is useful when a company wants to: Select product features. Assess consumers' sensitivity to price changes. Forecast its volumes and market share. Predict adoption of new products or services. Conjoint Analysis: Definition, Example, Types and Model Conjoint analysis example. For example, assume a scenario where a product marketer needs to measure individual product features' impact on the estimated market share or sales revenue. In this conjoint study example, we'll assume the product is a mobile phone. The competitors are Apple, Samsung, and Google. What is a Conjoint Analysis? Types & Use Cases Conjoint analysis can be defined as a popular survey-based statistical technique used in market research. It is the optimal approach for measuring the value that consumers place on features of a product or service. This commonly used approach combines real-life scenarios and statistical techniques with the modeling of actual market decisions. What is Conjoint Analysis? (Guide, Types, Tools, Examples) // OpinionX Conjoint analysis is a type of survey that measures how important customers think different attributes (like price, brand, or features) are when comparing products in a purchase decision. A conjoint survey shows two or more "profiles" that represent variations of the same product. Conjoint Analysis—Overview, Types, Uses & Examples Conjoint analysis in research & development With conjoint analysis, product developers can define customer needs and bring the right product or service idea to life. In addition, at the beginning of product development , a conjoint analysis will help reveal the concepts that aren't valued by customers, allowing businesses to eliminate them at ... Conjoint Analysis: A Research Method to Study Patients' Preferences and 1. Introduction. The popularity of conjoint analysis (CA) in health outcomes research has been increasing in recent years [1,2].Yet, the untraditional concept of this research method is still unclear for many healthcare researchers and clinicians in terms of the design complexity and the absence of confirmed sample size [1,3,4].Throughout clinical practice, healthcare professionals have been ... Conjoint Analysis: Definition, Types, Benefits, Examples The evolution of Conjoint Analysis has led to the development of various methodologies, including Discrete Choice-Based Conjoint (CBC), MaxDiff, and Best-Worst Scaling, each offering unique advantages for specific research needs. These advancements have solidified Conjoint Analysis as a vital tool in marketing research, product development, and ... How to use conjoint analysis Conjoint analysis, sometimes called trade-off analysis, is a form of statistical analysis used in market research to understand how customers value different components or features of a company's products or services. Conjoint analysis is based on the principle that any product or offering can be broken down into a set of attributes that ... The Plain-English Guide to Conjoint Analysis Conjoint analysis is a market research tactic that attempts to understand how people make decisions. A common approach, the conjoint analysis combines realistic hypothetical situations to measure buying decisions and consumer preferences. Think about buying a new phone. Attributes you might consider are color, size, and model. What is Conjoint Analysis Conjoint analysis is a marketing research method that leverages statistical analyses and mathematical models to quantify survey respondents' preferences for product features, determine what attributes impact product demand, and can also predict how the market will respond to new products pre-launch. These research methods use specialized ... Conjoint Analysis Explained + Survey Template Definition: Conjoint analysis is a survey-based research technique used to quantify how people value the individual features of a product or service. A conjoint survey question shows respondents a set of concepts, asking them to choose or rank the most appealing ones. When the results are displayed, each feature is scored, giving you actionable data. What Is A Conjoint Analysis A conjoint analysis is a market research approach that helps you understand how people make decisions. With conjoint analyses, you see how people make trade-offs in real time, giving you much needed intel on what features to emphasize, and which ones to deprioritize. In essence, it replicates the real-time decisions individuals make during any ... Conjoint Analysis: Definition, Types, and Examples Definition, Types, and Examples. Conjoint analysis is a market research technique used to understand how consumers value different features of a product or service. It involves presenting respondents with a series of hypothetical scenarios and asking them to choose their preferred option. Table of contents. What Is Conjoint Analysis? How It Works and When To Use It Conjoint analysis is invaluable for any research into the impact of different product features on consumers' purchase intent. If asked directly, in either quantitative or qualitative research, consumers will often say that all attributes are equally important, or won't be able to say exactly which are more motivating than others in their ... What is the Conjoint Analysis? Examples & Definition The conjoint analysis is a versatile market research method suitable for a variety of use cases. Three common applications of conjoint analysis are: Concept testing. Conjoint analysis is useful for testing product concepts in the early stages of development. By identifying consumer preferences and potential flaws early on, resources can be ... 13 Types of Conjoint Analysis Explained (With Image Examples) Conjoint analysis is a popular research method for understanding which attributes are most important to customers when they consider purchasing your product. However, "conjoint analysis" is a catch-all term that actually means a lot of different things. This post covers the 13 most common types of Conjoint Analysis survey, explains how each ... What is Conjoint Analysis? Defining conjoint analysis. Conjoint analysis is a survey-based statistical analysis technique used during market research that quantifies the value customers place on attributes of a product or service. Already that may sound complicated, so let's break it down a little more. What is conjoint analysis? The complete guide Conjoint analysis is an advanced market research method that gets under the skin of how people make decisions. It is used to quantify what customers really value in products and services to create models and forecasts based on presenting people with realistic product choices and then analysing what features most drive purchasing decisions.. Conjoint analysis is used to build market models and ... Conjoint Analysis Abstract. This article chapter provides an up-to-date review of methods that have come to be called conjoint analysis. These methods enable marketing researchers to determine trade-offs among attributes of a new product based on responses of stated preferences and stated choices. These trade-offs can assist in product design, pricing, market ... How To Calculate Conjoint Analysis Results [8 Steps] Step 8: Calculate Ratio For Each Level. If we take an individual level score (for example Pork's score is -2.281) and divide that by the sum of all attribute ranges, we get the influence each 'level' has on respondents' preference. This number is our second key output for Conjoint Analysis research — it allows us to visualize the ... assignment essay homework paper phd review speech thesis