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15 Real-Life Case Study Examples & Best Practices

Written by: Oghale Olori

Case studies are more than just success stories.

They are powerful tools that demonstrate the practical value of your product or service. Case studies help attract attention to your products, build trust with potential customers and ultimately drive sales.

It’s no wonder that 73% of successful content marketers utilize case studies as part of their content strategy. Plus, buyers spend 54% of their time reviewing case studies before they make a buying decision.

To ensure you’re making the most of your case studies, we’ve put together 15 real-life case study examples to inspire you. These examples span a variety of industries and formats. We’ve also included best practices, design tips and templates to inspire you.

Let’s dive in!

Table of Contents

What is a case study, 15 real-life case study examples, sales case study examples, saas case study examples, product case study examples, marketing case study examples, business case study examples, case study faqs.

• A case study is a compelling narrative that showcases how your product or service has positively impacted a real business or individual. 
• Case studies delve into your customer's challenges, how your solution addressed them and the quantifiable results they achieved.
• Your case study should have an attention-grabbing headline, great visuals and a relevant call to action. Other key elements include an introduction, problems and result section.
• Visme provides easy-to-use tools, professionally designed templates and features for creating attractive and engaging case studies.

A case study is a real-life scenario where your company helped a person or business solve their unique challenges. It provides a detailed analysis of the positive outcomes achieved as a result of implementing your solution.

Case studies are an effective way to showcase the value of your product or service to potential customers without overt selling. By sharing how your company transformed a business, you can attract customers seeking similar solutions and results.

Case studies are not only about your company's capabilities; they are primarily about the benefits customers and clients have experienced from using your product.

Every great case study is made up of key elements. They are;

• Attention-grabbing headline: Write a compelling headline that grabs attention and tells your reader what the case study is about. For example, "How a CRM System Helped a B2B Company Increase Revenue by 225%.
• Introduction/Executive Summary: Include a brief overview of your case study, including your customer’s problem, the solution they implemented and the results they achieved.
• Problem/Challenge: Case studies with solutions offer a powerful way to connect with potential customers. In this section, explain how your product or service specifically addressed your customer's challenges.
• Solution: Explain how your product or service specifically addressed your customer's challenges.
• Results/Achievements : Give a detailed account of the positive impact of your product. Quantify the benefits achieved using metrics such as increased sales, improved efficiency, reduced costs or enhanced customer satisfaction.
• Graphics/Visuals: Include professional designs, high-quality photos and videos to make your case study more engaging and visually appealing.
• Quotes/Testimonials: Incorporate written or video quotes from your clients to boost your credibility.
• Relevant CTA: Insert a call to action (CTA) that encourages the reader to take action. For example, visiting your website or contacting you for more information. Your CTA can be a link to a landing page, a contact form or your social media handle and should be related to the product or service you highlighted in your case study.

Now that you understand what a case study is, let’s look at real-life case study examples. Among these, you'll find some simple case study examples that break down complex ideas into easily understandable solutions.

In this section, we’ll explore SaaS, marketing, sales, product and business case study examples with solutions. Take note of how these companies structured their case studies and included the key elements.

We’ve also included professionally designed case study templates to inspire you.

1. Georgia Tech Athletics Increase Season Ticket Sales by 80%

Georgia Tech Athletics, with its 8,000 football season ticket holders, sought for a way to increase efficiency and customer engagement.

Their initial sales process involved making multiple outbound phone calls per day with no real targeting or guidelines. Georgia Tech believed that targeting communications will enable them to reach more people in real time.

Salesloft improved Georgia Tech’s sales process with an inbound structure. This enabled sales reps to connect with their customers on a more targeted level. The use of dynamic fields and filters when importing lists ensured prospects received the right information, while communication with existing fans became faster with automation.

As a result, Georgia Tech Athletics recorded an 80% increase in season ticket sales as relationships with season ticket holders significantly improved. Employee engagement increased as employees became more energized to connect and communicate with fans.

Why Does This Case Study Work?

In this case study example , Salesloft utilized the key elements of a good case study. Their introduction gave an overview of their customers' challenges and the results they enjoyed after using them. After which they categorized the case study into three main sections: challenge, solution and result.

Salesloft utilized a case study video to increase engagement and invoke human connection.

Incorporating videos in your case study has a lot of benefits. Wyzol’s 2023 state of video marketing report showed a direct correlation between videos and an 87% increase in sales.

The beautiful thing is that creating videos for your case study doesn’t have to be daunting.

With an easy-to-use platform like Visme, you can create top-notch testimonial videos that will connect with your audience. Within the Visme editor, you can access over 1 million stock photos , video templates, animated graphics and more. These tools and resources will significantly improve the design and engagement of your case study.

Simplify content creation and brand management for your team

• Collaborate on designs , mockups and wireframes with your non-design colleagues
• Lock down your branding to maintain brand consistency throughout your designs
• Why start from scratch? Save time with 1000s of professional branded templates

Sign up. It’s free.

2. WeightWatchers Completely Revamped their Enterprise Sales Process with HubSpot

WeightWatchers, a 60-year-old wellness company, sought a CRM solution that increased the efficiency of their sales process. With their previous system, Weightwatchers had limited automation. They would copy-paste message templates from word documents or recreate one email for a batch of customers.

This required a huge effort from sales reps, account managers and leadership, as they were unable to track leads or pull customized reports for planning and growth.

WeightWatchers transformed their B2B sales strategy by leveraging HubSpot's robust marketing and sales workflows. They utilized HubSpot’s deal pipeline and automation features to streamline lead qualification. And the customized dashboard gave leadership valuable insights.

As a result, WeightWatchers generated seven figures in annual contract value and boosted recurring revenue. Hubspot’s impact resulted in 100% adoption across all sales, marketing, client success and operations teams.

Hubspot structured its case study into separate sections, demonstrating the specific benefits of their products to various aspects of the customer's business. Additionally, they integrated direct customer quotes in each section to boost credibility, resulting in a more compelling case study.

Getting insight from your customer about their challenges is one thing. But writing about their process and achievements in a concise and relatable way is another. If you find yourself constantly experiencing writer’s block, Visme’s AI writer is perfect for you.

Visme created this AI text generator tool to take your ideas and transform them into a great draft. So whether you need help writing your first draft or editing your final case study, Visme is ready for you.

3. Immi’s Ram Fam Helps to Drive Over $200k in Sales

Immi embarked on a mission to recreate healthier ramen recipes that were nutritious and delicious. After 2 years of tireless trials, Immi finally found the perfect ramen recipe. However, they envisioned a community of passionate ramen enthusiasts to fuel their business growth.

This vision propelled them to partner with Shopify Collabs. Shopify Collabs successfully cultivated and managed Immi’s Ramen community of ambassadors and creators.

As a result of their partnership, Immi’s community grew to more than 400 dedicated members, generating over $200,000 in total affiliate sales.

The power of data-driven headlines cannot be overemphasized. Chili Piper strategically incorporates quantifiable results in their headlines. This instantly sparks curiosity and interest in readers.

While not every customer success story may boast headline-grabbing figures, quantifying achievements in percentages is still effective. For example, you can highlight a 50% revenue increase with the implementation of your product.

Take a look at the beautiful case study template below. Just like in the example above, the figures in the headline instantly grab attention and entice your reader to click through.

Having a case study document is a key factor in boosting engagement. This makes it easy to promote your case study in multiple ways. With Visme, you can easily publish, download and share your case study with your customers in a variety of formats, including PDF, PPTX, JPG and more!

4. How WOW! is Saving Nearly 79% in Time and Cost With Visme

This case study discusses how Visme helped WOW! save time and money by providing user-friendly tools to create interactive and quality training materials for their employees. Find out what your team can do with Visme. Request a Demo

WOW!'s learning and development team creates high-quality training materials for new and existing employees. Previous tools and platforms they used had plain templates, little to no interactivity features, and limited flexibility—that is, until they discovered Visme.

Now, the learning and development team at WOW! use Visme to create engaging infographics, training videos, slide decks and other training materials.

This has directly reduced the company's turnover rate, saving them money spent on recruiting and training new employees. It has also saved them a significant amount of time, which they can now allocate to other important tasks.

Visme's customer testimonials spark an emotional connection with the reader, leaving a profound impact. Upon reading this case study, prospective customers will be blown away by the remarkable efficiency achieved by Visme's clients after switching from PowerPoint.

Visme’s interactivity feature was a game changer for WOW! and one of the primary reasons they chose Visme.

“Previously we were using PowerPoint, which is fine, but the interactivity you can get with Visme is so much more robust that we’ve all steered away from PowerPoint.” - Kendra, L&D team, Wow!

Visme’s interactive feature allowed them to animate their infographics, include clickable links on their PowerPoint designs and even embed polls and quizzes their employees could interact with.

By embedding the slide decks, infographics and other training materials WOW! created with Visme, potential customers get a taste of what they can create with the tool. This is much more effective than describing the features of Visme because it allows potential customers to see the tool in action.

To top it all off, this case study utilized relevant data and figures. For example, one part of the case study said, “In Visme, where Kendra’s team has access to hundreds of templates, a brand kit, and millions of design assets at their disposal, their team can create presentations in 80% less time.”

Who wouldn't want that?

Including relevant figures and graphics in your case study is a sure way to convince your potential customers why you’re a great fit for their brand. The case study template below is a great example of integrating relevant figures and data.

This colorful template begins with a captivating headline. But that is not the best part; this template extensively showcases the results their customer had using relevant figures.

The arrangement of the results makes it fun and attractive. Instead of just putting figures in a plain table, you can find interesting shapes in your Visme editor to take your case study to the next level.

5. Lyte Reduces Customer Churn To Just 3% With Hubspot CRM

While Lyte was redefining the ticketing industry, it had no definite CRM system . Lyte utilized 12–15 different SaaS solutions across various departments, which led to a lack of alignment between teams, duplication of work and overlapping tasks.

Customer data was spread across these platforms, making it difficult to effectively track their customer journey. As a result, their churn rate increased along with customer dissatisfaction.

Through Fuelius , Lyte founded and implemented Hubspot CRM. Lyte's productivity skyrocketed after incorporating Hubspot's all-in-one CRM tool. With improved efficiency, better teamwork and stronger client relationships, sales figures soared.

The case study title page and executive summary act as compelling entry points for both existing and potential customers. This overview provides a clear understanding of the case study and also strategically incorporates key details like the client's industry, location and relevant background information.

Having a good summary of your case study can prompt your readers to engage further. You can achieve this with a simple but effective case study one-pager that highlights your customer’s problems, process and achievements, just like this case study did in the beginning.

Moreover, you can easily distribute your case study one-pager and use it as a lead magnet to draw prospective customers to your company.

Take a look at this case study one-pager template below.

This template includes key aspects of your case study, such as the introduction, key findings, conclusion and more, without overcrowding the page. The use of multiple shades of blue gives it a clean and dynamic layout.

Our favorite part of this template is where the age group is visualized.

With Visme’s data visualization tool , you can present your data in tables, graphs, progress bars, maps and so much more. All you need to do is choose your preferred data visualization widget, input or import your data and click enter!

6. How Workato Converts 75% of Their Qualified Leads

Workato wanted to improve their inbound leads and increase their conversion rate, which ranged from 40-55%.

At first, Workato searched for a simple scheduling tool. They soon discovered that they needed a tool that provided advanced routing capabilities based on zip code and other criteria. Luckily, they found and implemented Chili Piper.

As a result of implementing Chili Piper, Workato achieved a remarkable 75–80% conversion rate and improved show rates. This led to a substantial revenue boost, with a 10-15% increase in revenue attributed to Chili Piper's impact on lead conversion.

This case study example utilizes the power of video testimonials to drive the impact of their product.

Chili Piper incorporates screenshots and clips of their tool in use. This is a great strategy because it helps your viewers become familiar with how your product works, making onboarding new customers much easier.

In this case study example, we see the importance of efficient Workflow Management Systems (WMS). Without a WMS, you manually assign tasks to your team members and engage in multiple emails for regular updates on progress.

However, when crafting and designing your case study, you should prioritize having a good WMS.

Visme has an outstanding Workflow Management System feature that keeps you on top of all your projects and designs. This feature makes it much easier to assign roles, ensure accuracy across documents, and track progress and deadlines.

Visme’s WMS feature allows you to limit access to your entire document by assigning specific slides or pages to individual members of your team. At the end of the day, your team members are not overwhelmed or distracted by the whole document but can focus on their tasks.

7. Rush Order Helps Vogmask Scale-Up During a Pandemic

Vomask's reliance on third-party fulfillment companies became a challenge as demand for their masks grew. Seeking a reliable fulfillment partner, they found Rush Order and entrusted them with their entire inventory.

Vomask's partnership with Rush Order proved to be a lifesaver during the COVID-19 pandemic. Rush Order's agility, efficiency and commitment to customer satisfaction helped Vogmask navigate the unprecedented demand and maintain its reputation for quality and service.

Rush Order’s comprehensive support enabled Vogmask to scale up its order processing by a staggering 900% while maintaining a remarkable customer satisfaction rate of 92%.

Rush Order chose one event where their impact mattered the most to their customer and shared that story.

While pandemics don't happen every day, you can look through your customer’s journey and highlight a specific time or scenario where your product or service saved their business.

The story of Vogmask and Rush Order is compelling, but it simply is not enough. The case study format and design attract readers' attention and make them want to know more. Rush Order uses consistent colors throughout the case study, starting with the logo, bold square blocks, pictures, and even headers.

Take a look at this product case study template below.

Just like our example, this case study template utilizes bold colors and large squares to attract and maintain the reader’s attention. It provides enough room for you to write about your customers' backgrounds/introductions, challenges, goals and results.

The right combination of shapes and colors adds a level of professionalism to this case study template.

8. AMR Hair & Beauty leverages B2B functionality to boost sales by 200%

With limits on website customization, slow page loading and multiple website crashes during peak events, it wasn't long before AMR Hair & Beauty began looking for a new e-commerce solution.

Their existing platform lacked effective search and filtering options, a seamless checkout process and the data analytics capabilities needed for informed decision-making. This led to a significant number of abandoned carts.

Upon switching to Shopify Plus, AMR immediately saw improvements in page loading speed and average session duration. They added better search and filtering options for their wholesale customers and customized their checkout process.

Due to this, AMR witnessed a 200% increase in sales and a 77% rise in B2B average order value. AMR Hair & Beauty is now poised for further expansion and growth.

This case study example showcases the power of a concise and impactful narrative.

To make their case analysis more effective, Shopify focused on the most relevant aspects of the customer's journey. While there may have been other challenges the customer faced, they only included those that directly related to their solutions.

Take a look at this case study template below. It is perfect if you want to create a concise but effective case study. Without including unnecessary details, you can outline the challenges, solutions and results your customers experienced from using your product.

Don’t forget to include a strong CTA within your case study. By incorporating a link, sidebar pop-up or an exit pop-up into your case study, you can prompt your readers and prospective clients to connect with you.

9. How a Marketing Agency Uses Visme to Create Engaging Content With Infographics

SmartBox Dental , a marketing agency specializing in dental practices, sought ways to make dental advice more interesting and easier to read. However, they lacked the design skills to do so effectively.

Visme's wide range of templates and features made it easy for the team to create high-quality content quickly and efficiently. SmartBox Dental enjoyed creating infographics in as little as 10-15 minutes, compared to one hour before Visme was implemented.

By leveraging Visme, SmartBox Dental successfully transformed dental content into a more enjoyable and informative experience for their clients' patients. Therefore enhancing its reputation as a marketing partner that goes the extra mile to deliver value to its clients.

Visme creatively incorporates testimonials In this case study example.

By showcasing infographics and designs created by their clients, they leverage the power of social proof in a visually compelling way. This way, potential customers gain immediate insight into the creative possibilities Visme offers as a design tool.

This example effectively showcases a product's versatility and impact, and we can learn a lot about writing a case study from it. Instead of focusing on one tool or feature per customer, Visme took a more comprehensive approach.

Within each section of their case study, Visme explained how a particular tool or feature played a key role in solving the customer's challenges.

For example, this case study highlighted Visme’s collaboration tool . With Visme’s tool, the SmartBox Dental content team fostered teamwork, accountability and effective supervision.

Visme also achieved a versatile case study by including relevant quotes to showcase each tool or feature. Take a look at some examples;

Visme’s collaboration tool: “We really like the collaboration tool. Being able to see what a co-worker is working on and borrow their ideas or collaborate on a project to make sure we get the best end result really helps us out.”

Visme’s library of stock photos and animated characters: “I really love the images and the look those give to an infographic. I also really like the animated little guys and the animated pictures. That’s added a lot of fun to our designs.”

Visme’s interactivity feature: “You can add URLs and phone number links directly into the infographic so they can just click and call or go to another page on the website and I really like adding those hyperlinks in.”

You can ask your customers to talk about the different products or features that helped them achieve their business success and draw quotes from each one.

10. Jasper Grows Blog Organic Sessions 810% and Blog-Attributed User Signups 400X

Jasper, an AI writing tool, lacked a scalable content strategy to drive organic traffic and user growth. They needed help creating content that converted visitors into users. Especially when a looming domain migration threatened organic traffic.

To address these challenges, Jasper partnered with Omniscient Digital. Their goal was to turn their content into a growth channel and drive organic growth. Omniscient Digital developed a full content strategy for Jasper AI, which included a content audit, competitive analysis, and keyword discovery.

Through their collaboration, Jasper’s organic blog sessions increased by 810%, despite the domain migration. They also witnessed a 400X increase in blog-attributed signups. And more importantly, the content program contributed to over $4 million in annual recurring revenue.

The combination of storytelling and video testimonials within the case study example makes this a real winner. But there’s a twist to it. Omniscient segmented the video testimonials and placed them in different sections of the case study.

Video marketing , especially in case studies, works wonders. Research shows us that 42% of people prefer video testimonials because they show real customers with real success stories. So if you haven't thought of it before, incorporate video testimonials into your case study.

Take a look at this stunning video testimonial template. With its simple design, you can input the picture, name and quote of your customer within your case study in a fun and engaging way.

Try it yourself! Customize this template with your customer’s testimonial and add it to your case study!

11. How Meliá Became One of the Most Influential Hotel Chains on Social Media

Meliá Hotels needed help managing their growing social media customer service needs. Despite having over 500 social accounts, they lacked a unified response protocol and detailed reporting. This largely hindered efficiency and brand consistency.

Meliá partnered with Hootsuite to build an in-house social customer care team. Implementing Hootsuite's tools enabled Meliá to decrease response times from 24 hours to 12.4 hours while also leveraging smart automation.

In addition to that, Meliá resolved over 133,000 conversations, booking 330 inquiries per week through Hootsuite Inbox. They significantly improved brand consistency, response time and customer satisfaction.

The need for a good case study design cannot be over-emphasized.

As soon as anyone lands on this case study example, they are mesmerized by a beautiful case study design. This alone raises the interest of readers and keeps them engaged till the end.

If you’re currently saying to yourself, “ I can write great case studies, but I don’t have the time or skill to turn it into a beautiful document.” Say no more.

Visme’s amazing AI document generator can take your text and transform it into a stunning and professional document in minutes! Not only do you save time, but you also get inspired by the design.

With Visme’s document generator, you can create PDFs, case study presentations , infographics and more!

Take a look at this case study template below. Just like our case study example, it captures readers' attention with its beautiful design. Its dynamic blend of colors and fonts helps to segment each element of the case study beautifully.

12. Tea’s Me Cafe: Tamika Catchings is Brewing Glory

Tamika's journey began when she purchased Tea's Me Cafe in 2017, saving it from closure. She recognized the potential of the cafe as a community hub and hosted regular events centered on social issues and youth empowerment.

One of Tamika’s business goals was to automate her business. She sought to streamline business processes across various aspects of her business. One of the ways she achieves this goal is through Constant Contact.

Constant Contact became an integral part of Tamika's marketing strategy. They provided an automated and centralized platform for managing email newsletters, event registrations, social media scheduling and more.

This allowed Tamika and her team to collaborate efficiently and focus on engaging with their audience. They effectively utilized features like WooCommerce integration, text-to-join and the survey builder to grow their email list, segment their audience and gather valuable feedback.

The case study example utilizes the power of storytelling to form a connection with readers. Constant Contact takes a humble approach in this case study. They spotlight their customers' efforts as the reason for their achievements and growth, establishing trust and credibility.

This case study is also visually appealing, filled with high-quality photos of their customer. While this is a great way to foster originality, it can prove challenging if your customer sends you blurry or low-quality photos.

If you find yourself in that dilemma, you can use Visme’s AI image edit tool to touch up your photos. With Visme’s AI tool, you can remove unwanted backgrounds, erase unwanted objects, unblur low-quality pictures and upscale any photo without losing the quality.

Constant Contact offers its readers various formats to engage with their case study. Including an audio podcast and PDF.

In its PDF version, Constant Contact utilized its brand colors to create a stunning case study design.  With this, they increase brand awareness and, in turn, brand recognition with anyone who comes across their case study.

With Visme’s brand wizard tool , you can seamlessly incorporate your brand assets into any design or document you create. By inputting your URL, Visme’s AI integration will take note of your brand colors, brand fonts and more and create branded templates for you automatically.

You don't need to worry about spending hours customizing templates to fit your brand anymore. You can focus on writing amazing case studies that promote your company.

13. How Breakwater Kitchens Achieved a 7% Growth in Sales With Thryv

Breakwater Kitchens struggled with managing their business operations efficiently. They spent a lot of time on manual tasks, such as scheduling appointments and managing client communication. This made it difficult for them to grow their business and provide the best possible service to their customers.

David, the owner, discovered Thryv. With Thryv, Breakwater Kitchens was able to automate many of their manual tasks. Additionally, Thryv integrated social media management. This enabled Breakwater Kitchens to deliver a consistent brand message, captivate its audience and foster online growth.

As a result, Breakwater Kitchens achieved increased efficiency, reduced missed appointments and a 7% growth in sales.

This case study example uses a concise format and strong verbs, which make it easy for readers to absorb the information.

At the top of the case study, Thryv immediately builds trust by presenting their customer's complete profile, including their name, company details and website. This allows potential customers to verify the case study's legitimacy, making them more likely to believe in Thryv's services.

However, manually copying and pasting customer information across multiple pages of your case study can be time-consuming.

To save time and effort, you can utilize Visme's dynamic field feature . Dynamic fields automatically insert reusable information into your designs.  So you don’t have to type it out multiple times.

14. Zoom’s Creative Team Saves Over 4,000 Hours With Brandfolder

Zoom experienced rapid growth with the advent of remote work and the rise of the COVID-19 pandemic. Such growth called for agility and resilience to scale through.

At the time, Zoom’s assets were disorganized which made retrieving brand information a burden. Zoom’s creative manager spent no less than 10 hours per week finding and retrieving brand assets for internal teams.

Zoom needed a more sustainable approach to organizing and retrieving brand information and came across Brandfolder. Brandfolder simplified and accelerated Zoom’s email localization and webpage development. It also enhanced the creation and storage of Zoom virtual backgrounds.

With Brandfolder, Zoom now saves 4,000+ hours every year. The company also centralized its assets in Brandfolder, which allowed 6,800+ employees and 20-30 vendors to quickly access them.

Brandfolder infused its case study with compelling data and backed it up with verifiable sources. This data-driven approach boosts credibility and increases the impact of their story.

Bradfolder's case study goes the extra mile by providing a downloadable PDF version, making it convenient for readers to access the information on their own time. Their dedication to crafting stunning visuals is evident in every aspect of the project.

From the vibrant colors to the seamless navigation, everything has been meticulously designed to leave a lasting impression on the viewer. And with clickable links that make exploring the content a breeze, the user experience is guaranteed to be nothing short of exceptional.

The thing is, your case study presentation won’t always sit on your website. There are instances where you may need to do a case study presentation for clients, partners or potential investors.

Visme has a rich library of templates you can tap into. But if you’re racing against the clock, Visme’s AI presentation maker is your best ally.

15. How Cents of Style Made $1.7M+ in Affiliate Sales with LeadDyno

Cents of Style had a successful affiliate and influencer marketing strategy. However, their existing affiliate marketing platform was not intuitive, customizable or transparent enough to meet the needs of their influencers.

Cents of Styles needed an easy-to-use affiliate marketing platform that gave them more freedom to customize their program and implement a multi-tier commission program.

After exploring their options, Cents of Style decided on LeadDyno.

LeadDyno provided more flexibility, allowing them to customize commission rates and implement their multi-tier commission structure, switching from monthly to weekly payouts.

Also, integrations with PayPal made payments smoother And features like newsletters and leaderboards added to the platform's success by keeping things transparent and engaging.

As a result, Cents of Style witnessed an impressive $1.7 million in revenue from affiliate sales with a substantial increase in web sales by 80%.

LeadDyno strategically placed a compelling CTA in the middle of their case study layout, maximizing its impact. At this point, readers are already invested in the customer's story and may be considering implementing similar strategies.

A well-placed CTA offers them a direct path to learn more and take action.

LeadDyno also utilized the power of quotes to strengthen their case study. They didn't just embed these quotes seamlessly into the text; instead, they emphasized each one with distinct blocks.

Are you looking for an easier and quicker solution to create a case study and other business documents? Try Visme's AI designer ! This powerful tool allows you to generate complete documents, such as case studies, reports, whitepapers and more, just by providing text prompts. Simply explain your requirements to the tool, and it will produce the document for you, complete with text, images, design assets and more.

Still have more questions about case studies? Let's look at some frequently asked questions.

How to Write a Case Study?

• Choose a compelling story: Not all case studies are created equal. Pick one that is relevant to your target audience and demonstrates the specific benefits of your product or service.
• Outline your case study: Create a case study outline and highlight how you will structure your case study to include the introduction, problem, solution and achievements of your customer.
• Choose a case study template: After you outline your case study, choose a case study template . Visme has stunning templates that can inspire your case study design.
• Craft a compelling headline: Include figures or percentages that draw attention to your case study.
• Work on the first draft: Your case study should be easy to read and understand. Use clear and concise language and avoid jargon.
• Include high-quality visual aids: Visuals can help to make your case study more engaging and easier to read. Consider adding high-quality photos, screenshots or videos.
• Include a relevant CTA: Tell prospective customers how to reach you for questions or sign-ups.

What Are the Stages of a Case Study?

The stages of a case study are;

• Planning & Preparation: Highlight your goals for writing the case study. Plan the case study format, length and audience you wish to target.
• Interview the Client: Reach out to the company you want to showcase and ask relevant questions about their journey and achievements.
• Revision & Editing: Review your case study and ask for feedback. Include relevant quotes and CTAs to your case study.
• Publication & Distribution: Publish and share your case study on your website, social media channels and email list!
• Marketing & Repurposing: Turn your case study into a podcast, PDF, case study presentation and more. Share these materials with your sales and marketing team.

What Are the Advantages and Disadvantages of a Case Study?

Advantages of a case study:

• Case studies showcase a specific solution and outcome for specific customer challenges.
• It attracts potential customers with similar challenges.
• It builds trust and credibility with potential customers.
• It provides an in-depth analysis of your company’s problem-solving process.

Disadvantages of a case study:

• Limited applicability. Case studies are tailored to specific cases and may not apply to other businesses.
• It relies heavily on customer cooperation and willingness to share information.
• It stands a risk of becoming outdated as industries and customer needs evolve.

What Are the Types of Case Studies?

There are 7 main types of case studies. They include;

• Illustrative case study.
• Instrumental case study.
• Intrinsic case study.
• Descriptive case study.
• Explanatory case study.
• Exploratory case study.
• Collective case study.

How Long Should a Case Study Be?

The ideal length of your case study is between 500 - 1500 words or 1-3 pages. Certain factors like your target audience, goal or the amount of detail you want to share may influence the length of your case study. This infographic has powerful tips for designing winning case studies

What Is the Difference Between a Case Study and an Example?

Case studies provide a detailed narrative of how your product or service was used to solve a problem. Examples are general illustrations and are not necessarily real-life scenarios.

Case studies are often used for marketing purposes, attracting potential customers and building trust. Examples, on the other hand, are primarily used to simplify or clarify complex concepts.

Where Can I Find Case Study Examples?

You can easily find many case study examples online and in industry publications. Many companies, including Visme, share case studies on their websites to showcase how their products or services have helped clients achieve success. You can also search online libraries and professional organizations for case studies related to your specific industry or field.

If you need professionally-designed, customizable case study templates to create your own, Visme's template library is one of the best places to look. These templates include all the essential sections of a case study and high-quality content to help you create case studies that position your business as an industry leader.

Get More Out Of Your Case Studies With Visme

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Problem-Solving in Business: CASE STUDIES

• ABOUT THIS LIBGUIDE
• PROBLEM-SOLVING DEFINED AND WHY IT IS IMPORTANT
• SKILLS AND QUALIFICATIONS NEEDED IN PROBLEM-SOLVING
• PROBLEM-SOLVING STEPS
• CASE STUDIES
• MORE HELPFUL RESOURCES

• << Previous: PROBLEM-SOLVING STEPS
• Next: MORE HELPFUL RESOURCES >>
• Last Updated: Mar 23, 2024 4:47 PM
• URL: https://libguides.nypl.org/problem_solving_in_business

• Our Mission

Making Learning Relevant With Case Studies

The open-ended problems presented in case studies give students work that feels connected to their lives.

To prepare students for jobs that haven’t been created yet, we need to teach them how to be great problem solvers so that they’ll be ready for anything. One way to do this is by teaching content and skills using real-world case studies, a learning model that’s focused on reflection during the problem-solving process. It’s similar to project-based learning, but PBL is more focused on students creating a product.

Case studies have been used for years by businesses, law and medical schools, physicians on rounds, and artists critiquing work. Like other forms of problem-based learning, case studies can be accessible for every age group, both in one subject and in interdisciplinary work.

You can get started with case studies by tackling relatable questions like these with your students:

• How can we limit food waste in the cafeteria?
• How can we get our school to recycle and compost waste? (Or, if you want to be more complex, how can our school reduce its carbon footprint?)
• How can we improve school attendance?
• How can we reduce the number of people who get sick at school during cold and flu season?

Addressing questions like these leads students to identify topics they need to learn more about. In researching the first question, for example, students may see that they need to research food chains and nutrition. Students often ask, reasonably, why they need to learn something, or when they’ll use their knowledge in the future. Learning is most successful for students when the content and skills they’re studying are relevant, and case studies offer one way to create that sense of relevance.

Teaching With Case Studies

Ultimately, a case study is simply an interesting problem with many correct answers. What does case study work look like in classrooms? Teachers generally start by having students read the case or watch a video that summarizes the case. Students then work in small groups or individually to solve the case study. Teachers set milestones defining what students should accomplish to help them manage their time.

During the case study learning process, student assessment of learning should be focused on reflection. Arthur L. Costa and Bena Kallick’s Learning and Leading With Habits of Mind gives several examples of what this reflection can look like in a classroom: 

Journaling: At the end of each work period, have students write an entry summarizing what they worked on, what worked well, what didn’t, and why. Sentence starters and clear rubrics or guidelines will help students be successful. At the end of a case study project, as Costa and Kallick write, it’s helpful to have students “select significant learnings, envision how they could apply these learnings to future situations, and commit to an action plan to consciously modify their behaviors.”

Interviews: While working on a case study, students can interview each other about their progress and learning. Teachers can interview students individually or in small groups to assess their learning process and their progress.

Student discussion: Discussions can be unstructured—students can talk about what they worked on that day in a think-pair-share or as a full class—or structured, using Socratic seminars or fishbowl discussions. If your class is tackling a case study in small groups, create a second set of small groups with a representative from each of the case study groups so that the groups can share their learning.

4 Tips for Setting Up a Case Study

1. Identify a problem to investigate: This should be something accessible and relevant to students’ lives. The problem should also be challenging and complex enough to yield multiple solutions with many layers.

2. Give context: Think of this step as a movie preview or book summary. Hook the learners to help them understand just enough about the problem to want to learn more.

3. Have a clear rubric: Giving structure to your definition of quality group work and products will lead to stronger end products. You may be able to have your learners help build these definitions.

4. Provide structures for presenting solutions: The amount of scaffolding you build in depends on your students’ skill level and development. A case study product can be something like several pieces of evidence of students collaborating to solve the case study, and ultimately presenting their solution with a detailed slide deck or an essay—you can scaffold this by providing specified headings for the sections of the essay.

Problem-Based Teaching Resources

There are many high-quality, peer-reviewed resources that are open source and easily accessible online.

• The National Center for Case Study Teaching in Science at the University at Buffalo built an online collection of more than 800 cases that cover topics ranging from biochemistry to economics. There are resources for middle and high school students.
• Models of Excellence , a project maintained by EL Education and the Harvard Graduate School of Education, has examples of great problem- and project-based tasks—and corresponding exemplary student work—for grades pre-K to 12.
• The Interdisciplinary Journal of Problem-Based Learning at Purdue University is an open-source journal that publishes examples of problem-based learning in K–12 and post-secondary classrooms.
• The Tech Edvocate has a list of websites and tools related to problem-based learning.

In their book Problems as Possibilities , Linda Torp and Sara Sage write that at the elementary school level, students particularly appreciate how they feel that they are taken seriously when solving case studies. At the middle school level, “researchers stress the importance of relating middle school curriculum to issues of student concern and interest.” And high schoolers, they write, find the case study method “beneficial in preparing them for their future.”

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• A Global Experience

The HBS Case Method

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How the HBS Case Method Works

How the Case Method Works

• Read and analyze the case. Each case is a 10-20 page document written from the viewpoint of a real person leading a real organization. In addition to background information on the situation, each case ends in a key decision to be made. Your job is to sift through the information, incomplete by design, and decide what you would do.
• Discuss the case. Each morning, you’ll bring your ideas to a small team of classmates from diverse professional backgrounds, your discussion group, to share your findings and listen to theirs. Together, you begin to see the case from different perspectives, better preparing you for class.
• Engage in class. Be prepared to change the way you think as you debate with classmates the best path forward for this organization. The highly engaged conversation is facilitated by the faculty member, but it’s driven by your classmates’ comments and experiences. HBS brings together amazingly talented people from diverse backgrounds and puts that experience front and center. Students do the majority of the talking (and lots of active listening), and your job is to better understand the decision at hand, what you would do in the case protagonist’s shoes, and why. You will not leave a class thinking about the case the same way you thought about it coming in! In addition to learning more about many businesses, in the case method you will develop communication, listening, analysis, and leadership skills. It is a truly dynamic and immersive learning environment.
• Reflect. The case method prepares you to be in leadership positions where you will face time-sensitive decisions with limited information. Reflecting on each class discussion will prepare you to face these situations in your future roles.

Student Perspectives

“I’ve been so touched by how dedicated other people have been to my learning and my success.”

Faculty Perspectives

“The world desperately needs better leadership. It’s actually one of the great gifts of teaching here, you can do something about it.”

Alumni Perspectives

“You walk into work every morning and it's like a fire hose of decisions that need to be made, often without enough information. Just like an HBS case.”

Celebrating the Inaugural HBS Case

“How do you go into an ambiguous situation and get to the bottom of it? That skill – the skill of figuring out a course of inquiry, to choose a course of action – that skill is as relevant today as it was in 1921.”

Case Study Mastery: Examples & Step-by-Step Templates

Master case study: Uncover key strategies to conduct & present findings that influence decisions charachters.

February 9, 2024

What's Inside?

Understanding and sharing success stories in the business management world is crucial for grasping the growth journey of a business.

In this article, we will delve into the concept of a business management case study, exploring its definition, benefits, limitations, step-by-step process, types, and essential elements.

What is a Case Study?

A case study research is a detailed analysis of a particular subject, often a real-world situation or scenario, to draw insights and conclusions. It serves as a valuable tool for learning from successful strategies, identifying challenges, and making informed decisions.

Key Characteristics of a Case Study:

Specific Focus: Case studies concentrate on a particular subject, narrowing down the scope to delve deeply into specific aspects.

Real-world Context: Unlike theoretical studies, case studies are grounded in the real world. They often involve the examination of actual events, circumstances, or challenges.

Comprehensive Exploration: Case studies involve a thorough investigation of multiple facets of the chosen subject. This may include collecting and analyzing data, conducting interviews, and reviewing relevant documents.

Contextualization: Each case study is set within a context, providing background information to help readers or viewers understand the circumstances surrounding the case.

Problem-Solving Orientation: While exploring the intricacies of a case, case studies often aim to identify problems, challenges, or opportunities. They can be used as tools for problem-solving and decision-making.

In-depth Analysis: The analysis in a case study goes beyond surface-level observations. It involves a detailed examination of factors contributing to the situation, allowing for a nuanced understanding.

Presentation of Findings: A case study concludes with the presentation of findings, insights, and conclusions. Leveraging a visually compelling presentation plays a vital role for a case study to speak out.

Why You Should Write a Case Study?

Writing a case study offers several compelling reasons for individuals and businesses alike:

Demonstrate Success: A case study allows you to showcase your achievements and successes. It provides tangible evidence of your capabilities, helping build trust and credibility with potential clients, customers, or collaborators.

Educate and Inform: Use case studies to share valuable insights, lessons learned, and best practices. By documenting your experiences, you contribute to the collective knowledge within your industry, positioning yourself as an authority and resource.

Problem-Solving Showcase: If your case study revolves around overcoming challenges, it highlights your problem-solving abilities. This can be particularly impactful in industries where complex issues require innovative solutions.

Engage Your Audience: Well-crafted case studies are engaging and resonate with your audience. They tell a story, making information more relatable and memorable. This storytelling aspect can captivate readers and enhance their understanding of your work.

Build Brand Awareness: Case studies provide an opportunity to promote your brand in a context that goes beyond traditional marketing. Through real-world examples, you can reinforce your brand message and values.

Attract New Opportunities: A compelling case study can attract new opportunities, whether it be clients, partnerships , or collaborations. It serves as a powerful marketing tool, showcasing your expertise and capabilities to a wider audience.

Validate Your Methods: For businesses, case studies serve as a validation of their methods and strategies. Employing a robust case study methodology is a way to demonstrate the effectiveness of your products, services, or approaches to potential clients or customers through a thorough research process.

Internal Learning: Writing a case study requires reflection on your processes and approach case outcomes. This internal learning process can contribute to continuous improvement within your organization , fostering a culture of innovation and adaptability.

SEO Benefits: Case studies can be optimized for search engines, contributing to your online visibility. Including relevant keywords and internal links in your case studies can improve your website's SEO , attracting more organic traffic.

Differentiation: In competitive industries, a well crafted case study sets you apart from the competition. It allows you to highlight what makes your approach unique and why clients or customers should choose your products or services.

Benefits and Limitations of Case Studies

Benefits of Case Studies:

• Evident Success Stories: Case studies serve as tangible evidence of a business's success, allowing them to showcase real-world achievements and build credibility with potential clients or customers.
• Effective Marketing Tool: They function as powerful marketing tools by providing in depth insights into a business's capabilities , differentiating it from competitors, and influencing the decision making process of potential clients.
• Client Relationship Building: Through detailed case studies, businesses can strengthen relationships with existing clients by demonstrating their commitment, problem solving abilities, and delivering measurable results.
• Versatile Content: Case studies offer versatile content that can be repurposed across various marketing channels, including websites, social media, presentations, and promotional materials.
• Educational Value: Businesses can use case studies to educate their target audience about their industry, innovative solutions, and best practices, positioning themselves as thought leaders.

Limitations of Case Studies:

• Resource Intensive: Creating comprehensive case studies demands significant resources, including time, effort, and potential costs, making them resource-intensive for businesses.
• Limited Generalization: Findings from a specific case study may not be universally applicable, limiting their generalizability to other scenarios or industries.
• Potential Bias: There is a risk of bias in the selection and presentation of information, as businesses may be inclined to emphasize positive outcomes and downplay challenges.
• Confidentiality Concerns: Businesses may face challenges in sharing detailed information, especially if it involves sensitive data or strategies, raising concerns about confidentiality.
• Difficulty in Replication: The unique circumstances of a case study may make it challenging to replicate the same success in different contexts, limiting the broader applicability of the insights gained.

How to Conduct a Case Analysis: Step-by-step

1. define the objective:.

• Clearly outline the purpose of the case study. What do you aim to achieve or understand through this analysis?

2. Select the Case:

• Identify a relevant and specific case that aligns with your objective. For an important case study this could be a real-world situation, event, or phenomenon.

3. Background Research:

• Gather background information about the case. This may include historical context, key players involved, and any existing literature on the subject.

4. Identify Key Issues or Questions:

• Formulate specific research questions or highlight key issues you want to address through the case study.

5. Choose the Research Method:

• Decide on the case study method or approach for data collection. A case study research method could involve qualitative methods such as interviews, observations, or document analysis.

6. Develop Data Collection Plan:

• Outline a detailed plan for collecting data. Specify sources, methods, and tools you will use to gather relevant information.

7. Data Collection:

• Execute the data collection plan. Conduct interviews , observe events, and analyze documents to accumulate necessary data.

8. Data Analysis:

• Apply appropriate analytical techniques to interpret the gathered data. This may involve coding, categorizing, and identifying patterns or themes.

9. Construct the Case Study Narrative:

• Organize the findings into a coherent and structured narrative. Develop sections that cover the introduction, background, analysis, and conclusion.

10. Draw Conclusions:

• Based on your analysis, after you conduct case study , draw conclusions that address the research questions or objectives. Consider the implications of your findings.

11. Peer Review or Feedback:

• Seek feedback from colleagues, mentors, or peers to ensure the validity and reliability of your case study.

12. Finalize the Case Study:

• Incorporate feedback and make necessary revisions. Finalize the case study, ensuring clarity, coherence, and adherence to ethical guidelines.

13. Document and Share:

• Prepare the case study for publication or presentation and take advantage of Decktopus AI, a user-friendly and efficient presentation generator powered by AI. Easily convert your case study insights into a visually compelling deck.

• Decktopus ensures your case studies are presented in a format that engages your audience, making your narratives more impactful and memorable. Explore the benefits of Decktopus AI to elevate your case study presentations effortlessly.

What are the Components of a Case Study

The format of a case study typically comprises several key components to present information in a structured and comprehensive manner. While variations may exist based on the context and purpose, a standard case study format often includes the following elements:

1. Introduction:

Provide a brief overview of the case and set the stage for the reader. Outline the main objectives and establish the context of the study.

2. Background:

Present relevant background information about the subject of the case. This may include the history, industry context, or any pertinent details necessary for understanding the situation.

3. Problem Statement or Objectives:

Clearly state the problem or the main objectives of the case study. Define the issues or challenges that the study aims to address.

4. Analysis:

Dive into the analysis of the case. This section often comprises multiple sub-sections, each exploring different aspects such as market conditions, internal factors, external influences, etc.

5. Solution or Action:

Propose solutions or actions to address the identified problems. Detail the steps taken or recommended strategies based on the analysis.

6. Results:

Present the outcomes of the solutions or actions taken. Include any measurable results, impacts, or changes observed.

7. Conclusion:

Summarize the key points, outcomes, and lessons learned. Revisit the problem statement and emphasize the significance of the study, highlighting how the research design shaped the results.

Types of Case Studies

Case Study Examples

1. marketing case study template.

The Marketing Case Study Template is tailored for marketers, highlighting successful marketing strategies . Uncover the methods employed, target audience engagement, and measurable outcomes.

Ideal for marketing professionals seeking insights into effective campaign executions. With Decktopus AI , spending your precious time perpetually recreating your product's presentation has become an ancient practice.

Along with our collection of case-study templates, with our one-click platform, you can easily create beautiful presentations for yourself or your clients.

Also check out: creative marketing case study template .

2. Sales Case Study Template

The Sales Case Study Template is designed for salespeople to present and discuss case studies in sales meetings. With its professional look and engaging layout, your clients will be impressed with the level of detail you put into your analysis.

This professionally designed template is easy to use and easy to customize, making it the perfect way to show off your small business expertise.

So whether you're looking to wow potential clients or just need a little more confidence in your sales meetings, our client case study template will help you make an impact.

Also check-out: case study template for sales teams .

3. Design Case Study Example: UI Case Study Template

The UI Case Study Template is specifically designed for UI designers, making it easy to discuss your design process and findings. Present your design case studies like a pro with our target-spesific case study templates. With our design case study template , you'll be able to showcase your work in a clear, professional manner.

Looking to create a stunning case study presentation for your next client meeting? Look no further than our case study templates! Our professional and easy-to-use templates are perfect for designers of all experience levels, and will help you showcase your work in a clear and concise way.

Also check out: Art Case Study Template .

Explore More Case Study Templates

Discover a vast collection of case study templates from various fields, including marketing, sales, and design, in our dedicated Case Study Examples Blog. Gain insights into diverse business scenarios and find inspiration for your own projects.

Case Study Presentation Creation with Decktopus AI

Streamlining the creation of engaging visual case studies has never been easier than with Decktopus AI . This innovative platform offers a seamless experiencensimply write your input, and Decktopus takes care of the rest, ensuring that your templates not only boast a polished visual appeal but also integrate relevant and impactful content effortlessly.

Discover how easy it is to create engaging case study templates using Decktopus AI . Our platform ensures your templates look great and contain relevant content. With the help of our AI assistant, you not only get support during presentations but also receive tips, facilitate Q&A, and increase overall engagement.

Explore the unique storytelling format that Decktopus offers, making your case studies more relatable. For a step-by-guide on how to easily create a visually stunning case study with Decktopus, see our case study examples blog.

This approach allows you to present information in a narrative style, connecting better with your audience. Find practical tips for smoother case study presentations, from effective storytelling to engaging your audience. Improve your presentation experience with Decktopus AI , where simplicity meets interactivity and storytelling for effective communication.

It features, practical design, mobilizing easy principles of marketing ecosystem platform design. Making it by far the easiest thing to use in your daily practice of mobilizing marketing ecosystems through platform strategies.

Frequently Asked Questions

1) what is a marketing case study.

A marketing case study is a concise analysis of a business's marketing strategy, showcasing its objectives, challenges, tactics, and outcomes. It offers practical insights into real-world marketing applications, serving as a valuable learning tool for understanding successful practices and lessons learned in achieving specific marketing goals.

2) What is a case study?

A case study, or case report, is a concise examination of a specific subject, often real-world situations or problems, providing detailed insights and analysis for learning or decision-making purposes.

3) How should you write a case study?

To create an impactful case study, define objectives, choose a relevant case, gather key information, and use Decktopus for a polished presentation. Employ data analysis, construct a clear narrative, and offer actionable recommendations.

Validate findings and consider broader implications. Decktopus streamlines this process, providing a user-friendly platform for creating compelling case study presentations effortlessly.

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Problem-Solving

Problem-solving is a complex skill used in times where solutions are not readily available. It is defined as “the skill of finding solutions for difficult or complex issues.” Using problem-solving in our daily lives helps us think through what options are possible in difficult situations and also helps create new solutions to move past the issue at hand.

Learning in Observation

The following video case studies are a learning tool that helps students, families, and educators examine the context of the social problems that might arise for youth and young adults. Visual media gives everyone a common viewing experience and centralizes the discussion around making choices and decisions in situations where problems exist. The activities extend learning around problem-solving and help learners understand why many of the problems that exist in everyday life are more complex than we think.

Watch the high school video case study below and think about how the opportunities for problem-solving involve both advocacy and the ability to receive and listen to other people’s perspectives. Hand out Character Profile Cards to students or groups of students and have them watch the video from the perspective of that character.

Referencing the profile cards , facilitate a discussion after watching the video. Some questions for discussion may include:

• What does my character know that the other characters need to know?
• What do the other characters know that my character needs to know?
• What are some possible solutions to the problem?
• Who can support the solution to make sure it is effective?
• Who needs to be a part of the solution?

The following video case study takes place at home between two brothers. Similar to the case study above, use questions to reflect on the problem and the process of problem-solving taken up by the parents.

Use the following questions to guide small groups in discussion around problem-solving.

• Why does this approach work well to discover the truth around the problem?
• What are some things that the parents learned about their children in the process of working through the problem?
• As a parent, why would it be important to get both perspectives?
• As a child, why would it be important to be heard, but also to hear your sibling’s perspective?
• What are some complex or difficult issues that are addressed in this process?

Observation to Practice

It might seem easy to find solutions when we are outside of the situation, however, it can be hard to solve problems when they pop up in our daily lives. The following are steps you can take to start problem-solving.

• Identify the problem
• Invite others affected by the problem to help understand what happened
• With help, collect more information about what happened
• Using the collected information, consider what process is most appropriate for reaching positive solutions
• Use the process to collaborate with others and generate plans for moving towards a solution
• Act towards goals that will help bring about a solution

Try it out for yourself! Use the following link to practice problem-solving in real-life situations.

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Blog Business How to Present a Case Study like a Pro (With Examples)

How to Present a Case Study like a Pro (With Examples)

Written by: Danesh Ramuthi Sep 07, 2023

Okay, let’s get real: case studies can be kinda snooze-worthy. But guess what? They don’t have to be!

In this article, I will cover every element that transforms a mere report into a compelling case study, from selecting the right metrics to using persuasive narrative techniques.

And if you’re feeling a little lost, don’t worry! There are cool tools like Venngage’s Case Study Creator to help you whip up something awesome, even if you’re short on time. Plus, the pre-designed case study templates are like instant polish because let’s be honest, everyone loves a shortcut.

Click to jump ahead: 

What is a case study presentation?

What is the purpose of presenting a case study, how to structure a case study presentation, how long should a case study presentation be, 5 case study presentation examples with templates, 6 tips for delivering an effective case study presentation, 5 common mistakes to avoid in a case study presentation, how to present a case study faqs.

A case study presentation involves a comprehensive examination of a specific subject, which could range from an individual, group, location, event, organization or phenomenon.

They’re like puzzles you get to solve with the audience, all while making you think outside the box.

Unlike a basic report or whitepaper, the purpose of a case study presentation is to stimulate critical thinking among the viewers. 

The primary objective of a case study is to provide an extensive and profound comprehension of the chosen topic. You don’t just throw numbers at your audience. You use examples and real-life cases to make you think and see things from different angles.

The primary purpose of presenting a case study is to offer a comprehensive, evidence-based argument that informs, persuades and engages your audience.

Here’s the juicy part: presenting that case study can be your secret weapon. Whether you’re pitching a groundbreaking idea to a room full of suits or trying to impress your professor with your A-game, a well-crafted case study can be the magic dust that sprinkles brilliance over your words.

Think of it like digging into a puzzle you can’t quite crack . A case study lets you explore every piece, turn it over and see how it fits together. This close-up look helps you understand the whole picture, not just a blurry snapshot.

It’s also your chance to showcase how you analyze things, step by step, until you reach a conclusion. It’s all about being open and honest about how you got there.

Besides, presenting a case study gives you an opportunity to connect data and real-world scenarios in a compelling narrative. It helps to make your argument more relatable and accessible, increasing its impact on your audience.

One of the contexts where case studies can be very helpful is during the job interview. In some job interviews, you as candidates may be asked to present a case study as part of the selection process.

Having a case study presentation prepared allows the candidate to demonstrate their ability to understand complex issues, formulate strategies and communicate their ideas effectively.

The way you present a case study can make all the difference in how it’s received. A well-structured presentation not only holds the attention of your audience but also ensures that your key points are communicated clearly and effectively.

In this section, let’s go through the key steps that’ll help you structure your case study presentation for maximum impact.

Let’s get into it. 

Open with an introductory overview 

Start by introducing the subject of your case study and its relevance. Explain why this case study is important and who would benefit from the insights gained. This is your opportunity to grab your audience’s attention.

Explain the problem in question

Dive into the problem or challenge that the case study focuses on. Provide enough background information for the audience to understand the issue. If possible, quantify the problem using data or metrics to show the magnitude or severity.

Detail the solutions to solve the problem

After outlining the problem, describe the steps taken to find a solution. This could include the methodology, any experiments or tests performed and the options that were considered. Make sure to elaborate on why the final solution was chosen over the others.

Key stakeholders Involved

Talk about the individuals, groups or organizations that were directly impacted by or involved in the problem and its solution. 

Stakeholders may experience a range of outcomes—some may benefit, while others could face setbacks.

For example, in a business transformation case study, employees could face job relocations or changes in work culture, while shareholders might be looking at potential gains or losses.

Discuss the key results & outcomes

Discuss the results of implementing the solution. Use data and metrics to back up your statements. Did the solution meet its objectives? What impact did it have on the stakeholders? Be honest about any setbacks or areas for improvement as well.

Include visuals to support your analysis

Visual aids can be incredibly effective in helping your audience grasp complex issues. Utilize charts, graphs, images or video clips to supplement your points. Make sure to explain each visual and how it contributes to your overall argument.

Pie charts illustrate the proportion of different components within a whole, useful for visualizing market share, budget allocation or user demographics.

This is particularly useful especially if you’re displaying survey results in your case study presentation.

Stacked charts on the other hand are perfect for visualizing composition and trends. This is great for analyzing things like customer demographics, product breakdowns or budget allocation in your case study.

Consider this example of a stacked bar chart template. It provides a straightforward summary of the top-selling cake flavors across various locations, offering a quick and comprehensive view of the data.

Not the chart you’re looking for? Browse Venngage’s gallery of chart templates to find the perfect one that’ll captivate your audience and level up your data storytelling.

Recommendations and next steps

Wrap up by providing recommendations based on the case study findings. Outline the next steps that stakeholders should take to either expand on the success of the project or address any remaining challenges.

Acknowledgments and references

Thank the people who contributed to the case study and helped in the problem-solving process. Cite any external resources, reports or data sets that contributed to your analysis.

Feedback & Q&A session

Open the floor for questions and feedback from your audience. This allows for further discussion and can provide additional insights that may not have been considered previously.

Closing remarks

Conclude the presentation by summarizing the key points and emphasizing the takeaways. Thank your audience for their time and participation and express your willingness to engage in further discussions or collaborations on the subject.

Well, the length of a case study presentation can vary depending on the complexity of the topic and the needs of your audience. However, a typical business or academic presentation often lasts between 15 to 30 minutes. 

This time frame usually allows for a thorough explanation of the case while maintaining audience engagement. However, always consider leaving a few minutes at the end for a Q&A session to address any questions or clarify points made during the presentation.

When it comes to presenting a compelling case study, having a well-structured template can be a game-changer. 

It helps you organize your thoughts, data and findings in a coherent and visually pleasing manner. 

Not all case studies are created equal and different scenarios require distinct approaches for maximum impact. 

To save you time and effort, I have curated a list of 5 versatile case study presentation templates, each designed for specific needs and audiences. 

Here are some best case study presentation examples that showcase effective strategies for engaging your audience and conveying complex information clearly.

1 . Lab report case study template

Ever feel like your research gets lost in a world of endless numbers and jargon? Lab case studies are your way out!

Think of it as building a bridge between your cool experiment and everyone else. It’s more than just reporting results – it’s explaining the “why” and “how” in a way that grabs attention and makes sense.

This lap report template acts as a blueprint for your report, guiding you through each essential section (introduction, methods, results, etc.) in a logical order.

Want to present your research like a pro? Browse our research presentation template gallery for creative inspiration!

2. Product case study template

It’s time you ditch those boring slideshows and bullet points because I’ve got a better way to win over clients: product case study templates.

Instead of just listing features and benefits, you get to create a clear and concise story that shows potential clients exactly what your product can do for them. It’s like painting a picture they can easily visualize, helping them understand the value your product brings to the table.

Grab the template below, fill in the details, and watch as your product’s impact comes to life!

3. Content marketing case study template

In digital marketing, showcasing your accomplishments is as vital as achieving them. 

A well-crafted case study not only acts as a testament to your successes but can also serve as an instructional tool for others. 

With this coral content marketing case study template—a perfect blend of vibrant design and structured documentation, you can narrate your marketing triumphs effectively.

4. Case study psychology template

Understanding how people tick is one of psychology’s biggest quests and case studies are like magnifying glasses for the mind. They offer in-depth looks at real-life behaviors, emotions and thought processes, revealing fascinating insights into what makes us human.

Writing a top-notch case study, though, can be a challenge. It requires careful organization, clear presentation and meticulous attention to detail. That’s where a good case study psychology template comes in handy.

Think of it as a helpful guide, taking care of formatting and structure while you focus on the juicy content. No more wrestling with layouts or margins – just pour your research magic into crafting a compelling narrative.

5. Lead generation case study template

Lead generation can be a real head-scratcher. But here’s a little help: a lead generation case study.

Think of it like a friendly handshake and a confident resume all rolled into one. It’s your chance to showcase your expertise, share real-world successes and offer valuable insights. Potential clients get to see your track record, understand your approach and decide if you’re the right fit.

No need to start from scratch, though. This lead generation case study template guides you step-by-step through crafting a clear, compelling narrative that highlights your wins and offers actionable tips for others. Fill in the gaps with your specific data and strategies, and voilà! You’ve got a powerful tool to attract new customers.

Related: 15+ Professional Case Study Examples [Design Tips + Templates]

So, you’ve spent hours crafting the perfect case study and are now tasked with presenting it. Crafting the case study is only half the battle; delivering it effectively is equally important. 

Whether you’re facing a room of executives, academics or potential clients, how you present your findings can make a significant difference in how your work is received. 

Forget boring reports and snooze-inducing presentations! Let’s make your case study sing. Here are some key pointers to turn information into an engaging and persuasive performance:

• Know your audience : Tailor your presentation to the knowledge level and interests of your audience. Remember to use language and examples that resonate with them.
• Rehearse : Rehearsing your case study presentation is the key to a smooth delivery and for ensuring that you stay within the allotted time. Practice helps you fine-tune your pacing, hone your speaking skills with good word pronunciations and become comfortable with the material, leading to a more confident, conversational and effective presentation.
• Start strong : Open with a compelling introduction that grabs your audience’s attention. You might want to use an interesting statistic, a provocative question or a brief story that sets the stage for your case study.
• Be clear and concise : Avoid jargon and overly complex sentences. Get to the point quickly and stay focused on your objectives.
• Use visual aids : Incorporate slides with graphics, charts or videos to supplement your verbal presentation. Make sure they are easy to read and understand.
• Tell a story : Use storytelling techniques to make the case study more engaging. A well-told narrative can help you make complex data more relatable and easier to digest.

Ditching the dry reports and slide decks? Venngage’s case study templates let you wow customers with your solutions and gain insights to improve your business plan. Pre-built templates, visual magic and customer captivation – all just a click away. Go tell your story and watch them say “wow!”

Nailed your case study, but want to make your presentation even stronger? Avoid these common mistakes to ensure your audience gets the most out of it:

Overloading with information

A case study is not an encyclopedia. Overloading your presentation with excessive data, text or jargon can make it cumbersome and difficult for the audience to digest the key points. Stick to what’s essential and impactful. Need help making your data clear and impactful? Our data presentation templates can help! Find clear and engaging visuals to showcase your findings.

Lack of structure

Jumping haphazardly between points or topics can confuse your audience. A well-structured presentation, with a logical flow from introduction to conclusion, is crucial for effective communication.

Ignoring the audience

Different audiences have different needs and levels of understanding. Failing to adapt your presentation to your audience can result in a disconnect and a less impactful presentation.

Poor visual elements

While content is king, poor design or lack of visual elements can make your case study dull or hard to follow. Make sure you use high-quality images, graphs and other visual aids to support your narrative.

Not focusing on results

A case study aims to showcase a problem and its solution, but what most people care about are the results. Failing to highlight or adequately explain the outcomes can make your presentation fall flat.

How to start a case study presentation?

Starting a case study presentation effectively involves a few key steps:

• Grab attention : Open with a hook—an intriguing statistic, a provocative question or a compelling visual—to engage your audience from the get-go.
• Set the stage : Briefly introduce the subject, context and relevance of the case study to give your audience an idea of what to expect.
• Outline objectives : Clearly state what the case study aims to achieve. Are you solving a problem, proving a point or showcasing a success?
• Agenda : Give a quick outline of the key sections or topics you’ll cover to help the audience follow along.
• Set expectations : Let your audience know what you want them to take away from the presentation, whether it’s knowledge, inspiration or a call to action.

How to present a case study on PowerPoint and on Google Slides?

Presenting a case study on PowerPoint and Google Slides involves a structured approach for clarity and impact using presentation slides :

• Title slide : Start with a title slide that includes the name of the case study, your name and any relevant institutional affiliations.
• Introduction : Follow with a slide that outlines the problem or situation your case study addresses. Include a hook to engage the audience.
• Objectives : Clearly state the goals of the case study in a dedicated slide.
• Findings : Use charts, graphs and bullet points to present your findings succinctly.
• Analysis : Discuss what the findings mean, drawing on supporting data or secondary research as necessary.
• Conclusion : Summarize key takeaways and results.
• Q&A : End with a slide inviting questions from the audience.

What’s the role of analysis in a case study presentation?

The role of analysis in a case study presentation is to interpret the data and findings, providing context and meaning to them. 

It helps your audience understand the implications of the case study, connects the dots between the problem and the solution and may offer recommendations for future action.

Is it important to include real data and results in the presentation?

Yes, including real data and results in a case study presentation is crucial to show experience,  credibility and impact. Authentic data lends weight to your findings and conclusions, enabling the audience to trust your analysis and take your recommendations more seriously

How do I conclude a case study presentation effectively?

To conclude a case study presentation effectively, summarize the key findings, insights and recommendations in a clear and concise manner. 

End with a strong call-to-action or a thought-provoking question to leave a lasting impression on your audience.

What’s the best way to showcase data in a case study presentation ?

The best way to showcase data in a case study presentation is through visual aids like charts, graphs and infographics which make complex information easily digestible, engaging and creative. 

Don’t just report results, visualize them! This template for example lets you transform your social media case study into a captivating infographic that sparks conversation.

Choose the type of visual that best represents the data you’re showing; for example, use bar charts for comparisons or pie charts for parts of a whole. 

Ensure that the visuals are high-quality and clearly labeled, so the audience can quickly grasp the key points. 

Keep the design consistent and simple, avoiding clutter or overly complex visuals that could distract from the message.

Choose a template that perfectly suits your case study where you can utilize different visual aids for maximum impact. 

Need more inspiration on how to turn numbers into impact with the help of infographics? Our ready-to-use infographic templates take the guesswork out of creating visual impact for your case studies with just a few clicks.

Related: 10+ Case Study Infographic Templates That Convert

Congrats on mastering the art of compelling case study presentations! This guide has equipped you with all the essentials, from structure and nuances to avoiding common pitfalls. You’re ready to impress any audience, whether in the boardroom, the classroom or beyond.

And remember, you’re not alone in this journey. Venngage’s Case Study Creator is your trusty companion, ready to elevate your presentations from ordinary to extraordinary. So, let your confidence shine, leverage your newly acquired skills and prepare to deliver presentations that truly resonate.

Go forth and make a lasting impact!

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• Problem Solving Workshop

How to Approach a Case Study in a Problem Solving Workshop

John Palfrey and Lisa Brem

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16 case study examples (+ 3 templates to make your own)

I like to think of case studies as a business's version of a resume. It highlights what the business can do, lends credibility to its offer, and contains only the positive bullet points that paint it in the best light possible.

Imagine if the guy running your favorite taco truck followed you home so that he could "really dig into how that burrito changed your life." I see the value in the practice. People naturally prefer a tried-and-true burrito just as they prefer tried-and-true products or services.

To help you showcase your success and flesh out your burrito questionnaire, I've put together some case study examples and key takeaways.

What is a case study?

A case study is an in-depth analysis of how your business, product, or service has helped past clients. It can be a document, a webpage, or a slide deck that showcases measurable, real-life results.

For example, if you're a SaaS company, you can analyze your customers' results after a few months of using your product to measure its effectiveness. You can then turn this analysis into a case study that further proves to potential customers what your product can do and how it can help them overcome their challenges.

It changes the narrative from "I promise that we can do X and Y for you" to "Here's what we've done for businesses like yours, and we can do it for you, too."

16 case study examples 

While most case studies follow the same structure, quite a few try to break the mold and create something unique. Some businesses lean heavily on design and presentation, while others pursue a detailed, stat-oriented approach. Some businesses try to mix both.

There's no set formula to follow, but I've found that the best case studies utilize impactful design to engage readers and leverage statistics and case details to drive the point home. A case study typically highlights the companies, the challenges, the solution, and the results. The examples below will help inspire you to do it, too.

1. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Volcanica Coffee and AdRoll

People love a good farm-to-table coffee story, and boy am I one of them. But I've shared this case study with you for more reasons than my love of coffee. I enjoyed this study because it was written as though it was a letter.

In this case study, the founder of Volcanica Coffee talks about the journey from founding the company to personally struggling with learning and applying digital marketing to finding and enlisting AdRoll's services.

It felt more authentic, less about AdRoll showcasing their worth and more like a testimonial from a grateful and appreciative client. After the story, the case study wraps up with successes, milestones, and achievements. Note that quite a few percentages are prominently displayed at the top, providing supporting evidence that backs up an inspiring story.

Takeaway: Highlight your goals and measurable results to draw the reader in and provide concise, easily digestible information.

2. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Taylor Guitars and Airtable

This Airtable case study on Taylor Guitars comes as close as one can to an optimal structure. It features a video that represents the artistic nature of the client, highlighting key achievements and dissecting each element of Airtable's influence.

It also supplements each section with a testimonial or quote from the client, using their insights as a catalyst for the case study's narrative. For example, the case study quotes the social media manager and project manager's insights regarding team-wide communication and access before explaining in greater detail.

Takeaway: Highlight pain points your business solves for its client, and explore that influence in greater detail.

3. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} EndeavourX and Figma

My favorite part of Figma's case study is highlighting why EndeavourX chose its solution. You'll notice an entire section on what Figma does for teams and then specifically for EndeavourX.

It also places a heavy emphasis on numbers and stats. The study, as brief as it is, still manages to pack in a lot of compelling statistics about what's possible with Figma.

Takeaway: Showcase the "how" and "why" of your product's differentiators and how they benefit your customers.

4. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} ActiveCampaign and Zapier

Zapier's case study leans heavily on design, using graphics to present statistics and goals in a manner that not only remains consistent with the branding but also actively pushes it forward, drawing users' eyes to the information most important to them. 

The graphics, emphasis on branding elements, and cause/effect style tell the story without requiring long, drawn-out copy that risks boring readers. Instead, the cause and effect are concisely portrayed alongside the client company's information for a brief and easily scannable case study.

Takeaway: Lean on design to call attention to the most important elements of your case study, and make sure it stays consistent with your branding.

5. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Ironclad and OpenAI

In true OpenAI fashion, this case study is a block of text. There's a distinct lack of imagery, but the study features a narrated video walking readers through the product.

The lack of imagery and color may not be the most inviting, but utilizing video format is commendable. It helps thoroughly communicate how OpenAI supported Ironclad in a way that allows the user to sit back, relax, listen, and be impressed. 

Takeaway: Get creative with the media you implement in your case study. Videos can be a very powerful addition when a case study requires more detailed storytelling.

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GitHub's case study on Shopify is a light read. It addresses client pain points and discusses the different aspects its product considers and improves for clients. It touches on workflow issues, internal systems, automation, and security. It does a great job of representing what one company can do with GitHub.

To drive the point home, the case study features colorful quote callouts from the Shopify team, sharing their insights and perspectives on the partnership, the key issues, and how they were addressed.

Takeaway: Leverage quotes to boost the authoritativeness and trustworthiness of your case study. 

7 . .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Audible and Contentful

Contentful's case study on Audible features almost every element a case study should. It includes not one but two videos and clearly outlines the challenge, solution, and outcome before diving deeper into what Contentful did for Audible. The language is simple, and the writing is heavy with quotes and personal insights.

This case study is a uniquely original experience. The fact that the companies in question are perhaps two of the most creative brands out there may be the reason. I expected nothing short of a detailed analysis, a compelling story, and video content. 

Takeaway: Inject some brand voice into the case study, and create assets that tell the story for you.

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Asana's case study on Zoom is longer than the average piece and features detailed data on Zoom's growth since 2020. Instead of relying on imagery and graphics, it features several quotes and testimonials. 

It's designed to be direct, informative, and promotional. At some point, the case study reads more like a feature list. There were a few sections that felt a tad too promotional for my liking, but to each their own burrito.

Takeaway: Maintain a balance between promotional and informative. You want to showcase the high-level goals your product helped achieve without losing the reader.

9 . .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Hickies and Mailchimp

I've always been a fan of Mailchimp's comic-like branding, and this case study does an excellent job of sticking to their tradition of making information easy to understand, casual, and inviting.

It features a short video that briefly covers Hickies as a company and Mailchimp's efforts to serve its needs for customer relationships and education processes. Overall, this case study is a concise overview of the partnership that manages to convey success data and tell a story at the same time. What sets it apart is that it does so in a uniquely colorful and brand-consistent manner.

Takeaway: Be concise to provide as much value in as little text as possible.

10. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} NVIDIA and Workday

The gaming industry is notoriously difficult to recruit for, as it requires a very specific set of skills and experience. This case study focuses on how Workday was able to help fill that recruitment gap for NVIDIA, one of the biggest names in the gaming world.

Though it doesn't feature videos or graphics, this case study stood out to me in how it structures information like "key products used" to give readers insight into which tools helped achieve these results.

Takeaway: If your company offers multiple products or services, outline exactly which ones were involved in your case study, so readers can assess each tool.

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I'm personally not a big KFC fan, but that's only because I refuse to eat out of a bucket. My aversion to the bucket format aside, Contentful follows its consistent case study format in this one, outlining challenges, solutions, and outcomes before diving into the nitty-gritty details of the project.

Say what you will about KFC, but their primary product (chicken) does present a unique opportunity for wordplay like "Continuing to march to the beat of a digital-first drum(stick)" or "Delivering deep-fried goodness to every channel."

Takeaway: Inject humor into your case study if there's room for it and if it fits your brand. 

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Twilio does an excellent job of delivering achievements at the very beginning of the case study and going into detail in this two-minute read. While there aren't many graphics, the way quotes from the Intuit team are implemented adds a certain flair to the study and breaks up the sections nicely.

It's simple, concise, and manages to fit a lot of information in easily digestible sections.

Takeaway: Make sure each section is long enough to inform but brief enough to avoid boring readers. Break down information for each section, and don't go into so much detail that you lose the reader halfway through.

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Salesforce created a video that accurately summarizes the key points of the case study. Beyond that, the page itself is very light on content, and sections are as short as one paragraph.

I especially like how information is broken down into "What you need to know," "Why it matters," and "What the difference looks like." I'm not ashamed of being spoon-fed information. When it's structured so well and so simply, it makes for an entertaining read.

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Benchling is an impressive entity in its own right. Biotech R&D and health care nuances go right over my head. But the research and digging I've been doing in the name of these burritos (case studies) revealed that these products are immensely complex. 

And that's precisely why this case study deserves a read—it succeeds at explaining a complex project that readers outside the industry wouldn't know much about.

Takeaway: Simplify complex information, and walk readers through the company's operations and how your business helped streamline them.

15. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Chipotle and Hubble

The concision of this case study is refreshing. It features two sections—the challenge and the solution—all in 316 words. This goes to show that your case study doesn't necessarily need to be a four-figure investment with video shoots and studio time. 

Sometimes, the message is simple and short enough to convey in a handful of paragraphs.

Takeaway: Consider what you should include instead of what you can include. Assess the time, resources, and effort you're able and willing to invest in a case study, and choose which elements you want to include from there.

16. .css-12hxxzz-Link{all:unset;box-sizing:border-box;-webkit-text-decoration:underline;text-decoration:underline;cursor:pointer;-webkit-transition:all 300ms ease-in-out;transition:all 300ms ease-in-out;outline-offset:1px;-webkit-text-fill-color:currentColor;outline:1px solid transparent;}.css-12hxxzz-Link[data-color='ocean']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='ocean']:hover{outline-color:var(--zds-text-link-hover, #2b2358);}.css-12hxxzz-Link[data-color='ocean']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='white']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='white']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='white']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='primary']{color:var(--zds-text-link, #3d4592);}.css-12hxxzz-Link[data-color='primary']:hover{color:var(--zds-text-link, #2b2358);}.css-12hxxzz-Link[data-color='primary']:focus{color:var(--zds-text-link-hover, #3d4592);outline-color:var(--zds-text-link-hover, #3d4592);}.css-12hxxzz-Link[data-color='secondary']{color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-color='secondary']:hover{color:var(--zds-gray-warm-5, #a8a5a0);}.css-12hxxzz-Link[data-color='secondary']:focus{color:var(--zds-gray-warm-1, #fffdf9);outline-color:var(--zds-gray-warm-1, #fffdf9);}.css-12hxxzz-Link[data-weight='inherit']{font-weight:inherit;}.css-12hxxzz-Link[data-weight='normal']{font-weight:400;}.css-12hxxzz-Link[data-weight='bold']{font-weight:700;} Hudl and Zapier

I may be biased, but I'm a big fan of seeing metrics and achievements represented in branded graphics. It can be a jarring experience to navigate a website, then visit a case study page and feel as though you've gone to a completely different website.

The case study is essentially the summary, and the blog article is the detailed analysis that provides context beyond X achievement or Y goal.

Takeaway: Keep your case study concise and informative. Create other resources to provide context under your blog, media or press, and product pages.

3 case study templates

Now that you've had your fill of case studies (if that's possible), I've got just what you need: an infinite number of case studies, which you can create yourself with these case study templates.

Case study template 1

If you've got a quick hit of stats you want to show off, try this template. The opening section gives space for a short summary and three visually appealing stats you can highlight, followed by a headline and body where you can break the case study down more thoroughly. This one's pretty simple, with only sections for solutions and results, but you can easily continue the formatting to add more sections as needed.

Case study template 2

For a case study template with a little more detail, use this one. Opening with a striking cover page for a quick overview, this one goes on to include context, stakeholders, challenges, multiple quote callouts, and quick-hit stats. 

Case study template 3

Whether you want a little structural variation or just like a nice dark green, this template has similar components to the last template but is designed to help tell a story. Move from the client overview through a description of your company before getting to the details of how you fixed said company's problems.

Tips for writing a case study

Examples are all well and good, but you don't learn how to make a burrito just by watching tutorials on YouTube without knowing what any of the ingredients are. You could , but it probably wouldn't be all that good.

Have an objective: Define your objective by identifying the challenge, solution, and results. Assess your work with the client and focus on the most prominent wins. You're speaking to multiple businesses and industries through the case study, so make sure you know what you want to say to them.

Focus on persuasive data: Growth percentages and measurable results are your best friends. Extract your most compelling data and highlight it in your case study.

Use eye-grabbing graphics: Branded design goes a long way in accurately representing your brand and retaining readers as they review the study. Leverage unique and eye-catching graphics to keep readers engaged. 

Simplify data presentation: Some industries are more complex than others, and sometimes, data can be difficult to understand at a glance. Make sure you present your data in the simplest way possible. Make it concise, informative, and easy to understand.

Use automation to drive results for your case study

A case study example is a source of inspiration you can leverage to determine how to best position your brand's work. Find your unique angle, and refine it over time to help your business stand out. Ask anyone: the best burrito in town doesn't just appear at the number one spot. They find their angle (usually the house sauce) and leverage it to stand out.

Case study FAQ

Got your case study template? Great—it's time to gather the team for an awkward semi-vague data collection task. While you do that, here are some case study quick answers for you to skim through while you contemplate what to call your team meeting.

What is an example of a case study?

An example of a case study is when a software company analyzes its results from a client project and creates a webpage, presentation, or document that focuses on high-level results, challenges, and solutions in an attempt to showcase effectiveness and promote the software.

How do you write a case study?

To write a good case study, you should have an objective, identify persuasive and compelling data, leverage graphics, and simplify data. Case studies typically include an analysis of the challenge, solution, and results of the partnership.

What is the format of a case study?

While case studies don't have a set format, they're often portrayed as reports or essays that inform readers about the partnership and its results. 

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Hachem Ramki

Hachem is a writer and digital marketer from Montreal. After graduating with a degree in English, Hachem spent seven years traveling around the world before moving to Canada. When he's not writing, he enjoys Basketball, Dungeons and Dragons, and playing music for friends and family.

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How to Solve Problems

• Laura Amico

To bring the best ideas forward, teams must build psychological safety.

Teams today aren’t just asked to execute tasks: They’re called upon to solve problems. You’d think that many brains working together would mean better solutions, but the reality is that too often problem-solving teams fall victim to inefficiency, conflict, and cautious conclusions. The two charts below will help your team think about how to collaborate better and come up with the best solutions for the thorniest challenges.

• Laura Amico is a former senior editor at Harvard Business Review.

Partner Center

How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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A DQN based approach for large-scale EVs charging scheduling

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• Published: 21 August 2024

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• Yingnan Han 1 ,
• Tianyang Li   ORCID: orcid.org/0009-0001-5545-8700 1 &
• Qingzhu Wang 1  

This paper addresses the challenge of large-scale electric vehicle (EV) charging scheduling during peak demand periods, such as holidays or rush hours. The growing EV industry has highlighted the shortcomings of current scheduling plans, which struggle to manage surge large-scale charging demands effectively, thus posing challenges to the EV charging management system. Deep reinforcement learning, known for its effectiveness in solving complex decision-making problems, holds promise for addressing this issue. To this end, we formulate the problem as a Markov decision process (MDP). We propose a deep Q-learning (DQN) based algorithm to improve EV charging service quality as well as minimizing average queueing times for EVs and average idling times for charging devices (CDs). In our proposed methodology, we design two types of states to encompass global scheduling information, and two types of rewards to reflect scheduling performance. Based on this designing, we developed three modules: a fine-grained feature extraction module for effectively extracting state features, an improved noise-based exploration module for thorough exploration of the solution space, and a dueling block for enhancing Q value evaluation. To assess the effectiveness of our proposal, we conduct three case studies within a complex urban scenario featuring 34 charging stations and 899 scheduled EVs. The results of these experiments demonstrate the advantages of our proposal, showcasing its superiority in effectively locating superior solutions compared to current methods in the literature, as well as its efficiency in generating feasible charging scheduling plans for large-scale EVs. The code and data are available by accessing the hyperlink: https://github.com/paperscodeyouneed/A-Noisy-Dueling-Architecture-for-Large-Scale-EV-ChargingScheduling/tree/main/EV%20Charging%20Scheduling .

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Introduction

Electric Vehicles (EVs) contribute to numerous benefits such as reduced using cost, environment friendly, and decreased consumption of fossil fuels [ 1 ]. As a result, the amount of EVs has been increasing rapidly [ 2 , 3 ]. However, the lack of convenient charging infrastructure and low effective charging scheduling algorithm for large-scale EVs charging scheduling becomes the key barrier to the EVs penetration [ 4 ]. In some specific scenarios, such as rush hours or extreme weather with low temperatures, there is a noticeable increase in the demand for charging scheduling services [ 4 ]. Uncoordinated charging scheduling strategy could lead to a long queuing time for EVs and traffic congestion [ 5 ]. Therefore, it remains crucial to develop an effective algorithm for managing the large-scale EVs charging schedules while improving the quality of EVs charging service by minimizing the average queuing time of EVs and maximizing charging performance for both EV and charging stations (CSs).

Previous research about EVs charging scheduling has predominantly employed the metaheuristic-based and the Reinforcement Learning (RL)-based methods [ 6 ]. Some of the studies use the metaheuristic-based method to deal with the large-scale real-time scheduling problems. In such scenarios, the genetic algorithm (GA) [ 7 , 8 , 9 ], artificial bee colony (ABC) [ 10 , 11 , 12 ] and particle swarm optimization (PSO) [ 13 , 14 , 15 ] are widely used to search the optimal/suboptimal solution. However, in other studies, the reinforcement learning based algorithms are used to get a better scheduling plan for larger or more complex scenarios. For example, studies in [ 16 , 17 , 19 ] aimed to employ reinforcement learning based algorithm to generate a feasible EV charging scheduling plan to reduce charging costs for users. Similarly, some studies focus on reinforcement learning based algorithms to balance electric load [ 16 , 19 , 20 , 21 ] and increase CSs revenue [ 22 ]. Moreover, some studies proposed the model-based algorithms [ 16 , 23 , 24 ] or multi-agent reinforcement learning (MARL) [ 16 , 21 , 25 ] in different scheduling environments. Generally, metaheuristic-based algorithms can find the optimal/suboptimal solution in real-time, but they need lots of searching time. The aforementioned reinforcement learning based studies primarily generated scheduling plan by using the historical data (such as time series, this needs more steps to update the state/reward information rather get feedback from environment directly) instead of states in real-time. Consequently, these algorithms are difficult to deal with the high real-time EVs charging scheduling problems with high requirement of quick response. To deal with this difficulty, some studies formulated the EVs charging scheduling problem as a sophisticate sequences decision problem or Markov Decision Process (MDP) [ 16 , 21 , 25 ] and the deep reinforcement learning (DRL) is utilized to generate EV charging scheduling plan in the complex situation with multiple of constraints. While, due to the lack of reasonable design of states and exploration mechanism, this type of RL based methods hardly cope with the large state/solution space and cannot find the optimal/suboptimal scheduling solution, which means the large-scale EVs charging scheduling problem may be solved in an inefficient way.

To overcome these gaps, creating an effective deep reinforcement learning-based approach must be undertaken. The states that could represent the global information should be firstly designed and the design of reward should consider the appropriate optimizing target which reflects the service quality of EVs charging scheduling. Furthermore, for obtaining the optimal scheduling plan, DRL-based networks that need to consider the utilization of historical experience of environment interaction and high global search ability should be designed based on the scheduling process. Finally, the training algorithm of the models should pay more attention to scheduling details to avoid being trapped into local optimal solutions.

Despite the importance of providing an effective DRL-based approach to improve service quality (to get lower average queueing time for EVs and lower average idling time for CSs), current research only focuses on historical experience utilization or inefficient solution space explorations. Moreover, existing DRL-based EVs charging scheduling methods only focus on optimizing EV-side or CS-side targets, without providing a comprehensive approach to optimizing service quality from both perspectives. The lack of effective method precedent raises three questions:

What is the state and reward patterns that cover comprehensive information for both EVs and CSs sides?

How to design deep models for solving MDP of EVs charging scheduling efficiently?

How to train the deep models to effectively obtain the optimal EVs charging scheduling solution while avoiding the local optimal and exploring the action space thoroughly?

To solve these questions, on the basis of our pervious works [ 4 ], we proposed an innovative DQN based approach, including a fined-grained state representation that contains the global information, the reward based on an appropriate optimization target for guiding models to improve service qualities and deep neural networks with novel training algorithms. The deep neural networks, combined with the advantages of dueling net [ 26 ] and noisy layer [ 27 ], is designed to not only make effectively use of historical experience, but also improve the ability of space exploring, which makes the proposed approach have the extraordinary performance. Through a series of experiments, we demonstrate the superiority and effectiveness of our proposal.

The remainder of this paper is organized as follows. “Related Works” review the research related to RL/DRL-based EVs charging scheduling. “Problem Formulation” provides the mathematical background of our proposal. Next, MDP formulation of large-scale EVs charging scheduling is introduced. The “DQN based scheduling network and training algorithm” section details the designing of our proposal. “Experiments and Discussion” shows the efficiency and superiority of our algorithm by a series of experiments and analyzes and discusses the effective mechanism of our proposal from the perspective of capability of solution space exploring”. Finally, the “Conclusion” section summarizes our work, provides an outlook for our research.

Related works

With the rapid penetration of EVs in urban transportation systems, there has been a corresponding increase in related research about EVs charging scheduling [ 6 , 28 , 29 ]. Many studies have focused on utilizing DRL-based methods to address EVs charging scheduling problems from different perspectives. In recent research, more attentions were paid to the design of the optimizing target, state, and deep model.

The design of the optimizing target varies across different studies. A well-designed optimizing target can guide the model effectively achieving the optimal goals. In research [ 30 − 32 ], the authors presented the DRL-based algorithms to reduce the energy costs by managing the scheduling process and predicting the price trends. Similarly, authors in [ 19 , 33 , 34 ] developed the DRL based strategies to maximize the revenue or welfare for charging station in energy distributed system. On another hand, some study focuses on optimizing multiple optimizing targets or the weighted sum (of multiple optimizing targets). For instance, a double-Q learning based algorithm in [ 35 ] was designed to optimize the optimizing targets such as satisfying EV charging demand, minimizing charging operation costs, and reducing transformer overloading at same time. Additionally, studies in [ 36 , 37 ] combined waiting time with energy costs as their optimization targets.

The designing of the state usually depends on the optimizing targets [ 38 ]. For example, studies that aimed to meet EVs charging requirements, such as studies [ 37 , 39 ], used the battery state of charge as a part of state. Parameters like charging or discharging costs were usually included in the state representation to optimize the operational costs [ 40 , 41 ]. Other factors, such as the waiting time [ 42 ]and charging price, were also considered when designing state representations to improve the individual satisfaction, decrease energy consumption levels or the expected charging duration. However, there is still a lack of appropriate state representation that incorporates the global information to determine the EVs scheduling priorities and charging station selection in large-scale EVs scheduling scenarios. Based on carefully designed states, suitable actions for minimizing or maximizing the optimizing goal can be generated by the models. Generally, a model used to map the state to an action-value can take the form of a table that consisting of state-action values or a combination of several neural networks.

For simple problems with low-dimensional action and state spaces, such as the problem in study [ 43 ], a Q-Table was commonly employed as a state-action value function to predict the quality of the charging scheduling. However, this method is not widely applicable to real-world problems due to the complexity and randomness [ 44 ]. For more sophisticated situations, the most commonly used algorithms are DQN, Deep Deterministic Policy Gradient (DDPG) and their variants [ 38 ]. In study [ 40 ], the model combining DDPG and DQN was proposed to predict the action-values based on the features of historical data extracted by a long short-term memory (LSTM) and to obtain feasible solutions. Besides, the MARL was used in some studies, such as [ 45 ] and [ 41 ], where it was used to generate EVs charging scheduling strategies by deeply exploring the state and action spaces. The advantages of the multi-agent over the single agent are that it can easily get the local optimum in a large space and is suitable for more complex scenarios such as high dimensions of features or action spaces [ 6 ]. While, effectively obtaining an optimal scheduling plan remains challenging due to the complexity of surging EVs charging scheduling demands. Existing literature pays little attention to this problem.

In summary, the current studies mainly addressed EVs charging scheduling problems with a small state space or low-dimensional action space from an economic or operational perspective. Some of them focused on getting feasible solutions from historical experience, the others devoted to gain the local optimum. Few studies focus on finding the optimal solution in large-scale state an action spaces, especially for scenarios of EVs charging scheduling with surging demands to improve the quality of charging service. Therefore, an effective scheduling method is needed for managing the surging requests in large-scale EVs charging. To solve this problem, a DRL-based algorithm should be proposed with: (i) well-defined state that can represent the global information of both EVs and CSs sides, and reward that could represent the service quality; (ii) deep networks and training algorithms that can solve MDP of EVs charging scheduling and accurately map the state to action values while incorporating an action space exploration mechanism.

Problem formulation

Entity definition.

The EVs charging scheduling problem typically involves three key entities: EVs, CSs and Charging Devices (CDs). The EV and CD can be represented as a quadruple <  i , elo i , ltd i , ct i  > and a five-tuple <  j , k , ot (k, j) , qn kj  > respectively, while the CS can be described as a triple <  j , clo j , dn j  > .

Without loss of generality, we consider the scheduling of EVs with specific charging requirements. Each EV numbered with i , denoted as EV i , is located at position elo i (represents the current position of EV i ) and requires ct i (represents the charging duration of EV i ) minutes for charging. Each charging station numbered with j , denoted as CS j , is equipped with dn j (the number of charging devices on CS j ) available CDs. The scheduling of EVs charging is to assign a charging station CS j located at position clo j (the position of charging station CS j ) for each EV i with the limitation ltd i (the max left travel distance by remaining battery capacity of EV i ).

We represent the k th CD of CS j as CD kj . Additionally, ot (k, j) denotes the working time of CD kj that occupied by the assigned qn kj (the number of EVs that scheduled at CD kj ) EVs.

In this study, we focus on the scheduling of large-scale EVs charging in a city scenario with a complex traffic situation. Both the EVs and the CSs have the capability to communicate with a Global Aggregator which is a third party that interested in EVs charging scheduling. Our approach may help GA to determine the scheduling sequence of EVs for an appropriate CS with the purpose of high charging efficiency in terms of minimized queueing time of EVs and idling time of CDs.

Mathematical background

In general, an EV will submit a charging request when the battery is low, which mainly includes the expected charging time and the remaining travel distance. Specifically, for any given time, the travel time from EV i to CS j , which is denoted as tt ij , is determined by equation tt ij  =  d (i, j) /(α  ×  v i ) , where the d (i, j) represents the distance between EV i to CS j , the v i represents the average driving speed of EV i and the α  ∈ [0, 1] represents a factor that indicates the level of traffic congestion of route between EV i to CS j . The value of α is determined based on the real-time traffic situation and obtained from the data interface of map Apps (Such as Google map or Baidu map). A lower value of α indicates a higher level of congestion, while a higher value suggests a lower congestion level. If EV i is scheduled at CD kj , which is occupied before EV i arrives, the expected queueing time qt ijk can be calculated by qt ijk  =  (ot (k, j) –tt ij )  ×  I (ot (k, j)  >  tt ij ) . Otherwise, if CD kj is not occupied, its idling time it jk can be updated as it jk  =  (tt ij –ot (k, j) )  ×  I (ot (k, j)  ≤  tt ij ) . I (x) denote the indicator function and can be described as ( 1 ).

Additionally, the occupied time of CD jk can be updated as ( 2 ):

For improving the charging efficiency and quality for both EV and CS sides, the target of charging decision is to minimize the expected queueing time of each EV and the idling time of each CD. To simplify the problem, we transform the EVs charging scheduling problem from a multi-objective optimization to a single-objective optimization. The optimization objective is formulated as ( 3 ), where ω 1 and ω 2 are the hyper-parameters.

Markov decision process of large-scale EVs charging scheduling

The EVs charging scheduling problem can be defined within a discrete MDP considering a discrete time model. Based on the updated state information provided by GA (GA, i.e., global aggregator, is a third party which provides the information of the state for EVs and CSs in real-time; the state of EVs and CSs are denoted as SEV and SCS separately), deep models which include the EV selection model and CS selection model are used as the agent to make decisions regarding which EV should be prioritized for charging and where it should be scheduled. The state, action and reward in our approach are denoted as follows:

STATE : SEV t represents the state used to select which EV is to be scheduled at time step t (the selected EV is denoted as EV s ). It includes the global state information of the CSs and all the not scheduled EVs. Similarly, SCS t refers to the state used to select the optimal CS for EV i at time step t . It includes the information of selected EV and all available CSs.

ACTION : a et represents the action taken at time step t to select an EV that is scheduled. The range of a et is from 0 to N ev , where N ev is the total number of schedulable EVs, indicating the available actions for selecting a schedulable EV. Similarly, a ct represents the action used to select a CS for EV s . The range of a ct is from 0 to N cs , where N cs denotes the total number of available charging stations.

REWARD : In our proposal, we have designed two types of rewards r e and r c . Each reward is based on the weighted values of average queueing time and idling time. The weights are hyperparameters. r e is designed for training the EV selection model. It is a K -steps discounted weighted value with discounted factor γ , where K is a hyper-parameter. This allows to consider the long-term consequences of the selected actions. Additionally, r c is the average queueing time returned from the environment after each step is executed. This reward provides direct feedback on the impact of the chosen charging station for the EVs.

Improved DQN based networks and training algorithms

In the large-scale EV charging scheduling scenario, the high-dimensional action space and state space pose the challenges to find the optima solution effectively when applying the current DRL-based algorithms. To overcome this disadvantage, we carefully designed the state representation and reward setting. Based on this, an improved DQN-based network with feature extracting module is proposed for enhancing EV charging scheduling performance. In addition, for efficiently utilizing the scheduling experience that is obtained from the interaction between the agent and environment, a scheduling-order fine-tunning algorithm is proposed.

Designing of state, action, and reward

Designing of state.

For a schedulable EV, its feature is formed as SE i  = [ dis_to_cs(i), ct i , ltd i , scheduled_flag ], which includes the distance to all available charging stations (denoted as dis_to_cs(i) ), the expected charging time (denoted as ct i ), the remaining travel distance of the EV i (denoted as ltd i ) and the scheduling flag (denoted as scheduled_flag ) used to indicate whether the EV i is scheduled.

In addition, feature of CSs consist of AQT , AIT , CNS and occup_T . Therein, the AQT is a list contains the average queueing time of EVs in each CS, AIT denotes a list contains the average idling time of EVs in each CS and the CNS is a list that contains the number of EVs scheduled at each charging station. Therefore, the state of charging station is collectively denoted as SC  = [ AQT, AIT, CNS, occup_T ].

With above defined features, the state SEV t used to the EV selection model can be represented as SEV  = [ SE 1 , \( \cdot \cdot \cdot \) , SE Nev , SC ] with dimensions = ( N ev  + 4) ×  L , where L represents the dimensions of SE i . For the CS selection that is used to select a charging station for EV s (the selected EV at time step t ), the state SCS is constructed as SCS  = [ SE s , SC ] with dimensions = 5 ×  L .

Designing of action and reward

At each time step t , the models interact with the environment by using the current states SEVt, SCSt to select the optimal EV and CS. The EV selection model operates on schedulable EVs, each output with an associated Q -value representing the expected reward upon selection. Similarly, the CS selection model involves reachable CSs for the chosen EV, each output linked to a Q-value denoting the expected rewards for allocating the CS to EV.

The rewards acquired from the environment rely on both the states and actions taken for optimal EV and CS selection. The reward re for EV selection is a K -step discounted weighted sum of the queueing time about EVi and idling times of the target CDkj , enabling long-term information acquisition. It can be collectively described as \({r}_{et}={\sum }_{m=0}^{K}{\gamma }^{m}\times \left({\omega }_{1}\times {it}_{kj\left(t+m\right)}+{\omega }_{2}\times {qt}_{ikj\left(t+m\right)}\right)\) where itkj ( t  +  m ) represents the idling time of CDkj due to waiting for the arrival of m th EV that scheduled at CDkj from time step t and qtijk ( t  +  m ) represents the queueing time of the m th EV that scheduled at CDkj from time step t . Specifically, the discounted factor γ is set as 0.8 and K is set as 2 (as an experience value) enabling long-term information acquisition.

For CS selection, the reward is the weighted sum of the current average idling and average queueing times of the selected EV and their target CS, providing direct scheduling feedback within a single time-step.

Architecture of Neural networks

In this section, the detail of the overall structure of deep models is introduced, which shown as Fig.  1 . For improving the performance of feature extraction from states of EV selection, a fine-grained feature.

The overall structure of our proposal

extraction module (FFEM) is proposed for EV selection model. Based on the extracted fine-grained features, an improved noisy-based exploring module (INEM) is employed to explore the solution space thoroughly. In the end, a dueling module is used to improve the fitting of performance of Q-value.

Fine-grained feature extracting module (FFEM)

In traditional reinforcement learning/deep reinforcement learning based EV charging scheduling methods, states that acquired from the environment are usually featured with low-dimensional or can be easily transformed into a simpler form [ 6 ]. However, for the large-scale EV charging scheduling scenario, states are high-dimensional and based on the total number of EVs and CSs, which cannot be transformed into a simpler form. Moreover, it is difficult to extract features for interacting with the scheduling environment. Therefore, a more effective feature extraction module is proposed.

For solving this problem, a fine-grained feature extracting module (FFEM) is proposed. The FFEM, which used for helping to extract the feature of the input state SEV, contains two same sub-modules. For each sub-module, as shown in the Fig.  2 , a 3 × 3 convolutional layer is used to extract the key information of the input feature map. Given that fact that the feature of the feature map might be sparse in some scheduling scenarios, a batch normalization is therefore adopted to prevent the disappearance of the gradient. Furthermore, the ReLU (rectifier linear unit) is adopted after each batch normalization layer to improve the ability of nonlinear sub-module feature extracting. In each sub-module, two of sub-structure, i.e., Convolutional-Batch Normlized-ReLU combination, is concatenated linearly and a 1 × 1 convolutional layer is adopted which links the input and the output of the sub-module. It implements the cross-channel integration of input and output features, significantly increasing nonlinearity of the sub-model. Given the reality that the complexity of state space and for extracting more refined features of the input states, the proposed FFEM consists of two linearly concatenated sub-modules.

The overall sub-module structure of FFEM

For each sub-module of FFEM, let X denotes the input state, W 1 , B 1 , W 2 , B 2 denotes the weights and biases of the convolutional layers and W 3 , B 3 denotes the weight and bias of the 1 × 1 convolutional layer. Each sub-module of fine-grained feature extraction module can be collectively summarized as ( 4 ) and ( 5 ):

where σ 1 and σ 2 are the standard deviation of the X and Y , μ 1 and μ 2 are the mean of the X and Y , ε 1 and ε 2 are the hyper-parameters that bigger than 0, β 1 , β 2 , γ 1 , γ 2 are the learnable parameters.

It can be concluded from ( 4 ) and ( 5 ) that the proposed module introduces more weights and biases parameters. By combining with the ReLU function, this structure help improves the ability for the complex non-linearized Q-value function fitting and feature extraction in larger state space. Therein, the ReLU activation function is defined as f ( x ) =  max ( 0, x ), introducing nonlinearity into the model. By allowing only positive values to pass through and setting negative values to zero, ReLU facilitates the learning of complex nonlinear relationships in the data. Furthermore, its sparse activation property promotes feature extraction by selecting relevant features from the input. When integrated into the proposed module, ReLU enhances the model's capability to capture complex patterns in the Q-value function and extract meaningful features, particularly in larger state spaces. The introduction of the proposed module, through the incorporation of additional weights and biases parameters along with the utilization of the ReLU activation function, significantly improves the model's performance in fitting complex nonlinear Q-value functions and extracting features. This approach proves particularly advantageous in scenarios involving larger state spaces, where traditional models may struggle to adequately capture the underlying relationships. Future research could explore further improvements and applications of this framework in various reinforcement learning tasks.

Improved noisy-based exploring module (INEM)

In our research, the selection of an appropriate exploration strategy within the solution space is crucial for identifying the optimal electric vehicle charging scheduling scheme. Generally, traditional strategies such as ε -greedy and Boltzmann probability-based methods are two primary approaches for exploring the solution space. However, when dealing with large-scale, high-dimensional solution spaces, these methods exhibit inefficiencies in the exploration and the balancing between exploration and exploitation [ 27 ]. To address this issue, we proposed an improved noisy-based exploring module (INEM) that integrates the dropout and noisy linear layer. The aim is to augment the model's exploration capabilities.

The Fig.  3 illustrates the structure of INEM, which comprising three key neural network layers. Initially, the Dropout layer processes the state features extracted by the FFEM module. During training stage, it stochastically deactive a portion of neurons with a probability of p  = 0.5 to inhibit their forward and backward propagation. Following the dropout processing of features, they are fed into two layers of noisy layers designed to incorporate random noise subject to normal distribution, enhancing the model’s generalization capabilities. Each noisy linear layer is adopted the ReLU function for non-linearity, thereby augmenting the module's capacity.

The structure of INEM

Specifically, for the Dropout layer, during each forward propagation, a portion of neurons is randomly deactivated with a probability of p  = 0.5, discarding their original computed results and setting their outputs as 0. Furthermore, concerning these deactivated neurons, their parameter updates are halted during backpropagation. This allows effectively training a sub-model of the original model in each training iteration. This strategy helps reduce reliance between specific neurons, thereby enhancing the model's generalization and mitigating overfitting risks. Mathematically, this layer can be represented as Y  =  X ⨂ m , where ⨂ denotes element-wise multiplication of matrices, and m follows a Bernoulli distribution ( p  = 0.5).

For the noisy linear layers within the module, the generation of parameterized noise adopts the factorized gaussian noise method proposed in [ 27 ]. Based on this method, the representation of the noisy layer can be summarized as Y n  = ( μ ω  +  σ ω ⨂ ε ω ) ×  X n  + ( μ b  +  σ b ⨂ ε b ), where the X n and Y n denotes the input and output of the layer, and the μ ε , μ b , σ ε , σ b are the parameter matrices of the noisy linear layer and each value in the matrices is subjected to normal distribution. The introduction of noisy layers aims to enhance the model's robustness and generalization capabilities. By introducing Gaussian noise at each layer, the model can better adapt to different data distributions and noise conditions, thereby improving its generalization and solution space exploration ability. Additionally, noisy layers help prevent overfitting, thereby enhancing the model’s performance in practical applications.

Combining the dropout skill, the noisy linear layer described in this section, and the Fig.  3 , our proposed module can be mathematically summarized as the form shown in Eq. ( 6 ).

where f in represents the input to the model, \(\mu^{{\omega_{i} }} ,\mu^{{b_{i} }} ,\sigma^{{\omega_{i} }} ,\sigma^{{b_{i} }}\) are the hyper-parameters of the ith noisy linear layer, and \(\varepsilon^{{\omega_{{\text{i}}} }} ,\varepsilon^{{b_{{\text{i}}} }}\) signify the noise introduced by the Noise layer’s weights and biases, respectively.

This module's design yields significant advantages for the model in various aspects. Primarily, it contributes to enhancing the overall training speed, crucial for efficiently handling large-scale tasks. Secondly, the introduction of the Noise layer amplifies the collective adaptability among neural nodes, notably enhancing the model's robustness and performance when dealing with intricate datasets. Additionally, the incorporation of the Noise layer aids in balancing the model's exploration and exploitation, thereby reinforcing its generalization capabilities. These combined factors propel the model towards better adaptation to the challenges posed by large-scale electric vehicle charging scheduling tasks.

Dueling block for a better Q-value fitting

When addressing problems involving large-scale action spaces and high-dimensional state spaces, directly mapping from states to different actions through state-action values (i.e., Q-values) typically demands increased computational resources and struggles to accurately reflect the value information of diverse states. To mitigate this issue, we devised a Q-value estimation module based on Noised Features, employing the structure of a Dueling Network to tackle these challenges.

The Fig.  4 illustrates this module, which takes processed features from the Noisy Linear Layer as input and ultimately produces Q-values for actions. In contrast to other networks (such as the Q-network in DQN), the dueling module employs two parallel sub-network branches that separately estimate the advantage value of state-action pairs and the value of the current state. Here, the advantage value refers to the relative merit of an action concerning the state value, representing the relative value of a particular action in a specific state. To merge the outputs of these two branches into the actual Q-values, an Aggregation Layer is employed to integrate the outputs of these sub-networks.

The dueling module

Let the noised feature as shown in the Fig.  4 be denoted as S noised , the v η and a ψ represent the state value predictor branch and advantage value predictor branch respectively. Therein, η and ψ represents the parameters of the v η and a ψ branches. In this context, a signifies the action chosen in the current state,

while a′ represents the action chosen according to the current action-selection policy in the subsequent state s′ . Hence, the mathematical formalization of this module can be articulated as follows:

where θ  = { η, ψ }.

In the proposed module, the s noised is separately fed into the v η and a ψ branches, thereby decomposing the estimation of Q-values into estimations of the advantage value A and the state value V . Subsequently, the aggregation layer integrating outputs derived from both branches into the Q-value. The design uniquely determines the action advantage values and state values [ 26 ]. This structure aids in optimizing the model's training stage, enhancing both the speed and precision of state value estimation, thus improving the capability for policy evaluation. Simultaneously, this decomposition stabilizes the model's exploration of solution spaces, enhancing its ability to search for the optimal solution to electric vehicle charging scheduling problems within large-scale solution spaces.

Training algorithm

For training the proposed architecture effectively, an assist algorithm which are used to scheduling order fine-tunning is proposed. Based on this algorithm, the models are trained by the sampled experiences.

Charging order fine-tunning

EV charging scheduling models training algorithm

In a large-scale action space, it is difficult to directly identify a scheduling scheme with relatively good performance at the beginning of the training stage. This can lead to a relatively poor quality of experience gathered by the model while interacting with the environment (longer EV queueing times, longer charging equipment idling times). As a result, models trained based on the experience typically perform poorly on EV charging scheduling. To minimize this deficiency, this paper proposes a charging sequence fine-tunning algorithm, which is outlined as Algorithm 1 . In this algorithm, the epat(ev, j) denotes the predicted driving time to the target charging station j of EV ev in current state . And the ct ev denotes the charging time for EV ev .

The main idea of the proposed algorithm is derived from SJF (shortest job first), i.e., to have the shortest process time of the scheduling process is to schedule the not scheduled job/EVs with shortest processing time firstly. However, the situation becomes more complex in the EV charging scheduling process, i.e., both the traveling time and charging time could be the key point for the scheduling result.

To solve this problem, two of skills is introduced. Firstly, calculated the expected occupied time of each EV need to be scheduled at CD (k, j) , which could be described as the sum of the charging time and the expected traveling time (the 1–3 lines of Algorithm 1 ). Based on the calculating results, the optimal EV selection can be simply defined as the EV with minimum summation (i.e., M ev ) (the 4–5 lines of Algorithm 1 ). However, the 6–7 lines of Algorithm 1 shows a special situation. For situations that more than one EVs has the same M ev -value, select the EV that with minimum epat (ev, j) can derive the better plan. This can be simply proved as follows.

Considering the situation where two EVs (denoted as A and B ) need to be scheduled at the charging station CD (j, k) , let C A and C B denote the charging time of A and B , and R A and R B denote the expected travel time to the charging station. If C A  >  C B , R A  <  R B , and C A  +  C B  =  R A  +  R B . If A is firstly selected, then the total finish time of the charging process can be calculated as follows (Eq.  8 ):

otherwise, the total finish time is calculated as ( 9 ):

It can be derived from ( 8 ) and ( 9 ) that,

Based on the assumption, it can be derived that L—F  < 0. Therefore, to select an EV which is closer to the charging station could lead to a better scheduling performance when the M -value of EVs is equal.

The 9–13 lines gives a scheme to update the M -value of EVs, which ensures that the re-arranged scheduling order could lead to a maximum performance on both EV (minimized queueing time) and CS (minimized idling time).

In our model training algorithm, the training process can be divided into two-main part i.e., the pre-training stage and training stage. In this algorithm, two experience storage, i.e., the prioritized replay buffer (PER) [ 35 ], is denoted as ERB and CRB for storing the experience that used to training the EV-selecting model and CS-selecting model respectively. Therein, the PER is used for enhancing the utilization of collected experiences. Based on the experience, the models are pre-trained for PTS time trained for TS times where both the PTS and TS are scalars. The training process is summarized as Algorithm-2.

In Algorithm 2 , it can be divided into two main parts, the pre-training stage and training stage. The advantages of this training setting can be summarized as follows: given the fact that the problem setting has the large-scale and large-dimensional action space and state space, a pre-training process is introduced to accelerating the convergence of models. This could scale the search space back and exclude some solutions with relatively worse performance to alleviate some of the unnecessary exploration in the subsequent training stage.

At the pre-training stage (i.e., the line 1–8), the algorithm collected experiences by Monte Carlo with a sampling process. A sampling mechanism is introduced to re-sampling the original sampled experiences and only 30% of experiences with better scheduling performance is keep. This allows that the pre-training process could lead to a faster performance enhancing in EV charging scheduling task.

The training stage (i.e., the line 9–22) has two main processes, the interacting and training. In interacting process, the models interact with the environment to explore the solution space (i.e., the line 10–16). To prevent introducing the worse experience into replay buffer, the scheduling order fine-tunning is introduced (i.e., the line 17–18). In line 20–21, each trajectory is re-evaluated after fine-tunning. If the re-evaluated experience is better than in the replay buffer, this experience will be added to the experience buffer. This is beneficial for improving the overall quality of the training model. This can be simply explained as follows:

Assuming that N A trajectories are added into the replay buffer with the final weighted reward A , and a new experience is sampled with the final weighted reward r . If the new experiences are added into the replay buffer, the change of A (denoted as B ) follows:

It follows that:

if and only if r  <  A . It shows that, this experience collection method helps to improve the quality of experience used for training models, which is beneficial for accelerating model training.

Experiments and discussion

To demonstrate the superiority of our proposal, we have designed the case studies to showcase its performance. The experiments in each case study were conducted using PyCharm Integrated Development Environment version 2019.1.1 with Python 3.7 on a PC equipped with an 11th Gen Intel(R) Core (TM) i7-11800H processor running at 2.30 GHz, 32 GB of memory, and an Nvidia RTX 3060 graphics card with 6 GB of memory.

Yardstick and dataset

In our experiments, we collected a real-world dataset from an EV charging service company inter-face. The dataset consists of 1000 EVs and 34 charging stations (CSs) of a city. Each CS is equipped with 5 high voltage charging devices. The 1000 EVs are located at different positions and need charging services. During our research, we identified that 101 EVs were unable to be scheduled for charging due to poor battery conditions. These EVs required battery exchanging services. Consequently, only 899 EVs were able to reach at least one charging station with their remaining battery capacity. The details of the dataset can be summarized as Table  1 .

Experiment setting

In our experiment setting, three sets of case studies were conducted to showcase the performance of our model from different perspectives.

In case study I, we investigate the influences of different reward setting, hyperparameters setting on the performance of models in EV charging scheduling. In this part, a group of optimal hyper-parameter setting is identified. The experiments discussed after this section are all conducted based on the optimal parameters shows in this part.

In case study II, we investigated the effectiveness of each proposed module (fine-grained features extraction module, Improved noisy-based exploring module and dueling module) and scheduling order fine-tunning algorithm. A series ablation experiments, and analysis are conducted/proposed based on the results of experiments.

In case study III, some heuristic-based, DRL-based, and dispatching rule-based EV charging scheduling algorithms are compared. The results showcase the advantages of our proposal over other algorithms.

Case study I

In this section, our primary focus lies in presenting and discussing the influence of different reward settings, neural network optimizers and hyperparameters, such as learning rates, on the exploration of the solution space. To investigate this topic comprehensively, we examine various reward combinations (with learning rate = 0.0001and optimizer Adagrad for EV-model, Adam for CS-model), as depicted in Table  2 . Based on these reward settings, a series of controlled experiments are conducted (therein, we sampled experience by Monte Carlo to pre-training the models), and the experiment results are illustrated in Fig.  5 . Furthermore, the effects of different hyper-parameter (mainly about the learning rate) on the performance of the models are then discussed based on the optimal reward setting.

The result of compare experiments

As shown in the Table  2 and Fig.  5 , the result #1—#8 indicates that the bigger weight for the queueing time can lead to a better scheduling performance to some extent. For some extremely situation (i.e., one of the factors (queueing time or idling time) is neglect, such as the #11—#12 version of the control experiment), however, the performance has a downward trend. Therefore, it can be concluded from the Fig.  5 that the performance for EV charging scheduling cannot be improved by simply increasing the weight of the queueing time. Given the fact that the idling time increasing far slower than the queueing time, it can be therefore concluded that it is easier to find the optimal EV charging scheduling solution by considering the target that changes more frequently (such as the queuing time) in this type of problem.

Learning rate and optimizer are of the most important elements in the models training. However, given the complexity of the problem (large solution space) that it is hard to find an appropriate learning rate or an optimizer in large-scale searching space (i.e., solution space in our proposal). Therefore, we conduct a series of experiments to investigate the influence of optimizer selecting and learning rate setting on scheduling results.

In these experiments, we adopt different optimizers and learning rate for training EV-selection model and CS-selection model for comparison. The mainly tested optimizers are Adagrad, Adam, SGD, and the learning rate we adopt are 0.1, 0.01, 0.05, 0.001, 0.005, 0.0001. For simplifying the criterion of scheduling results, we adopt the weighted sum of the average queueing time and weighted idling time (with ω 1  = 0.8, ω 2  = 0.2). The result of experiments is shown as Table  3 .

It can be concluded from the Table  3 that the combination of Adagrad for EV-selecting model training and Adam for CS-selecting model training has the best performance among the scheduling result. It can be inferred that the features of Adagrad (suitable for sparse data and able to automatically adjust the learning rate) is suitable for the solution space of EV charging scheduling with high dimensionality. Therefore, there exists the sparsity in the solution space of EV charging scheduling problem to some extent can be then inferred (i.e., the solution space of EV selecting). This might be contributed to the scheduling constraints that not all the EV could be scheduled in the current state. Given the complexity of solution space, the learning rate adjusts automatically lead to a more convenient paradigm to training the model without introducing the additional hand labor. On the other hand, it could lead to best results by adopt the Adam for CS-model training. This might be since the high-efficient in gradient calculating which lead to a better performance in EV charging scheduling. Therefore, based on the result shown in the Table  3 , all the following comparison experiments are conducted in the following manners:

The optimizers are set as Adam for CS-selecting model with learning rate = 0.0001 and Adagrad for EV-selecting model with learning rate = 0.0001.

The reward for EV selecting is set as the discounted weighted reward with ω 1  = 0.8 , ω 2  = 0.2 and the discounted factor is set as 0.7 (the 0.7 is experienced value).

Case study II

This part, we conducted a series of ablation/compare experiments to investigate the advantages of our proposal. Firstly, we study the feature extract module to show that it is beneficial for enhancing the features extraction for state representing in the large-scale state space. Then, the advantage for noisy block in exploring solution space is analyzed by comparing the results with exploring by Boltzmann probability and ε -greedy. After that, the merits for advantage-value module are further analyzed by the ablation experiments.

For investigating the advantages of the feature extract module, a group of ablation experiments are conducted. In the compared module, the 1 × 1 conv shortcut, batch normalize are erased. As the results shown as the Figs.  6 and 7 , the overall trend from the experimental results suggests that adding a feature extraction module leads to a better large-scale EV scheduling result. On the one hand, the average queuing time corresponding to the overall scheduling result has a decreasing trend compared to the control group as shown in Fig.  7 , which suggests that the enhanced extraction of features helps to improve the ability of the model in generating scheduling plan. On the other hand, it can be concluded from the Fig.  6 that the average idling time corresponding to the scheduling results generated in the model training stage has a greater than its control group in some time steps. This is since the refined feature extraction layer helps to some extent in the exploration of the solution space by other subsequent modules (e.g., the noise module in the EV selecting model) (the oscillations in the idle time indicate that the queuing order is constantly changing during this stage, which suggests that the model is exploring the solution space thoroughly).

The result of average idling time

The result of average queueing time

Based on the features extracted by the proposed feature extraction module, we further investigate the important role played by the noise layer in our proposed network architecture for the exploration of the solution spaces. For clarifying the comparison, we use the weighted sum of the average idling time and the average queueing time with weight ( ω 1  = 0.8 , ω 2  = 0.2) as the indicator to measure the scheduling performance.

To investigate the effectiveness of the INEM, the ablation experiment is conducted. In the ablation control experiment, we uniformly sampled 50 samples at equal intervals during both the pre-training and training stages in the control group and our proposed model. The horizontal axis represents sample steps, where 0–50 denotes the pre-training stage, and 51–100 represents the training stage. The vertical axis represents the weighted criterion, i.e., the weighted sum of the average queue time of electric vehicles and the average idle time of CS after each scheduling, with weights set as ω 1  = 0.8 and ω 2  = 0.2. As shown in Fig.  8 , during the pre-training stage (steps 0–50), our proposal and the version without INEM exhibit similar convergence trends. However, our model shows superior performance compared to the control group. This might attribute to the fact that the model with the IENM module has more parameters, a relatively complex structure, i.e., higher model fitting capacity, resulting in a better fit to the pre-training data. Furthermore, in the training stage (steps 51–100), our proposal demonstrates significant fluctuation in the optimal weighted criterion sampled at different time points compared to the control group. This fluctuation stabilizes after a certain period. This phenomenon is attributed to the Noisy Linear Layer embedded in the INEM module, which introduces learnable noise following a normal distribution. In the early stages of model training, these noise in Noise Linear Layer guides the model to explore the solution space thoroughly. By leveraging the scheduling order fine-tuning algorithm, the model can explore strategies with better scheduling results. The trends in the curve from step 70 to step 100 indicate that the model acquires the capability to generate high-quality scheduling strategies. Moreover, the change in the weighted criterion becomes gradually smoother, possibly because the model adapts to the impact of learnable noise and could effectively removing non-critical information from the state features for generating optimal scheduling strategies. These crucial factors contribute to the superiority of INEM over the control group.

The results of the ablation for INEM

We compare the proposed improved noisy-based exploring module with the ε -greedy and Boltzmann probability, where the ε -greedy based algorithm is to select an action with the optimal Q-value by a probability ε , and select the actions randomly by a probability 1– ε . In the Boltzmann probability, the selection probability of each action is proportional to its Q-value, The Boltzmann probability of an action is shown as \(pt(a|st) = e^{Q(st,a)} /\sum\nolimits_{a} {e^{Q(st,a)} }\) where the p t (a |s t ) denotes the selection probability of action a in the state s t and the e denotes the Euler’s number (which equals to 2.71828).

The Fig.  9 shows the results of the comparison for different exploration mechanism ( ε -greedy, Boltzmann probability and our proposal). It can be inferred that the epsilon-greedy based exploration mechanism is less capable of exploring on larger scale action spaces than the other two approaches. This may be because this method only considers a simple trade-off between "utilization" and "exploration", and the actual environmental information is not considered in the exploration stage. As can be seen in the green part of the figure, in a large-scale solution space, the Boltzmann probability-based exploration mechanism tends to explore more solutions in the space (i.e., it is able to search a much larger solution space than the epsilon-greedy-based method). This is reflected in the larger fluctuations in the reward values obtained from the environment during the search stage. However, this property might introduce instability to problems with large solution spaces. Unlike the first two classes of traditional methods, our proposed module has a gentler character in the exploration stage from an overall perspective. Additionally, the final exploration results are better compared to the two types of traditional schemes. This may be since the model directly adds noise to the states during the exploration of the solution space, which on the one hand introduces randomness, and on the other hand the model adapts to the effects brought by the noise as it approaches convergence. This indirectly leads to a strong adaptive ability of the model, which makes the model perform relatively well in solving problems concerning large-scale action space exploration.

The result of weighted reward comparison

Dueling block

For exploring the advantages of the proposed advantage-value module, a group of comparison experiments is conducted. In this experiment, the dueling block is replaced by the fully connected layers.

The structure of the compared model has similarities compare to the original however, the part of value-module is eliminated. Therefore, it can be viewed as a DQN-shaped structure. The experiment result is collectively shown as the Table  4 .

It can be concluded that our proposed structure performs better than the conventional DQN for large-scale EV charging scheduling. This may be due to the decoupling of Q- values by the dueling structure. This makes the advantage value, which really reflects the importance of the action, separated from the original Q-value, as well as the network is used to predict the state value more specialized, which also enhances the ability to fit the actual state value function at same time.

To validate the effect of the Dueling Block on improving the accuracy of Q-value evaluation, we conducted further ablation experiments, with results presented in rows 1 and 3 of Table  5 . The experimental findings indicate that removing the Dueling module resulted in a 16% increase in the weighted reward corresponding to the scheduling policies generated by the model compared to the control group. This suggests that simply using the output of the noise layer as Q -values cannot meet the accuracy requirements for predicting state-action values. On the other hand, experimentally, both the Dueling Block and the CNN module were able to reasonably predict Q-values and maintain their results within appropriate ranges. However, there was variation in prediction accuracy, with a difference of approximately 1.8% observed in the evaluation metrics corresponding to the optimal scheduling strategy for large-scale electric vehicle charging. This indicates that in larger-scale electric vehicle charging scheduling scenarios, our proposed architecture demonstrates a more pronounced advantage, generating strategies more suitable for large-scale electric vehicle charging scheduling.

Case study III

In this case study, we demonstrate the advantages of our proposal over other EV charging scheduling algorithms in our scheduling scenarios and investigate the mechanism of solution space exploration. To achieve this, we surveyed six categories (consisting of fourteen kinds) of algorithms. The aim was to assess their effectiveness in generating feasible EV charging scheduling plans. We conducted rigorous experiments on all these algorithms within our carefully designed EV charging scheduling environment. The summarized results of all comparison experiments can be found in Table  5 .

From Table  5 , it is evident that experiments 1 and 2 yield poorer scheduling performance compared to the others. This suggests that these two algorithms result in a gathering of EVs at the charging station, leading to longer average queuing times. Different from our proposal, solely considering the user or the charging service provider for charging scheduling may overlook global information, resulting in subpar performance of the scheduling strategy generated. Among experiments 3, 4, and 5, there is a notable improvement in EV charging scheduling results compared to experiments 1 and 2. This indicates that the GA-based algorithm effectively addresses the problem. However, searching through the vast solution space with GA may lead to local optima or slow search speeds. The EDA-based algorithm (lines 6 and 7 in Table  5 ) demonstrates better results in solution space exploration. Nevertheless, increasing the number of iterations does not necessarily yield better results for large-scale EV charging scheduling (as observed in experiments 6 and 7, where more iterations led to worse scheduling results). This suggests instability in the EDA-GA based algorithm when tackling such problems. Despite the potential for better solutions with more iterations, the long runtime for solution space exploration is impractical for real-time applications. We further tested the EDA-GA based algorithm with more than 3000 iterations, such as 3500 and 5000. Although some experiments showed improved results, the extended runtime remains a barrier for practical application.

Clearly, the DRL/RL based algorithm proves to be more effective than other algorithms. In the realm of electric vehicle (EV) charging scheduling, deep reinforcement learning (DRL) methods exhibit significant advantages over genetic algorithms (GAs), dispatching rules, and other conventional methods. Firstly, DRL autonomously explores optimal charging strategies through continual trial and error learning, effectively adapting to intricate and ever-changing environments and requirements. In contrast, GAs necessitates evaluating numerous individuals in each generation and rely on randomness to explore solution spaces, potentially resulting in inefficient exploration particularly in high-dimensional and complex problem domains. Dispatching rules, often based on static rules or heuristic methods, lack the flexibility to adjust to real-time changes. Secondly, DRL methods dynamically adjust strategies based on environmental feedback, facilitating adaptation to fluctuating charging demands and grid conditions, thereby enhancing charging efficiency and grid utilization. This dynamic adaptability is lacking in traditional approaches. Consequently, RL offers superior adaptability and efficiency in EV charging scheduling, better aligning with practical application requirements.

For instance, when comparing scheduling outcomes in similar scenarios, such as our observations from experiments 11 and 6, we found that scheduling strategies based on reinforcement learning methods achieve computational speeds approximately 47 times faster than other methods when addressing the large-scale electric vehicle charging scheduling problem. This finding isn't merely reflective of a single experimental result but has been validated through multiple repeated experiments. Moreover, our approach notably outperforms other prominent reinforcement learning-based scheduling algorithms, especially those based on Deep Q-Networks (DQN) and Deep Deterministic Policy Gradients (DDPG). The superiority of our approach can be attributed to several key factors. Firstly, our model incorporates a refined feature extraction module, significantly enhancing the model's capability to extract state features. By fully leveraging this refined state feature extraction module, coupled with pre-training and constrained solution space exploration, our method efficiently explores the solution space while mitigating the issue of excessive randomization. Secondly, we judiciously decompose the actual Q-values, enabling the model to understand the value of each state more accurately, thereby making more effective decisions when exploring the solution space. Such design choices result in our model performing commendably in addressing the large-scale electric vehicle charging scheduling problem. It's worth noting that compared to other reinforcement learning methods, such as Advantage Actor-Critic (A2C) based on policy gradient methods, our proposed approach still holds a slight advantage. This observation underscores the practicality and efficiency of our method over A2C in the field of electric vehicle scheduling. This is because our proposed model architecture bears similarity to the A2C method in predicting Q-values, whereby rational decomposition of Q -values enhances model stability and prediction accuracy. However, in addition, effective exploration mechanisms and a sound understanding of state features within the vast state space are more advantageous for the model to attain optimal solutions compared to traditional Actor-Critic frameworks.

In our study, we conducted experiments comparing the use of Multi-Agent Reinforcement Learning (MARL) and Single-Agent Reinforcement Learning methods for addressing the large-scale electric vehicle (EV) charging scheduling problem. Surprisingly, the experimental results indicated that the MARL-based approach did not exhibit the anticipated superiority in solving this problem and, in fact, performed worse compared to the Single-Agent Reinforcement Learning method. This outcome may stem from the inherent complexity of the problem itself and the associated technical challenges. Firstly, the involvement of numerous electric vehicles in large-scale EV systems led to a proportional increase in the number of agents within the MARL framework. As the number of agents increased, the complexity of interactions within the system grew exponentially. Seeking the optimal scheduling strategy inevitably introduced additional interaction parameters among the agents. However, these supplementary interaction parameters often did not directly correlate with the essence of the problem but rather augmented the computational complexity and uncertainty of the system. Furthermore, the collaboration and competition dynamics inherent in multi-agent systems may have contributed to the performance degradation. When addressing the EV charging scheduling problem, individual agents may engage in competition due to conflicting interests, thereby resulting in instability, and decreased overall system performance. In contrast, our proposal offered a more straightforward approach, enabling more effective exploration of the optimal solution within the solution space and avoiding the complexities of interactions and competition present in multi-agent systems.

Additionally, given the widespread application of Long Short-Term Memory (LSTM) in decision problems involving time series data, we conducted a comparative analysis of our proposal against several LSTM-based decision models (Experiment 12 and Experiment 13). In the control experiments, considering the characteristics of LSTM and the electric vehicle charging scheduling problem, we utilized these models for decision-making regarding the sequence of electric vehicle scheduling, while maintaining the original charging station selection network unchanged. The experimental results, as presented in the "Other" column of Table  5 , indicate that Algorithms 12 and 13 achieved results like our proposal. However, models based on the INEM module exhibited a more thorough exploration of the solution space, particularly in generating optimal strategies for EV charging schedules. This might be attributed to the fact that, in the context of EV charging scheduling problems, the design of states incorporates current global information. On the other hand, LSTM-based models, in the process of addressing sequential decision-making problems, appropriately utilize memory gates to retain information from the previous state. Due to this, the combination of this information with the current state might adversely impact the accuracy of the Q-value prediction module, potentially hindering the overall accuracy of Q-value predictions. From an experimental standpoint, it is observed that algorithm 12 and 13 exhibit slightly longer inference speeds compared to our proposal. Consequently, our proposal demonstrates a superior advantage in exploring the solution space.

Based on the three proposed modules, we further investigate the mechanism of action space exploration. As illustrated in Fig.  10 , the training process of our proposed model can be divided into two parts: the pre-training stage and the training stage with exploration. During the pre-training stage, the EV charging service quality, represented as the weighted sum of the average queueing time of EVs and the average idling time of charging stations, shows a gradual improvement for the solutions (depicted as blue dots). This improvement suggests that the model's performance in electric vehicle scheduling tasks has been enhanced through pre-training. The solutions tend to concentrate within the solution space and gradually extend towards the optimal solution (represented by the blue dot). This observation indicates that training the model using pretraining samples obtained from better-performing trajectories can expedite the model's training progress in the initial stage. However, as depicted in the figure, it can be concluded that solely conducting pre-training may result in a limited exploration of the solution space. During the model's training and exploration stages (represented by yellow dots), the positions of these solutions obtained through our model exhibit relatively more dispersion. This dispersion signifies that sufficient exploration of the solution space has taken place during the model training process. In summary, the pre-training stage contributes to the initial performance improvement of the model in EV scheduling tasks. While pre-training accelerates the training speed and leads to concentrated solutions, further training and exploration stages are essential for achieving a more diverse and comprehensive exploration of the solution space. This two-stage training approach and our proposed architecture/modules ensure thorough exploration of solutions and enhance the algorithm’s capability to find optimal solutions for EV scheduling.

The pattern for exploration

The advantage of our model lies in the appropriate combination of the proposed three modules i.e., the fine-grained features extraction module (FFEM), the Improved noisy-based exploring module (INEM) and the dueling block. The model utilizes the fine-grained features extraction module and the dueling block to efficiently extract state features to fit the empirical data to roughly determine the approximate location of the subspace where the optimal solution is located. Based on this, the model introduces constrained noise through the improved noisy-based exploring module in the training stage to help the model explore the subspace adequately. In this process, our proposed an effective empirical filtering strategy (refer to the line 20–21 in Algorithm 2 ) which ensures that the model can explore the solution space and identifies the location of the optimal solution by iteratively. The reasonable combination of the three main modules in the training process can enable the model to efficiently obtain the global optimal solution in a large-scale solution space (This phenomenon can be well verified in Fig.  10 ). Unlike our proposal, the swarm intelligence algorithm can only continue to explore those subspaces that have already been explored. This model has some drawbacks in the search of global optimal solutions, i.e., the swarm intelligence algorithm can only generate new solutions based on the existing solutions through basic operations (e.g., crossover or mutation operation in genetic algorithms), which is relatively easy to ignore the subspace of the global optimal solutions.

Based on the analysis, our proposed model helps to improve the current large-scale EV charging efficiency and has higher execution efficiency compared to traditional algorithms as well as other reinforcement learning based algorithms. This has an enlightening significance for further research on large-scale electric vehicle charging scheduling methods in more complex scenarios.

The growing significance of large-scale EV charging scheduling challenges across diverse scenarios underscores the urgent need for effective solutions in this domain. In this study, we introduce a novel model architecture tailored specifically to tackle the complexities inherent in large-scale EV charging scheduling. Our approach revolves around the creation of two distinct state representations encapsulating the global scheduling information. Furthermore, we introduce a performance metric aimed at quantifying the quality of EV charging service. To optimize scheduling performance, we employ the Fine-Grained Feature Extraction Module (FFEM), the Improved Noise-Based Exploration Module (INEM), and a dueling block for enhancing feature extraction, solution space exploration ability, and Q-value prediction accuracy, respectively. To ensure effective model training, we propose a two-stage algorithm encompassing model pretraining proficient trajectory sampling, and action space exploration.

The effectiveness of our proposed methodology is rigorously demonstrated through three comprehensive case studies. Our experimental findings clearly indicate that our approach excels in generating optimal EV charging schedules, outperforms existing methodologies in terms of efficiency and effectivity. Overall, our proposal furnishes valuable insights for large-scale EV charging scheduling service providers and furnishes a roadmap for addressing challenges characterized by extensive action spaces using Deep Reinforcement Learning (DRL) techniques.

Our future research endeavors will be geared towards further enhancing the performance of the EV charging algorithm. By focusing on improving computational efficiency and optimizing the scheduling process, we aim to elevate the overall efficiency and effectiveness of EV charging services.

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This work was supported by the Scientific Research Funds of Northeast Electric Power University (No. BSZT07202107) and the science and technology development program of Jilin province (No. 20240101362JC).

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Han, Y., Li, T. & Wang, Q. A DQN based approach for large-scale EVs charging scheduling. Complex Intell. Syst. (2024). https://doi.org/10.1007/s40747-024-01587-w

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Received : 16 December 2023

Accepted : 06 July 2024

Published : 21 August 2024

DOI : https://doi.org/10.1007/s40747-024-01587-w

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