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Harness the Power of Cohort Analysis to Build Customer Acquisition and Loyalty

Keeping customers is just as important as attracting new ones. Use cohort analysis to understand your audience and boost customer retention.

Finding and keeping new customers is the lifeblood of any business. There are many ways to build a strong customer base and keep loyal clients coming back, but not all customers are the same and not all sales or marketing techniques will be effective for everyone.

Many businesses turn to data analytics tools like cohort analysis to identify patterns that can be used to find and keep customers. Follow this guide to learn about a powerful method to dig deeper into your customer base to determine what works best and then maximize your customer engagement and retention.

What is cohort analysis?

Cohort analysis is a data analytics tool in which users are divided into groups, known as cohorts, that have shared characteristics. By charting data for each group, it's possible to determine the success of business functions like marketing and sales for every cohort.

What is a cohort?

A cohort refers to a group of individuals who share a common characteristic or experience within a specific timeframe. These groups are typically identified based on a particular event or action, such as the date of first purchase, signup, or another significant interaction with a product or service.

How cohort analysis is used in marketing

Marketers use cohort analysis to identify trends and understand which strategies are most effective. This helps optimize marketing efforts by improving customer targeting and focusing on the unique characteristics and behaviors of different customer groups.

Benefits of customer cohort analysis

Cohort analysis is a powerful tool for businesses, allowing them to identify trends and take action based on large amounts of data. Read on for some of the top benefits.

Get to know your customers

Cohort analysis helps businesses understand preferences and potential issues unique to each cohort throughout the customer lifecycle. Consequently, companies can make data-driven decisions to optimize their offerings, leading to a deeper understanding of customer dynamics and fostering long-term relationships with distinct customer segments.

Increase customer retention

Cohort analysis plays an important role in retaining existing customers. Businesses gain valuable insight into trends that influence retention rates and can follow up with targeted strategies in response to that information.

In addition, businesses can tailor retention efforts to the needs of different segments and boost marketing effectiveness by choosing the most successful communication strategies and channels for each cohort. This helps reduce customer churn and strengthen ongoing customer relationships.

Improve the health of your business

Cohort analysis enables strategies targeted at a specific group of users, optimizing customer acquisition and retention. This data-driven approach enhances decision-making precision, minimizing resource waste and maximizing return on investment. It’s a proactive and adaptive business approach that is critical for long-term success.

Identify trends

Another benefit of cohort analysis is the ability to identify trends. Marketing teams can uncover insights that lead to better marketing strategies, keeping them agile and responsive to shifting factors in the marketplace.

Whether performing cohort analyses to study marketing channels, user engagement, or revenue during a specific period, the resulting data can give marketers a deeper understanding of how user behaviors are changing. This proactive approach to cohort data helps to take advantage of emerging opportunities and anticipate challenges.

Understand the effects of changes

Cohort analysis also allows organizations to discern the true impact of changes over time by comparing cohorts before and after the implementation of new products, features, or initiatives.

In addition, analyzing retention rates, conversion metrics, and engagement levels among cohorts provides valuable insights into user behavior, aiding in data-driven decision-making and refinement of strategies for sustainable growth.

Customize your marketing campaigns

With cohort analysis, businesses can see the unique preferences, challenges, and response patterns for each cohort. This detailed understanding of how different groups behave enables highly targeted and personalized marketing messages.

Businesses can adjust factors like tone, product recommendations, or contact frequency based on user data so that campaigns' content, channel, timing, and type can be aligned with specific user groups. This results in increased relevance to improve engagement metrics and increase user retention.

Types of cohorts to analyze

While there are an infinite number of ways to organize and segment your audience, cohorts are usually organized into 2 overall groups—acquisition cohorts and behavioral cohorts. The type of cohort you choose to analyze will be based on your goals.

Acquisition cohorts

Acquisition cohorts are groups of users who became customers, joined a mailing list, or otherwise began engaging with a company during a specific time period. It’s often helpful to break out cohorts by specific acquisition event; a user who has only signed up for your mailing list is different from one who is a loyal, longtime customer. Essentially, acquisition cohort analysis looks at users who are at the same stage of their customer journey with your company.

This type of cohort analysis helps businesses evaluate the effectiveness of user onboarding strategies or product launches by tracking the performance and behavior of users acquired during a particular period.

Behavioral cohorts

Behavioral cohorts, on the other hand, group users based on an action they take regarding your product or service. Behavioral cohort analysis is useful for understanding customer behavior like usage frequency, product updates, and sharing content on social media. It also helps tailor your customer service and retention strategies to each cohort.

How to perform a cohort analysis

Cohort analysis can involve numerous variables and a lot of data. The best way to get started is by deciding what you want to analyze and the steps involved.

Set your goal

Before putting together your cohort metrics or breaking down various cohorts, choose a goal. Your goal might be to improve user retention rates, increase conversions, or encourage adoption of new product features. It's important to be clear about what you want to achieve to make sure the analysis is well designed.

Cohort analysis is particularly useful for goals that can be tracked over time, throughout all stages of the customer lifecycle. This can offer insights into areas for improvement and help businesses build longer-term relationships with their customers.

Choose the metrics you want to measure

If it can be measured, almost any aspect of your customer relationship can be studied with cohort analysis. Following are some of the most popular metrics.

User acquisition rate

User acquisition rate is the pace at which a company gains new users over a specific period. This is a common metric for cohort analysis, which studies those who became users during the period in question and analyzes their behavior over time.

It's also common to analyze cohorts based on acquisition channels, like digital marketing or referral programs, to understand the effectiveness of different marketing efforts.

Churn rate

Churn rate is the percentage of customers or subscribers who stop using a product or service within a certain period. This is a key metric for businesses, especially those with a subscription-based revenue model or those that rely on customers to make regular, repeat purchases.

A high rate can be an indication that customers are not happy, service quality is not good, or a competing company is offering better value. Understanding your churn rate and doing everything possible to minimize it is important for customer retention.

Engagement rate

The more users who spend time on your website, engaging with your content or products, the better. Users can be evaluated based on things like frequency of logins or interactions, purchases, or time spent using an app or product. This data allows you to set goals to enhance engagement by all cohorts in general or by users in the same cohort.

Conversion rate

Converting website visitors or sales leads into paying customers is the key to business growth. The rate of these conversions is a useful metric you can glean through cohort reports. Decide what conversion action you want to understand (a first purchase or filling out a contact form, for example) then divide customers into cohorts to determine which groups have the highest conversion rates.

Product or feature adoption

Cohort analysis can also be used to examine how different groups purchase new products or adopt new features. Studying the resulting cohort table allows you to better address customer needs and keep them coming back. It also makes it easier to set goals that can increase adoption rates by targeting underperforming cohorts.


Making sure you're on track for revenue growth and finding ways to maximize revenue across cohort groups is another useful goal. A cohort analysis report allows you to determine which groups of users contribute the most revenue and where there's room for growth. This enables you to design a marketing campaign to target specific cohorts with sales and marketing incentives.

Customer lifetime value (CLV)

Customer lifetime value (CLV) is the total amount of money a business expects to earn from a customer throughout their relationship. Studying this metric using cohort analysis allows a business to track the CLV of different groups with shared characteristics. The data helps businesses understand the overall value of acquiring and retaining customers and can inform decisions about marketing strategies and other acquisition costs.

Retention rate

Businesses know that keeping customers is just as important as finding new ones. A company might use cohort analysis to identify users who signed up during specific time periods and analyze the users' retention rate over their customer lifecycle. This allows a business to implement strategies targeted at specific cohorts to improve retention.

Vanity metrics

Cohort analysis can also measure some data, known as vanity metrics, that may look impressive but do not always provide meaningful insights into a business's performance or success.

For example, the number of your social media followers or website visitors. On its own, this information might not be useful, but your analysis can combine that with the number of active engagements to pinpoint opportunities for improvement. These metrics tend to be most useful in the early stages of a project to gauge initial interest or to assess the reach of your marketing efforts.

Just be sure that the metrics you choose to track are an accurate representation of your sales and marketing effectiveness.

Decide on relevant cohorts

What cohorts and cohort size you want to study will depend on your goals and the nature of your business. If the date range of your analysis covers a year or more, you may want to group users into monthly cohorts. On the other hand, if you’re looking at the quarter immediately following a new product launch, weekly cohorts might make more sense.

It's often beneficial to adjust the cohort size and observe its impact on the results. You can start with a moderate size and adjust based on the insights you gain and the practicality of handling the data. The goal is to strike a balance between detail and statistical significance.

Gather cohort data

Next, it’s time to gather all the relevant data for each group of users. The type of raw data you’re looking for will depend on your goal. It may include customer demographics, acquisition date, engagement metrics, and other relevant information. You may use your company’s own data, data from an outside source like Google Analytics, or your website’s hosting company.

Use cohort analysis charts

Once you've gathered the data, it can be formatted into a chart or table, with the metrics for each cohort of users over a time period. Having the data in a visual format makes it easier to spot patterns and identify areas of opportunity.

Cohort analysis examples

Cohort analysis often involves a lot of data and a significant amount of business analytics. It can be helpful to look at a few cohort analysis examples to understand how you might use cohort analysis in your business.

Scenario #1: How quickly are users from different cohorts adopting the new feature introduced in the latest app update?

Software companies need to make sure that their products are regularly updated and optimized for security and functionality. Even when updates are released, they don't do much good if users don't take advantage of them.

A mobile app company has recently released a new app update and would like to know which users are using the new features. Are the product's most loyal, long-term users taking advantage of the update, or are the newest users adapting more effectively?

In this case, the company might decide to do an acquisition cohort analysis. The company might start by categorizing users based on the date they installed the app, creating cohorts for each month of user acquisition.

Next, the company chooses a metric that gives them useful information, such as the number of daily active users of the feature. Customers are divided into cohorts based on when they became users, and then the company then uses analytics tools to gather data on usage.

This data is represented visually so that adoption rates can easily be compared across cohorts. Next, the company analyzes the data to identify trends and differences among cohorts, evaluating which groups adopted the new feature more rapidly.

Finally, the company uses this information to refine future updates and improve outreach about useful product features to groups who are underutilizing them.

Scenario #2: What percentage of users who signed up each month in 2023 are still active after 1 month, 3 months, and 6 months?

The subscription-based business in this cohort analysis example is hoping to determine the churn rate for different user groups. Because the company wants to determine which monthly cohorts have continued to be active for the longest time, they'll first divide users into 12 cohorts—one for each month of 2023.

Next, they'll track the number of users in each cohort and how many of them are still active after 1, 3, and 6 months.

Churn rate is calculated by dividing the number of customers remaining after a specific period by the total number of customers at the beginning of that period, subtracting that number from 1, and then multiplying the result by 100 to express it as a percentage. For example, if 200 users signed up in January and 175 of them were still active after a month, the churn rate formula is 1 minus the result of 175 divided by 200, then multiplied by 100. So the churn rate for the January cohort after 1 month is 12.5%.

The company calculates the churn rate for each cohort at each time period and discovers that there is a sharp drop-off for all cohorts at 3 months and that users who sign up in the summer are most likely to become inactive in the first month.

The company can use this information to offer incentives at certain times or to certain groups to keep them as active users of the service.

Scenario #3: What is the conversion rate for users acquired through social media advertising compared to users acquired through email campaigns?

A direct-to-consumer clothing company has an extensive social media presence and a large email marketing list. To allocate limited marketing resources, the company wants to know which of these channels is most effective.

They start by creating cohorts based on the acquisition channel (social media or email) and tracking their behavior over time. They then measure conversion rates by calculating the percentage of users within each cohort who made a purchase.

The company compares the performance of social media and email cohorts at different stages, such as weeks or months after acquisition. This makes it possible to identify trends, drop-offs, or variations in conversion rates and retention between the 2 channels.

In this cohort analysis example, analyzing the data helps to identify which channel yields more conversions, allowing the company to focus its digital marketing campaigns there to increase sales.

Tools for generating a cohort analysis report

Depending on the type of analysis, there can be a large amount of raw data. There are various tools available to manage the data and generate cohort analysis reports.

Use a data analytics program

When choosing a tool, look at factors like ease of use, specific features offered for cohort analysis, and the program's ability to integrate with other tools like your customer relationship management system or your marketing automation tools.

Google Analytics

Google Analytics is a popular choice. The basic service is free, and provides cohort analysis features that allow you to track user cohorts based on various dimensions and metrics. Users can analyze user behavior over time and understand how different cohorts perform.


Mixpanel is a specialized analytics tool that does cohort analysis, among other features. It offers some free, basic default analytic dashboards and is a good choice for companies that want a more robust behavioral analytics program than Google Analytics but also want to jump in right away without learning how to navigate advanced features.


Like Mixpanel, Amplitude offers a free basic plan so you can try it out first and then upgrade to a paid plan if you need more functionality or larger data capacity. It requires more customization than Mixpanel, but also has more flexibility and features for advanced users.

Do it yourself

It's not necessary to use an analytics tool. If your data is relatively simple and your team has the expertise, it's possible to create cohort analysis charts with any spreadsheet program like Microsoft Excel or Google Sheets.

Customer cohort analysis is a powerful data analytics tool for identifying trends, improving your retention curve, and tailoring your marketing strategies to groups of customers with common characteristics. Hopefully, this guide to cohort analysis has helped you start understanding your customers and making sure you’re meeting their needs.

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