Cohort Analysis: The Ultimate Guide for Understating Business Analytics

Marketers going over cohort analysis data at a table

Have you ever wished that you could take a deep dive into the behavior of your customers to understand them better and ultimately improve your marketing strategy?

Cohort analysis can help you do just that.

It may sound intimidating, but at its core, cohort analysis is a simple business analytics tool that can give you valuable insights such as customer lifetime value, user engagement, and more.

In this guide, we’ll explore what cohort analysis is, how to use it, and the advantages it offers your organization. So let’s dive in!

What is Cohort Analysis?

Cohort Analysis is an analytical technique used to study the behaviors and outcomes of groups of individuals (cohorts) who share common characteristics within a specified timeframe. It is widely used in various fields like marketing, product management, user experience research, and health studies.

In marketing, cohort analysis is a way to measure the performance of different customer segments over time. By tracking how cohorts interact with your product, website, or emails, you can gain valuable insights such as user engagement and customer lifetime value.

Still, sound a little abstract? Have no fear! Below, we are going to dive into different types of cohort analysis, examples, and how to conduct it yourself.

5 Types of Cohorts to Analyze

Person using wooden people figurines to demonstrate customer segmentation
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Before you can understand how to execute a cohort analysis, you need to understand the different types of cohorts. Let’s get into it:

1. Acquisition Cohorts

Acquisition cohorts are groups of users or customers who first interacted with your product or service within the same time frame. This could be the month they signed up, the week they made their first purchase, or the day they downloaded your app. The primary focus here is on the initial engagement point. By analyzing acquisition cohorts, businesses can understand how different groups respond to the product over time. 

For instance, a cohort that signed up during a promotional period may exhibit different long-term behavior compared to those who joined without any incentives. This analysis is particularly useful for evaluating the effectiveness of marketing campaigns, understanding seasonal trends, and identifying shifts in customer behavior over time.

2. Behavioral Cohorts

Behavioral cohorts group users based on specific actions they take while interacting with your product or service. Unlike acquisition cohorts, which are defined by when users started their journey, behavioral cohorts are defined by what users do during their journey. This could include actions like making a purchase, utilizing a specific feature, or reaching a certain level in a game. 

Behavioral cohorts allow businesses to see how different user behaviors correlate with retention, engagement, and lifetime value. For example, users who regularly use a key feature might have a higher retention rate than those who don’t. This insight can drive targeted strategies to encourage specific behaviors that are linked to positive outcomes.

3. Time-Based Cohorts

Time-based cohorts are defined by the specific period during which users engage with a product or service. This could be a daily, weekly, monthly, or even yearly cohort. As opposed to acquisition cohorts that focus on the initial engagement, time-based analysis is crucial for understanding how user behavior evolves over a set period. For instance, a monthly cohort analysis might reveal how user engagement changes from the first month to the sixth month. 

This type of analysis is invaluable for identifying patterns in customer lifecycle, such as when customers are most likely to upgrade, renew, or churn. It helps businesses in pinpointing critical intervention points to enhance the user experience and extend customer lifetime value.

Marketers discussing the positive results of their recent cohort analysis
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4. Segment-Based Cohorts

Segment-based cohorts divide users based on specific characteristics or attributes. These characteristics could be demographic (like age or location), psychographics (like interests or values), or based on user behavior (like frequent buyers or occasional users). Segment-based analysis is vital for personalizing marketing efforts, product development, and customer service. 

By understanding the needs and behaviors of different segments, businesses can tailor their offerings to meet these specific needs, improving customer satisfaction and loyalty. For example, a business might find that its younger user segment prefers mobile engagement, prompting a shift in its marketing strategy to focus more on mobile platforms.

5. Size-Based Cohorts

Size-based cohorts categorize users or customers based on the size of their engagement or purchase. This could mean grouping users based on the size of their orders, the level of subscription they choose, or the frequency of their purchases. 

Analyzing size-based cohorts helps businesses understand how different customer groups contribute to revenue. It can reveal, for instance, whether most of the revenue comes from a small number of high-value customers or a large number of lower-value customers. This insight is crucial for resource allocation, pricing strategies, and understanding the impact of different customer groups on overall business health.

Each of these cohort types offers unique insights into customer behavior and business performance. By leveraging them effectively, businesses can gain a comprehensive understanding of their customer base, tailor their strategies for maximum impact, and ultimately drive growth and success.

Example of Cohort Analysis: E-commerce Platform Retention Study

Let’s consider a hypothetical example of a cohort analysis conducted by an e-commerce platform. The primary objective is to understand customer retention and identify patterns that can help improve long-term user engagement and sales strategies.

Marketer looking at a credit card at her desk
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Setting the Stage

Business Context: The e-commerce platform has observed fluctuating sales and customer engagement levels over the past year. The management wants to understand how different groups of customers behave over time, especially focusing on their retention and purchasing patterns.

Data Collection: Using its data analytics system, possibly integrated with tools like Google Analytics, the company collects data on user activities, including acquisition dates, purchase history, frequency of visits, and types of products purchased.

Identifying Cohorts

The company decided to analyze two primary types of cohorts:

Acquisition Cohorts: Customers are grouped based on the month they made their first purchase. For instance, all customers who first purchased in January form one cohort, February purchasers form another, and so on.

Behavioral Cohorts: Customers are segmented based on their purchasing behavior, such as frequent buyers (customers who make purchases more than once a month) and occasional buyers (customers who make purchases less than once a month).

Key Metrics Defined

The following key metrics are identified for analysis:

  • Retention Rate: The percentage of customers who continue to make purchases after their initial buy.
  • Average Order Value (AOV): The average amount spent by customers in each transaction.
  • Purchase Frequency: How often customers come back to make a purchase.
Businessperson with a laptop in one hand and printed marketing analytics in the other
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Executing Cohort Analysis

Cohort Analysis Charts: Using cohort analysis charts, the company visualizes the retention rate of each acquisition cohort over 12 months. For example, the chart shows what percentage of customers who first purchased in January returned to purchase in each subsequent month.

Behavioral Analysis: Similarly, the company analyzes the behavioral cohorts to understand how frequent buyers differ from occasional buyers in terms of AOV and purchase frequency.

Insights Gained

  1. Time-Based Patterns: The analysis reveals that customers acquired during the holiday seasons (November and December) have a higher retention rate compared to those acquired in other months.
  2. Behavioral Trends: Frequent buyers have a lower AOV but a higher overall lifetime value due to their regular purchasing habits.
  3. Segment Differences: The January cohort showed a decline in retention after six months, suggesting potential issues with customer satisfaction or engagement.

Strategic Actions

Based on these insights, the e-commerce platform decides to:

  • Implement targeted marketing campaigns during holiday seasons to acquire more customers.
  • Develop loyalty programs for frequent buyers to encourage continuous engagement.
  • Investigate the cause of the January cohort’s decline and strategize on improving mid-year retention, possibly through personalized offers or feedback surveys.

This hypothetical case offers a snapshot of how data-driven decisions can inform a brand’s strategy and ultimately improve customer experience. Analysis of user information can reveal valuable insights into buying behavior, segment differences, and more – all of which help companies enhance their product or service and better serve customers. In the end, it’s the combination of customer understanding and strategic actions that creates an optimal brand experience.

5 Steps to Conduct a Cohort Analysis

Conducting a cohort analysis is a systematic process that involves several key steps. Let’s explore these steps in detail:

Step 1: Crafting Targeted Queries

The first step in this process is to craft targeted queries with precision. This involves defining the questions that you want your analysis to answer, aligned with your business objectives. Whether it’s understanding user retention rates or identifying which features drive the most engagement, your expertise in formulating these questions is critical. 

For example, we often explore how the purchasing behavior of users acquired through a specific marketing campaign differs from those acquired organically. These targeted queries guide the structure of our analysis and guarantee that the insights we gather are relevant, actionable, and grounded in authoritative practice

Hand writing on a notepad with a pen
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Step 2: Defining Key Metrics

Once you have your targeted queries, the next step is to define the key metrics that will provide answers to these queries. These metrics could include user retention rate, average revenue per user, session length, frequency of purchase, etc. 

The choice of metrics will depend on the nature of your queries and the aspects of user behavior you are most interested in. We’ve found that if your focus is on customer loyalty, metrics like repeat purchase rate and churn rate will be significant. We can’t emphasize enough that defining the right metrics is essential as they form the basis of your analysis, enabling you to measure and compare the performance of different cohorts effectively.

Step 3: Identifying Specific Cohorts

Identifying specific cohorts involves segmenting your user base into distinct groups based on shared characteristics. This could be based on their acquisition date (acquisition cohorts), behaviors (behavioral cohorts), demographics (segment-based cohorts), or any other relevant criteria. 

We’ve found that the key is to ensure that the cohorts are meaningful and relevant to your business questions. One of our clients was looking to improve user engagement, so they created cohorts based on the month of the first subscription or based on user activity levels. We promise that accurate identification of cohorts is critical as it allows for more precise analysis and insights.

Step 4: Executing Cohort Analysis

With your queries set, metrics defined, and cohorts identified, you’re ready to execute the cohort analysis. This involves collecting and analyzing data related to your defined metrics for each cohort. We use data that has been collected through analytics tools, databases, or CRM software. Our analysis often includes observing how metrics change over time within a cohort, comparing metrics across different cohorts, and looking for patterns or anomalies in the data. 

Step 5: Assessing Test Outcomes

The final step is assessing the outcomes of your cohort analysis. This involves interpreting the data to draw meaningful conclusions and insights. It is helpful to compare the results against your initial hypotheses and business objectives to understand the success of different strategies or initiatives. This step often involves visualizing data in charts or graphs to better understand trends and patterns. Based on your findings, you can make informed decisions to improve your product, service, or marketing strategies.

Marketers discussing cohort analysis data taped to a marker board
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Final Thoughts

Cohort analysis is a powerful tool that can help you understand user behavior and improve your overall business performance. By tracking user cohorts, you can gain insights into trends and patterns over time, allowing you to make data-driven decisions based on accurate information. As with any form of analysis, it’s important to ensure the accuracy and validity of your data in order to draw meaningful conclusions from your results. With the right strategies in place, cohort analysis is an invaluable asset as part of your overall marketing strategies. If you are interesting in learning more about what you should be tracking, check out our blog on marketing KPIs.

And if you need help with tracking user cohorts and using your data to make informed decisions, our team at WGM is here to help. With our advanced search engine optimization (SEO) techniques and analytics tools, we can make sure your website is optimized for success while monitoring the performance of your keywords, traffic sources, and more. Contact us today to learn how cohort analysis can help you maximize your business potential.

Frequently Asked Questions

What is deep actionable cohort analytics?

Deep actionable cohort analytics is an advanced approach to cohort analysis that involves delving into cohort data to extract meaningful insights that can drive strategic decisions. It goes beyond basic analysis to explore relevant differences and common characteristics within different cohorts, such as behavioral cohorts and acquisition cohorts. This type of analysis often uses a variety of data points, key metrics, and visual aids like cohort analysis charts or cohort charts to understand and predict customer lifecycle trends, identify shifts in monthly active users, or observe changes over a specific time period. 

By utilizing tools like Google Analytics and principles of behavioral analytics, deep actionable cohort analytics enables businesses to not just observe, but also act on the insights gained, thereby enhancing their strategies based on real, data-driven evidence.

What is an example of cohort analysis data?

An example of cohort analysis data could involve analyzing acquisition cohorts in a SaaS business, like Base a job costing and spend management tool. Imagine a scenario where a business tracks new users (acquisition date) over several months (time period). This cohort data might include metrics like monthly active users, engagement rates, and churn rates. 

For instance, a cohort analysis report might reveal that users who signed up in January (a specific acquisition cohort) show higher engagement compared to those who signed up in March. These findings, presented through cohort analysis charts, provide valuable insights into user behavior and help in understanding cohort analysis more deeply.

What is the cohort analysis theory?

The cohort analysis theory is based on the concept that analyzing groups of people (cohorts) who share common characteristics within a specific time period provides deeper insights than analyzing an entire population as a whole. 

This theory applies to various domains, from behavioral analytics to customer lifecycle studies. It posits that by understanding the unique behaviors and patterns of different cohorts, whether they are acquisition cohorts or behavioral cohorts, organizations can more accurately identify trends, assess the impact of specific actions or changes, and make more informed decisions. Cohort analysis theory emphasizes the importance of considering the relevant differences among cohorts to derive meaningful conclusions from the data.

What is a cohort analysis in simple terms?

In simple terms, cohort analysis is a method of analyzing data where you group people (cohorts) based on a shared characteristic, like when they first used a service (acquisition date) or a specific behavior they exhibit. These groups, or cohorts, are then tracked over a period of time (time period), allowing businesses to observe patterns and changes in behavior. 

For example, a company might use cohort analysis to see how often users who signed up in a particular month (an acquisition cohort) return to use their app (monthly active users). Cohort analysis helps in understanding the life cycle of these users, from their initial interaction to their long-term engagement, and it is used to identify trends and make data-driven decisions.

Noelle Thuillier

Noelle Thuillier

Noelle is a Content Specialist for WGM. She writes and edits all business content, including blogs, press releases, social media posts, and technical writing pieces. With over ten years of experience writing, Noelle has been published on many reputable sites during her career, including TheScriptLab.com and HelloGiggles.com. Before coming to WGM, she worked as a News Director at KWHI in Brenham, Texas. Noelle oversaw the newsroom writing stories, producing content, and being an on-air personality. In addition to her bachelor’s degree in Political Science, Noelle holds a Master in Arts degree in Communication and Media Studies from the University of Greenwich in London, England. Noelle’s other passion in life is her family. She enjoys spending time with her husband, Joe, daughter, Frankie, and English Bulldog, Alvin. She also loves to travel, with her favorite destinations so far being Ireland and the Czech Republic.

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