What is Cohort Analysis & How to Get Started With It

Csaba Zajdo
May 31, 2024
Conversion
6 min read

Cohort analysis might sound like something straight out of a stats class, but fear not! It’s actually a super handy tool that businesses use to understand the behavior of different groups of customers over time.

In simpler terms, it helps them figure out who’s sticking around, who’s leaving, and why.

In this article, we’re diving headfirst into the world of cohort analysis. We’ll break down what it is, why it matters, and most importantly, how you can start using it to supercharge your own business strategies.

Let’s dive right in!

What is cohort analysis?

Cohort analysis is a type of behavioral analytics that divides data into groups (cohorts) that share common characteristics within a defined time.

This method helps in analyzing how specific groups of users behave over time. Thus, it reveals insights that go far beyond surface-level metrics such as download counts or active users.

The ultimate goals? To enhance customer retention strategies and develop a more profound understanding of user behavior within each unique cohort.

Employing this analytical tool helps businesses gain a more contextual view of customer behavior, paving the way for targeted and effective decision-making.

Cohort analysis plays a pivotal role in decoding the complex language of customer behavior. By exposing preferences and actions over time, it assists companies in crafting tailored marketing strategies and optimizing product offerings.

Types of cohort analysis

As we dissect cohort analysis further, we find that it’s not a one-size-fits-all approach.

There are various sub-categories, such as acquisition, behavior, and time-based cohorts, each offering a unique lens through which to view user behavior and preferences.

Acquisition cohort analysis and behavioral cohorts are the two main types that provide a wealth of insights, particularly when it comes to making informed decisions that can reduce churn and propel revenue growth.

1. Acquisition cohorts: Tracking new users

Acquisition cohorts are akin to a business’s growth rings, revealing the story of user adoption from the moment of sign-up.

These cohorts segment users based on their entry point into the product, allowing for a chronological analysis of their behavior over specific periods.

This method is especially illuminating when assessing the immediate impact of a product launch on user behavior and engagement with core features, which is closely linked to churn rates.

By grouping new users into user groups, such as daily, weekly, or monthly cohorts, businesses can discern retention and churn trends, pinpointing issues that may be impeding feature adoption.

Furthermore, retention curves for these cohorts can spotlight critical drop-off points, aiding businesses in understanding and tackling early-stage engagement challenges.

2. Behavioral cohorts: Analyzing user actions

If acquisition cohorts are the timeline of user adoption, then behavioral cohorts are the narrative of user interaction.

These cohorts categorize users based on their engagement with a product or service, such as making a purchase or reading reviews before buying.

Such granular analysis is crucial for pinpointing the specific user actions that correlate with heightened retention or signal a higher risk of churn.

Examining the varied responses of users to the same action, behavioral cohorts reveal different levels of engagement, essential for crafting personalized product strategies.

Advanced tools like Amplitude take this a step further, allowing businesses to construct detailed charts on product usage and retention, tailored to their needs.

3. Time-based cohorts: Measuring engagement over time

Time-based cohorts serve as a watchtower for businesses, tracking the ups and downs of user engagement over specified periods.

They are essential for:

Identifying the strategies that resonate with users and those that fall flat
Observing variations in retention and engagement durations across different cohorts
Refining strategies to better cater to customer needs

Time-based cohorts provide the necessary data to drive user engagement and conversion, whether through testing new user experiences or refining existing ones, while minimizing customer churn risks.

How do I get started with cohort analysis?

Here’s a detailed guide to help you get started with cohort analysis:

Step 1: Collect data

The first step in cohort analysis is collecting relevant data. This involves:

Defining the characteristic for cohort grouping: Decide on the characteristic you want to use for creating cohorts. Common characteristics include sign-up date, first purchase date, or the date of a specific action taken within your app or website.
Gathering the data: Ensure you have a robust data collection system in place. This could be through your website analytics platform, CRM, or any other data tracking tool you use. The data should be detailed and accurate, including timestamps for when users performed specific actions.

Step 2: Segment the data

Once you have collected the data, the next step is to segment it into cohorts. Here’s how:

Define time intervals: Cohorts are typically segmented by time intervals, such as days, weeks, or months. For example, you might create cohorts based on the month users signed up for your service.
Group users into cohorts: Based on the defined time intervals, group users accordingly. For example, all users who signed up in January would form the January cohort, those who signed up in February would form the February cohort, and so on.

Step 3: Track key metrics

After segmenting your users into cohorts, you need to track key metrics over time. Important metrics include:

Retention rate: This measures how many users from each cohort continue to use your product over time. For example, if 1000 users signed up in January and 200 are still active three months later, the three-month retention rate for the January cohort is 20%.
Engagement rate: This tracks how actively users from each cohort are interacting with your product. Metrics could include the number of sessions per user, time spent on the app, or specific actions taken within the product.
Conversion rate: If your goal is to drive conversions (e.g., from free to paid plans), track how many users from each cohort convert over time.

Step 4: Analyze and visualize

Analyzing and visualizing the data helps in understanding patterns and drawing actionable insights. Here’s how to do it effectively:

Create tables and charts: Use tables to list out metrics for each cohort over time. Charts, such as line graphs or bar charts, can visually represent trends and make it easier to spot patterns.
Look for trends: Identify trends such as when users are most likely to drop off, which cohorts have the highest engagement, and how changes in your product or marketing efforts impact different cohorts.

Step 5: Take action

Based on your analysis, take strategic actions to improve user engagement and retention:

Identify problem areas: If you notice a significant drop-off at a certain point, investigate what might be causing it. This could involve user feedback, session recordings, or further analysis.
Test and implement changes: Based on your findings, implement changes aimed at improving the metrics. This could involve improving onboarding processes, enhancing user experience, or adjusting your marketing strategy.
Monitor and iterate: Continuously monitor the impact of your changes. Cohort analysis should be an ongoing process where you regularly track new cohorts and measure the effectiveness of your interventions.

Cohort analysis example for an ecommerce store

Let’s consider a practical example to illustrate the process:

Collect data: Let’s say you run an ecommerce store selling clothing and accessories. You collect data on customer purchases, including the date of their first purchase and subsequent purchases, as well as other relevant information like demographics and referral source.
Segment the data: You segment your customers into cohorts based on the month of their first purchase. For example, customers who made their first purchase in January form the January cohort, February for the February cohort, and so on.
Track key metrics: For each cohort, you track several key metrics over time. For example, you can measure retention rate (the percentage of customers from each cohort who make repeat purchases in subsequent months). Or track the average order value for each cohort over time. Finally, you can mmeasure how often customers from each cohort make purchases.
Analyze and visualize: Create tables and charts to visualize the data and identify trends. Plot the retention rate for each cohort over time to see if there are any patterns or trends. Are certain cohorts more likely to remain active customers over the long term? You can also create a line graph showing the average order value for each cohort over time. Do customers tend to spend more or less as they continue to engage with your platform? Finally, you can compare the purchase frequency of different cohorts to see if there are any differences in how often they make purchases.
Take action: Based on your analysis, take strategic actions to improve customer retention and increase sales. For example, offer discounts or promotions to customers who haven’t made a purchase in a while to encourage them to return. Or leverage data on customer preferences and purchase history to provide personalized product recommendations.

What are some tools that can help with cohort analysis?

Fortunately, there are several tools available that can streamline the process and provide valuable insights into user cohorts.

Here are some popular tools for cohort analysis:

1. Google Analytics

Google Analytics is a widely used web analytics service that offers a range of features for tracking and analyzing website and app data.

One of its key features is built-in cohort analysis reports, which allow you to segment users into cohorts based on various criteria such as acquisition date, behavior, or demographics.

With Google Analytics, you can track key metrics, visualize cohort data, and gain insights into user retention and engagement over time.

2. Mixpanel

Mixpanel is a comprehensive product analytics platform that specializes in user behavior tracking and cohort analysis.

It provides detailed cohort analysis capabilities, allowing you to segment users based on a wide range of attributes and behaviors.

Mixpanel offers advanced features such as funnel analysis, retention reports, and cohort comparison, enabling you to understand how different user cohorts interact with your product and identify opportunities for optimization.

3. Amplitude

Amplitude is another popular product analytics platform that offers powerful cohort analysis tools. It is designed to help businesses understand user behavior, measure product performance, and drive growth.

Amplitude provides robust cohort analysis capabilities with features such as cohort segmentation, retention analysis, and cohort comparison.

Additionally, Amplitude offers advanced analytics features such as behavioral segmentation, predictive analytics, and A/B testing, making it a comprehensive solution for cohort analysis and beyond.

4. Excel/Google Sheets

While specialized analytics platforms offer advanced features for cohort analysis, basic cohort analysis can also be performed using spreadsheet software such as Excel or Google Sheets.

These tools allow you to organize and analyze data manually, create tables and charts, and visualize cohort trends.

While they may not offer the same level of sophistication as dedicated analytics platforms, Excel and Google Sheets are useful for basic cohort analysis and can be a cost-effective option for businesses with limited resources.

Applying cohort analysis to customer retention strategies

Diving into the realm of customer retention, cohort analysis emerges as a compass pointing towards the trends and behaviors that herald loyalty or signal departure. It’s a strategic ally in identifying the most lucrative user cohorts, steering targeted retention and upselling efforts.

Cohort analysis insights prove invaluable in understanding customer growth, engagement, and revenue dynamics, forming the pillars of a robust retention strategy.

Moreover, by optimizing the user experience based on these insights, you can significantly uplift customer lifetime value, making cohort analysis a central tool in retention strategy toolkits.

1. Identify at-risk customers

Cohort analysis is not only about celebrating the champions but also about saving those at the brink of departure. Identifying the period after which customers are likely to churn enables businesses to devise strategies to prevent early customer departure.

Segmenting users into cohorts based on behaviors and profile properties reveals patterns and predictors that signal a higher likelihood of churn, enabling companies to intervene before customers slip away.

Such segmentation strategies, like dividing users by onboarding completion, allow for tailored interventions that directly touch upon the customer’s experience, significantly boosting retention.

2. Optimize onboarding processes

Cohort analysis guides the crucial first steps of a customer within a product, ensuring they don’t stumble. By identifying when and why new users disengage, it uncovers potential onboarding deficiencies and points towards the essential features that need to be highlighted.

SaaS companies, in particular, rely on cohort analysis to pinpoint onboarding bottlenecks, enhancing feature adoption and, subsequently, user retention.

Comparing the onboarding interactions of retained users with those who churn, businesses can fine-tune onboarding flows to resonate more effectively with newcomers. Furthermore, cohort analysis pinpoints exact phases in onboarding where users commonly drop-off, allowing for targeted improvements that smooth out the onboarding process.

3. Refine product features

Cohort analysis acts as a magnifying glass, allowing product teams to zoom in on how different groups use features, directing focus to those that significantly enhance user engagement.

Monitoring feature adoption rates provides actionable data for targeted training, which can amplify the use of key features. Identifying when users abandon a product, cohort analysis highlights the features requiring attention and where A/B testing could yield the most effective improvements.

Analyzing drop-offs with specificity is essential to tailoring product enhancements that engage users most effectively. Furthermore, segmenting by product packages can shed light on how features impact customer lifetime value, guiding service enhancements that resonate with users.

FAQ

How does cohort analysis improve customer retention?

Cohort analysis improves customer retention by identifying at-risk customers and refining product features based on user behavior and engagement patterns within specific cohorts. This can help optimize onboarding processes and tailor offerings to specific customer segments.

Can cohort analysis predict customer churn?

Absolutely, cohort analysis can indeed predict customer churn by identifying patterns that signal a higher likelihood of customer attrition, allowing businesses to take preemptive actions to mitigate churn.

What are some tools used for cohort analysis?

Commonly used tools for cohort analysis include Amplitude, Mixpanel, Mosaic, and SQL, along with data visualization platforms like Google Sheets and Chameleon.

How often should cohort charts be reviewed?

Cohort charts should be reviewed regularly to stay updated on user behavior and retention trends, ensuring effective strategies. Regular monitoring and iteration based on these reviews are crucial for maintaining success.

Wrapping up

As we wrap up our comprehensive guide, it’s clear that cohort analysis is an indispensable tool for businesses aiming to harness the power of data analytics.

From the fundamentals of grouping users into cohorts to the intricate strategies of customer retention and advanced analytical techniques, we’ve explored the myriad ways cohort analysis can illuminate the path to business growth.

Embracing this method means not just understanding your customers but predicting trends, personalizing experiences, and ultimately, driving loyalty and revenue. Let cohort analysis be your guide to a future where decisions are informed, strategic, and transformative.

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Written by

Csaba Zajdo

Csaba Zajdo is the founder of OptiMonk. As an ecommerce veteran, Csaba has over 15 years of experience in working with ecommerce stores. His mission is to reinvent the ecommerce industry and help stores, by creating delightful shopping experiences for each customer.

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