Cohort Analysis: Definition, Key Steps & Examples

Cohort analysis is a statistical tool used to track user behavior over time. It is essential for understanding retention, user patterns, feature usage, and growth.

Using this statistical method involves grouping customers based on shared characteristics or conducts within a specific time frame, to find opportunities for sustained business growth and profitability.

Imagine you've launched a new product feature, only to see 75% of your new users leave within the first week. This stark drop-off highlights a critical need: understanding user behavior before releasing new features. Cohort analysis gives you the ability to dissect user journeys, spot exactly where drop-offs occur, and make data-driven improvements to retain users.

In case you're a product marketer trying to connect with the right audience. This method helps uncover hidden patterns in customer segments, enabling effective personalized campaigns that truly resonate and boost lifetime value (LTV).

This guide provides you with the essential knowledge to conduct a standard cohort analysis. You'll gain a practical foundation for extracting valuable insights from your data.

What Is a Cohort?

A cohort is a group of individuals who share a common characteristic or experience within a defined period. In the context of business analytics, cohorts are typically groups of customers or users who have similar traits or behaviors.

Types of Cohorts Used in Product Analytics

Acquisition Cohorts

Acquisition cohorts classify customers by when they were acquired or first engaged with a product or service. By segmenting users in this way, businesses can track changes in customer behavior starting from the acquisition stage.

Example: A mobile app's product team could create weekly acquisition cohorts to observe how long users from each specific week's downloads continue using the app. The findings might show that users who downloaded the app during promotional periods or targeted campaigns have higher long-term engagement rates compared to those who downloaded it during regular periods.

Time-based cohorts

With time-based cohorts, customers or users are grouped based on when they began using a product or service—whether during a particular day, week, or month. Analyzing these cohorts helps businesses spot behavioral patterns and evaluate the impact of product changes over time.

Example: A SaaS company might use monthly time-based cohorts to examine user retention. The analysis could indicate that users who signed up during months featuring major product updates or promotional campaigns show better 6-month retention rates than those who signed up in other months. This could influence decisions regarding the optimal timing for future product updates and marketing activities.

Demographic cohorts

These cohorts enable companies to understand how distinct demographic groups engage with their product or service over time. Demographic cohorts segment customers according to shared attributes, such as age, gender, income, or location.

Example: An e-commerce platform might use demographic cohorts based on age to explore purchasing behaviors across generations. The data could reveal that millennial customers are more likely to make repeat purchases of sustainable products, while Gen Z customers are more inclined to engage with mobile app features. This insight can guide marketing strategies and product development tailored to each demographic group.

Behavioral cohorts

Behavioral cohorts group customers who have exhibited specific behaviors or performed particular actions within a set timeframe. This helps companies assess how various user behaviors relate to outcomes such as retention or revenue.

Example: A SaaS provider offering project management software could create behavioral cohorts by examining actions users take within their first week, such as:

• Creating at least one project
• Inviting team members
• Integrating third-party tools

For instance, they may find that users who integrate third-party tools during their first week have a 50% higher retention rate after three months compared to users who don't.

Advanced analytics tools like Amplitude enable the prediction of future user behaviors through Predictive Cohorts. This powerful feature goes beyond traditional cohort analysis by leveraging machine learning algorithms to forecast user actions and behaviors.

By analyzing historical data patterns, Predictive Cohorts allow businesses to anticipate user needs and take preemptive actions, ultimately improving retention, and maximizing customer lifetime value.

Common Applications of Cohort Analysis

Improving Customer Retention - Track cohort behavior over time to find drop-off points, craft targeted retention strategies, and assess retention efforts across segments.

Optimization of Marketing Efforts - Use cohort data to tailor campaigns, allocate budgets effectively, identify top acquisition channels, refine messaging, and improve product promotions.

User Experience Enhancement - Analyze which features drive engagement, implement interface improvements and create personalized experiences for different cohorts.

Finding Growth Opportunities - Spot trends in successful cohorts, find patterns that help you increase conversions, uncover underserved segments and use cohort data to forecast growth and revenue potential.

Calm's 3X their Retention Using Behavioral Cohorts and Amplitude

Calm, a leading mindfulness app, leveraged Amplitude to enhance user retention. They analyzed user behavior data with the cohort feature, comparing the retention rates of users who set reminders against those who didn't. They found that users who set Daily Reminders for meditation had 3x higher retention.

Initially, very few users (less than 1%) used this feature as it was buried in the app's settings. After making Daily Reminders more visible with a prompt, 40% of users set reminders, which boosted overall new user retention.

Is Your Startup Ready to Use Cohort Analysis?

Using this type of analysis can provide relevant insights for startups, but it is important to consider your startup's stage. Cohort analysis is most valuable when you have a stable user base and consistent metrics to analyze.

Here are key indicators to confirm that your startup is ready to leverage cohort analysis:

Clear OKRs

Clear OKRs help guide your analysis and measure success. Common goals include improving retention, reducing churn, or enhancing engagement.

Established KPIs

Have well-defined KPIs that align with your growth goals. Cohort analysis works best when tracking specific metrics like retention or conversion rates.

Sufficient Data Volume

To get meaningful insights, you need enough users to form cohorts. Aim for at least 1,000 active users or several months of consistent data to guarantee statistically significant results.

Data Availability

Ensure you have a proper tracking implementation to access to the data that solves your main questions. Track metrics like retention and churn rates to gain actionable insights into user behavior. At Adasight, we can support you to have consistent tracking.

Defined Customer Journey

You need a clear understanding of your user lifecycle—from acquisition to retention—to create relevant cohorts that align with user experiences.

Data Infrastructure

Ensure you have the tools and systems to collect, store, and analyze user data effectively. A solid data setup makes tracking and generating insights easier.

Resources for Analysis

Cohort analysis requires dedicated time and effort. Make sure you have team members ready to review cohort data and act on the insights.

Team Readiness

Ensure your team has the skills to conduct and interpret cohort analysis. Training or workshops can enhance data analytics skills, ensuring everyone contributes effectively.

Steps to Conduct a Cohort Analysis

1. Define the question you want to uncover:

Your questions should align with the company's core goals, encompassing both product and marketing objectives. It's acceptable to have multiple questions—up to three—as long as you're clear on which ones drive the most value toward reaching the goal. These questions may vary depending on the department requiring the data. Consider inquiries such as:

• Evaluating feature adoption rates and their impact on user retention
• Understanding how different user segments interact with the product
• Assessing the effectiveness of various marketing campaigns on user acquisition and engagement
• Analyzing the correlation between specific product features and customer lifetime value
• Identifying the most effective user onboarding paths for different cohorts
• Optimizing the product roadmap based on cohort behavior and preferences

Bonus:

Applying the 5W framework to cohort analysis can provide beginners with a clear and systematic method to come up with questions about user behavior. By focusing on Who, What, When, Where, and Why, you can better understand the nuances of each cohort, identify actionable insights, and improve your product strategies based on data.

This framework is a useful tool for breaking down the data you need to analyze, here we apply the 5W framework to cohort analysis using a fitness app example:

• Who: Users who signed up
• What: Completed at least 3 workouts per week
• When: During their first 30 days of January 2024
• Where: In the app's "Beginner's Program"
• Why: To understand the impact of the number of workouts completed on monetization

2. Evaluate Which Metrics Are Most Relevant to Answer Your Core Questions

When analyzing product performance, selecting the right metrics is critical for gaining better insights. A simple example to illustrate this step is to start with the question you defined in step one, and then think about the metrics that affect it.

Let's begin with a question example:

• How effective is our new onboarding process in retaining users?

To answer this, you may need supporting metrics that will guide the analysis. Here are some key metrics:

1. Retention Rate: This measures the percentage of users who continue to use the product after a set period. We could measure retention on Day 1, Day 7, and Day 30 to assess whether users are successfully engaging after their initial onboarding experience.
2. Time to Complete Onboarding: This metric will tell us how long it takes for a new user to complete the onboarding process.
3. Activation Rate: Activation rate is a critical metric used to measure user engagement, specifically the percentage of users who complete a defined set of actions that signify they are deriving value from a product or service, like setting up a profile or completing a tutorial.
4. Time to First Key Action: This metric measures the time it takes for a new user to complete a significant action (e.g., making their first purchase or using a core feature). A shorter time to first key action often correlates with higher retention rates.

3. Determine the Time Frame

Selecting the right time frame for your cohort analysis is crucial as it ensures the data you analyze aligns with your marketing cycles, product changes, or external trends. The time frame you choose will directly impact the accuracy and relevance of the insights you gather. Here are some key considerations when determining the time frame:

Align with Marketing Cycles:

If you are analyzing user behavior about marketing efforts, the time frame of your cohort analysis should match the duration of your marketing campaigns. For instance, if you’re running a 3-month email campaign, you’d want to track user engagement and conversion rates over that same 3-month period.

Consider Seasonal Trends:

Seasonality can have a significant impact on user behavior, especially for businesses in industries like retail, travel, or hospitality. Align your cohort analysis with relevant seasonal trends to assess how user behavior fluctuates during high or low-demand periods.

Monitor Short-term vs. Long-term Trends:

To get a complete picture of user behavior, it’s important to choose a time frame that captures both short-term engagement (e.g., Day 1, Week 1) and long-term behavior (e.g., Day 30, Day 90). Short-term trends might reveal initial user reactions to a feature, while long-term trends can indicate whether users continue to find value over time.

4. Choose Your Cohort Type

Your main question will serve as a compass, guiding you to choose the most suitable cohort type and ensuring your analysis aligns with your specific goal. The right cohort type will provide the focused lens to derive actionable insights. Choose wisely among behavioral, demographic, time-based, or acquisition cohorts to uncover the most relevant insights for your analysis.

Then, you can evaluate other relevant factors such as:

Relevance to business goals: Make sure the cohorts you create are directly related to your key performance indicators (KPIs) and strategic objectives.

Data availability and quality: Select cohorts based on data that is readily available and reliable within your analytics ecosystem.

Actionability: Choose cohorts that will lead to actionable insights, allowing you to make improvements in your product or marketing strategies.

Statistical significance: Ensure your cohorts are large enough to provide statistically significant results and avoid making conclusions from small sample sizes.

5. Read Your Cohort Data Strategically

Perform Horizontal Analysis

Analyze each row of the cohort table to track how a single cohort’s behavior evolves. This is critical for observing retention and engagement drops as time progresses. For example, if retention declines rapidly after onboarding, it may signal friction in the product experience.

Perform Vertical Analysis

Analyze the columns to compare different cohorts at the same point in their lifecycle. This helps assess if product changes, like new features or onboarding tweaks, had an impact on user retention or engagement. By comparing cohorts, you can track the success of these initiatives over time.

Perform Diagonal Analysis

Looking diagonally helps identify retention trends across the lifecycle of different cohorts. This will give you insights into whether long-term user retention is improving or declining across cohorts, regardless of their acquisition date.

Focus on Significant Changes

Identify major increases or decreases in metrics like feature adoption rates, conversions, churn or retention for specific cohorts. Tie these changes to product updates or campaigns to understand the underlying causes. This analysis helps refine product decisions based on real user behavior.

Compare Cohorts

Conduct a comparative analysis of cohorts with distinct characteristics or behaviors to extract deeper insights. For example, compare the retention rates of users who engaged with a new feature within their first week versus those who didn't. This granular examination unveils the key drivers of user retention to pinpoint areas for targeted product enhancements.

Comparing cohorts allows product managers to quantify the impact of specific product experiences on user retention and revenue generation. By identifying the features or interactions that correlate with higher retention and monetization you can prioritize product development efforts easier.

From a marketing standpoint, compare cohorts based on different acquisition channels, campaign strategies, or onboarding experiences. For example, analyze if users from a paid ad campaign convert at a higher rate than those from an organic channel. This comparison gives insight into which marketing efforts yield the best ROI.

Amplitude makes cohort comparison simple and intuitive. In this video, Gregor Spielmann, Co-founder of Adasight, walks you through the process step-by-step:

Classify Cohorts by Importance

After conducting cohort analysis, you can enhance your insights by integrating RFM analysis—a customer segmentation technique used in marketing—or by scoring your cohorts with a custom framework tailored to your needs. RFM evaluates and categorizes customers based on three key factors: Recency, Frequency, and Monetary value. This approach helps you pinpoint where to allocate resources and efforts to maximize growth.

For example, if you have monthly acquisition cohorts, evaluate each cohort based on:

• Recency: How recently have members of the cohort interacted with your product?
• Frequency: How often members of the cohort engage or make purchases.
• Monetary Value: The total spending or value contributed by the cohort over a defined period.

By scoring cohorts using RFM metrics, you can identify the high-impact cohorts that contribute the most value to your business. For instance, a cohort acquired during a specific campaign may have a high frequency and monetary value score, indicating that users acquired during that campaign are more loyal and valuable.

Note:

This guide outlines the general steps for cohort analysis, but as it's an extensive topic that can vary depending on the type of analysis you conduct, we recommend exploring more advanced approaches. For a deeper dive, check out "Cohort Analysis: Reduce Churn and Improve Retention" on Amplitude's blog. This article explains how cohort analysis can enhance retention by segmenting users based on shared characteristics or behaviors.

6. Visualize Cohort Progression with Line Charts

Line charts are a powerful tool for visualizing how different groups of users (cohorts) are progressing over time. They can help you understand important metrics like retention rates (how many users continue to use your product) and drop-off trends (when users stop using your product).

By visualizing these metrics on a line chart, you can gain valuable insights into how your users behave and what changes might be happening in their journey.

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How to Use Cohorts In Amplitude?

Define Behavioral Cohorts Based on User Actions:

• Navigate to the Cohorts section in Amplitude.
  • Click on Create New and select Cohort.

• In the cohort definition interface, specify the criteria based on user actions. For example, to create a cohort of users who performed a specific event multiple times within a certain timeframe:
  • Choose the event of interest.
  • Set the condition (e.g., performed the event greater than five times).
  • Define the time frame (e.g., in the last 30 days).
• Add any additional filters or conditions as needed.
• Save the cohort for use in analyses

Use Microscope to Create Cohorts from Specific Data Points in Charts:

• Open a chart (e.g., Event Segmentation) that displays the data point of interest.
• Hover over the specific data point to reveal the Microscope icon.
• Click on the icon to open the Microscope menu.
• Select Create Cohort.
• Define the cohort parameters as prompted.
• Save the cohort for further analysis.

This feature enables you to drill down into specific data points and create cohorts directly from chart insights.

Import Cohorts from CSV Files:

• Navigate to the Cohorts section (Access from the button "Create" in Amplitude's Dashboard)
• Click on Import from CSV.

• Upload a CSV file containing user identifiers (e.g., User IDs or Amplitude IDs), with each ID on a separate line.
• Specify the type of IDs included in the file.
• Complete the import process to create a static cohort.

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The described method is useful for incorporating external user lists into Amplitude for analysis.

How to Use Cohorts in Google Analytics?

1. Access the Cohort Analysis Report

1. Login to Google Analytics:
   • Navigate to your Google Analytics account and select the property you want to analyze.
2. Locate the Cohort Analysis Report:
   • In the left-hand navigation menu, go to Reports > Audience > Cohort Analysis.

2. Understand the Cohort Report Interface

The cohort analysis report is divided into the following components:

• Cohort Type: Defines the grouping criteria (e.g., by acquisition date).
• Cohort Size: Specifies the size of the cohort, such as daily, weekly, or monthly groups.
• Metric: Choose a metric to analyze, such as retention rate, revenue, or session duration.
• Date Range: Defines the timeframe for cohort analysis (e.g., last 7 days, 14 days, or 30 days).

3. Customize the Report

1. Choose a Cohort Type:
   • The default option is "Acquisition Date," which groups users by when they first interacted with your site or app.
2. Set the Cohort Size:
   • Daily cohorts are useful for short-term campaigns.
   • Weekly or monthly cohorts are better for analyzing long-term user trends.
3. Select a Metric:
   • For example, use "User Retention" to evaluate how well you retain users over time or "Goal Completions" to assess conversion trends.
4. Adjust the Date Range:
   • Shorter ranges provide more detailed views, while longer ranges help analyze trends.

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Challenges and Considerations

Potential Biases in Cohort Studies

When conducting cohort analysis, it's crucial to be aware of potential biases that can skew results and lead to misinterpretation. Some common biases to watch out for include:

• Survivorship Bias: This occurs when the analysis focuses only on cohorts that have "survived" or remained active, potentially overlooking valuable insights from churned customers. To mitigate this, ensure your analysis includes data from both active and inactive cohorts.
• Selection Bias: This happens when a method of selecting cohorts introduces a systematic error. For example, if you only analyze cohorts that signed up during promotional periods, you might miss insights about customers who join during non-promotional times.
• Period Bias: External factors specific to certain periods (e.g., seasonal trends, economic conditions) can affect cohort behavior. Be sure to consider these factors when interpreting results across different time-based cohorts.

Overcoming Challenges in Cohort Analysis

Dealing with common obstacles such as fragmented data sources, rapidly changing product features, and unpredictable user behaviors can make cohort analysis challenging. However, as a product manager or marketer, asking the right questions can lay the foundation for transforming data into clear, actionable insights:

Are you using multiple cohort definitions?

Analyzing customers based on different criteria, like acquisition channel, first purchase category, or engagement level, can reveal various patterns and opportunities. Relying on just one cohort type can limit your understanding.

Is your data validated regularly?

Inaccurate data can lead to misleading conclusions. Conduct regular audits of data collection and processing to ensure data quality and completeness.

Are you considering external factors?

Market trends, competitive changes, or major world events can significantly influence customer behavior. Contextualizing your cohort analysis with these factors to better understand shifts in user behavior.

Are you combining quantitative and qualitative data? Quantitative data shows trends, while qualitative insights from customer feedback and interviews provide context. Bridge the gap between what users do and why they do it.

Is your cohort analysis approach up to date?

As your product and market evolve, you must adjust your cohort definitions, metrics, and techniques to stay relevant and ensure your insights are meaningful.

Is there a potential bias in cohort selection?

Choosing cohorts without a clear rationale can introduce bias. Make sure your cohorts are representative of the broader user base and not selected based on convenience.

Are you selecting appropriate time frames for analysis?

Choosing the wrong time frame can skew results. Short time frames may miss long-term trends, while long periods may dilute the relevance of data due to external changes in user behavior or market conditions.

Are you accounting for attribution errors in multi-touch customer journeys?

Attributing conversions or key events to the correct touchpoint can be challenging. Be clear about your attribution model and understand its limitations.

By asking these questions, you can avoid many common pitfalls and relevant angles for cohort analysis.

Conclusion

Cohort analysis is a powerful tool for product managers and marketers to gain deep insights into user behavior, retention, and overall product performance. By grouping users based on shared characteristics or experiences, businesses can identify patterns, trends, and areas for improvement that might otherwise go unnoticed.

The key steps outlined in this guide provide a comprehensive framework for conducting effective cohort analysis. From defining clear objectives and choosing appropriate metrics to selecting cohort types and interpreting data strategically, each step is crucial in extracting actionable insights that drive product development and marketing strategies.

While cohort analysis has challenges, such as potential biases and data interpretation complexities, the benefits far outweigh the difficulties.