Cohort analysis: churn and retention by cohorts

If you have watched the movie "Social Network" (if you don't, highly recommend it) you may remember one of the key scenes in the film – the moment when Mark Zuckerberg comes up with the idea of creating "Relationship Statuses", which later leads to the creation of the "In a relationship", "Dating", "Married", etc. for Facebook users.

This moment in the film can be seen as an example of using cohort analysis. Mark Zuckerberg came to the idea that structuring users into certain "relationships" can make the user experience more interesting and engaging. Mark understands that grouping users by their "relationships" creates cohorts – groups of people with common characteristics. This allows you to more precisely customize content, advertising, and functionality to the interests and needs of each cohort of users.

As you can see understanding cohort analysis provides many advantages that allow a deeper and more systematic analysis of the behavior of customers or users in different periods.

Introduction

In a nutshell, cohort analysis is a data analysis technique that involves grouping individuals with shared characteristics or experiences into cohorts and tracking their behavior over time. It provides insights into how different groups of customers or users interact with a product, service, or platform, allowing businesses to understand trends, patterns, and changes in customer behavior.

The primary purpose of cohort analysis is to uncover actionable insights about customer segments' engagement, retention, and churn rates. All this will be discussed in detail in this topic with SQL examples.

Understanding cohorts

Cohorts are groups of individuals who share a common characteristic or experience during a specific period. For instance, a cohort could be customers who made their first purchase in the same month. By segmenting customers into cohorts, businesses gain insights into how different groups interact with their products or services over time.

Suppose an e–commerce company wants to analyze the purchasing behavior of customers who joined during different months. This can reveal whether there are any trends or differences in spending patterns between cohorts.

A cohort can be divided into segments. This process involves dividing a group of customers or users into smaller parts based on shared characteristics or behaviors. This segmentation helps businesses gain a deeper understanding of customer behavior and preferences over time. There are several types of cohort segmentation and their insights that data analysts can gain:

Calculating retention rate

The two most important indicators in cohort analysis are Retention Rate and Churn Rate. Let's talk about them in more detail.

Retention rate measures the percentage of customers who continue to engage with a product or service over a specific period. It's a key indicator of customer loyalty and satisfaction.

Let's say a subscription–based streaming service wants to calculate the retention rate of customers who signed up in January. The retention rate for February would be the percentage of January customers who continued their subscriptions in February.

SELECT
    cohort_month,
    COUNT(DISTINCT customer_id) AS initial_customers,
    COUNT(DISTINCT CASE WHEN MONTH(subscription_end_date) = cohort_month + 1 THEN customer_id END) AS retained_customers,
    (COUNT(DISTINCT CASE WHEN MONTH(subscription_end_date) = cohort_month + 1 THEN customer_id END) / COUNT(DISTINCT customer_id)) AS retention_rate
FROM subscriptions
GROUP BY cohort_month;

Step–by–step guide to calculating retention rates using SQL queries.

Calculating retention rates using SQL queries involves analyzing how many customers return in subsequent periods after their initial interaction with a product or service. Here's a step–by–step guide to calculating retention rates using SQL queries:

Assumption:

• You have a table named user_activity with columns user_id and activity_date.

Step 1: Calculate the initial period for each user Identify the period when each user first interacted with your product or service.

SELECT
    user_id,
    MIN(activity_date) AS initial_date
FROM
    user_activity
GROUP BY
    user_id;

Step 2: Calculate returning users in subsequent periods Determine the count of returning users who interacted after their initial period.

SELECT
    ua.user_id,
    ua.initial_date,
    COUNT(DISTINCT ua2.activity_date) AS returning_count
FROM
    (SELECT
        user_id,
        MIN(activity_date) AS initial_date
    FROM
        user_activity
    GROUP BY
        user_id) ua
LEFT JOIN
    user_activity ua2 ON ua.user_id = ua2.user_id
    AND ua2.activity_date > ua.initial_date
GROUP BY
    ua.user_id, ua.initial_date;

Step 3: Calculate retention rate Compute the retention rate for each period.

SELECT
    initial_date,
    COUNT(DISTINCT user_id) AS initial_users,
    SUM(returning_count) AS returning_users,
    COUNT(DISTINCT user_id) / (COUNT(DISTINCT user_id) + SUM(returning_count)) AS retention_rate
FROM
    (SELECT
        ua.user_id,
        ua.initial_date,
        COUNT(DISTINCT ua2.activity_date) AS returning_count
    FROM
        (SELECT
            user_id,
            MIN(activity_date) AS initial_date
        FROM
            user_activity
        GROUP BY
            user_id) ua
    LEFT JOIN
        user_activity ua2 ON ua.user_id = ua2.user_id
        AND ua2.activity_date > ua.initial_date
    GROUP BY
        ua.user_id, ua.initial_date) retention_data
GROUP BY
    initial_date
ORDER BY
    initial_date;

This query will give you the retention rates for each initial period, helping you understand how many users continue to interact with your product or service over time.

Steps to analyze retention rate trends and factors:

1. Visualize retention rate trends: Create visualizations (line charts, bar charts) of retention rates over time (initial periods). Observe trends, spikes, or drops in retention rates. Look for patterns that might indicate changes in user behavior.

2. Segment analysis: Segment your data based on different attributes like acquisition channel, user type, or product category. Calculate retention rates for each segment separately. Compare retention rates among segments to identify differences.

3. Time–based trends: Analyze retention rates based on the time when users first interacted. Do users acquired during holidays have different retention rates than those acquired during regular days?

4. Cohort Analysis: Group users based on their initial period and analyze how their retention rates evolve over time. Compare the behavior of different cohorts.

5. User Behavior Patterns: Identify actions that returning users commonly take. Are there specific behaviors correlated with higher retention rates? This can guide you in enhancing user engagement.

6. Correlation with Features: Analyze if users who interact with specific features have better retention rates. Determine if certain features positively impact user engagement and retention.

7. Communication Impact: Investigate whether users who receive regular communication (emails, notifications) exhibit improved retention rates. Assess the effectiveness of engagement campaigns.

8. Product Updates: Study whether retention rates change after introducing updates. Determine if positive changes lead to better retention.

9. Experimentation: Implement strategies targeting factors identified in the analysis. Monitor retention rates to evaluate the impact of changes. Iterate based on results.

10. Customer Feedback: Collect qualitative feedback to understand why users may churn or stay. This complements quantitative analysis by providing insights into user sentiment.

Analyzing churn rate

Another vital indicator is the churn rate. It represents the percentage of customers who stop using a product or service over a specific period. Understanding churn is crucial for identifying areas of improvement and retaining valuable customers.

Let's suppose that a mobile app company wants to calculate the churn rate for users who signed up in the first quarter of the year. The churn rate for the second quarter would be the percentage of users who stopped using the app in the second quarter.

SELECT
    cohort_quarter,
    COUNT(DISTINCT customer_id) AS initial_users,
    COUNT(DISTINCT CASE WHEN MONTH(last_activity_date) = cohort_quarter + 3 THEN customer_id END) AS churned_users,
    (COUNT(DISTINCT CASE WHEN MONTH(last_activity_date) = cohort_quarter + 3 THEN customer_id END) / COUNT(DISTINCT customer_id)) AS churn_rate
FROM user_activity
GROUP BY cohort_quarter;

Let's calculate churn rates for different cohorts:

Assumption:

• You have a table named user_activity with columns user_id, activity_date, and churn_date

Step 1: Calculate cohort churn rates. Let's calculate the churn rate for each cohort based on their initial interaction period.

SELECT
    initial_date,
    COUNT(DISTINCT user_id) AS initial_users,
    SUM(CASE WHEN churn_date IS NOT NULL THEN 1 ELSE 0 END) AS churned_users,
    SUM(CASE WHEN churn_date IS NOT NULL THEN 1 ELSE 0 END) / COUNT(DISTINCT user_id) AS churn_rate
FROM
    (SELECT
        user_id,
        MIN(activity_date) AS initial_date
    FROM
        user_activity
    GROUP BY
        user_id) ua
LEFT JOIN
    user_activity ua2 ON ua.user_id = ua2.user_id
GROUP BY
    ua.initial_date
ORDER BY
    ua.initial_date;

Understanding high churn rates and strategies to reduce churn:

High churn rate causes:

1. Lack of engagement: Users who do not actively engage with your product or service are more likely to churn.

2. Poor user experience: If users find it challenging to navigate or use your product, they might churn.

3. Competitive alternatives: The availability of similar products with better features can lead to higher churn rates.

4. Unmet expectations: If your product does not meet user expectations, they might seek alternatives.

5. Pricing concerns: High prices relative to perceived value can lead to churn.

6. Lack of value: If users do not perceive value in continuing to use your product, they might churn.

7. Lack of personalization: Users appreciate personalized experiences. Generic interactions can lead to churn.

8. Communication gaps: Inadequate communication or lack of relevant updates can cause churn.

9. Technical issues: Frequent technical glitches can frustrate users and contribute to churn.

10. Changing circumstances: Changes in users' needs or circumstances can lead to churn.

Strategies to reduce churn:

1. Improve onboarding: Provide a smooth and guided onboarding experience to help users understand your product.

2. Enhance engagement: Offer features that encourage regular interaction and provide value to users.

3. Personalization: Tailor experiences based on user behavior and preferences to increase engagement.

4. Communication: Regularly update users with relevant information, new features, and tips.

5. Customer support: Offer responsive and effective customer support to address user concerns.

6. Feedback loop: Collect user feedback and use it to make improvements.

7. Re–engagement campaigns: Design campaigns to re–engage inactive users and remind them of your product's value.

8. Segmentation: Segment users based on behavior and demographics for targeted approaches.

9. Competitive analysis: Understand why users choose competitors. Adapt based on insights.

10. Value proposition: Clearly communicate the unique value your product offers.

Interpreting cohort analysis results

Interpreting cohort analysis results is crucial for optimizing marketing and product strategies. Let's break down how to interpret cohort analysis results, extract actionable insights, and provide real-world examples of businesses leveraging cohort analysis:

Interpreting cohort analysis results:

1. Understanding Cohort Behavior: Analyze how different cohorts (groups of users who share a common characteristic) behave over time. Track their engagement, retention, and conversion rates.

2. Comparing Retention Rates: Compare the retention rates of cohorts. Identify cohorts with higher or lower retention rates compared to the average. This reveals if certain groups of users are more likely to stay engaged over time.

3. Identifying Trends: Observe patterns in cohort behavior. Look for trends like early high engagement followed by a drop, indicating potential issues in the onboarding process.

4. Segmentation Impact: Analyze if segmentation by demographic, location, or behavior affects cohort behavior differently. This helps tailor strategies for specific user groups.

5. Product Adoption: Determine if cohorts from different periods have similar adoption rates for new features or updates. This shows if certain cohorts are quicker to adopt changes.

Extracting Actionable Insights:

1. Optimizing Onboarding: If you notice cohorts with low engagement after the initial period, focus on improving the onboarding experience to drive better retention.

2. Feature Enhancement: Identify cohorts that show high engagement with specific features. These guides feature prioritization and improvements.

3. Targeted Marketing: Tailor marketing campaigns based on cohort behavior. For example, if a cohort has high churn after a specific period, create re–engagement campaigns.

4. User Education: If some cohorts exhibit low product adoption, create educational content or tutorials to help them understand the value better.

5. Customer Support: Use cohort insights to identify cohorts that tend to contact customer support frequently. Address their pain points to improve satisfaction.

Cohort analysis empowers businesses to make data–driven decisions. By understanding cohort behavior and extracting actionable insights, companies can optimize their strategies, enhance user experience, and drive sustainable growth.

Conclusion

In conclusion, cohort analysis stands as a guiding light, illuminating the path toward a deeper understanding of customer interactions, loyalty, and attrition. Through the journey of this topic, we've embarked on a voyage of discovery, unveiling the power and potential that cohort analysis holds. Here are some of the main takeaways:

• What is cohort analysis and its purpose: a strategic tool that enables organizations to unlock patterns hidden within customer data. With churn and retention rates being the main metrics;

• Cohort segmentation: creation of meaningful groups based on shared characteristics or periods;

• Churn and retention rate calculation with SQL queries.

Now, that we have got acquainted with cohort analysis – we are ready to apply our new knowledge.