Cohort analytics: An overview

Cohort analytics tracks user behavior over time, providing detailed insights. You can observe how different user groups interact with your product. This helps identify trends and patterns.

Tailoring experiences to specific user groups becomes easier. By understanding distinct behaviors, you can customize features. This ensures each user group has a better experience.

Identifying areas where users face friction is crucial. Cohort analytics pinpoints these trouble spots. You can then address issues promptly, improving overall user satisfaction.

Documentation to get you started with implementation can be beneficial. Additionally, you can explore REST API and SDKs in more than 20 frameworks to enhance your analytics. For a deeper understanding, check out How Statsig Works.

How to conduct cohort analysis

Step-by-step process

Frame the question: First, decide what you want to discover. This step sets the direction for your analysis.

Define criteria: Establish the parameters to identify your cohort. Clear criteria ensure accurate segmentation. For example, you can learn more about cohort metrics and how they can be used to analyze user behavior.

Create the cohort: Use analytics tools to segment users based on your criteria. This step groups users with similar traits. You can refer to guides for creating a metric source and assignment source.

Analyze results: Interpret the segmented data to uncover insights. This helps in understanding user behavior. It is crucial to read the results and ensure everything looks reasonable. You may also want to see how to scope to specific cohorts for a more detailed analysis.

For more detailed instructions on setting up and analyzing cohorts, you can explore the Statsig documentation.

Examples of cohort analytics

Power Users: Identify users who made multiple purchases recently. Analyze their behavior to understand what drives repeat buying. This helps in tailoring features to keep them engaged. For instance, by understanding Cohort Metrics, you can track how these users behave over time, providing insights into their purchasing patterns. Additionally, using tools like Customer Journey Management can help map out their interactions across various touchpoints. Behavioral Targeting can also be crucial in personalizing experiences for power users based on their past behaviors.

Inactive Users: Study users who haven't logged in for a set period. Understand the reasons behind their drop-off. This insight helps in crafting re-engagement strategies. For instance, Retention Charts can help visualize the rate at which users disengage over time, highlighting critical periods where users tend to drop off. By analyzing Churn Rate, you can quantify the proportion of users who have stopped using your product, allowing you to develop targeted re-engagement campaigns. Furthermore, understanding the Monthly Active Users (MAU) metric can help gauge the overall health of your user base and identify trends over time.

Recent Upgrades: Examine users who upgraded their plan. Look for patterns in their new usage. This can guide future feature development and marketing efforts. By analyzing Cohort Metrics, you can identify the specific triggers that led to the upgrade. Additionally, tracking Conversion Rate Optimization (CRO) helps in understanding how to increase the percentage of users who perform the desired upgrade action. Lastly, using insights from Enterprise Analytics can provide a comprehensive view of how different features impact user behavior post-upgrade.