{"id":83433,"date":"2024-12-26T10:00:00","date_gmt":"2024-12-26T10:00:00","guid":{"rendered":"https:\/\/www.prodpad.com\/?post_type=pp_glossary&p=83433"},"modified":"2024-12-20T15:49:20","modified_gmt":"2024-12-20T15:49:20","slug":"cohort-analysis","status":"publish","type":"pp_glossary","link":"https:\/\/www.prodpad.com\/glossary\/cohort-analysis\/","title":{"rendered":"Cohort Analysis"},"content":{"rendered":"\n
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What is cohort analysis?<\/h2>\n\n\n\n

Cohort analysis is a method for evaluating user interactions with your product by grouping users into distinct cohorts based on shared characteristics and then tracking their behavior over time. By examining these groups, you can uncover how user behavior evolves over time, allowing you to identify problem areas and opportunities across the user journey and conversion funnel.<\/p>\n\n\n\n

A cohort is simply a group of users who share a common trait, often tied to the time when they completed an action. For example, you might group users based on the month they first signed up for your product. <\/p>\n\n\n\n

This is similar to how you\u2019d divide school classes based on the year they started. One cohort for the Class of 1996, another for the Class of 1997.<\/p>\n\n\n\n

This time-based grouping enables you to analyze behavioral patterns and trends that develop over specific periods, offering valuable insights into the effectiveness of your onboarding, feature releases, or other major product changes.<\/p>\n\n\n\n

Cohort analysis is like a time-lapse of your product data, revealing how user groups behave and adapt across their lifecycle. For instance, you might discover that users who signed up in January have lower retention rates than those who signed up in February. With this insight, you can investigate what might have impacted these behaviors (perhaps a feature change or a tweak to your onboarding process) and take corrective action.<\/p>\n\n\n\n

It\u2019s important to distinguish cohort analysis from segmentation, as the two are often confused. Segmentation groups users based on shared characteristics, such as their role or geographic location, but it typically provides a static snapshot of user behavior. For example, segment analysis might help you understand how certain stakeholders are using your product at a given moment in time. While useful for personalization or targeting, it doesn\u2019t provide insights into how behaviors change over time.<\/p>\n\n\n\n

Cohort analysis, on the other hand, adds a time dimension to segmentation. By slicing segments into time-based cohorts, you can observe how specific user groups respond to changes or progress through the customer journey. For instance, analyzing acquisition cohorts – who are users grouped by their signup date – can highlight shifts in user retention following a major product update or a new onboarding flow.<\/p>\n\n\n\n

This dynamic view of your user base is invaluable for uncovering actionable insights. It allows you to understand how behaviors evolve, pinpoint challenges, and identify what\u2019s driving success or failure in specific groups. In short, cohort analysis is a powerful tool for tracking user behavior over time, helping you make data-driven decisions to improve your product and keep users engaged.<\/p>\n\n\n\n

A quick word on customer cohort analysis<\/h3>\n\n\n\n

When going about your day as a Product Manager, you may often see cohort analysis described as customer cohort analysis. If you\u2019re going to be a stickler for the rules, you need to be aware that this isn\u2019t correct. These two terms shouldn\u2019t be used interchangeably, as there are subtle differences between the two. <\/p>\n\n\n\n

Cohort analysis is a general approach to grouping your users into a time-based cohort. Here, EVERYONE who uses your product is eligible to be grouped into different cohorts. This means people who are on a trial, reverse trial<\/a>, freemium users, and long-term customers. If they\u2019ve adopted your product, they\u2019re included in cohort analysis.<\/p>\n\n\n\n

Customer cohort analysis is essentially a filter, where you have a more specified look at just the users who have become customers. This means people who are generating revenue for you and users who have been activated. In everyday talk – these are actual paying customers. <\/p>\n\n\n\n

Customer cohort analysis helps Product Teams<\/a> learn how revenue-driving customers are using their product over their lifetime, and from those learnings, they can work out what to do to make the product better. <\/p>\n\n\n\n

It\u2019s important to know the difference here, as the actions and behaviors of customers are going to be very different from all your other users. You\u2019re going to be learning different things depending on what form of cohort analysis you\u2019re doing. It\u2019s like one is monitoring the behaviors of all the bugs in your backyard, while the other is just looking at butterflies. <\/p>\n\n\n\n

Got the difference? Sweet, let\u2019s crack on.<\/p>\n\n\n\n

Why use cohort analysis?<\/h2>\n\n\n\n

As a Product Manager, few things sting more than customer churn<\/a>. It\u2019s never nice to watch the users you\u2019ve worked so hard to acquire and nurture start to drop off. Cohort analysis is one method that gives you the chance to learn why this is happening and how you might fix it. It\u2019s your detective toolkit for getting answers. <\/p>\n\n\n\n

By grouping users based on shared traits or behaviors – like when they signed up or how they interacted with a feature – you can pinpoint where engagement drops off. Is the onboarding process letting you down? Is a specific feature failing to deliver on the value proposition<\/a>? Cohort analysis reveals the weak links in your product experience.<\/p>\n\n\n\n

But cohort analysis does more than simply spot problems, it can also show you opportunities. By dividing users by their actions and measuring retention over time, you\u2019ll be able to see the stickiest areas of your product and the specific actions users are taking that are leading to long-term loyalty.

Cohort analysis is particularly useful when you launch a new feature, make changes to a user flow, alter the UI – or any other significant changes that you want to understand the impact of. For example, you can compare pre-change cohorts who signed up without the new experience to post-change cohorts and analyze differences in behavior. This comparison can provide valuable feedback on the success of your updates and guide further improvements.<\/p>\n\n\n\n

With this data in hand, you\u2019ll be able to make targeted improvements to get more users to perform this certain action, improving the performance of your product. <\/p>\n\n\n\n

If the thought of customer churn is keeping you up at night, cohort analysis can be that nightlight that helps you dream sweetly. <\/p>\n\n\n\n

Benefits of cohort analysis<\/h3>\n\n\n\n

To fully understand why you should give cohort analysis a go, let\u2019s explore some of the key benefits of this analysis framework. Cohort analysis lets you dig deep, giving you the insight to make smart, data-driven Product Management<\/a> decisions. Here\u2019s why it can be a game-changer:<\/p>\n\n\n\n