Cohort analysis use cases

Last update: 2024-08-14
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This article describes some use case examples for Cohort Analysis.

App engagement use case

Suppose that you want to analyze how users who install your app engage with it over time. Do they install it and never use it? Do they use it for a while, then fall away? Or do they remain engaged over time?

You can create a six-month Cohort Analysis:

Granularity: Monthly, from January 2015 through June 2015.

Inclusion metric: App Installs.

Return metric: Sessions or Launches

Persons do not count as engaged in subsequent months, unless they are having a session or at least launching the app. Cohort Analysis would then show you patterns in usage where App Install always occurs in Month 0. You might notice that usage dips in Month 2, regardless of when users installed the app. (For those users who installed the app in January 2015, Month 2 is March 2015. For those persons who installed the app in February 2015, Month 2 is April 2015, and so on.) This analysis allows you to send an email or a push message to all users during the second month after they install the app to remind them to use the app.

Subscription use case

You work at Adobe.com and offer a free Creative Cloud subscription. The goal is for users to upgrade from the free version to the 30-day trial version or, ultimately, the paid version.

Granularity: Monthly

Inclusion metric: Download Link

Return metric: Purchase Paid Creative Cloud

Using Cohort Analysis, you could see, for example, that anywhere between 8% and 10% of free Creative Cloud users upgrade in the first month after installation. Regardless of when the users installed. 12-15% upgrade in the second month of use. After that, upgrading significantly drops off: 4-5% in month three, 3-4% in month four, and 1-2% in month five.

You do not want to lose potential customers in month three. So, you set up an email campaign designed to go out in the middle of the third month to a sample of users. Offering a $50 coupon to users who have not yet upgraded.

Check back with your cohort analysis reports a few months later. For cohorts formed after the launch of the campaign, conversion to paid Creative Cloud subscriptions in month three has risen from 4-5% to 13-14%. This rise in conversion results in hundreds of thousands of dollars per cohort, for every monthly cohort that hits month three from that point forward.

Complex Cohort filters use case

A major hotel chain targets multiple customer groups for promotions and tracks against performance. To identify the best groups of user cohorts to target, they want to create very specific cohort groups. Using the augmented Inclusion and Return Criteria within Cohort Tables, the hotel chain is able to define just the right cohort groupings with multiple metrics and filters. So, the hotel chain can identify underperforming customer groups to target customers with promotions and deals to increase bookings.

App Version Adoption use case

A large insurance company drives customer engagement through the use of its mobile app. However, as new features are added to their app, it is critical that their customers upgrade to the latest app version. They can analyze and compare all of their app versions side-by-side using Custom Dimension Cohort to see which customers on which app version to target. Additionally, they can track both retention and churn to see if specific app versions are driving customers away from using the app over time. Through mobile messaging efforts, they can re-engage with these users to get them to upgrade to the latest version to take advantage of their latest features.

Campaign Stickiness use case

A multinational media company uses targeted campaigns to drive users to their various platforms to drive engagement. Ad spend per platform is based off customer engagement and retention; therefore, successful campaigns are critical to the success of their business. They use the new Custom Dimension Cohort feature in Cohort Tables to compare various campaigns side-by-side to identify which campaigns are most effective at acquiring and retaining users to increase engagement. They can then identify which aspects make a campaign successful and apply it to other campaigns to increase engagement across their various platform.

Product Launch use case

A large apparel retailer has many specific customer filters that drive large portions of revenue for their business. Each filter has specific products designed and created with the filter in mind. With each product launch, they want to know how the new product has boosted sales to various cohorts over time. Using the new Latency Table setting in Cohort Analysis, they are able to analyze a given customer filter’s pre-launch and post-launch behavior and revenue. Using this information, they can identify which products are driving new revenue and which are not gaining traction with customers.

Individual Stickiness - Most Loyal Users use case

A major airline derives the majority of their success and revenue from repeat and loyal customers. In many cases, their loyal travelers comprise the majority of their revenue and retaining those customers is critical to their long-term success. Identifying their most loyal and consistent customers can often be difficult. However, using the new Rolling Calculation setting in Cohort Analysis, the airline is able to analyze loyal customer filters and find out which travelers were repeat purchasers month-over-month. The airline is also able to target these travelers with rewards and perks for their loyalty. Additionally, by switching the Cohort type from retention to churn, the airline is able to identify which customers are not repeat purchasers month-over-month and target those customers with promotions. So, the airline can re-engage with these customers and ensure they remain loyal customers in the future.

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