Complex Cohort Segments use case

A major hotel chain targets multiple customer groups for promotions and tracks against performance. In order 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, they are able to define just the right cohort groupings with multiple metrics and segments to identify underperforming customers groups in order to target them with promotions and deals to increase bookings.

App Version Adoption use case

A large insurance company drives a lot of 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 our 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 segments that drive large portions of revenue for their business. Each segment has specific products designed and created with the segment 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 segment’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, they were able to analyze loyal customer segments and find out which travelers were repeat purchasers month-over-month. They were then able to target these travelers with rewards and perks for their loyalty. Additionally, by switching the Cohort type from retention to churn, they were also able to identify which customers were not repeat purchasers month-over-month and target those segments with promotions in order to re-engage with them and ensure they remain loyal customers in the future.

Analytics