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Runtastic pro







Runtastic is happy with the performance of this new segment targeting, especially finding that there are no overlaps with any of the current segments they target. This means this campaign is on par with the best performing targeted campaigns that Runtastic has launched so far. Overall, the LAL-LTV performance is more or less the same as the LAL-PREM performance (based on the mentioned main KPIs). Because of this, they’ve been able to acquire more subscribers, which are more important for them in the long-term than simple users (installers) and registrants. Working with Audience Builder in conjunction with Facebook’s new targeting feature has added a lot of value to the Runtastic campaigns - they’ve been able to quickly pull seed lists and upload these seamlessly into Facebook.

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When Runtastic evaluated the CPP of the LAL-LTV campaign against the average of all other target segments that ran during the same period, it saw that the CPP was a whopping - 61.63% cheaper! Since there was no significant overlap found, the test proceeded as planned. So, before launching the LAL-LTV segment, the overlap between the segments were checked through Facebook, to avoid competing segments. Due to similarity of these segments, the LAL-PREM segment was taken as a benchmark to compare against the LAL-LTV targeting. The LAL-LTV (lookalikes of lifetime value users / value-based targeting) segment is in theory and by logic highly similar to the Runtastic LAL-PREM segment, which are lookalike of all users who are active or who have a Premium Membership (with or without a renewal). Runtastic tracks all of its subscription revenue events with Adjust so it was able to segment the users based on the purchase type. With Audience Builder, Runtastic was able to easily pull the complex user data needed, without having to request information from other teams. Unfortunately, it required the work of different internal stakeholders to create an audience with user level LTV. Runtastic was eager to test out Facebook’s new value-based lookalikes feature.









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