App developers are constantly looking for methods and strategies to identify valuable users as early as possible.
AdLibertas customer and frequent contributor Geoff Hladik shares how he’s leveraging day-1 user time to identify and predict future valuable users.
Marketing performance articles tend to focus on apps focused on IAP and subscription revenue. Apps that have a meaningful amount of revenue earned by in-app advertising face different sets of challenges. We aim to add clarity and provide feedback on some successful methods for managing campaigns on ad-supported apps.
Many of our customers tap the AdLibertas platform to build custom KPIs to monitor their app’s performance, and the one constant we see is an absolute variety of metrics that app developers use to track their app’s performance.
Since one of the early questions we get with new customers is “which KPIs would you recommend” we thought it important to write up some of the findings we’ve seen, taught to us by our customers.
“First-party data” often gets a bad rap, and has become just another synonym for data onboarding and lookalike audience targeting. This means the term now comes with plenty of baggage, often including hugely negative connotations around shady data marketplaces and invasions of user privacy.
But what does “first-party” really mean?
When onboarding new customers, one of the most common questions we get is “how reliable are predicted LTVs? Put another way, this becomes the single biggest question that every marketer faces: how confident should I be in the projected success of my campaigns?
We share some of our expertise in how you can shortcut gaining confidence in your predicted LTVs by relying on historical predictions.