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Game Developer Meets ROAS Goals With Over 95% Accurate LTV Predictions
A large casual mobile game developer in the U.S. reached out to AlgoLift by Vungle to help them better predict lifetime value (LTV). The developer had a D7 ROAS model that used a historical multiplier between D7/D365 to measure the ongoing performance of their user acquisition campaigns. The developer wanted a more sophisticated and accurate methodology for measuring returns of their UA spend with a goal to hit their D365 payback goals.
AlgoLift by Vungle introduced their Premium Intelligence solution to help the game developer build finely-tuned LTV predictions. By building tighter user-level LTV predictions, the studio was able to generate more accurate bids to meet their D365 payback window for a stronger ROAS performance.
In order to train the LTV model, the developer shared their anonymous app-specific attribution, revenue, and engagement data. In total, AlgoLift by Vungle’s model ingested 40 different in-app engagement events to improve prediction accuracy. This data was incorporated into AlgoLift by Vungle’s proprietary model to project expected revenue from both non-paying users, which represented a significant portion of the game developer’s future revenue at D7, and paying users.
The AlgoLift by Vungle models combine machine learning techniques which do the heavy lifting to find complex patterns and relationships between user demographic information, engagement and purchase behavior, and value. In addition, AlgoLift by Vungle properly upheld and ensured privacy standards for the game developer by anonymizing all of the data.
After adopting AlgoLift by Vungle’s Premium Intelligence solution, the game developer was able to accurately predict a 60-day LTV window on a daily basis with over 95% accuracy. This was based on data collected across 60 days (July 25 to September 8) and 1.4 million installs with a revenue contribution of 76% from IAP and 24% from serving ads. Thanks to AlgoLift by Vungle’s Premium Intelligence, the game studio was able to evaluate 60-day payback with a high level of accuracy, maximizing ROAS as a result.
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