Adaptive Ad Load Optimizes Revenue and User Experience in Sponsored Search.

Sponsored search platforms face a critical challenge in balancing revenue generation from ads with maintaining a positive user experience. This study investigates the impact of varying ad loads, from one to six sponsored slots, on an Android app store. Findings indicate that while higher ad loads can increase revenue by up to 43%, they also risk reducing total search conversions by 5% and daily engagement by 2.2%. The research highlights that the optimal ad load is not uniform; it varies significantly based on query characteristics, such as the ad conversion rate for specific queries and the presence of brand advertisers. To address this complexity, the authors developed and deployed an adaptive algorithm called e-LAAL. In a large-scale production deployment involving millions of users and searches, e-LAAL demonstrated superior performance compared to static benchmarks. It effectively improved the revenue-conversion trade-off, outperforming both uniform and historically-dependent static ad-load policies by dynamically optimizing ad placements.