Leveraged comprehensive user data analysis to drive personalized experiences and measurable engagement improvements through strategic A/B experimentation.
Analyzed geographic user patterns to identify location-specific app preferences and usage behaviors across global markets.
Examined device types, specifications, and user-declared intent from onboarding to create targeted recommendation strategies.
Deep-dived into app usage frequency, daily activity cycles, and engagement patterns to build comprehensive user profiles.
Over several months, I led a comprehensive analysis of user signals including region, device characteristics, onboarding intent, app usage patterns, and daily activity rhythms. This data mining effort enabled us to identify meaningful patterns that would inform personalized app recommendation campaigns directly within the Windows Start menu.
The project culminated in designing and executing multiple A/B experiments to optimize click-through rates and user engagement, with successful campaigns being scaled to retail audiences across Windows devices globally.
Our goal structure followed a clear hierarchy, focusing on Windows Daily Active Devices (WDAD) as the north star metric while tracking supporting indicators that would provide deeper insight into user behavior and platform health.
The diversification goal ensured we weren't just optimizing for one app, but genuinely expanding the Windows app ecosystem engagement across our user base.
Targeted percentage increase in Windows Daily Active Devices, measuring overall platform engagement improvement.
Track number of app instances launched from Start menu.
Achieve +1 app increase in the number of Windows Active Apps, expanding user engagement breadth.
Help users rediscover previously installed apps that have fallen into disuse, particularly region-specific popular applications.
If we recommend (x app) to users in (y region), Then we will see x% increase in WDAD (Windows Daily Active Devices), Because users will find a relevant app they had previously installed right at their fingertips.
Existing devices with specific app installed, haven't launched the app in 28 days, located in specific region.
Utilized internal targeting system combined with internal experiment framework to ensure precise user segmentation and reliable scorecards.
Implemented experimental design with control group receiving standard experience and treatment group seeing personalized recommendations.
Help users discover new apps that they might like based on users similar to them.
If we recommend (x app) to users (given a specific criteria), Then we will see x% increase in WAA (Windows Active Apps), Because other users (with a similar criteria) are highly engaged with (x app), we will help users find a new and relevant app.
Utilized internal targeting system combined with internal experiment framework to ensure precise user segmentation and reliable scorecards.
Implemented experimental design with control group receiving standard experience and treatment group seeing personalized recommendations.
Windows Start Menu Personalization & A/B Testing