Why badi chose Adjust: Leveraging user-level data for granular visibility CASE STUDY: BADIFinding the right place to live can be one of the most stressful processes. Carlos Pierre, saw a big need for matching roommates and apartments simultaneously, and consequently created an app to meet this demand. Not long after its 2015 launch, badi has experienced a rapid growth, and needed to build a larger, more sophisticated data infrastructure with an attribution partner. Receiving good quality raw data in real-time was critical because badi optimized its marketing strategy with in-app events rather than installs. At the time, badi used a solution that didn’t allow user-level insight, preventing the company from analyzing behavior and improving the user journey. A mobile-first bus

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