ZEPTO CASE STUDY

PROCESS

To acquire new users for Zepto we used our proprietary Machine Learning technology that helps brands connect with consumers with a full-funnel solution that propels growth with the right audience. We applied user-level targeting based on machine- learning algorithms, real-time data, and aggregated data from our DMP to run an effective UA campaign. Focusing on user profiles, rather than placements, allowed us to help online delivery services companies such as Zepto in this case, to acquire the most relevant users for their app. When we started running the UA campaign for Zepto, our initial approach was intent-based targeting — targeting users that showed direct interest in the multiple relevant Delivery apps like Dunzo, Swiggy Instamart, and more. This approach allowed us to acquire significantly higher LTV users. Then, our machine learning models gradually learned the patterns of the users that were most engaged from the day of their installation, which allowed us to target similar users and eventually resulted in beating the defined ROAS KPIs.