Accurate recruitment has been an age-old problem in the world of quantitative research. Think about the last seven days: How many hours did you spend watching Netflix? What is the total amount of money that you spent buying groceries, both offline and online? How many days ago did you last open an app that you do not open every day? The human mind falters in recalling the details of actions taken today, let alone in the last week or month. Nonetheless, traditional recruitment still relies heavily on the user's claim of her category and product usage, purchase history, etc., which leads to inaccurate targeting. An error in recruitment can lead to a much higher gap between the derived insights and the truth. This problem is accentuated even further for mobile-first brands where 'micro-segments' are based on not one but multiple parameters, like purchase history, wallet size, dormancy, product category, etc. In cases where the client's database is used to connect with users accurately (through email/SMS/call), the outcome ends up being inefficient in terms of scale, investments and/or timelines. Can technology really help in accurately recruiting users on the basis of all these parameters with zero margin of error, and complement the insights collected through claimed research at scale, as well as in a cost-efficient manner? This paper demonstrates how a mobile-first brand and its research partner came together to solve the micro-segment recruitment of its app users, in order to solve the core problem of user retention.