Machine Learning Times
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7 months ago
Predicting Long-Term User Engagement from Short-Term Behavior

Originally published in Insight, Nov 17, 2021.

User engagement is one of many crucial elements to study and understand deeply for both well-established companies and nascent startups throughout the development of a product. A product’s health can be broadly measured from the delicate balance between new user adoption, engagement, retention, and churn, all of which are closely related. With the fierce competition of the mobile app market today, many developers are vying for the attention of large populations of smartphone users around the world. It is not uncommon for the average person to use an app only a handful of times before quitting once the initial novelty wears off, leaving companies with the very tough problem of finding new ways to increase the level of engagement with their product.

As an Insight Fellow, I partnered with a company that provides a mobile app payment service allowing its users to make simple cash transfers to one another. In addition to its peer-to-peer social transaction features akin to Venmo or Cash App, one of its unique core functionalities is the ability for users to create ‘Pools’ in which many users across the platform may contribute to a central cash pot. These collections can be used for any general purpose: casual outings, event planning, roommate rent payments, betting pools, charities, etc. The platform is seeing a monthly growth in registered users of 2–3% with over 390K current registered users. About 38% (150k) of those users have been active in the last year and 8% (31k) within the last month.

A problem that the company wanted to address was how to derive insights from data on already engaged users to identify any common behavior patterns that can be leveraged to promote the same level of engagement in new users.

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