Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network

TL;DR

This paper develops machine learning models to predict user lifetime in an anonymous location-based social network, leveraging community-specific and countrywide data, with a focus on practical binary classification for churn prediction.

Contribution

It introduces a novel approach to predict user lifetime in disjoint communities within a location-based social network using off-the-shelf machine learning techniques, including a practical binary classifier.

Findings

Random Forest achieved strong predictive performance.

Countrywide models generalize well across communities.

Binary classification effectively predicts user churn.

Abstract

In this work, we predict the user lifetime within the anonymous and location-based social network Jodel in the Kingdom of Saudi Arabia. Jodel's location-based nature yields to the establishment of disjoint communities country-wide and enables for the first time the study of user lifetime in the case of a large set of disjoint communities. A user's lifetime is an important measurement for evaluating and steering customer bases as it can be leveraged to predict churn and possibly apply suitable methods to circumvent potential user losses. We train and test off the shelf machine learning techniques with 5-fold crossvalidation to predict user lifetime as a regression and classification problem; identifying the Random Forest to provide very strong results. Discussing model complexity and quality trade-offs, we also dive deep into a time-dependent feature subset analysis, which does not work very well; Easing up the classification problem into a binary decision (lifetime longer than timespan $x$) enables a practical lifetime predictor with very good performance. We identify implicit similarities across community models according to strong correlations in feature importance. A single countrywide model generalizes the problem and works equally well for any tested community; the overall model internally works similar to others also indicated by its feature importances.