Churn Analysis in a Music Streaming Service : Predicting and understanding retention

Sammanfattning: Churn analysis can be understood as a problem of predicting and understanding abandonment of use of a product or service. Different industries ranging from entertainment to financial investment, and cloud providers make use of digital platforms where their users access their product offerings. Usage often leads to behavioural trails being left behind. These trails can then be mined to understand them better, improve the product or service, and to predict churn. In this thesis, we perform churn analysis on a reallife data set from a music streaming service, Spotify AB, with different signals, ranging from activity, to financial, temporal, and performance indicators. We compare logistic regression, random forest, along with neural networks for the task of churn prediction, and in addition to that, a fourth approach combining random forests with neural networks is proposed and evaluated. Then, a meta-heuristic technique is applied over the data set to extract Association Rules that describe quantified relationships between predictors and churn. We relate these findings to observed patterns in aggregate level data, finding probable explanations to how specific product features and user behaviours lead to churn or activation. For churn prediction, we found that all three non-linear methods performed better than logistic regression, suggesting the limitation of linear models for our use case. Our proposed enhanced random forest model performed mildly better than conventional random forest.