  User churn, characterized by customers ending their relationship with a
business, has profound economic consequences across various
Business-to-Customer scenarios. For numerous system-to-user actions, such as
promotional discounts and retention campaigns, predicting potential churners
stands as a primary objective. In volatile sectors like fantasy sports,
unpredictable factors such as international sports events can influence even
regular spending habits. Consequently, while transaction history and
user-product interaction are valuable in predicting churn, they demand deep
domain knowledge and intricate feature engineering. Additionally, feature
development for churn prediction systems can be resource-intensive,
particularly in production settings serving 200m+ users, where inference
pipelines largely focus on feature engineering. This paper conducts an
exhaustive study on predicting user churn using historical data. We aim to
create a model forecasting customer churn likelihood, facilitating businesses
in comprehending attrition trends and formulating effective retention plans.
Our approach treats churn prediction as multivariate time series
classification, demonstrating that combining user activity and deep neural
networks yields remarkable results for churn prediction in complex
business-to-customer contexts.
