Product Madness (an Aristocrat co.) on Predicting 1st-Day Churn in Real Time
Product Madness’ Head of Data Science discusses how technology and new work processes can help the gaming and mobile app industries predict and mitigate 1st-day (or D0) user churn in real time — down to minutes and seconds using modern streaming data architectures such as KAPPA. Also, we explore feature engineering improvements to the RFM (Recency, Frequency, and Monetary) churn prediction framework: The Discrete Wavelet Transform (DWT).
In this session you will learn:
• Why first-day churn is a critical challenge for gaming and mobile app companies worldwide
• Why retaining users is much easier than acquiring new users, and why it is so important to understand churn intent specifically on the first day
• What kind of data should be taken into account when building a time series model for first-day churn prediction
• How to run predictive churn analysis with real-time AI using behavioral data
• The key role of MLOps (machine learning operations) in cutting time to market and slashing costs of building AI applications that predict and mitigate first-day churn in real time
This post has been republished by AIIA. To view the original article, please click HERE.