Early Churn Prediction and Personalised Interventions in Top Eleven game
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Transcript of Early Churn Prediction and Personalised Interventions in Top Eleven game
About me
Data engineer at Nordeus.
Main focus: Predictive machine learning pipelines; Their deployment and maintenance.
About Nordeus
● Award winning gaming company
● Offices in London, Dublin, San Francisco, Skopje and Belgrade
● Creators of Top Eleven
Agenda
IDENTIFY- Predict early churners using machine
learning
TARGET- Construct a personalized message for every
churner
INTERVENE- Send messages via a scalable notification
system
Models● Best model -
Gradient Boosting Trees
● Model we use - Logistic Regression
● Model to play with - Recurrent Neural Network
What kind of notifications work well on what type of user?
How to find the perfect match?
Use the user’s first day activity to create meaningful personalized notifications.
Targeting
Our results
● On average, baseline group achieved 30% better retention
● Test group achieved 40% better retention than baseline
Notification engine
Specs:● Modular design● Scalable● On time delivery● Near real time message creation
● Logging system● A/B test support
Conclusion
● Everyone can do early churn prediction
● Target with care
● When in doubt, Apache Spark