Application of

Stream Mining

for Churn

Prediction

David Manzano Macho, Ericsson Research

Ricard Gavaldà, Universitat Politècnica de Catalunya

February 2012

Ericsson Internal | 2012-01-27 | Page 2

Churn prediction

› Churning = customers discontinuing a service or leaving a

company during a specified period

› It is more difficult to get a customer than to retain it

› If we can predict that a customer will churn, we can take action to retain him/her

Ericsson Internal | 2012-01-27 | Page 3

WHY Stream mining?

› Patterns and reasons for churning change over time, often

abruptly and unpredictably. High volatility.

› Traditional data mining techniques require human intervention. Adaption to changes is slow.

› Stream mining techniques detect and adapt to time

immediately, and autonomously.

Show the potential of stream mining techniques in churn prediction scenarios Able to keep prediction rules updated at all times for fast reaction to changes

Ericsson Internal | 2012-01-27 | Page 4

The PoC

› Based on simulated data generated by a synthetic data generator. Events:

– Subscriber joins company

– Calls from or to a subscriber

– Subscriber complains / calls customer service

– Bill emitted for subscriber

– Subscriber churns (leaves company)

› Applies Adaptive Hoeffding trees algorithm to learn the

classifier

Ericsson Internal | 2012-01-27 | Page 5

The PoCThe simulation

User sets (for simulation):– Number of subscribers

– Various parameters describing their probabilistic behavior & churn propensity

– Cost and effectiveness of retention actions

System tracks & displays:– Event statistics, churn rates, prediction accuracy

– Business edge if actions taken on (predicted) churners

– Profiles of subscribers most likely to churn

When user changes a parameter (concept drift), the system compares old vs. adapting model performance

run the demo

Ericsson Internal | 2012-01-27 | Page 7

Conclusion

Stream mining techniques for quickly and autonomously reacting to changes in the data.

Contrast with traditional mining techniques:› Requires human (analyst) intervention to rebuild models› Much higher adaptation time

Other scenarios where potentially applicable› Mobile advertising› Electronic commerce› Energy management› Transportation and mobility› …