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Stream analytics for churn prediction from Ericsson Research
1. Application of
Stream Mining
for Churn
Prediction
David Manzano Macho, Ericsson Research
Ricard Gavaldà, Universitat Politècnica de Catalunya
February 2012
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 2
3. WHY Stream mining?
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
› 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.
Ericsson Internal | 2012-01-27 | Page 3
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 4
5. The PoC
The 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
Ericsson Internal | 2012-01-27 | Page 5
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
›…
Ericsson Internal | 2012-01-27 | Page 7
