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

6. run the demo

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