Application ofStream Miningfor ChurnPredictionDavid Manzano Macho, Ericsson ResearchRicard Gavaldà, Universitat Politècnic...
Churn prediction› Churning = customers discontinuing a service or leaving a  company during a specified period› It is more...
WHY Stream mining?Show the potential of stream mining techniques in churn prediction scenariosAble to keep prediction rule...
The PoC› Based on simulated data generated by a synthetic data  generator. Events:       – Subscriber joins company       ...
The PoCThe simulationUser sets (for simulation):       – Number of subscribers       – Various parameters describing their...
run the demo
ConclusionStream mining techniques for quickly and autonomously reacting to changes in the data.Contrast with traditional ...
Stream analytics for churn prediction from Ericsson Research
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Stream analytics for churn prediction from Ericsson Research

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Stream analytics for churn prediction from Ericsson Research

  1. 1. Application ofStream Miningfor ChurnPredictionDavid Manzano Macho, Ericsson ResearchRicard Gavaldà, Universitat Politècnica de CatalunyaFebruary 2012
  2. 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/herEricsson Internal | 2012-01-27 | Page 2
  3. 3. WHY Stream mining?Show the potential of stream mining techniques in churn prediction scenariosAble 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. 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 classifierEricsson Internal | 2012-01-27 | Page 4
  5. 5. The PoCThe simulationUser sets (for simulation): – Number of subscribers – Various parameters describing their probabilistic behavior & churn propensity – Cost and effectiveness of retention actionsSystem tracks & displays: – Event statistics, churn rates, prediction accuracy – Business edge if actions taken on (predicted) churners – Profiles of subscribers most likely to churnWhen user changes a parameter (concept drift), the system compares old vs. adapting model performanceEricsson Internal | 2012-01-27 | Page 5
  6. 6. run the demo
  7. 7. ConclusionStream 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 timeOther scenarios where potentially applicable› Mobile advertising› Electronic commerce› Energy management› Transportation and mobility›…Ericsson Internal | 2012-01-27 | Page 7
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