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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