Customer Defection Prediction Model

A Customer Defection Prediction Model is a binary predictive model that predicts customer defections (customer churn).

• AKA: Churn Model.
• Context:
  • It can predict Customer Defection through machine learning algorithms.
  • It can evaluate Customer Risk through behavioral pattern analysis.
  • It can identify Flight Risk Signals through statistical analysis.
  • It can detect Churn Patterns through historical data analysis.
  • It can measure Defection Probability through predictive scoring.
  • ...
  • It can (often) segment Customer Groups through risk level classification.
  • It can (often) generate Early Warning Alerts through threshold monitoring.
  • It can (often) track Customer Engagement through interaction analysis.
  • It can (often) analyze Usage Patterns through temporal analysis.
  • ...
  • It can range from being a Simple Logistic Model to being an Advanced Neural Network, depending on its model complexity.
  • It can range from being a Monthly Batch Predictor to being a Real-Time Risk Monitor, depending on its prediction frequency.
  • ...
  • It can integrate with Customer Database Systems for historical data access.
  • It can connect to Marketing Automation Systems for intervention triggering.
  • It can support Customer Service Platforms for proactive outreach.
  • ...
• Example(s):
  • Subscription Service Models, such as:
    • PSN+ Defection Model for gaming subscriptions.
    • Netflix Churn Predictor for streaming services.
    • Spotify Retention Model for music subscriptions.
  • Telecom Service Models, such as:
    • Mobile Service Churn Model for cellular services.
    • Broadband Defection Predictor for internet services.
  • Financial Service Models, such as:
    • Credit Card Attrition Model for banking services.
    • Insurance Lapse Predictor for insurance services.
  • ...
• Counter-Example(s):
  • Customer Acquisition Model, which predicts new customer likelihood rather than defection risk.
  • Customer Reclaiming Model, which focuses on churned customer recovery rather than churn prevention.
  • Customer Value Model, which predicts customer lifetime value rather than defection probability.
  • Customer Segmentation Model, which groups customer types without defection prediction.
• See: Customer Profile, Customer Retention Task, Churn Analysis Model, Predictive Analytics System, Customer Behavior Model.

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References

2015

• http://predictionimpact.com/retention-with-predictive-analytics.html
  • QUOTE: How much higher would your revenue climb if you could predict which customers are likely to churn? Predictive analytics targets customer retention campaigns to ensure bottom-line ROI. By predicting which customers are at risk for defection, campaign dollars are applied effectively -- without predictive targeting, a retention campaign may cost more than it gains.

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