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USE CASE

Customer Churn

Objective

  • Predict customer churn based on behavioral patterns and cadence of transactions

  • Identify leading indicators for churn, improving customer retention insights

Outcome

  • Optimized models up to the granularity of each customer

  • Calculated daily probability for customer churn for early detection

  • Distributed in-memory infrastructure trains and scores entire customer base in minutes instead of hours

  • Reduced model building time from 6-7 hours to < 30 min

Business Value

●    Intercepted customer churn before it happened

●    Know what leads a client towards the decision to leave the company.

●    Develop loyalty programs and retention campaigns to keep as many customers as possible.

●    Improving the customer retention rate for existing customers by just 5 percent can improve a company’s profitability by 25 to 95 percent (Bain & Co).

H2O's AI and Data Approaches

  • Identify customers with high probability of churning by creating models and custom recipes specifically built for generating features/variables. 

  • Distributed algorithms (Random Forest, GBM)

  • Capabilities to iterate through models with different parameters within time constraints, instead of relying on just one model

Customer Churn Customer Churn
  • H2O Customer Churn Demo AI App, built with H2O Wave App:
      • To interact with the Customer Churn app, you must log in with your H2O Cloud Account. If you do not have an account, you can sign up for a 90-day free trial and create your account today. This app demonstrates the use of machine learning as part of an overall plan to minimize employee attrition. Users can view predictions of employee departure, forecast churn rates, and identify relevant factors contained in employee data.

Resources

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