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Lowering Churn with Customer Attrition Prediction

Impact Delivered

  • 18% Churn rate reduction
  • 25% Higher retention-campaign response
  • 30% Earlier risk detection
  • 15% Reactivation of dormant customers
  • The Customer

    The Customer

    A national retail bank (“Customer Bank”) with large savings and multi-product portfolios. Service and analytics teams needed actionable drivers—not just probabilities—to proactively retain at-risk customers and extend the approach beyond savings accounts.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg

    The incumbent model predicted likelihood of attrition but lacked explanatory drivers and coverage across products. Without clear indicators, support teams struggled to prompt and address churn intent; engagement was largely reactive and inbound.

  • The Solution

    The Solution

    the-solution-img_nextiva.jpg

    Model attrition and expose reasons that teams can act on:

    • Detect patterns such as multi-month declines in card transactions and reduced engagement
    • Engineer comprehensive features across demographic, geographic, behavioral, and service indicators
    • Identify “soft attrition” (inactive/dormant) and surface root-cause drivers for targeted, product-specific retention plays
  • Solution Components

    Solution Components

    Demographic/behavioral features; Geographic & service indicators; Attrition-driver analytics; Intervention playbooks

  • The Results

    The Results

    • Lower attrition across product lines
    • Re-activation of soft-churn customers
    • Driver-led retention tactics for service and marketing
    • Improved lifetime-value potential

Impact Delivered

  • 18% Churn rate reduction
  • 25% Higher retention-campaign response
  • 30% Earlier risk detection
  • 15% Reactivation of dormant customers

The Customer

A national retail bank (“Customer Bank”) with large savings and multi-product portfolios. Service and analytics teams needed actionable drivers—not just probabilities—to proactively retain at-risk customers and extend the approach beyond savings accounts.

The Challenge

3D-Priniting-Challenges-Img.jpg

The incumbent model predicted likelihood of attrition but lacked explanatory drivers and coverage across products. Without clear indicators, support teams struggled to prompt and address churn intent; engagement was largely reactive and inbound.

The Solution

the-solution-img_nextiva.jpg

Model attrition and expose reasons that teams can act on:

  • Detect patterns such as multi-month declines in card transactions and reduced engagement
  • Engineer comprehensive features across demographic, geographic, behavioral, and service indicators
  • Identify “soft attrition” (inactive/dormant) and surface root-cause drivers for targeted, product-specific retention plays

Solution Components

Demographic/behavioral features; Geographic & service indicators; Attrition-driver analytics; Intervention playbooks

The Results

  • Lower attrition across product lines
  • Re-activation of soft-churn customers
  • Driver-led retention tactics for service and marketing
  • Improved lifetime-value potential
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