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Protecting Recurring Revenue with Renewal-Risk Prediction

Impact Delivered

  • 40% Faster risk detection
  • 25% Churn-risk reduction
  • 30% Campaign efficiency improvement
  • 20% Revenue-leakage reduction
  • The Customer

    The Customer

    A software corporation needing to know in advance if a customer will renew, so teams can take timely actions to retain customers and prevent revenue loss across geos, sales orgs, customer groups, and product lines.

  • The Challenge

    The Challenge

    3D-Priniting-Challenges-Img.jpg
    • Complex, time-consuming manual analytics from legacy warehouses
    • Finance unable to act in time due to slow insights
    • Difficulty scaling use cases and keeping customers loyal
  • The Solution

    The Solution

    the-solution-img_nextiva.jpg

    Enable rapid ML-driven renewal prediction and revenue-risk insights:

    • Build models to flag likely churn in upcoming periods
    • Surface revenue-risk by geo, org, and product segments
    • Feed prioritized actions to marketing/success; unlock new analytics use cases
  • Solution Components

    Solution Components

    Cloud data lake/warehouse; Ingestion & ETL/ELT pipelines; Machine learning platform; Feature store; MLOps (CI/CD, monitoring); BI/analytics layer with CRM/reverse ETL.

  • The Results

    The Results

    • Early interventions for at-risk renewals
    • Targeted retention plays by segment
    • Clear visibility into revenue at risk
    • Foundation for new analytics use cases

Impact Delivered

  • 40% Faster risk detection
  • 25% Churn-risk reduction
  • 30% Campaign efficiency improvement
  • 20% Revenue-leakage reduction

The Customer

A software corporation needing to know in advance if a customer will renew, so teams can take timely actions to retain customers and prevent revenue loss across geos, sales orgs, customer groups, and product lines.

The Challenge

3D-Priniting-Challenges-Img.jpg
  • Complex, time-consuming manual analytics from legacy warehouses
  • Finance unable to act in time due to slow insights
  • Difficulty scaling use cases and keeping customers loyal

The Solution

the-solution-img_nextiva.jpg

Enable rapid ML-driven renewal prediction and revenue-risk insights:

  • Build models to flag likely churn in upcoming periods
  • Surface revenue-risk by geo, org, and product segments
  • Feed prioritized actions to marketing/success; unlock new analytics use cases

Solution Components

Cloud data lake/warehouse; Ingestion & ETL/ELT pipelines; Machine learning platform; Feature store; MLOps (CI/CD, monitoring); BI/analytics layer with CRM/reverse ETL.

The Results

  • Early interventions for at-risk renewals
  • Targeted retention plays by segment
  • Clear visibility into revenue at risk
  • Foundation for new analytics use cases
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