Case Study: Proactive Churn Prediction Transforms Customer Success

The Challenge

Our client’s Global Customer Success division needed to improve their ability to identify high-risk customers for their Customer Success Managers (CSM). Their rule-based churn prediction approach was yielding below-industry benchmarks and couldn’t support the proactive, data-driven strategy required for customer retention.

Key challenges included:

• Low predictive accuracy with existing rules-based model

• Siloed customer data across systems

• Limited scalability for growth

• Inadequate foresight into churn risk

The Solution: CloudEQS + Snowflake Snowpark

CloudEQS implemented a scalable machine-learning solution within Snowflake’s Snowpark platform. Including:

• Centralized Customer Data – Unified customer entitlements, product telemetry, support cases, CSAT/NPS scores, product defects, renewal insights, and sentiment—all within Snowflake.

• Advanced ML Engineering – Leveraged Snowpark’s Feature Store to power:

  • ML-based classification models

  • Continuous learning for dynamic model updates

  • Combined algorithm selection

  • Hyperparameter optimization for accuracy

• Scalable Implementation – Delivered an end-to-end solution—from data ingestion to ML modeling—within a modern data architecture.