Overview
Learn how CloudEQS implemented a machine learning model in Snowflake to drive $6.6M in revenue insights and 4x improvement in churn prediction.
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.
Project Highlights:
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.
The Results
Now, the client’s Customer Success and Renewal teams now proactively identify at-risk accounts up to four quarters in advance, enabling timely and strategic engagement.
Key Metrics:
- 85% accuracy in identifying churn risk accounts
- 61% accuracy in identifying down-sell risk accounts
- $6.6m in potential down-sell revenue identified
- 4x improvement in churn prediction capabilities
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