How to Reduce Customer Churn with AI Voice of Customer
The Churn Reduction Protocol connects usage, support, and sentiment data using multi-model adversarial verification — low-confidence churn signals are refused, not shown.
Customers don't just leave. They give off warning signs for weeks—but those signs are scattered across different tools.
The Churn Reduction Protocol connects usage, support, and sentiment data using multi-model adversarial verification — low-confidence churn signals are refused, not shown.
Here is how LoomPin turns scattered signals into one confident, human-approved decision.
Compound signals, not a single survey
The insight
Modeling Probability: Compound Signal (Usage + Export + Champion) = Elevated Churn Risk
The recommended action
Recommend "VIP Rescue" sequence for approval. Alert CSM via Slack. Draft retention proposal.
A human approves before anything fires.
Target Outcome: 33% churn reduction. $2.1M ARR preserved.
LoomPin is in pilot with design partners. Target outcomes describe what the workflow is designed to achieve, not measured client results — we publish real outcome data only once it is measured by our outcomes ledger.