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Executive's Guide to AI-First Churn Reduction: From Fighting Fires to Surgical Churn Prevention

This guide provides executives with a blueprint to:

• Transform reactive, resource-intensive customer retention efforts into proactive, targeted interventions using ML-based prediction
• Identify at-risk customers 6-9 months before traditional warning signs appear
• Efficiently action risk consistently without overwhelming resources
• Measure score performance, revenue impact, and ROI
• Improve gross retention by 5-10 percentage points while reducing operating costs

Braze Customer Case Study

Most customer scoring tools give you a "score"—Reef AI gives you a plan.

Braze’s data science team was stretched thin. Retention was critical. Time was short. The answer? Partnering with Reef AI for predictive churn insights 6–9 months ahead of renewals.

• Risk identified up to 9 months in advance
• Explainable recommendations, not black-box scores
• Fast to implement
• $5M+ in ARR opportunity identified

See how they transformed churn from a reaction into a revenue growth engine.

Organizational Playbook

The Organizational Playbook provides a step-by-step playbook with instructions on how to prepare, build, operationalize, and measure ROI on an ML churn prediction model.

1. Organize essential customer data

2. Benchmark current performance

3. Create an ML-based churn prediction model to isolate customers likely to churn

4. Backtest and validate model performance

5. Introduce your model with a PR campaign to establish trust

6. Deliver recommendations and mandate acknowledgement

7. Measure improvement, learn, and adapt