Features that most strongly increase churn probability.
Values represent the average percentage point increase in risk (+15.0 means this
feature adds 15% to the risk probability).
Features that most strongly prevent churn.
Values represent the average percentage point reduction in risk.
Strategic Overview of Churn Risk & Revenue Impact
Revenue leakage due to customer churn has the potential to erode annual growth. Retention efforts are currently reactive rather than proactive, leading to loss of high-value customers who show early warning signs of dissatisfaction.
Analysis performed on policyholder dataset, utilizing historical behavioral data, claims history, and policy interactions. Data quality is high with coverage of key demographics and policy variables. This can be converted to a database for future analysis. And constant updates can be made to the model and predictions.
We classify customers into three distinct tiers:
Immediate Action: Shift focus from specific "Save Teams" to equipping front-line agents with "Likely Churn" flags.
Strategy: Implement a "Value Review" call 60 days pre-renewal for all policies >$500 in the Likely Churn bucket.
Model assumes historical behavior predicts future outcomes. External market shocks (competitor price wars) are not modeled. "Reason for churn" is inferred from data patterns, not explicit customer feedback.
Strategic Guidance for Sales Leadership
Who to target? Focus immediately on "Likely Churn" (46-85% risk) customers with high annual premiums.
Rationale: These customers are undecided. "Certain Churn" (>85%) are often too costly to save. "Likely Churn" offers the highest ROI on intervention.
| Lever (Effort) | Impact | Action |
|---|---|---|
| Policy Review (Med) | Right-size coverage to reduce premium bloat without losing the policy. | |
| Discounts (Low) | Apply discretionary "Loyalty Discount" if NCD > 5 years. | |
| Payment Plans (Low) | Offer switch to interest-free monthly payments if affordability is the friction point. |