Policyholder Churn Prediction
“Which policyholders will not renew?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which policyholders will not renew?
P&C insurers face 10-15% annual non-renewal rates, with acquisition costs of $400-$600 per new policyholder (J.D. Power). For an insurer with 5M policyholders, a 12% churn rate means 600K lost customers and $240-360M in replacement acquisition costs annually. Worse, profitable low-risk policyholders are the most likely to leave because competitors aggressively poach them with lower rates. The signals of impending non-renewal are scattered: rate-shopping behavior (quote requests from competitors), claim dissatisfaction, premium increase reactions, and life changes (moving, marriage, new vehicle) that trigger a review of coverage.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects policyholders to their policy details, claims history, billing patterns, service interactions, rate changes, and competitive market data. The model identifies that Policyholder PH-6601 received a 12% rate increase, called customer service twice with billing questions, and lives in a zip code where a competitor just launched an aggressive acquisition campaign. These signals predict non-renewal 60-90 days before the renewal date, giving retention teams time to offer proactive rate adjustments or coverage enhancements to keep profitable customers.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
POLICYHOLDERS
| policyholder_id | name | policy_type | premium | tenure_years | loss_ratio |
|---|---|---|---|---|---|
| PH-6601 | Jennifer Adams | Home + Auto | $3,200 | 6.4 | 0.28 |
| PH-6602 | Mark Stevens | Auto Only | $1,800 | 2.1 | 0.65 |
| PH-6603 | Diana Lee | Home + Auto + Umbrella | $5,400 | 11.2 | 0.15 |
RATE_CHANGES
| policyholder_id | effective_date | old_premium | new_premium | pct_change |
|---|---|---|---|---|
| PH-6601 | 2025-07-01 | $2,860 | $3,200 | +11.9% |
| PH-6602 | 2025-08-01 | $1,720 | $1,800 | +4.7% |
| PH-6603 | 2025-06-01 | $5,200 | $5,400 | +3.8% |
SERVICE_INTERACTIONS
| policyholder_id | channel | type | sentiment | timestamp |
|---|---|---|---|---|
| PH-6601 | Phone | Billing Question | Negative | 2025-09-05 |
| PH-6601 | Phone | Coverage Question | Neutral | 2025-09-12 |
| PH-6603 | App | Document Request | Positive | 2025-09-10 |
COMPETITIVE_MARKET
| zip_code | competitor | campaign_type | avg_savings_offered | start_date |
|---|---|---|---|---|
| 90210 | Geico | Conquest | $400-$600 | 2025-08-15 |
| 90210 | Progressive | Switch & Save | $300-$500 | 2025-09-01 |
| 10001 | None Active | N/A | N/A | N/A |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(POLICYHOLDERS.STATUS = 'non_renewed', 0, 90, days) FOR EACH POLICYHOLDERS.POLICYHOLDER_ID WHERE POLICYHOLDERS.STATUS = 'active'
Prediction output
Every entity gets a score, updated continuously
| POLICYHOLDER_ID | PREMIUM | CHURN_PROB | LOSS_RATIO | RETENTION_ACTION |
|---|---|---|---|---|
| PH-6601 | $3,200 | 0.74 | 0.28 | Proactive Rate Review |
| PH-6602 | $1,800 | 0.31 | 0.65 | Standard Renewal |
| PH-6603 | $5,400 | 0.08 | 0.15 | Loyalty Reward |
Understand why
Every prediction includes feature attributions — no black boxes
Policyholder PH-6601 (Jennifer Adams)
Predicted: 74% probability of non-renewal
Top contributing features
Rate increase magnitude
+11.9%
28% attribution
Competitor conquest campaigns in zip
2 active
24% attribution
Negative service interactions
2 calls, 1 negative
21% attribution
Low loss ratio (attractive to competitors)
0.28
16% attribution
No bundling discount applied
Missing auto bundle
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Retain 25-35% of at-risk profitable policyholders with targeted rate adjustments, saving $60-120M in annual acquisition costs for a 5M-policyholder insurer.
Related use cases
Explore more insurance use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
