Cross-Sell Optimization
“Which policyholders should receive a bundling offer?”
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A real-world example
Which policyholders should receive a bundling offer?
Multi-line policyholders have 90% retention rates vs 70% for single-line (J.D. Power), yet only 35-40% of personal-lines customers bundle. Each additional line adds $800-$1,500 in annual premium and reduces churn risk by 15-20 percentage points. Insurers spend $50-100 per outbound sales contact, but untargeted campaigns convert at just 2-4%. The signals for bundling readiness are spread across policy records, claims history, life events, service interactions, and competitive pricing data. A targeted approach could double conversion rates while halving contact volume.
How KumoRFM solves this
Relational intelligence built for insurance data
Kumo connects policyholders to their coverage portfolio, life-event signals, billing patterns, service interactions, and market context. The model identifies that Policyholder PH-6601 (home-only) just purchased a new vehicle (DMV record match), has been searching for auto insurance quotes (digital signals), and has a low-loss-ratio profile that would qualify for a significant multi-policy discount. These cross-table signals produce a bundling-propensity score, ranking the right offer (auto + umbrella) for each household.
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 | current_lines | total_premium | tenure_years |
|---|---|---|---|---|
| PH-6601 | Jennifer Adams | Home Only | $2,100 | 6.4 |
| PH-6604 | Carlos Reyes | Auto Only | $1,400 | 3.2 |
| PH-6603 | Diana Lee | Home + Auto + Umbrella | $5,400 | 11.2 |
LIFE_EVENT_SIGNALS
| policyholder_id | event | confidence | detected_date |
|---|---|---|---|
| PH-6601 | New Vehicle Purchase | High | 2025-09-05 |
| PH-6604 | Home Purchase | Medium | 2025-09-12 |
| PH-6603 | None Detected | N/A | N/A |
QUOTE_ACTIVITY
| policyholder_id | line_quoted | competitor_quotes | last_quote_date |
|---|---|---|---|
| PH-6601 | Auto | 2 | 2025-09-10 |
| PH-6604 | Home | 1 | 2025-09-14 |
| PH-6603 | None | 0 | N/A |
DISCOUNT_ELIGIBILITY
| policyholder_id | multi_line_discount | loyalty_discount | claims_free_discount |
|---|---|---|---|
| PH-6601 | 15% if adds auto | 5% (5+ years) | 10% (0 claims) |
| PH-6604 | 12% if adds home | None | 5% (1 small claim) |
| PH-6603 | Already applied | 8% (10+ years) | 10% (0 claims) |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(POLICYHOLDERS.LINES_ADDED > 0, 0, 90, days) FOR EACH POLICYHOLDERS.POLICYHOLDER_ID WHERE POLICYHOLDERS.CURRENT_LINES != 'Home + Auto + Umbrella'
Prediction output
Every entity gets a score, updated continuously
| POLICYHOLDER_ID | CURRENT_LINES | BUNDLE_PROPENSITY | RECOMMENDED_LINE | EST_PREMIUM_ADD |
|---|---|---|---|---|
| PH-6601 | Home Only | 0.81 | Auto + Umbrella | +$2,200 |
| PH-6604 | Auto Only | 0.54 | Home | +$1,800 |
| PH-6603 | H+A+U (full) | N/A | Already Bundled | $0 |
Understand why
Every prediction includes feature attributions — no black boxes
Policyholder PH-6601 (Jennifer Adams, Home Only)
Predicted: 81% bundling propensity (Auto + Umbrella)
Top contributing features
New vehicle purchase detected
Sept 2025
30% attribution
Active auto insurance shopping
2 competitor quotes
26% attribution
Multi-line discount opportunity
15% savings
19% attribution
Strong retention profile (low loss ratio)
0.28
14% attribution
Tenure and loyalty discount eligible
6.4 yrs, 5%
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: Increase multi-policy households by 15-25% and reduce churn by 15-20 points per converted household, generating $80-150M in incremental annual premium for a top-10 personal-lines 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.
