Cross-Sell Optimization
“Which customers should receive a mortgage offer?”
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A real-world example
Which customers should receive a mortgage offer?
Banks spend $50-100 per direct-mail mortgage offer, yet conversion rates on untargeted campaigns hover at 0.5-1.0%. That means $5M-$10M spent on 100K mailers to acquire 500-1,000 loans. Worse, irrelevant offers erode customer trust. The signals for mortgage readiness are scattered across transaction data (rent payments, home-improvement spend, real-estate site visits), life-event indicators (income growth, new savings patterns), and product holdings (checking maturity, investment growth). No single table contains the answer.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo builds a relational graph connecting customers to their transactions, account balances, product holdings, digital behavior, and demographic data. The model learns that Customer C-10042 has rising direct-deposit amounts, increasing savings-account balances, recurring Zillow transactions, and home-improvement spend at Home Depot. These cross-table signals produce a mortgage-propensity score far more accurate than rule-based targeting, lifting conversion rates 3-5x while reducing offer volume by 60%.
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
CUSTOMERS
| customer_id | age | segment | products_held | tenure_years |
|---|---|---|---|---|
| C-10042 | 34 | Mass Affluent | 3 | 4.2 |
| C-10078 | 52 | Premier | 6 | 12.1 |
| C-10115 | 28 | Mass Market | 1 | 1.8 |
TRANSACTIONS
| txn_id | customer_id | merchant_category | amount | timestamp |
|---|---|---|---|---|
| T-001 | C-10042 | Real Estate Services | $350 | 2025-08-12 |
| T-002 | C-10042 | Home Improvement | $2,140 | 2025-08-20 |
| T-003 | C-10078 | Grocery | $187 | 2025-09-01 |
ACCOUNT_BALANCES
| customer_id | account_type | balance | 3mo_trend | snapshot_date |
|---|---|---|---|---|
| C-10042 | Savings | $67,400 | +$8,200 | 2025-09-01 |
| C-10042 | Checking | $12,300 | +$1,100 | 2025-09-01 |
| C-10078 | Investment | $340,000 | +$15,000 | 2025-09-01 |
PRODUCT_HOLDINGS
| customer_id | product | status | open_date |
|---|---|---|---|
| C-10042 | Checking | Active | 2021-06-15 |
| C-10042 | Savings | Active | 2021-06-15 |
| C-10042 | Credit Card | Active | 2022-01-10 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(PRODUCT_HOLDINGS.PRODUCT = 'Mortgage', 0, 6, months) FOR EACH CUSTOMERS.CUSTOMER_ID WHERE PRODUCT_HOLDINGS.PRODUCT != 'Mortgage'
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | NAME | MORTGAGE_PROPENSITY | RANK | RECOMMENDED_ACTION |
|---|---|---|---|---|
| C-10042 | James Whitfield | 0.74 | 1 | Priority Outreach |
| C-10115 | David Park | 0.31 | 2 | Nurture Campaign |
| C-10078 | Maria Gonzalez | 0.08 | 3 | Suppress |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C-10042 (James Whitfield)
Predicted: 74% mortgage propensity in next 6 months
Top contributing features
Real estate service transactions
3 in 60d
28% attribution
Savings account growth trajectory
+$8.2K/3mo
25% attribution
Home improvement spend spike
$4,300 in 90d
21% attribution
Income growth (direct deposit trend)
+12%
15% attribution
Age and tenure fit (34, 4.2 yrs)
High
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 mortgage cross-sell conversion from 0.8% to 3.5% while mailing 60% fewer offers, saving $3M in campaign costs and generating $40M+ in new loan originations per quarter.
Related use cases
Explore more financial services 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.
