Next Best Action
“What product should this customer see next?”
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A real-world example
What product should this customer see next?
Banks send the same promotional offers to broad segments, resulting in 1-2% engagement rates on digital banners and email campaigns. A large retail bank running 40+ campaigns per quarter found that 78% of customers received irrelevant offers, leading to opt-out rates above 15%. The challenge is that the right action depends on a customer's full context: recent transactions, life stage, product gaps, service interactions, and real-time digital behavior. Rule-based decisioning engines cannot weigh thousands of signals across dozens of tables in real time.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo builds a relational graph connecting each customer to their transactions, product holdings, service interactions, life events, and digital behavior. The model learns that Customer C-10042 just received a raise (higher direct deposits), started a family (baby-related spend), and has no life insurance. Instead of a generic credit-card upgrade offer, Kumo ranks 'term life insurance consultation' as the highest-propensity action. The NBA model re-scores daily as new transaction and event data flows in.
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 | life_stage | products_held | segment |
|---|---|---|---|---|
| C-10042 | 34 | New Parent | 3 | Mass Affluent |
| C-10078 | 52 | Pre-Retirement | 6 | Premier |
| C-10115 | 28 | Early Career | 1 | Mass Market |
TRANSACTIONS
| customer_id | merchant_category | amount | frequency_30d | timestamp |
|---|---|---|---|---|
| C-10042 | Baby & Kids | $420 | 8 | 2025-09-10 |
| C-10042 | Daycare | $2,100 | 1 | 2025-09-01 |
| C-10078 | Travel | $3,800 | 3 | 2025-09-05 |
PRODUCT_CATALOG
| product_id | product_name | category | eligibility_segment |
|---|---|---|---|
| P-101 | Term Life Insurance | Protection | Mass Affluent+ |
| P-102 | 529 College Savings | Investing | All |
| P-103 | Travel Rewards Card | Cards | Premier |
INTERACTION_HISTORY
| customer_id | channel | offer_shown | outcome | timestamp |
|---|---|---|---|---|
| C-10042 | Credit Card Upgrade | Ignored | 2025-08-15 | |
| C-10042 | App | Savings Rate Boost | Clicked | 2025-08-20 |
| C-10078 | Branch | Wealth Review | Accepted | 2025-09-02 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT ARGMAX(INTERACTION_HISTORY.OUTCOME = 'Accepted') FOR EACH CUSTOMERS.CUSTOMER_ID WHERE CUSTOMERS.SEGMENT IN ('Mass Affluent', 'Premier')
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | RECOMMENDED_ACTION | PROPENSITY | CHANNEL | PRIORITY |
|---|---|---|---|---|
| C-10042 | Term Life Insurance Consult | 0.72 | Mobile App | 1 |
| C-10042 | 529 College Savings | 0.58 | 2 | |
| C-10078 | Travel Rewards Card Upgrade | 0.81 | Branch | 1 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C-10042 (James Whitfield)
Predicted: Term Life Insurance Consultation (72% propensity)
Top contributing features
Life-stage signal (baby-related spend)
8 txns/30d
30% attribution
Income growth (direct deposit increase)
+18%
22% attribution
Protection product gap
No insurance
20% attribution
Prior offer response pattern
Savings clicked
16% attribution
Peer cohort adoption rate
34% of similar
12% 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 customer engagement rates from 1.5% to 6.2% by matching the right product to each customer's life context, generating $25-50M in incremental annual revenue.
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.
