Next Best Offer
“For each customer, which offer will they most likely redeem in the next 14 days?”
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A real-world example
For each customer, which offer will they most likely redeem in the next 14 days?
Marketing teams blast the same offers to broad segments. A premium customer who always buys full-price gets a 20% discount they never needed, destroying margin. A price-sensitive customer gets a free-shipping offer when they actually respond to percentage discounts. Mis-targeted offers cost retailers $15-30M annually in unnecessary discounting while leaving redemption rates below 5%. The right offer to the right customer at the right time changes everything.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo ranks offers for each customer by learning from the full redemption-purchase-customer graph. It captures that Customer C001 redeems category-specific discounts but ignores free-shipping offers, while customers in C001's graph neighborhood respond to bundled deals. The model simultaneously considers offer fatigue, purchase recency, tier-based behavior, and cross-customer redemption patterns to produce ranked offer recommendations.
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 | name | tier | signup_date |
|---|---|---|---|
| C001 | Sarah Chen | Gold | 2023-06-15 |
| C002 | James Wilson | Silver | 2024-02-10 |
| C003 | Maria Rodriguez | Platinum | 2022-04-22 |
OFFERS
| offer_id | offer_name | category | discount_pct |
|---|---|---|---|
| OFF01 | Spring Footwear 20% | Footwear | 20 |
| OFF02 | Free Shipping Weekend | All | 0 |
| OFF03 | Bundle & Save 15% | Outdoor | 15 |
REDEMPTIONS
| redemption_id | customer_id | offer_id | revenue | timestamp |
|---|---|---|---|---|
| R001 | C001 | OFF01 | 143.99 | 2025-01-28 |
| R002 | C002 | OFF02 | 67.50 | 2025-02-05 |
| R003 | C003 | OFF03 | 289.00 | 2025-02-12 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(REDEMPTIONS.OFFER_ID, 0, 14, days) RANK TOP 5 FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| C001 | OFF01 | 0.89 | 2025-03-12 |
| C001 | OFF03 | 0.72 | 2025-03-12 |
| C002 | OFF02 | 0.81 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C001 (Sarah Chen, Gold tier)
Predicted: Will redeem OFF01 (Spring Footwear 20%) — score 0.89
Top contributing features
Category-specific redemption history
4 of 5 past redemptions were Footwear
33% attribution
Days since last Footwear purchase
42 days (replenishment cycle)
24% attribution
Graph neighbors' offer affinity
78% of similar Gold members redeemed
21% attribution
Discount sensitivity score
0.71 (responds to 15%+ discounts)
14% attribution
Offer fatigue index
0.12 (low — hasn't been over-contacted)
8% 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: 25-50% lift in offer redemption rates while reducing unnecessary discounting by 30-40%. Drives $10-20M in incremental annual revenue for mid-size retailers.
Related use cases
Explore more personalization 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.
