Loyalty Program Optimization
“Which loyalty tier will each customer reach in the next 90 days?”
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A real-world example
Which loyalty tier will each customer reach in the next 90 days?
Loyalty programs are expensive — a typical retailer spends 2-3% of revenue on rewards. Without predicting tier movement, you over-reward customers who would have stayed anyway and under-reward those on the cusp of upgrading. For a retailer doing $2B in revenue, optimizing tier targeting by just 15% saves $9M in rewards spend while lifting tier-upgrade rates by 20%.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo predicts the loyalty tier each customer will reach as a multi-class classification — learning from transaction velocity, reward redemption patterns, cross-category purchase behavior, and how tier movement propagates through referral and household graphs. This lets marketing invest rewards where they change behavior, not where they reward inertia.
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 | loyalty_tier | signup_date | city |
|---|---|---|---|---|
| C301 | Grace Kim | Silver | 2023-04-10 | Seattle |
| C302 | Hank Morales | Gold | 2022-09-15 | Austin |
| C303 | Ivy Nguyen | Bronze | 2024-01-20 | Denver |
TRANSACTIONS
| txn_id | customer_id | amount | timestamp |
|---|---|---|---|
| T8001 | C301 | $185.00 | 2025-02-25 |
| T8002 | C302 | $420.00 | 2025-03-01 |
| T8003 | C303 | $67.50 | 2025-02-28 |
REWARDS
| reward_id | customer_id | points_earned | tier_at_time | timestamp |
|---|---|---|---|---|
| R601 | C301 | 370 | Silver | 2025-02-25 |
| R602 | C302 | 840 | Gold | 2025-03-01 |
| R603 | C303 | 135 | Bronze | 2025-02-28 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT CUSTOMERS.LOYALTY_TIER FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | TIMESTAMP | PRED_TIER | CONFIDENCE |
|---|---|---|---|
| C301 | 2025-03-05 | Gold | 0.74 |
| C302 | 2025-03-05 | Platinum | 0.61 |
| C303 | 2025-03-05 | Bronze | 0.88 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C301 — Grace Kim
Predicted: Gold tier (74% confidence)
Top contributing features
Transaction frequency trend (90d)
+42%
30% attribution
Points accumulation rate
1,240/month
25% attribution
Cross-category purchase diversity
4 categories
18% attribution
Referral network tier movement
2 contacts upgraded
16% attribution
Reward redemption rate
85%
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: A $2B retailer optimizing loyalty tier targeting by 15% saves $9M in rewards spend annually while lifting tier-upgrade rates by 20% — turning the loyalty program from a cost center into a growth engine.
Related use cases
Explore more retention 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.
