Store Clustering
“Which stores behave similarly for promotional planning?”
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A real-world example
Which stores behave similarly for promotional planning?
Retailers run 150-300 promotional campaigns per year, typically applying the same offers across all stores or using broad regional groupings. But two stores 5 miles apart can have completely different customer bases, price sensitivities, and category preferences. A suburban family-focused store responds to BOGO deals on bulk items, while an urban store near a college campus responds to single-serve discounts. Mismatched promotions waste 20-30% of promotional budgets ($40-80M annually for a large chain) and can actually decrease margin when high-margin items are discounted at stores where they would have sold at full price.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo builds a relational graph connecting stores to their transaction patterns, customer demographics, product category performance, promotional response history, and geographic context. The model generates embeddings that capture each store's behavioral DNA. Stores S-14 and S-38 cluster together because they share similar customer demographics, promotional response patterns, and category mix, even though they are in different states. Kumo surfaces 8-12 natural clusters that respond predictably to specific promotion types, replacing 500+ store-level rules with a small number of targeted strategies.
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
STORES
| store_id | name | region | format | sqft | demo_index |
|---|---|---|---|---|---|
| S-14 | Union Square Market | West | Urban | 42,000 | Young Professional |
| S-22 | Midtown Grocery | Northeast | Suburban | 55,000 | Family |
| S-38 | SoHo Fresh | Northeast | Urban | 38,000 | Young Professional |
CATEGORY_PERFORMANCE
| store_id | category | revenue_share | growth_yoy | avg_basket |
|---|---|---|---|---|
| S-14 | Organic & Natural | 28% | +18% | $42 |
| S-14 | Ready Meals | 22% | +12% | $18 |
| S-22 | Bulk & Family Pack | 35% | +8% | $65 |
PROMO_RESPONSE_HISTORY
| store_id | promo_type | avg_lift | avg_margin_impact | best_category |
|---|---|---|---|---|
| S-14 | % Off | +22% | +$1,200 | Organic |
| S-14 | BOGO | +8% | -$400 | Snacks |
| S-22 | BOGO | +35% | +$2,800 | Bulk Items |
CUSTOMER_DEMOGRAPHICS
| store_id | avg_age | avg_income | household_size | price_sensitivity |
|---|---|---|---|---|
| S-14 | 31 | $92,000 | 1.8 | Low |
| S-22 | 42 | $78,000 | 3.4 | Medium |
| S-38 | 29 | $88,000 | 1.6 | Low |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT MOST_COMMON(STORES.CLUSTER_ID) FOR EACH STORES.STORE_ID
Prediction output
Every entity gets a score, updated continuously
| STORE_ID | NAME | CLUSTER | CLUSTER_LABEL | TOP_PROMO_TYPE |
|---|---|---|---|---|
| S-14 | Union Square | C-1 | Urban Health-Forward | % Off Organic |
| S-38 | SoHo Fresh | C-1 | Urban Health-Forward | % Off Organic |
| S-22 | Midtown Grocery | C-3 | Suburban Family | BOGO Bulk |
Understand why
Every prediction includes feature attributions — no black boxes
Store S-14 (Union Square Market)
Predicted: Cluster C-1: Urban Health-Forward
Top contributing features
Organic category revenue share
28%
28% attribution
Customer demographic profile
Young Pro, 1.8 HH
24% attribution
Promotional response pattern
% Off > BOGO
22% attribution
Price sensitivity index
Low
15% attribution
Store format and size
Urban, 42K sqft
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 promotional ROI by 25-40% by targeting store clusters with the right promotion types, recovering $40-80M in wasted promotional spend for a 500-store chain.
Related use cases
Explore more retail & e-commerce 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.
