Dynamic Pricing
“What price maximizes margin for this product?”
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A real-world example
What price maximizes margin for this product?
Retailers reprice millions of SKUs manually or with basic rule engines (competitor price minus 5%), leaving $8-15B in annual margin on the table industry-wide (McKinsey). Price elasticity varies dramatically by product, customer segment, time of day, inventory level, and competitive context. A $0.50 price increase on a low-elasticity item yields pure margin, while the same increase on a high-elasticity item drives customers to competitors. Most pricing tools treat each SKU independently, missing the cross-product effects: raising the price of brand-name cereal by $0.30 shifts 12% of demand to the store brand.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo connects products, competitor prices, transaction history, customer segments, inventory levels, and promotional calendars into a relational graph. The model learns the demand curve for each product in context: SKU-4310 at $3.99 in Store S-14 will sell 480 units this week, but at $4.29 it will sell 440 units with a net margin gain of $14,400. The graph captures cross-product substitution effects, so the model also predicts that the $0.30 increase will shift 35 units to the competing SKU-4311, netting $11,200 in combined margin gain.
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
PRODUCTS
| sku_id | name | category | current_price | unit_cost | competitor_price |
|---|---|---|---|---|---|
| SKU-4310 | Casa Crunch Chips 12oz | Snacks | $3.99 | $1.40 | $4.19 |
| SKU-4311 | Store Brand Chips 12oz | Snacks | $2.99 | $0.90 | N/A |
| SKU-4520 | Premium Coffee Beans 1lb | Beverages | $12.99 | $6.50 | $13.49 |
PRICE_HISTORY
| sku_id | store_id | price | units_sold | date |
|---|---|---|---|---|
| SKU-4310 | S-14 | $3.99 | 68 | 2025-09-22 |
| SKU-4310 | S-14 | $3.49 | 95 | 2025-09-15 |
| SKU-4310 | S-14 | $4.29 | 58 | 2025-09-08 |
INVENTORY
| sku_id | store_id | on_hand | days_of_supply | next_delivery |
|---|---|---|---|---|
| SKU-4310 | S-14 | 520 | 7.6 | 2025-09-29 |
| SKU-4311 | S-14 | 340 | 12.1 | 2025-10-02 |
| SKU-4520 | S-14 | 85 | 4.2 | 2025-09-27 |
CUSTOMER_SEGMENTS
| segment | price_sensitivity | avg_basket | share_of_traffic |
|---|---|---|---|
| Value Seekers | High | $38 | 35% |
| Convenience | Low | $62 | 25% |
| Premium | Very Low | $95 | 15% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(PRICE_HISTORY.UNITS_SOLD, 0, 7, days) FOR EACH PRODUCTS.SKU_ID, STORES.STORE_ID ASSUMING PRODUCTS.CURRENT_PRICE = 4.29
Prediction output
Every entity gets a score, updated continuously
| SKU_ID | STORE_ID | OPTIMAL_PRICE | PRED_UNITS | PRED_MARGIN | VS_CURRENT |
|---|---|---|---|---|---|
| SKU-4310 | S-14 | $4.29 | 440 | $1,271.60 | +$142.00 |
| SKU-4311 | S-14 | $2.99 | 375 | $408.75 | +$31.50 |
| SKU-4520 | S-14 | $13.49 | 78 | $545.22 | +$38.50 |
Understand why
Every prediction includes feature attributions — no black boxes
SKU-4310 (Casa Crunch Chips) at Store S-14
Predicted: Optimal price $4.29, 440 units, +$142 margin vs current
Top contributing features
Price elasticity for this SKU
-0.35 (low)
28% attribution
Competitor price gap
$0.10 below comp.
24% attribution
Inventory level (sufficient stock)
7.6 days supply
19% attribution
Cross-product substitution rate
8% to store brand
17% attribution
Customer segment mix (low sensitivity)
40% Conv+Prem
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 gross margin by 3-8% across the assortment without sacrificing volume, adding $15-40M in annual profit for a $5B revenue retailer.
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.
