Product Recommendations
“Which products should this customer see?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which products should this customer see?
E-commerce retailers show the same trending products to every visitor, missing $50-100 per session in personalization uplift. Amazon attributes 35% of revenue to its recommendation engine (McKinsey). Most mid-market retailers lack the engineering resources to build similar systems. Collaborative filtering misses new customers (cold start), content-based filtering ignores purchase context (time of day, season, browsing path), and both fail to capture the rich relational structure between products, categories, brands, and customer segments.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo builds a relational graph connecting customers, products, orders, browsing sessions, reviews, and inventory data. The model learns that Customer CU-3012 who recently bought a DSLR camera, then browsed memory cards and camera bags, and has a purchase history skewing toward premium brands, should see a recommended set of compatible accessories ranked by purchase probability. The graph captures that customers who buy this camera model also purchase a specific lens 68% of the time within 30 days.
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 | segment | lifetime_orders | avg_order_value | joined_date |
|---|---|---|---|---|
| CU-3012 | Premium | 24 | $142 | 2022-06-15 |
| CU-3045 | Standard | 8 | $67 | 2023-11-20 |
| CU-3078 | New | 1 | $38 | 2025-09-10 |
ORDERS
| order_id | customer_id | total | items_count | timestamp |
|---|---|---|---|---|
| ORD-8801 | CU-3012 | $849.99 | 1 | 2025-09-05 |
| ORD-8802 | CU-3012 | $34.99 | 1 | 2025-09-12 |
| ORD-8803 | CU-3045 | $89.50 | 3 | 2025-09-14 |
PRODUCTS
| product_id | name | category | price | brand |
|---|---|---|---|---|
| P-5001 | Canon EOS R6 Mark II | Cameras | $2,499 | Canon |
| P-5002 | SanDisk 128GB SD Card | Accessories | $24.99 | SanDisk |
| P-5003 | Canon RF 50mm f/1.8 Lens | Lenses | $199.99 | Canon |
BROWSING_SESSIONS
| customer_id | product_id | action | duration_sec | timestamp |
|---|---|---|---|---|
| CU-3012 | P-5002 | view | 45 | 2025-09-14 10:22 |
| CU-3012 | P-5003 | view | 120 | 2025-09-14 10:25 |
| CU-3012 | P-5003 | add_to_wishlist | 5 | 2025-09-14 10:27 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ORDERS.PRODUCT_ID, 0, 14, days) FOR EACH CUSTOMERS.CUSTOMER_ID, PRODUCTS.PRODUCT_ID RANK TOP 5
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | PRODUCT_ID | PRODUCT_NAME | PURCHASE_PROB | RANK |
|---|---|---|---|---|
| CU-3012 | P-5003 | Canon RF 50mm f/1.8 Lens | 0.82 | 1 |
| CU-3012 | P-5002 | SanDisk 128GB SD Card | 0.74 | 2 |
| CU-3012 | P-5008 | Peak Design Camera Bag | 0.61 | 3 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer CU-3012 (Premium segment)
Predicted: Canon RF 50mm Lens: 82% purchase probability
Top contributing features
Compatible camera purchased recently
Canon EOS R6
32% attribution
Browsing session with wishlist add
120s + saved
26% attribution
Co-purchase pattern (68% of similar buyers)
High affinity
20% attribution
Premium brand preference
85% premium
13% attribution
Purchase recency pattern
Buys within 14d
9% 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 average order value by 15-25% and conversion rate by 2-4x through personalized recommendations, generating $20-50M in incremental annual revenue for a mid-size e-commerce 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.
