Product Recommendations
“For each customer, what products will they purchase in the next 30 days?”
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A real-world example
For each customer, what products will they purchase in the next 30 days?
Most retailers still show generic bestseller lists or rely on collaborative filtering that only considers the user-item interaction matrix. Cross-category purchase patterns, return signals, browse-to-buy sequences, and shared merchant affinities are invisible. Kumo learns from the full purchase-product-customer graph — capturing signals that collaborative filtering structurally cannot see. For a mid-size retailer doing $500M in ecommerce revenue, even a 1% conversion lift is worth $5M annually.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo's graph transformers traverse the full relational structure — customer demographics, purchase history, product attributes, browsing sessions, returns, and reviews — to predict which products each customer will buy next. Unlike matrix factorization that only sees (user, item) pairs, Kumo captures that Customer C001 bought running shoes, viewed trail gear, and shares purchase patterns with outdoor enthusiasts — surfacing cross-category recommendations that collaborative filtering misses entirely.
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 | segment | signup_date |
|---|---|---|---|
| C001 | Sarah Chen | premium | 2023-06-15 |
| C002 | Michael Torres | standard | 2024-01-20 |
| C003 | Priya Kapoor | premium | 2022-11-03 |
PURCHASES
| purchase_id | customer_id | product_id | amount | timestamp |
|---|---|---|---|---|
| PUR001 | C001 | P203 | 89.99 | 2025-02-10 |
| PUR002 | C001 | P087 | 124.50 | 2025-02-14 |
| PUR003 | C002 | P042 | 34.99 | 2025-02-11 |
PRODUCTS
| product_id | product_name | category | price |
|---|---|---|---|
| P203 | Trail Running Shoes | Footwear | 89.99 |
| P087 | Hydration Pack 2L | Outdoor Gear | 124.50 |
| P042 | Wireless Earbuds | Electronics | 34.99 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(PURCHASES.PRODUCT_ID, 0, 30, days) FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| C001 | P203 | 0.92 | 2025-03-12 |
| C001 | P087 | 0.85 | 2025-03-12 |
| C002 | P042 | 0.78 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C001 (Sarah Chen, premium segment)
Predicted: Will purchase P203 (Trail Running Shoes) — score 0.92
Top contributing features
Previous category purchases (Footwear)
4 purchases in 90 days
34% attribution
Graph neighbors with same product
12 similar customers bought P203
28% attribution
Browse-to-cart ratio (Outdoor)
0.38 (high intent)
19% attribution
Days since last Footwear purchase
47 days
12% attribution
Return rate for category
0.02 (very low)
7% 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: 15-30% lift in recommendation click-through rate. Each percentage point of conversion improvement equals $2-5M annually 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.
