Search Ranking
“For each user's search query, which products should rank highest?”
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A real-world example
For each user's search query, which products should rank highest?
Default search engines rank by text relevance and popularity. Two users searching "running shoes" see the same results even though one is a trail runner and the other runs on pavement. Click-through rates on search results average 15-20% when they could be 35-50% with personalization. For an ecommerce site doing $1B in GMV, search drives 40% of revenue — a 20% improvement in search conversion is worth $80M annually.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo re-ranks search results by learning from the full relational graph of user behavior, product attributes, and cross-user click patterns. It discovers that trail runners who search "running shoes" click on different products than road runners — and uses purchase history, return patterns, and graph neighborhood signals to personalize rankings. The model captures that users who bought hydration packs and trail GPS devices should see trail shoes first.
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
USERS
| user_id | segment | location |
|---|---|---|
| U001 | outdoor_enthusiast | Denver, CO |
| U002 | casual_fitness | Miami, FL |
| U003 | competitive_runner | Boston, MA |
SEARCHES
| search_id | user_id | query | timestamp |
|---|---|---|---|
| S001 | U001 | running shoes | 2025-02-20 |
| S002 | U002 | running shoes | 2025-02-20 |
| S003 | U003 | lightweight trainers | 2025-02-21 |
CLICKS
| click_id | search_id | product_id | position | timestamp |
|---|---|---|---|---|
| CL001 | S001 | P401 | 3 | 2025-02-20 |
| CL002 | S001 | P408 | 7 | 2025-02-20 |
| CL003 | S002 | P402 | 1 | 2025-02-20 |
PRODUCTS
| product_id | name | category | price |
|---|---|---|---|
| P401 | TrailMax Pro GTX | Trail Running | 159.99 |
| P402 | StreetRunner Lite | Road Running | 89.99 |
| P408 | Summit Ridge Trainer | Trail Running | 139.99 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(CLICKS.PRODUCT_ID, 0, 7, days) RANK TOP 20 FOR EACH USERS.USER_ID
Prediction output
Every entity gets a score, updated continuously
| USER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| U001 | P401 | 0.91 | 2025-03-12 |
| U001 | P408 | 0.87 | 2025-03-12 |
| U002 | P402 | 0.84 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
User U001 (outdoor_enthusiast, Denver, CO)
Predicted: P401 (TrailMax Pro GTX) ranked #1 — score 0.91
Top contributing features
Past trail running purchases
3 trail products in 6 months
31% attribution
Graph neighbors clicked P401
67% of similar users clicked
27% attribution
Location affinity (mountain region)
Trail product affinity 0.82
20% attribution
Click position bias correction
Clicked at position 3 (high intent)
14% attribution
Return rate for user segment
0.04 (low returns)
8% 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: 20-40% improvement in search conversion rate. For ecommerce sites with $1B+ GMV, personalized search ranking drives $50-80M in incremental annual revenue.
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.
