Conversion Rate Optimization
“Which visitors will convert after viewing a product page in the next 7 days?”
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A real-world example
Which visitors will convert after viewing a product page in the next 7 days?
Conversion rate optimization traditionally depends on aggregate funnel analysis and broad A/B tests. But two visitors who both view a product page can have wildly different conversion probabilities — one is a returning buyer with high session engagement, the other is a first-time visitor who bounced from three previous pages. Rule-based targeting treats them identically, wasting personalization budget on low-intent visitors while under-serving high-intent ones. With average e-commerce conversion rates at 2-3%, even a 0.5 percentage point improvement on a $50M GMV site translates to $250K in additional revenue per month.
How KumoRFM solves this
Relational intelligence for revenue growth
Kumo learns from the full relational graph connecting visitors, page views, session patterns, and historical orders to predict individual conversion probability conditioned on product page engagement. The backward window filter ensures predictions focus only on visitors who have actively viewed a product page, making the signal highly actionable. The model discovers that Visitor V-3301 (returning, 12-minute product page dwell) has 87% conversion probability while V-3302 (new, 8-second bounce) has just 4%.
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
VISITORS
| visitor_id | source | device | session_count |
|---|---|---|---|
| V-3301 | Google Organic | Desktop | 14 |
| V-3302 | Social Ad | Mobile | 1 |
| V-3303 | Email Campaign | Desktop | 8 |
PAGE_VIEWS
| view_id | visitor_id | page_type | duration | timestamp |
|---|---|---|---|---|
| PV-501 | V-3301 | product | 12m 18s | 2025-01-14 |
| PV-502 | V-3302 | product | 0m 08s | 2025-01-14 |
| PV-503 | V-3303 | product | 4m 42s | 2025-01-14 |
| PV-504 | V-3301 | checkout | 2m 05s | 2025-01-14 |
ORDERS
| order_id | visitor_id | amount | timestamp |
|---|---|---|---|
| ORD-9901 | V-3301 | $189 | 2025-01-10 |
| ORD-9902 | V-3303 | $64 | 2025-01-02 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(ORDERS.*, 0, 7, days) > 0 FOR EACH VISITORS.VISITOR_ID WHERE COUNT(PAGE_VIEWS.* WHERE PAGE_VIEWS.PAGE_TYPE = 'product', -1, 0, days) > 0
Prediction output
Every entity gets a score, updated continuously
| VISITOR_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| V-3301 | 2025-01-15 | True | 0.87 |
| V-3302 | 2025-01-15 | False | 0.04 |
| V-3303 | 2025-01-15 | True | 0.62 |
Understand why
Every prediction includes feature attributions — no black boxes
Visitor V-3301
Predicted: True (87% conversion probability)
Top contributing features
Product page dwell time
12m 18s
33% attribution
Prior purchase count (90d)
3 orders
27% attribution
Session count (lifetime)
14 sessions
19% attribution
Checkout page reached
Yes
14% attribution
Traffic source
Google Organic
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: Kumo predicts individual visitor conversion probability conditioned on product page engagement — enabling real-time personalization that targets high-intent visitors with the right offer at the right moment.
Related use cases
Explore more growth 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.
