Propensity to Buy
“Which website visitors will make a purchase in the next 7 days?”
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A real-world example
Which website visitors will make a purchase in the next 7 days?
E-commerce and SaaS companies drive millions of site visits, but fewer than 3% convert to a purchase. Marketing teams blast the same promotions to everyone, wasting ad spend on visitors who were never going to buy and under-investing in visitors on the verge of purchasing. Without visitor-level propensity scores, personalization engines, ad bidding, and on-site merchandising operate blind.
How KumoRFM solves this
Relational intelligence for smarter acquisition
Kumo ingests VISITORS, PAGE_VIEWS, and ORDERS into a temporal relational graph. The model learns sequences and cross-entity patterns — like 'visitors who viewed 5+ pages including pricing, from a paid source, within a session that lasted over 4 minutes' — and combines them with relational signals from other converting visitors. The WHERE clause filters to visitors with recent engagement, ensuring predictions are actionable. Scores update continuously as new page views stream in.
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 | first_seen |
|---|---|---|---|
| V001 | paid_search | desktop | 2025-11-10 |
| V002 | organic | mobile | 2025-11-11 |
| V003 | desktop | 2025-11-12 | |
| V004 | direct | tablet | 2025-11-12 |
PAGE_VIEWS
| view_id | visitor_id | page_url | duration_sec | timestamp |
|---|---|---|---|---|
| PV01 | V001 | /product/shoes | 45 | 2025-11-10 |
| PV02 | V001 | /pricing | 120 | 2025-11-10 |
| PV03 | V001 | /cart | 30 | 2025-11-11 |
| PV04 | V002 | /blog/guide | 90 | 2025-11-11 |
| PV05 | V003 | /product/jacket | 60 | 2025-11-12 |
| PV06 | V003 | /pricing | 85 | 2025-11-12 |
ORDERS
| order_id | visitor_id | amount | timestamp |
|---|---|---|---|
| O801 | V001 | $149 | 2025-11-12 |
| O802 | V003 | $225 | 2025-11-14 |
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.*, -7, 0, days) > 3
Prediction output
Every entity gets a score, updated continuously
| VISITOR_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| V001 | 2025-11-10 | True | 0.92 |
| V002 | 2025-11-11 | False | 0.08 |
| V003 | 2025-11-12 | True | 0.79 |
| V004 | 2025-11-12 | False | 0.15 |
Understand why
Every prediction includes feature attributions — no black boxes
Visitor V001 — paid_search / desktop
Predicted: True (92% probability)
Top contributing features
Visited cart page within 24 hours of product view
True
32% attribution
Time on pricing page > 90 seconds
120 sec
26% attribution
Source — paid_search (highest-converting channel)
paid_search
20% attribution
3+ page views in last 7 days
3 views
14% attribution
Desktop device (higher AOV segment)
desktop
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: Visitor-level propensity scores lift conversion rates by 2.5x when used for personalized offers, retargeting bid adjustments, and on-site merchandising — turning anonymous traffic into attributable revenue.
Related use cases
Explore more acquisition 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.
