Email Personalization
“For each subscriber, which products should we feature in their next email?”
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A real-world example
For each subscriber, which products should we feature in their next email?
Email campaigns feature the same top-selling products for every subscriber. Worse, they frequently recommend out-of-stock items — creating frustration and eroding trust. A subscriber who exclusively buys skincare products receives emails about electronics. Open rates hold at 18-22% but click-through rates languish at 2-3%. For a retailer sending 100M emails per month, each 0.1% improvement in click-through is worth $1.5-3M annually.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo predicts which in-stock products each subscriber will purchase, using a filtered recommendation query that automatically excludes out-of-stock items. The model learns from the subscriber-purchase-product graph — capturing that Subscriber S001 buys premium skincare every 45 days and is due for replenishment, while similar subscribers in their graph neighborhood have been converting on a newly launched serum. Every email becomes a personalized storefront.
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
SUBSCRIBERS
| subscriber_id | segment | last_open | |
|---|---|---|---|
| S001 | s.chen@email.com | high_value | 2025-02-18 |
| S002 | j.wilson@email.com | re_engage | 2025-01-05 |
| S003 | m.rodriguez@email.com | active | 2025-02-20 |
PURCHASES
| purchase_id | subscriber_id | product_id | amount | timestamp |
|---|---|---|---|---|
| PUR101 | S001 | P501 | 78.00 | 2025-01-15 |
| PUR102 | S001 | P502 | 42.00 | 2025-02-02 |
| PUR103 | S003 | P510 | 125.00 | 2025-02-10 |
PRODUCTS
| product_id | name | category | in_stock | price |
|---|---|---|---|---|
| P501 | Hydra Serum 30ml | Skincare | 1 | 78.00 |
| P502 | Night Repair Cream | Skincare | 1 | 42.00 |
| P510 | Vitamin C Booster | Skincare | 0 | 125.00 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT( PURCHASES.PRODUCT_ID WHERE PRODUCTS.IN_STOCK = 1, 0, 14, days ) FOR EACH SUBSCRIBERS.SUBSCRIBER_ID
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| S001 | P501 | 0.91 | 2025-03-12 |
| S001 | P502 | 0.84 | 2025-03-12 |
| S003 | P501 | 0.76 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber S001 (high_value segment, last open 2025-02-18)
Predicted: Will purchase P501 (Hydra Serum 30ml) — score 0.91
Top contributing features
Replenishment cycle match
45-day cycle, 56 days since last purchase
35% attribution
Category loyalty (Skincare)
100% of purchases in Skincare
26% attribution
Graph neighbors purchased P501
71% of similar high_value subscribers bought
19% attribution
Email engagement recency
Opened email 3 days ago
12% attribution
Price sensitivity (segment)
Low — buys at full price
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: 20-35% lift in email revenue per send with zero out-of-stock frustration. For retailers sending 100M+ emails monthly, this drives $4-8M in incremental annual email 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.
