Send-Time Optimization
“For each user, will they open an email sent in the next 4-hour window?”
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A real-world example
For each user, will they open an email sent in the next 4-hour window?
Email platforms send campaigns at a single time chosen by the marketer — usually 10am Tuesday. But users open emails at vastly different times: early risers check at 6am, commuters at 8am, night owls at 11pm. Sending at the wrong time means the email gets buried under 20 newer messages. Open rates for batch-sent emails average 18-22% when personalized send times can push them to 28-35%. For a brand sending 50M emails monthly, each 1% open rate improvement is worth $1-2M annually in downstream revenue.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo predicts whether each user will open an email in the next 4-hour window using binary classification on the user-email-open graph. By scoring each window throughout the day, the system identifies the optimal send time per user. The model captures that User U001 opens emails at 6:30am on weekdays but 10am on weekends, and that users in similar timezone-segment graph neighborhoods share open-time patterns — enabling predictions even for users with sparse history.
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 | timezone | segment |
|---|---|---|
| U001 | America/New_York | early_riser |
| U002 | America/Los_Angeles | night_owl |
| U003 | Europe/London | commuter |
EMAIL_SENDS
| send_id | user_id | campaign_id | send_hour | timestamp |
|---|---|---|---|---|
| ES001 | U001 | CAMP01 | 6 | 2025-02-18 |
| ES002 | U001 | CAMP02 | 14 | 2025-02-19 |
| ES003 | U002 | CAMP01 | 22 | 2025-02-18 |
EMAIL_OPENS
| open_id | send_id | user_id | timestamp |
|---|---|---|---|
| EO001 | ES001 | U001 | 2025-02-18 06:32 |
| EO002 | ES003 | U002 | 2025-02-18 22:15 |
| EO003 | ES001 | U001 | 2025-02-18 06:45 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(EMAIL_OPENS.*, 0, 4, hours) > 0 FOR EACH USERS.USER_ID
Prediction output
Every entity gets a score, updated continuously
| USER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| U001 | 2025-03-12 06:00 | True | 0.89 |
| U001 | 2025-03-12 14:00 | False | 0.14 |
| U002 | 2025-03-12 22:00 | True | 0.82 |
Understand why
Every prediction includes feature attributions — no black boxes
User U001 (America/New_York, early_riser segment)
Predicted: Will open email in 06:00-10:00 window — probability 0.89
Top contributing features
Historical open hour distribution
78% of opens between 6-7am
38% attribution
Weekday vs weekend pattern
Weekday — opens 2hrs earlier
22% attribution
Graph neighbors (same timezone + segment)
83% open before 7am weekdays
19% attribution
Time since last email open
18 hours (due for next check)
13% attribution
Campaign type affinity
Opens promotional emails 1.4x faster
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: 15-25% improvement in email open rates by sending at each user's optimal time. For brands sending 50M+ emails monthly, this drives $3-6M 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.
