Notification Reranking
“For each user, which notification type will drive the highest engagement in the next 7 days?”
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A real-world example
For each user, which notification type will drive the highest engagement in the next 7 days?
Apps send the same notification mix to every user — promotional, social, transactional, content updates — at the platform's preferred frequency. Users who only engage with social notifications get bombarded with promotions. Notification opt-out rates climb to 40-60%, and each opt-out permanently kills a high-value engagement channel. For a consumer app with 20M users, reducing opt-outs by 10 points preserves $15-25M in lifetime engagement value.
How KumoRFM solves this
Relational intelligence for true personalization
Kumo ranks notification types for each user by learning from the full interaction-notification-user graph. It discovers that User U001 taps on social notifications within 2 minutes but ignores promotional ones, while users in U001's graph neighborhood have started engaging with content update notifications. The model balances engagement probability, fatigue signals, and cross-user patterns to produce a ranked notification queue per user.
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 | platform | segment |
|---|---|---|
| U001 | iOS | power_user |
| U002 | Android | casual |
| U003 | iOS | new_user |
NOTIFICATIONS
| notif_id | user_id | notification_type | sent_at |
|---|---|---|---|
| N001 | U001 | social | 2025-02-18 09:00 |
| N002 | U001 | promotional | 2025-02-18 14:00 |
| N003 | U002 | content_update | 2025-02-19 10:00 |
INTERACTIONS
| interaction_id | user_id | notification_type | action | timestamp |
|---|---|---|---|---|
| INT001 | U001 | social | tap | 2025-02-18 09:02 |
| INT002 | U001 | promotional | dismiss | 2025-02-18 14:15 |
| INT003 | U002 | content_update | tap | 2025-02-19 10:08 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(INTERACTIONS.NOTIFICATION_TYPE, 0, 7, days) RANK TOP 3 FOR EACH USERS.USER_ID
Prediction output
Every entity gets a score, updated continuously
| USER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| U001 | social | 0.94 | 2025-03-12 |
| U001 | content_update | 0.71 | 2025-03-12 |
| U002 | content_update | 0.83 | 2025-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
User U001 (iOS, power_user segment)
Predicted: Social notification ranked #1 — score 0.94
Top contributing features
Social notification tap rate (7 days)
0.91 (taps 91% of social notifs)
36% attribution
Time-to-tap (social)
Avg 1.8 minutes (immediate)
24% attribution
Promotional dismiss rate
0.78 (high fatigue signal)
18% attribution
Graph neighbors' emerging preference
Content_update engagement rising 40%
14% attribution
Daily notification budget remaining
2 of 3 slots available
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: 30-50% reduction in notification opt-outs and 2x improvement in notification tap rates. For consumer apps with 20M+ users, this preserves $15-25M in lifetime engagement value.
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.
