CTR Prediction
“What is the click probability for this ad impression?”
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A real-world example
What is the click probability for this ad impression?
Traditional CTR models rely on user-level features and ad metadata, missing the cross-entity signals that actually drive clicks: which publishers attract which user segments, how creative fatigue propagates across campaigns, and which ad-user pairings historically convert. For an ad platform serving 10B impressions per day, a 5% CTR lift translates to $120M in additional annual revenue.
How KumoRFM solves this
Graph-powered intelligence for advertising
Kumo builds a heterogeneous graph connecting users, ads, impressions, clicks, campaigns, and publishers. The GNN learns latent patterns like 'users who clicked similar creatives on related publishers' without manual feature engineering. PQL lets you express the prediction in two lines while Kumo automatically discovers the cross-table signals that traditional models require months of feature work to approximate.
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 | segment | device_type | geo |
|---|---|---|---|
| U001 | Tech-savvy | Mobile | US-West |
| U002 | Bargain-hunter | Desktop | US-East |
| U003 | Luxury | Mobile | EU-West |
ADS
| ad_id | campaign_id | creative_type | category |
|---|---|---|---|
| A100 | CMP01 | Video | Electronics |
| A101 | CMP02 | Banner | Fashion |
| A102 | CMP01 | Native | Electronics |
IMPRESSIONS
| impression_id | user_id | ad_id | publisher_id | timestamp |
|---|---|---|---|---|
| IMP5001 | U001 | A100 | PUB01 | 2025-03-01 08:12 |
| IMP5002 | U002 | A101 | PUB02 | 2025-03-01 09:45 |
| IMP5003 | U003 | A102 | PUB03 | 2025-03-01 10:30 |
CLICKS
| click_id | impression_id | user_id | timestamp |
|---|---|---|---|
| CLK301 | IMP5001 | U001 | 2025-03-01 08:12 |
| CLK302 | IMP4990 | U002 | 2025-02-28 14:20 |
CAMPAIGNS
| campaign_id | advertiser | budget | objective |
|---|---|---|---|
| CMP01 | TechCorp | $500K | Conversions |
| CMP02 | FashionBrand | $200K | Awareness |
PUBLISHERS
| publisher_id | name | category | avg_ctr |
|---|---|---|---|
| PUB01 | TechNews | Technology | 2.1% |
| PUB02 | StyleMag | Fashion | 1.8% |
| PUB03 | LuxuryDigest | Lifestyle | 3.2% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CLICKS.click_id, 0, 1, hours) FOR EACH IMPRESSIONS.impression_id
Prediction output
Every entity gets a score, updated continuously
| IMPRESSION_ID | USER_ID | AD_ID | CLICK_PROB |
|---|---|---|---|
| IMP5001 | U001 | A100 | 0.087 |
| IMP5002 | U002 | A101 | 0.023 |
| IMP5003 | U003 | A102 | 0.142 |
Understand why
Every prediction includes feature attributions — no black boxes
Impression IMP5003 -- User U003 x Ad A102
Predicted: 14.2% click probability
Top contributing features
User affinity for Electronics category
High
31% attribution
Publisher LuxuryDigest avg CTR
3.2%
24% attribution
Native creative on mobile
True
19% attribution
User clicked similar ads (last 7d)
4 clicks
15% attribution
Campaign frequency cap remaining
8 of 10
11% 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: An ad platform serving 10B daily impressions that improves CTR prediction by 5% unlocks $120M in annual revenue. Kumo captures cross-entity signals between users, creatives, and publishers that flat feature tables miss entirely.
Related use cases
Explore more ad tech 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.
