Creative Performance Prediction
“Which creative will perform best for this segment?”
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A real-world example
Which creative will perform best for this segment?
Creative testing is slow and expensive. Brands test 5-10 variants per campaign, wait 1-2 weeks for statistical significance, and still make suboptimal decisions because results don't generalize across segments. For an advertiser spending $30M on creative production and testing, predicting winners before launch saves $4-6M in wasted test spend and accelerates time-to-performance by 3x.
How KumoRFM solves this
Graph-powered intelligence for advertising
Kumo connects creatives to impressions, clicks, segments, and campaigns in a graph that encodes creative attributes (format, length, CTA, color palette) alongside audience response patterns. The model learns which creative attributes resonate with which segments based on historical performance across the entire creative library, not just the current campaign.
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
CREATIVES
| creative_id | format | duration | cta_type | campaign_id |
|---|---|---|---|---|
| CR001 | Video-15s | 15s | Shop Now | CMP01 |
| CR002 | Static | N/A | Learn More | CMP01 |
| CR003 | Video-30s | 30s | Sign Up | CMP02 |
IMPRESSIONS
| impression_id | creative_id | segment_id | timestamp |
|---|---|---|---|
| IMP701 | CR001 | SEG-A | 2025-02-20 |
| IMP702 | CR002 | SEG-A | 2025-02-20 |
| IMP703 | CR001 | SEG-B | 2025-02-21 |
CLICKS
| click_id | impression_id | timestamp |
|---|---|---|
| CLK501 | IMP701 | 2025-02-20 |
| CLK502 | IMP703 | 2025-02-21 |
SEGMENTS
| segment_id | name | size | avg_order_value |
|---|---|---|---|
| SEG-A | Young-professionals | 2.4M | $85 |
| SEG-B | Parents-35-50 | 3.1M | $120 |
CAMPAIGNS
| campaign_id | objective | budget | start_date |
|---|---|---|---|
| CMP01 | Conversions | $200K | 2025-02-15 |
| CMP02 | Sign-ups | $150K | 2025-02-18 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(CLICKS.click_id, 0, 7, days) FOR EACH IMPRESSIONS.impression_id RANK TOP 1
Prediction output
Every entity gets a score, updated continuously
| SEGMENT_ID | CREATIVE_ID | PREDICTED_CTR | RANK |
|---|---|---|---|
| SEG-A | CR001 | 3.8% | 1 |
| SEG-A | CR002 | 1.9% | 2 |
| SEG-B | CR001 | 4.5% | 1 |
| SEG-B | CR003 | 2.1% | 2 |
Understand why
Every prediction includes feature attributions — no black boxes
Creative CR001 x Segment SEG-B (Parents-35-50)
Predicted: 4.5% predicted CTR (Rank #1)
Top contributing features
Video format preference for segment
2.3x vs static
32% attribution
15s duration engagement rate
87% completion
24% attribution
'Shop Now' CTA conversion history
3.1% avg
18% attribution
Segment purchase recency
12 days avg
15% attribution
Creative freshness (days since launch)
8 days
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 advertiser spending $30M on creative production saves $4-6M by predicting winning creatives before full deployment. Kumo's graph connects creative attributes to segment response patterns across the entire historical library, reducing test cycles from weeks to hours.
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.
