Campaign Optimization
“For each audience segment, which campaign creative will drive the highest conversion?”
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A real-world example
For each audience segment, which campaign creative will drive the highest conversion?
A/B testing campaign creatives takes weeks and only tests a handful of variants at a time. By the time results are significant, the campaign window has closed. Meanwhile, segment-level targeting treats millions of distinct customers as identical cohorts, leaving conversion lift on the table.
How KumoRFM solves this
Relational intelligence for optimal actions
Kumo connects segments, impressions, conversions, and creatives into a relational graph. The model learns which creative-segment combinations drive conversion from the full interaction history — including cross-segment spillover effects invisible to traditional A/B tests. Ranked predictions surface the top 3 creatives per segment before a single impression is served.
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
SEGMENTS
| segment_id | segment_name | size | channel |
|---|---|---|---|
| SEG-01 | High-value Millennials | 245K | |
| SEG-02 | Lapsed Buyers | 180K | push |
| SEG-03 | New Signups Q1 | 92K | in-app |
| SEG-04 | Enterprise Prospects | 15K |
IMPRESSIONS
| impression_id | segment_id | creative_id | cost | timestamp |
|---|---|---|---|---|
| IMP-1001 | SEG-01 | CR-10 | $0.42 | 2026-02-15 |
| IMP-1002 | SEG-01 | CR-12 | $0.38 | 2026-02-15 |
| IMP-1003 | SEG-02 | CR-10 | $0.55 | 2026-02-16 |
| IMP-1004 | SEG-03 | CR-15 | $0.28 | 2026-02-17 |
CONVERSIONS
| conversion_id | segment_id | creative_id | revenue | timestamp |
|---|---|---|---|---|
| CVR-201 | SEG-01 | CR-12 | $89 | 2026-02-16 |
| CVR-202 | SEG-02 | CR-10 | $145 | 2026-02-18 |
| CVR-203 | SEG-03 | CR-15 | $62 | 2026-02-19 |
| CVR-204 | SEG-01 | CR-10 | $112 | 2026-02-17 |
CREATIVES
| creative_id | name | format | message_type |
|---|---|---|---|
| CR-10 | Spring Sale Banner | display | promotional |
| CR-12 | Personalized Reco | recommendation | |
| CR-15 | Welcome Offer | in-app | onboarding |
| CR-18 | Win-back Video | video | re-engagement |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(CONVERSIONS.CREATIVE_ID, 0, 14, days) RANK TOP 3 FOR EACH SEGMENTS.SEGMENT_ID
Prediction output
Every entity gets a score, updated continuously
| SEGMENT_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| SEG-01 | CR-12 (Personalized Reco) | 0.89 | 2026-03-12 |
| SEG-01 | CR-10 (Spring Sale) | 0.71 | 2026-03-12 |
| SEG-02 | CR-18 (Win-back Video) | 0.84 | 2026-03-12 |
| SEG-02 | CR-10 (Spring Sale) | 0.62 | 2026-03-12 |
| SEG-03 | CR-15 (Welcome Offer) | 0.92 | 2026-03-12 |
| SEG-04 | CR-12 (Personalized Reco) | 0.77 | 2026-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
Segment SEG-02 (Lapsed Buyers)
Predicted: CR-18 — Win-back Video (0.84)
Top contributing features
Segment lapse duration avg 45 days (SEGMENTS)
45 days avg
31% attribution
Video creatives 2.3x higher re-engagement (CONVERSIONS)
2.3x lift
28% attribution
Push channel delivery rate (IMPRESSIONS)
94% delivered
23% attribution
Similar segments responded to CR-18 (graph)
72% peer conversion
18% 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: Eliminate weeks of A/B testing lag. Pre-select the top-converting creative per segment and lift campaign ROI by 25-40%, saving $1-3M in wasted ad spend per quarter.
Related use cases
Explore more next-best-action 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.
