Lookalike Audience Modeling
“Which users look like our best converters?”
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A real-world example
Which users look like our best converters?
Platform-native lookalike tools operate on limited signals and treat each user in isolation. They miss the behavioral graph: which content users consume, which products they browse, and how their engagement patterns cluster. For a DTC brand spending $20M on acquisition, a 25% improvement in lookalike quality means $5M in incremental revenue from the same ad spend.
How KumoRFM solves this
Graph-powered intelligence for advertising
Kumo encodes users, behaviors, segments, conversions, and demographics into a single graph. The GNN learns user embeddings that capture deep behavioral similarity, not just demographic overlap. PQL's RANK TOP operator surfaces the highest-scoring non-converters, giving media buyers a ready-to-activate audience list ranked by predicted conversion probability.
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 | signup_date | geo | device |
|---|---|---|---|
| U301 | 2024-06-15 | US-West | iOS |
| U302 | 2024-09-20 | US-East | Android |
| U303 | 2025-01-05 | EU-West | iOS |
BEHAVIORS
| event_id | user_id | action | category | timestamp |
|---|---|---|---|---|
| E601 | U301 | page_view | Electronics | 2025-02-28 |
| E602 | U302 | add_to_cart | Fashion | 2025-03-01 |
| E603 | U303 | page_view | Electronics | 2025-03-01 |
SEGMENTS
| segment_id | user_id | segment_name |
|---|---|---|
| SEG01 | U301 | High-intent |
| SEG02 | U302 | Browsers |
| SEG03 | U303 | New-visitor |
CONVERSIONS
| conversion_id | user_id | value | timestamp |
|---|---|---|---|
| CVR201 | U301 | $320 | 2025-02-28 |
DEMOGRAPHICS
| user_id | age_range | income_tier | interests |
|---|---|---|---|
| U301 | 25-34 | High | Tech, Fitness |
| U302 | 35-44 | Medium | Fashion, Travel |
| U303 | 25-34 | High | Tech, Gaming |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CONVERSIONS.conversion_id, 0, 30, days) FOR EACH USERS.user_id WHERE COUNT(CONVERSIONS.*, -365, 0, days) = 0 RANK TOP 100000
Prediction output
Every entity gets a score, updated continuously
| USER_ID | CONVERSION_PROB | RANK | SEGMENT |
|---|---|---|---|
| U303 | 0.34 | 1 | New-visitor |
| U302 | 0.18 | 2 | Browsers |
| U508 | 0.15 | 3 | Re-engaged |
Understand why
Every prediction includes feature attributions — no black boxes
User U303 -- New-visitor segment
Predicted: 34% conversion probability (Rank #1)
Top contributing features
Browsing pattern similarity to converters
92% match
33% attribution
Category affinity overlap
Electronics
25% attribution
Device and geo match to seed audience
iOS + US-West
18% attribution
Session depth last 7 days
12 pages
14% attribution
Connected users who converted
3 of 8
10% 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: A DTC brand spending $20M on acquisition generates $5M in incremental revenue by replacing platform-native lookalikes with Kumo's graph-learned audience models. Behavioral graph similarity outperforms demographic-only targeting by 25-40%.
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.
