Lookalike Modeling
“Which prospects most closely resemble our highest-value customers?”
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A real-world example
Which prospects most closely resemble our highest-value customers?
Traditional lookalike models match prospects to customers based on firmographic overlap — industry, company size, and geography. But the best customers share behavioral and relational patterns that demographics cannot capture: similar product usage trajectories, overlapping vendor ecosystems, and comparable buying cadences. Flat lookalike models miss these signals, diluting outbound targeting and inflating cost-per-acquisition.
How KumoRFM solves this
Relational intelligence for smarter acquisition
Kumo builds a graph connecting PROSPECTS, CUSTOMERS, and ORDERS. The GNN learns embeddings that encode not just who each entity is, but how they relate to everything else in the graph. Prospects whose relational neighborhoods resemble high-LTV customers surface automatically — even if their firmographics look nothing alike. The model discovers hidden patterns like 'prospects in the same vendor network as your top 10 accounts' that no rule-based system could find.
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
PROSPECTS
| prospect_id | company | industry | size | region |
|---|---|---|---|---|
| P001 | Apex Systems | Technology | Mid-Market | North America |
| P002 | Meridian Health | Healthcare | Enterprise | EMEA |
| P003 | Cascade Retail | Retail | SMB | APAC |
| P004 | Summit Financial | Finance | Enterprise | North America |
CUSTOMERS
| customer_id | company | industry | ltv_tier |
|---|---|---|---|
| CU01 | Atlas Corp | Finance | Platinum |
| CU02 | Pinnacle Tech | Technology | Gold |
| CU03 | Ironclad Health | Healthcare | Platinum |
ORDERS
| order_id | customer_id | amount | timestamp |
|---|---|---|---|
| O701 | CU01 | $156,000 | 2025-09-15 |
| O702 | CU01 | $89,000 | 2025-10-20 |
| O703 | CU02 | $62,000 | 2025-10-01 |
| O704 | CU03 | $134,000 | 2025-11-05 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(ORDERS.AMOUNT, 0, 90, days) > 5000 FOR EACH PROSPECTS.PROSPECT_ID
Prediction output
Every entity gets a score, updated continuously
| PROSPECT_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| P001 | 2025-11-01 | True | 0.76 |
| P002 | 2025-11-01 | True | 0.84 |
| P003 | 2025-11-01 | False | 0.14 |
| P004 | 2025-11-01 | True | 0.91 |
Understand why
Every prediction includes feature attributions — no black boxes
Prospect P004 — Summit Financial
Predicted: True (91% probability)
Top contributing features
Shares vendor network with 2 Platinum-tier customers
2 overlaps
33% attribution
Industry — Finance (matches top LTV segment)
Finance
25% attribution
Enterprise size with similar employee distribution
Enterprise
19% attribution
Region — North America (highest close-rate region)
North America
14% attribution
Behavioral similarity score to Platinum customers
0.88
9% 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: Graph-based lookalike models surface 40% more high-value prospects than demographic matching alone, reducing cost-per-acquisition by up to 35%.
Related use cases
Explore more acquisition 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.
