Referral Prediction
“Which customers will refer a new user in the next 30 days?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
Which customers will refer a new user in the next 30 days?
Referral programs are the lowest-cost acquisition channel, but most companies incentivize all customers equally. High-NPS customers with strong social connections refer at 10x the rate of average customers, yet referral nudges go out in blanket campaigns. The result: wasted incentive spend, referral fatigue among unlikely referrers, and missed opportunities with natural advocates. Companies need to identify who will refer — not just who is satisfied.
How KumoRFM solves this
Relational intelligence for smarter acquisition
Kumo builds a graph connecting CUSTOMERS, REFERRALS, and ORDERS. The GNN learns that referral behavior depends on more than NPS alone — it captures patterns like 'customers who purchased 3+ times, have tenure above 12 months, and are connected to other active referrers.' By modeling the referral graph directly, Kumo identifies the structural and behavioral signals that distinguish referrers from satisfied-but-passive customers.
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
CUSTOMERS
| customer_id | name | nps_score | tenure_months |
|---|---|---|---|
| CU101 | Sarah Chen | 9 | 24 |
| CU102 | James Park | 8 | 36 |
| CU103 | Maria Lopez | 7 | 6 |
| CU104 | Alex Kim | 10 | 18 |
REFERRALS
| referral_id | referrer_id | referee_id | status | timestamp |
|---|---|---|---|---|
| R01 | CU101 | CU103 | converted | 2025-09-15 |
| R02 | CU102 | CU105 | pending | 2025-10-01 |
| R03 | CU104 | CU106 | converted | 2025-10-20 |
ORDERS
| order_id | customer_id | amount | timestamp |
|---|---|---|---|
| O901 | CU101 | $340 | 2025-10-05 |
| O902 | CU101 | $520 | 2025-11-01 |
| O903 | CU102 | $280 | 2025-10-15 |
| O904 | CU104 | $610 | 2025-10-25 |
| O905 | CU104 | $445 | 2025-11-10 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(REFERRALS.*, 0, 30, days) > 0 FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| CU101 | 2025-11-01 | True | 0.85 |
| CU102 | 2025-11-01 | True | 0.72 |
| CU103 | 2025-11-01 | False | 0.11 |
| CU104 | 2025-11-01 | True | 0.93 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer CU104 — Alex Kim
Predicted: True (93% probability)
Top contributing features
Already referred 1 converted user in last 60 days
1 referral
31% attribution
NPS score of 10 (promoter)
10
26% attribution
2 purchases in last 30 days (high engagement)
2 orders
20% attribution
18-month tenure (established relationship)
18 months
15% attribution
Connected to 3 other active referrers in graph
3 connections
8% 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: Targeting the top 20% of predicted referrers with personalized incentives generates 4x more referrals per dollar spent than blanket referral campaigns, turning your best customers into a scalable acquisition engine.
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.
