Win-Back Targeting
“Which churned customers will return and make a purchase in the next 60 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 churned customers will return and make a purchase in the next 60 days?
Most win-back campaigns blast every churned customer with the same offer, wasting budget on customers who will never return while under-investing in those who would. Knowing which churned customers are persuadable turns a 2% response rate into 12%. For a retailer with 1M lapsed customers, that precision saves $8M in wasted campaign spend per year.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo filters to customers with zero orders in the past 180 days, then predicts which will re-purchase within 60 days. The graph captures dormant signals traditional models miss — similar customers who returned, prior campaign responses from connected accounts, and seasonal purchase patterns across the entire customer network.
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 | segment | last_order_date |
|---|---|---|---|
| C101 | Dana Lee | VIP | 2024-07-12 |
| C102 | Eli Brooks | Standard | 2024-08-30 |
| C103 | Fiona Diaz | VIP | 2024-06-05 |
ORDERS
| order_id | customer_id | amount | channel | timestamp |
|---|---|---|---|---|
| O5001 | C101 | $245.00 | Online | 2024-07-12 |
| O5002 | C102 | $89.50 | In-store | 2024-08-30 |
| O5003 | C103 | $312.00 | Online | 2024-06-05 |
CAMPAIGNS
| campaign_id | customer_id | offer_type | sent_date |
|---|---|---|---|
| CMP401 | C101 | 20% off | 2025-01-15 |
| CMP402 | C102 | Free shipping | 2025-01-20 |
| CMP403 | C103 | Loyalty bonus | 2025-02-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(ORDERS.*, 0, 60, days) > 0 FOR EACH CUSTOMERS.CUSTOMER_ID WHERE COUNT(ORDERS.*, -180, 0, days) = 0
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| C101 | 2025-03-05 | True | 0.71 |
| C102 | 2025-03-05 | False | 0.15 |
| C103 | 2025-03-05 | True | 0.64 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C101 — Dana Lee
Predicted: True (71% win-back probability)
Top contributing features
Prior campaign response rate
3 of 5 opened
31% attribution
Similar VIP customers returning
68% of cohort
24% attribution
Lifetime order value
$4,230
19% attribution
Days since last order
236 days
15% attribution
Seasonal purchase pattern match
Spring buyer
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: A retailer with 1M lapsed customers can lift win-back response rates from 2% to 12% by targeting only high-probability returners — saving $8M in wasted campaign spend and recovering $22M in dormant revenue.
Related use cases
Explore more retention 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.
