Churn Prediction
“Among members who visited in the past 60 days, which ones will have zero visits in the next 30 days?”
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A real-world example
Among members who visited in the past 60 days, which ones will have zero visits in the next 30 days?
Predicting churn for all members is noisy — many already left months ago. What you really need is to identify members who are still coming but are about to stop. The backward window focuses on the members you can still save. For a gym chain with 500K members, preventing 5% churn saves $15M annually.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo's backward time window filters to recently active members before predicting forward behavior. Traditional models predict over all members, flooding retention teams with false positives from already-churned users. Kumo focuses on the members you can still save — learning from visit frequency decay, location switching patterns, and cross-member social signals in the relational graph.
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
MEMBERS
| member_id | name | plan | signup_date |
|---|---|---|---|
| M001 | Alice Chen | Premium | 2024-01-15 |
| M002 | Bob Garcia | Basic | 2023-06-20 |
| M003 | Carol Patel | Premium | 2024-03-08 |
VISITS
| visit_id | member_id | location | timestamp |
|---|---|---|---|
| V9001 | M001 | Downtown | 2025-02-28 |
| V9002 | M002 | Midtown | 2025-02-10 |
| V9003 | M003 | Downtown | 2025-03-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(VISITS.*, 0, 30, days) = 0 FOR EACH MEMBERS.MEMBER_ID WHERE COUNT(VISITS.*, -60, 0, days) > 0
Prediction output
Every entity gets a score, updated continuously
| MEMBER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| M001 | 2025-03-05 | False | 0.09 |
| M002 | 2025-03-05 | True | 0.82 |
| M003 | 2025-03-05 | False | 0.14 |
Understand why
Every prediction includes feature attributions — no black boxes
Member M002 — Bob Garcia
Predicted: True (82% churn probability)
Top contributing features
Visit frequency (last 30d vs prior 30d)
-68%
34% attribution
Days since last visit
23 days
27% attribution
Workout buddies also churning
2 of 3
19% attribution
Plan downgrade in last 90d
Yes
12% attribution
Location switching frequency
3 locations
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: A 500K-member gym chain preventing just 5% of at-risk churn saves $15M per year. Kumo's backward window eliminates noise from already-churned members, letting retention teams focus on the members they can still save.
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.
