Subscriber Churn Prediction
“Which subscribers will cancel?”
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 subscribers will cancel?
Streaming platforms lose 5-7% of subscribers monthly. Traditional churn models rely on usage decline, catching subscribers only after they've mentally checked out. They miss the graph signals: when a subscriber's social circle churns, when content in their preferred genres dries up, or when payment friction increases. For a platform with 30M subscribers at $12/month ARPU, reducing churn by 1 percentage point saves $43M annually.
How KumoRFM solves this
Graph-powered intelligence for media platforms
Kumo connects subscribers, plans, watch history, payments, and devices into a temporal graph. The GNN learns early churn signals: viewing sessions getting shorter, binge completion rates dropping, payment method failures, and social graph erosion (friends leaving the platform). PQL filters to active subscribers and predicts cancellation in the next 30 days, giving retention teams a 2-4 week intervention window.
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
SUBSCRIBERS
| subscriber_id | plan | tenure_months | household_size |
|---|---|---|---|
| SUB101 | Premium | 18 | 3 |
| SUB102 | Standard | 4 | 1 |
| SUB103 | Premium | 24 | 4 |
PLANS
| plan_id | name | monthly_price | max_streams |
|---|---|---|---|
| PL01 | Standard | $9.99 | 2 |
| PL02 | Premium | $15.99 | 4 |
WATCH_HISTORY
| watch_id | subscriber_id | content_id | minutes_watched | timestamp |
|---|---|---|---|---|
| W6001 | SUB101 | MOV101 | 120 | 2025-02-28 |
| W6002 | SUB102 | SER201 | 15 | 2025-02-25 |
| W6003 | SUB103 | MOV305 | 95 | 2025-03-01 |
PAYMENTS
| payment_id | subscriber_id | amount | status | timestamp |
|---|---|---|---|---|
| PAY301 | SUB101 | $15.99 | Success | 2025-03-01 |
| PAY302 | SUB102 | $9.99 | Failed | 2025-03-01 |
| PAY303 | SUB103 | $15.99 | Success | 2025-03-01 |
DEVICES
| device_id | subscriber_id | type | last_active |
|---|---|---|---|
| D401 | SUB101 | Smart TV | 2025-03-01 |
| D402 | SUB102 | Mobile | 2025-02-20 |
| D403 | SUB103 | Smart TV | 2025-03-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(SUBSCRIBERS.is_cancelled, 0, 30, days) FOR EACH SUBSCRIBERS.subscriber_id WHERE COUNT(WATCH_HISTORY.*, -30, 0, days) > 0
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | PLAN | CHURN_PROB | RISK_TIER |
|---|---|---|---|
| SUB101 | Premium | 0.08 | Low |
| SUB102 | Standard | 0.81 | Critical |
| SUB103 | Premium | 0.15 | Medium |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB102 -- Standard plan, 4-month tenure
Predicted: 81% churn probability (Critical)
Top contributing features
Watch time decline (30d vs prior 30d)
-72%
32% attribution
Payment failure in billing cycle
1 failed
24% attribution
Days since last active session
9 days
20% attribution
Content completion rate decline
-55%
14% attribution
Single-device household
1 device
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 30M-subscriber streaming platform saves $43M annually by reducing churn just 1 percentage point. Kumo detects early signals like social graph erosion and viewing pattern decay weeks before traditional models flag declining usage.
Related use cases
Explore more media & entertainment 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.
