Subscriber Churn Prediction
“Which subscribers will port out?”
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A real-world example
Which subscribers will port out?
Telecom carriers lose 1.5-2% of subscribers monthly. For a carrier with 30M subscribers at $55 ARPU, each percentage point of churn costs $198M annually. The cost of acquiring a replacement subscriber ($300-$500) is 6-10x the cost of retaining one. Traditional churn models built on billing data miss the network effects: when a subscriber's frequently-called contacts switch carriers, that subscriber follows within 60 days.
How KumoRFM solves this
Graph-learned network intelligence across your entire subscriber base
Kumo builds a call-graph connecting subscribers through their communication patterns, overlaid with plan details, usage trends, support tickets, and network quality events. It learns that subscribers whose top-5 contacts have ported out, who experienced 3+ dropped calls in poor-coverage areas, and who called a competitor's store number are 9x more likely to churn. This social contagion signal is invisible to traditional feature-based models that treat each subscriber independently.
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 | contract_end |
|---|---|---|---|
| SUB001 | Unlimited Plus | 24 | 2025-04-15 |
| SUB002 | Basic 5GB | 8 | 2025-03-30 |
| SUB003 | Family Share | 36 | 2025-06-01 |
PLANS
| plan_id | name | monthly_cost | data_gb | hotspot |
|---|---|---|---|---|
| PLN01 | Unlimited Plus | $75 | Unlimited | 50GB |
| PLN02 | Basic 5GB | $35 | 5 | None |
| PLN03 | Family Share | $120 | Shared 30GB | 15GB |
USAGE
| usage_id | subscriber_id | date | data_gb | calls_min | texts |
|---|---|---|---|---|---|
| U001 | SUB001 | 2025-03-01 | 8.2 | 320 | 150 |
| U002 | SUB002 | 2025-03-01 | 4.8 | 45 | 280 |
| U003 | SUB003 | 2025-03-01 | 12.4 | 580 | 420 |
TICKETS
| ticket_id | subscriber_id | category | created_date | resolved |
|---|---|---|---|---|
| T001 | SUB002 | Network quality | 2025-02-20 | N |
| T002 | SUB002 | Billing dispute | 2025-02-25 | Y |
| T003 | SUB001 | Plan inquiry | 2025-03-01 | Y |
NETWORK_EVENTS
| event_id | subscriber_id | type | timestamp | cell_tower |
|---|---|---|---|---|
| NE01 | SUB002 | Dropped call | 2025-02-28 | TWR_445 |
| NE02 | SUB002 | No service | 2025-03-01 | TWR_445 |
| NE03 | SUB001 | Normal | 2025-03-01 | TWR_102 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(SUBSCRIBERS.STATUS = 'Ported', 0, 30, days) FOR EACH SUBSCRIBERS.SUBSCRIBER_ID WHERE SUBSCRIBERS.CONTRACT_END <= '2025-06-01'
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | PLAN | TENURE | CHURN_30D_PROB |
|---|---|---|---|
| SUB001 | Unlimited Plus | 24mo | 0.11 |
| SUB002 | Basic 5GB | 8mo | 0.86 |
| SUB003 | Family Share | 36mo | 0.04 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB002 -- Basic 5GB, 8-month tenure
Predicted: 86% port-out probability within 30 days
Top contributing features
Top-5 contacts ported (last 60d)
3 of 5
30% attribution
Network quality events (last 30d)
7 events
24% attribution
Open support tickets
1 unresolved
18% attribution
Data usage vs plan limit
96% utilized
16% attribution
Contract end proximity
29 days
12% 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 carrier that reduces monthly churn by 0.3% saves $71M per year in avoided acquisition costs. Kumo detects social contagion churn through the call graph, learning that when a subscriber's frequent contacts port out, that subscriber follows within 60 days.
Related use cases
Explore more telecom 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.
