Customer Lifetime Value Prediction
“What is each subscriber's 24-month value?”
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A real-world example
What is each subscriber's 24-month value?
Carriers treat all subscribers with the same retention playbook, spending $200 to save a $35/month subscriber and $200 to save a $120/month family plan. Without accurate LTV, retention spend is misallocated by $40M+ annually. The lifetime value depends not just on the current plan but on household composition, product add-on trajectory, network quality experience, and the subscriber's influence on their social graph (a churned influencer takes 5-10 contacts with them).
How KumoRFM solves this
Graph-learned network intelligence across your entire subscriber base
Kumo connects subscribers, plans, usage trends, payment history, and product add-ons into a graph where LTV propagates through household and social relationships. It learns that subscribers who add a wearable line within 6 months, whose household has 3+ lines, and who refer 2+ new subscribers have 4.5x higher 24-month value. The model captures product adoption velocity, payment reliability signals, and network influence value that flat ARPU projections cannot.
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_id |
|---|---|---|---|
| SUB401 | Unlimited Plus | 18 | HH001 |
| SUB402 | Basic 5GB | 6 | HH002 |
| SUB403 | Family Share | 42 | HH001 |
PLANS
| plan_id | name | monthly_cost | tier |
|---|---|---|---|
| PLN01 | Basic 5GB | $35 | Entry |
| PLN02 | Unlimited Plus | $75 | Premium |
| PLN03 | Family Share | $120 | Family |
USAGE
| usage_id | subscriber_id | month | data_gb | intl_min |
|---|---|---|---|---|
| U401 | SUB401 | 2025-02 | 22.5 | 45 |
| U402 | SUB402 | 2025-02 | 3.1 | 0 |
| U403 | SUB403 | 2025-02 | 18.8 | 120 |
PAYMENTS
| payment_id | subscriber_id | amount | date | on_time |
|---|---|---|---|---|
| PAY01 | SUB401 | $75.00 | 2025-02-01 | Y |
| PAY02 | SUB402 | $40.00 | 2025-02-05 | N |
| PAY03 | SUB403 | $120.00 | 2025-02-01 | Y |
PRODUCTS
| product_id | subscriber_id | type | added_date | monthly_cost |
|---|---|---|---|---|
| PRD01 | SUB401 | Watch line | 2025-01-15 | $10 |
| PRD02 | SUB401 | Tablet line | 2024-12-01 | $15 |
| PRD03 | SUB403 | Home internet | 2024-10-15 | $50 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(PAYMENTS.AMOUNT, 0, 24, months) FOR EACH SUBSCRIBERS.SUBSCRIBER_ID
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | PLAN | TENURE | PREDICTED_24M_LTV |
|---|---|---|---|
| SUB401 | Unlimited Plus | 18mo | $2,880 |
| SUB402 | Basic 5GB | 6mo | $420 |
| SUB403 | Family Share | 42mo | $4,560 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB401 -- Unlimited Plus, 18-month tenure
Predicted: $2,880 predicted 24-month LTV
Top contributing features
Product add-on velocity
2 add-ons in 3 months
28% attribution
Household line count
3 lines (shared HH001)
23% attribution
Payment reliability
100% on-time
19% attribution
Referral network value
Referred 2 subscribers
17% attribution
Usage growth trend
+15% data/month
13% 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 carrier that allocates retention spend proportional to predicted 24-month LTV saves $40M annually in misallocated retention budgets while reducing high-value churn by 25%. Kumo factors in household composition, product adoption velocity, and social influence value that flat ARPU projections underestimate by 3x for the most valuable subscribers.
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.
