Customer Lifetime Value
“For each customer, what will their total spend be over the next 12 months?”
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A real-world example
For each customer, what will their total spend be over the next 12 months?
Most LTV models use cohort averages or simple RFM scores — treating customers as isolated rows in a flat table. But a customer who buys premium products, contacts support rarely, and shares purchase patterns with other high-value customers is fundamentally different from one with identical spend but rising ticket volume. Traditional models miss these relational signals, leading to under-investment in high-potential customers and over-investment in declining ones. With average customer acquisition costs exceeding $200 in B2C and $400 in B2B, misallocating retention and upsell budgets by even 10% translates to millions in wasted spend.
How KumoRFM solves this
Relational intelligence for revenue growth
Kumo's graph transformers learn from the full relational structure — transaction history, product affinity, support interactions, and behavioral similarity to other customers — to produce individual-level LTV predictions. Unlike flat-table models that require months of feature engineering, Kumo automatically discovers that Customer C-1042 shares purchase trajectories with the top 5% of spenders and has zero support escalations, boosting the predicted 12-month spend to $48,200. Every prediction includes feature attributions so marketing and finance teams can understand and act on the drivers.
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 | tier | signup_date |
|---|---|---|---|
| C-1042 | Meridian Holdings | Platinum | 2023-03-15 |
| C-2187 | J. Vasquez | Gold | 2024-01-08 |
| C-3391 | NovaTech Inc. | Silver | 2024-06-22 |
ORDERS
| order_id | customer_id | amount | category | timestamp |
|---|---|---|---|---|
| ORD-8801 | C-1042 | $4,250 | Enterprise | 2025-01-05 |
| ORD-8802 | C-2187 | $320 | Standard | 2025-01-06 |
| ORD-8803 | C-1042 | $1,890 | Add-on | 2025-01-12 |
| ORD-8804 | C-3391 | $85 | Trial | 2025-01-14 |
SUPPORT_TICKETS
| ticket_id | customer_id | priority | status | timestamp |
|---|---|---|---|---|
| TK-401 | C-2187 | High | Resolved | 2025-01-03 |
| TK-402 | C-3391 | Low | Open | 2025-01-10 |
| TK-403 | C-2187 | Medium | Resolved | 2025-01-15 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(ORDERS.AMOUNT, 0, 365, days) FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| C-1042 | 2025-02-01 | $48,200 |
| C-2187 | 2025-02-01 | $2,850 |
| C-3391 | 2025-02-01 | $12,400 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C-1042
Predicted: $48,200 in 12-month spend
Top contributing features
Rolling 90-day order value
$18,420
38% attribution
Product category breadth
4 categories
24% attribution
Support ticket count (lifetime)
0 tickets
18% attribution
Similar-customer spend trajectory
Top 5% cohort
13% attribution
Account tier
Platinum
7% 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: Kumo predicts individual customer lifetime value from relational transaction, support, and product data — no feature engineering, no cohort approximations. Marketing and finance teams get explainable, per-customer dollar forecasts they can act on immediately.
Related use cases
Explore more growth 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.
