Customer Lifetime Value
“What is each customer's 3-year lifetime value?”
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A real-world example
What is each customer's 3-year lifetime value?
Banks allocate relationship-manager time, fee waivers, and retention budgets uniformly across segments, wasting resources on low-value accounts while under-serving high-potential customers. A regional bank with 2M retail customers found that its top 8% of households generated 42% of total revenue, yet these customers received the same service level as accounts generating $200/year. Without accurate forward-looking LTV, banks cannot differentiate treatment, leading to $20-40M in misallocated service costs and lost high-value relationships annually.
How KumoRFM solves this
Relational intelligence built for banking and financial data
Kumo connects customer profiles, product holdings, transaction histories, branch interactions, digital engagement, and life-event signals into a relational graph. The model predicts that Customer C-10078 will generate $47,200 over the next 3 years because her investment account is growing, she is adding direct-deposit payroll, and her branch-visit frequency suggests she will consolidate a competitor mortgage. These cross-table signals produce LTV estimates 30-40% more accurate than segment-based averages.
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 | segment | tenure_years | products_held | relationship_mgr |
|---|---|---|---|---|
| C-10078 | Premier | 12.1 | 6 | RM-042 |
| C-10042 | Mass Affluent | 4.2 | 3 | RM-018 |
| C-10115 | Mass Market | 1.8 | 1 | None |
REVENUE_HISTORY
| customer_id | quarter | fee_revenue | interest_revenue | total |
|---|---|---|---|---|
| C-10078 | 2025-Q2 | $1,240 | $2,800 | $4,040 |
| C-10042 | 2025-Q2 | $320 | $890 | $1,210 |
| C-10115 | 2025-Q2 | $45 | $120 | $165 |
PRODUCT_HOLDINGS
| customer_id | product | balance | monthly_activity |
|---|---|---|---|
| C-10078 | Investment Account | $340,000 | 12 trades |
| C-10078 | Mortgage | $280,000 | 1 payment |
| C-10042 | Checking | $12,300 | 45 txns |
ENGAGEMENT_EVENTS
| customer_id | channel | event_type | frequency_30d |
|---|---|---|---|
| C-10078 | Branch | advisor_meeting | 2 |
| C-10078 | Mobile | investment_review | 8 |
| C-10042 | Mobile | balance_check | 22 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(REVENUE_HISTORY.TOTAL, 0, 36, months) FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | SEGMENT | PREDICTED_3YR_LTV | CURRENT_ANNUAL | LTV_TIER |
|---|---|---|---|---|
| C-10078 | Premier | $47,200 | $16,160 | Platinum |
| C-10042 | Mass Affluent | $18,400 | $4,840 | Gold |
| C-10115 | Mass Market | $2,100 | $660 | Standard |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C-10078 (Maria Gonzalez)
Predicted: $47,200 predicted 3-year lifetime value
Top contributing features
Investment account growth trajectory
+$15K/qtr
28% attribution
Product depth (6 products held)
Top 5%
23% attribution
Branch advisor meeting frequency
2x/month
19% attribution
Mortgage consolidation signals
Rate inquiry
17% attribution
Tenure and relationship stability
12.1 years
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: Allocate relationship-manager time and retention budgets to the top 8% of customers who drive 42% of revenue, recovering $20-40M in annual misallocated service costs.
Related use cases
Explore more financial services 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.
