Customer Lifetime Value (CLV)

See also: Clv REST API reference for request/response shapes, status codes, and error semantics.

Customer Lifetime Value (CLV) quantifies how much a customer is worth to your business over their entire relationship. KaireonAI computes CLV using an RFM model (Recency, Frequency, Monetary) derived from interaction summaries, then uses the score to segment customers and predict future revenue. CLV scores feed directly into the decisioning engine. A high-value customer at risk of churning can automatically receive retention offers, while a new customer with growing engagement can be fast-tracked into loyalty programs.

CLV is computed automatically on first access and cached. Batch recomputation can be triggered via the API or scheduled as a cron job.

Business Value

Use Case	How CLV Helps
Retention targeting	Identify `at_risk` customers before they churn and route them into retention flows
Budget allocation	Invest more in high-CLV segments, optimize spend on low-CLV segments
Offer prioritization	Use CLV as an input to the PRIE ranking formula’s value dimension
Executive reporting	Track CLV distribution across your customer base over time

How It Works

RFM Model

CLV is derived from three behavioral dimensions:

Dimension	What It Measures	Weight
Recency	Days since last interaction (lower is better)	35%
Frequency	Total number of interactions (impressions)	35%
Monetary	Total conversion value in currency	30%

Each dimension is normalized against tenant-wide percentiles so scores are relative to your customer base, not absolute values.

CLV Score Calculation

CLV Score = (RecencyNorm x 0.35 + FrequencyNorm x 0.35 + MonetaryNorm x 0.30) x 100

The score ranges from 0 to 100, where 100 represents the highest-value customer in your tenant.

Segment Assignment

Segment	Criteria
`high`	CLV score >= 75th percentile
`medium`	CLV score >= 25th percentile
`low`	CLV score < 25th percentile
`at_risk`	30+ days since last contact AND has prior interactions

The at_risk segment takes priority over score-based segments. A customer with a high CLV score who has gone inactive for 30+ days will be flagged as at_risk rather than high.

Churn Probability

Churn probability uses a sigmoid curve based on recency:

0 days inactive: ~7% churn probability
30 days inactive: 50% churn probability
90+ days inactive: approaches 100%

Predicted Revenue

Predicted revenue extrapolates from the customer’s monthly average spend over a 24-month projected lifespan:

Predicted Revenue = (Total Monetary Value / Months Active) x 24

Configuration

CLV computation requires no configuration. It automatically uses interaction summaries that the platform collects through the Respond API. To tune the model, you can adjust weights and thresholds through the platform settings:

Setting	Default	Description
Recency weight	0.35	Weight of recency dimension in CLV score
Frequency weight	0.35	Weight of frequency dimension
Monetary weight	0.30	Weight of monetary dimension
At-risk threshold	30 days	Days of inactivity before flagging at-risk
Lifespan projection	24 months	Months used for revenue projection

Using CLV

Single Customer Lookup

curl https://playground.kaireonai.com/api/v1/customers/CUST-001/clv \
  -H "X-Tenant-Id: my-tenant"

Returns the cached CLV or computes on-demand if no cached record exists.

Force Recompute

curl -X POST https://playground.kaireonai.com/api/v1/customers/CUST-001/clv \
  -H "X-Tenant-Id: my-tenant"

Batch Compute

Recompute CLV for all customers (or a specific segment):

curl -X POST https://playground.kaireonai.com/api/v1/clv/compute \
  -H "X-Tenant-Id: my-tenant" \
  -H "Content-Type: application/json" \
  -d '{ "segment": "at_risk" }'

Example Response

{
  "customerId": "CUST-001",
  "clvScore": 72,
  "predictedRevenue": 1440.00,
  "churnProbability": 0.119,
  "rfmRecency": 5,
  "rfmFrequency": 48,
  "rfmMonetary": 360.00,
  "segment": "high",
  "computedAt": "2026-04-03T10:30:00.000Z",
  "source": "cached"
}

CLV in Decision Flows

CLV scores are available as customer context in Decision Flows. You can use them in:

Decisioning gates with propensity_threshold type to restrict offers to high-value customers
Computed fields using customer.clv_score in formulas
Ranking profiles where CLV feeds the value dimension of the PRIE score

API Reference

See the full CLV API Reference for all endpoints, request/response schemas, and error codes.

Next Steps

CLV API Reference

Full endpoint documentation with examples.

Behavioral Metrics

Define custom metrics that feed into CLV and decisioning.

Decision Flows

Use CLV scores to personalize the decision pipeline.

Unified Profile

View CLV alongside all other customer data in one call.

Get Started

Tutorials

Use Cases

Decisioning

Studio

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AI & ML

Operations & Reporting

Governance & Security

Integrations

Reference

Customer Lifetime Value (CLV)

Business Value

How It Works

RFM Model

CLV Score Calculation

Segment Assignment

Churn Probability

Predicted Revenue

Configuration

Using CLV

Single Customer Lookup

Force Recompute

Batch Compute

Example Response

CLV in Decision Flows

API Reference

Next Steps

CLV API Reference

Behavioral Metrics

Decision Flows

Unified Profile

​Business Value

​How It Works

​RFM Model

​CLV Score Calculation

​Segment Assignment

​Churn Probability

​Predicted Revenue

​Configuration

​Using CLV

​Single Customer Lookup

​Force Recompute

​Batch Compute

​Example Response

​CLV in Decision Flows

​API Reference

​Next Steps

CLV API Reference

Behavioral Metrics

Decision Flows

Unified Profile

Business Value

How It Works

RFM Model

CLV Score Calculation

Segment Assignment

Churn Probability

Predicted Revenue

Configuration

Using CLV

Single Customer Lookup

Force Recompute

Batch Compute

Example Response

CLV in Decision Flows

API Reference

Next Steps