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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.

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Business Value

Use CaseHow CLV Helps
Retention targetingIdentify at_risk customers before they churn and route them into retention flows
Budget allocationInvest more in high-CLV segments, optimize spend on low-CLV segments
Offer prioritizationUse CLV as an input to the PRIE arbitration formula’s value dimension
Executive reportingTrack CLV distribution across your customer base over time

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How It Works

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RFM Model

CLV is derived from three behavioral dimensions:
DimensionWhat It MeasuresWeight
RecencyDays since last interaction (lower is better)35%
FrequencyTotal number of interactions (impressions)35%
MonetaryTotal conversion value in currency30%
Each dimension is normalized against tenant-wide percentiles so scores are relative to your customer base, not absolute values.

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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.

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Segment Assignment

SegmentCriteria
highCLV score >= 75th percentile
mediumCLV score >= 25th percentile
lowCLV score < 25th percentile
at_risk30+ 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.

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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%

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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

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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:
SettingDefaultDescription
Recency weight0.35Weight of recency dimension in CLV score
Frequency weight0.35Weight of frequency dimension
Monetary weight0.30Weight of monetary dimension
At-risk threshold30 daysDays of inactivity before flagging at-risk
Lifespan projection24 monthsMonths used for revenue projection

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Using CLV

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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.

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Force Recompute

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

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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" }'

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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"
}

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CLV in Decision Flows

CLV scores are available as customer context in Decision Flows. You can use them in:
  • Qualification rules with propensity_threshold type to restrict offers to high-value customers
  • Computed fields using customer.clv_score in formulas
  • Arbitration profiles where CLV feeds the value dimension of the PRIE score

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API Reference

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

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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.