100 lines
3.1 KiB
Markdown
100 lines
3.1 KiB
Markdown
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# Comparable Customer LTV
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Status: implementation-facing MVP for `ADAPTIVE-WP-0005`.
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## Purpose
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This document defines the first operational form of
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`average_comparable_customer_lifetime_value`.
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The goal is to compare pricing configurations using expected seller economics
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over time instead of only the current-period observatory snapshot.
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## Core Definition
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For the current MVP:
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`average_comparable_customer_lifetime_value`
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means discounted expected seller margin for a comparable customer profile over a
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finite horizon, minus acquisition and upfront seller investment costs.
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Inputs include:
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- validated monthly pricing economics from the boundary engine
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- comparable-customer usage expectations
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- churn and default risk assumptions
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- contract duration and commitment protections
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- seller acquisition and upfront investment costs
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- a seller-configurable discount rate and required-improvement factor
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## Reference Model Selection
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The comparison engine selects:
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`most_favorable_predefined_model`
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as the highest-LTV valid predefined model available to the comparable-customer
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profile. If no valid model exists, it falls back to the highest-LTV eligible
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predefined model so the comparison still produces an inspectable anchor.
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Eligibility is currently supplied by the simulation profile rather than derived
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solely from model metadata.
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## Required Improvement Semantics
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For non-reference configurations:
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```text
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average_comparable_customer_lifetime_value(candidate)
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>= average_comparable_customer_lifetime_value(reference)
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× required_improvement_factor
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```
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When the reference LTV is positive, the threshold is multiplicative.
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When the reference LTV is negative, the engine switches to additive improvement
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semantics so the candidate must become less negative by the requested
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percentage. This avoids the invalid outcome where multiplying a negative value
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would reward a worse configuration.
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## Risk Model
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Current risk handling is intentionally simple and explicit:
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- monthly churn risk applies after committed months expire
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- monthly default risk applies throughout the horizon
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- prepayment and guaranteed-fee commitments reduce default exposure
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- reduced cancellation flexibility lowers modeled churn exposure
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This is a policy approximation, not a retention model trained from history.
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## Sensitivity Model
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Each comparison runs the base case plus named sensitivity cases. The current
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Coulomb adapter includes:
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- usage downside
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- usage upside
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- risk spike
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Sensitivity output reports:
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- scenario LTV
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- delta versus base LTV
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- whether the configuration remains accepted, approval-only, or rejected
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## Coulomb Calibration
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The Coulomb observatory currently calibrates the generic engine with:
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- observed payment-fee rate from `payment_records.json`
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- observed AI usage unit cost from `usage_records.json`
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- segment profiles from `ltv_scenarios.json`
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- profile-specific fixed-cost allocation overrides for comparable future
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customers
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Those fixed-cost overrides are deliberate: the current single-member pilot cost
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structure is too distorted to act as a reusable comparable-customer baseline on
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its own.
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