# Comparable Customer LTV

Status: implementation-facing MVP for `ADAPTIVE-WP-0005`.

## Purpose

This document defines the first operational form of
`average_comparable_customer_lifetime_value`.

The goal is to compare pricing configurations using expected seller economics
over time instead of only the current-period observatory snapshot.

## Core Definition

For the current MVP:

`average_comparable_customer_lifetime_value`

means discounted expected seller margin for a comparable customer profile over a
finite horizon, minus acquisition and upfront seller investment costs.

Inputs include:

- validated monthly pricing economics from the boundary engine
- comparable-customer usage expectations
- churn and default risk assumptions
- contract duration and commitment protections
- seller acquisition and upfront investment costs
- a seller-configurable discount rate and required-improvement factor

## Reference Model Selection

The comparison engine selects:

`most_favorable_predefined_model`

as the highest-LTV valid predefined model available to the comparable-customer
profile. If no valid model exists, it falls back to the highest-LTV eligible
predefined model so the comparison still produces an inspectable anchor.

Eligibility is currently supplied by the simulation profile rather than derived
solely from model metadata.

## Required Improvement Semantics

For non-reference configurations:

```text
average_comparable_customer_lifetime_value(candidate)
>= average_comparable_customer_lifetime_value(reference)
   × required_improvement_factor
```

When the reference LTV is positive, the threshold is multiplicative.

When the reference LTV is negative, the engine switches to additive improvement
semantics so the candidate must become less negative by the requested
percentage. This avoids the invalid outcome where multiplying a negative value
would reward a worse configuration.

## Risk Model

Current risk handling is intentionally simple and explicit:

- monthly churn risk applies after committed months expire
- monthly default risk applies throughout the horizon
- prepayment and guaranteed-fee commitments reduce default exposure
- reduced cancellation flexibility lowers modeled churn exposure

This is a policy approximation, not a retention model trained from history.

## Sensitivity Model

Each comparison runs the base case plus named sensitivity cases. The current
Coulomb adapter includes:

- usage downside
- usage upside
- risk spike

Sensitivity output reports:

- scenario LTV
- delta versus base LTV
- whether the configuration remains accepted, approval-only, or rejected

## Coulomb Calibration

The Coulomb observatory currently calibrates the generic engine with:

- observed payment-fee rate from `payment_records.json`
- observed AI usage unit cost from `usage_records.json`
- segment profiles from `ltv_scenarios.json`
- profile-specific fixed-cost allocation overrides for comparable future
  customers

Those fixed-cost overrides are deliberate: the current single-member pilot cost
structure is too distorted to act as a reusable comparable-customer baseline on
its own.