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:

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.

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