adaptive-pricing/research/PricingResearchRoadmap.md

Pricing Research Roadmap

Status: draft.

Purpose

This document prioritizes adjacent research topics for adaptive-pricing into phased workstreams. It assumes the market patterns summarized in PricingPatternsAndStrategies.md and the vocabulary in PricingOntology.md.

The roadmap is research-first: each phase should produce inspectable artifacts, scenario tests, and downstream recommendations before implementation hardens.

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Roadmap Overview

Phase	Focus	Primary outputs
1	Ontology, lifecycle pricing, value metrics	Stable vocabulary, lifecycle playbooks, value-metric selection guide
2	Solver design, comparable customer LTV	Constraint model, optimization objectives, LTV estimation spec
3	Stripe/payment-provider abstraction	Provider mapping layer, artifact lifecycle, sync semantics
4	Dynamic/adaptive pricing governance	Policy model, explainability rules, fairness and audit patterns
5	AI-assisted pricing recommendations	Recommendation interfaces, guardrails, human-in-the-loop workflows

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Phase 1: Ontology, Lifecycle Pricing, Value Metrics

Goal: Finish the conceptual foundation and connect it to lifecycle-aware strategy.

Workstreams

1.1 Ontology completion

• Resolve open questions in PricingOntology.md
• Define primitive composition patterns for hybrid models
• Produce terminology conflict map for overloaded market terms (plan, price, tier, package)
• Specify canonical parameter classes and validation rules
• Draft scenario tests for common B2B, B2C, marketplace, and platform-fee models

1.2 Lifecycle pricing

• Define lifecycle phase objectives, risks, and acceptable tradeoffs
• Produce lifecycle playbooks for Exploration through Decline
• Specify migration patterns: grandfathering, opt-in upgrades, forced migration, sunset pricing
• Map strategy patterns from PricingPatternsAndStrategies.md to lifecycle phases
• Identify metrics that signal when to change pricing phase

1.3 Value metrics

• Research value metric selection criteria: value alignment, legibility, metering cost, gaming risk
• Compare seat, usage, outcome, and hybrid metrics for AI-heavy products
• Define anti-gaming mechanisms and abuse detection considerations
• Document outcome metric attribution requirements
• Produce a value-metric decision guide tied to offering type and lifecycle phase

Exit criteria

• Ontology terms are stable enough for schema design
• Lifecycle playbooks cover all six canonical phases
• Value-metric guide supports at least ten representative offering archetypes

Dependencies

• None. This phase is the foundation.

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Phase 2: Solver Design, Comparable Customer LTV

Goal: Specify how customer-tunable pricing is solved safely against seller economics.

Workstreams

2.1 Boundary and constraint model

• Formalize boundary conditions from INTENT.md into testable constraints
• Define hard versus soft constraints and approval escalation paths
• Model commitment offsets for discounts and concessions
• Specify invalid-configuration explanations

2.2 Comparable customer LTV

• Define average_comparable_customer_lifetime_value estimation inputs
• Specify segment normalization, risk classes, and usage expectations
• Define the most_favorable_predefined_model selection algorithm
• Specify required improvement factor semantics and seller overrides
• Identify data requirements and confidence intervals for estimates

2.3 Solver design

• Research constraint-solving approaches for customer-tunable parameters
• Evaluate multi-objective optimization: LTV, margin, predictability, adoption, churn risk
• Define explainable optimization outputs for sales and customer-facing flows
• Produce solver scenario suite: valid tuning, rejected tuning, edge cases, abuse attempts
• Draft simulation interface for pre-activation evaluation

Exit criteria

• LTV comparison spec is sufficient for prototype implementation
• Solver accepts tunable parameters and returns validated configurations or rejections with reasons
• Scenario suite covers volume discounts, commitment trades, prepayment trades, and hybrid models

Dependencies

• Phase 1 ontology and lifecycle framing

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Phase 3: Stripe/Payment-Provider Abstraction

Goal: Keep the internal pricing model as source of truth while executing cleanly on providers.

Workstreams

3.1 Provider abstraction model

• Define provider-neutral artifacts: product, charge component, meter, subscription, invoice, discount execution
• Specify mapping from canonical primitives to provider objects
• Model idempotent create/update/sync semantics and drift detection
• Define metadata strategy for auditability and reconciliation

3.2 Stripe reference mapping

• Map access fees, recurring prices, metered prices, tiers, credits, and coupons to Stripe Billing
• Research usage record ingestion, billing thresholds, and tax behavior
• Define customer-specific subscription configuration patterns
• Document limitations where provider models do not align cleanly with ontology primitives

3.3 Multi-provider survey

• Compare Stripe, Paddle, Chargebee, Lago, Orb, Stigg, and m3ter
• Identify provider capability gaps for outcome fees, complex commitments, and negotiated deals
• Recommend minimum abstraction surface for future provider adapters

Exit criteria

• Stripe mapping spec covers the primitive set defined in Phase 1
• Sync and reconciliation rules are documented
• Provider comparison informs adapter interface design

Dependencies

• Phase 1 primitive definitions
• Phase 2 validation semantics for publishable configurations

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Phase 4: Dynamic/Adaptive Pricing Governance

Goal: Enable adaptive pricing without sacrificing trust, fairness, or auditability.

Workstreams

4.1 Governance model

• Define policies for dynamic price changes, segment changes, and personalized offers
• Specify approval workflows and exposure limits
• Model grandfathering, price locks, and renewal behavior
• Define pricing version history and decision audit trails

4.2 Trust-preserving adaptation

• Research transparent dynamic pricing and customer-selected tradeoffs
• Define explainability requirements for customer-facing price changes
• Specify fairness constraints for personalized pricing
• Document regulatory and reputational considerations, including algorithmic pricing scrutiny

4.3 Experimentation and regulation

• Research pricing experiments: Van Westendorp, conjoint, Gabor-Granger, live A/B tests
• Define experiment guardrails tied to lifecycle phase and segment sensitivity
• Research revenue-recognition implications for discounts, credits, breakage, and bundles
• Produce compliance checklist for EU and other relevant jurisdictions

Exit criteria

• Governance policy model covers publish, change, rollback, and sunset operations
• Explainability standard is defined for both seller and customer views
• Experimentation playbook exists with explicit guardrails

Dependencies

• Phase 1 lifecycle playbooks
• Phase 2 solver and simulation outputs
• Phase 3 provider sync for operational enforcement

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Phase 5: AI-Assisted Pricing Recommendations

Goal: Support human and agent workflows for pricing discovery, tuning, and governance.

Workstreams

5.1 Recommendation surfaces

• Define seller-facing recommendations: model selection, packaging, migration, discount risk
• Define customer-facing safe tuning assistance within solver bounds
• Specify inputs: lifecycle phase, segment, usage forecast, competitive context, boundary conditions

5.2 Guardrails and evaluation

• Require recommendations to cite ontology terms, constraints, and comparison metrics
• Define refusal conditions for under-specified or high-risk requests
• Evaluate recommendation quality against scenario suite from Phase 2
• Specify human approval paths for non-self-serve changes

5.3 Agent integration

• Define agent-readable interfaces for pricing-model definition, validation, simulation, and publish
• Map recommendation outputs to workplans, ADRs, and implementation repositories
• Document prompt and tool boundaries for pricing agents operating on seller data

Exit criteria

• Recommendation contract is machine- and human-readable
• Guardrails prevent unmanaged discount suggestions
• Agent integration guide connects research artifacts to implementation repos

Dependencies

• Phases 1–4 artifacts
• Operational telemetry and simulation data from pilot implementations

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Cross-Cutting Research Backlog

These topics span multiple phases and should be revisited as the canon matures:

Topic	Primary phase	Notes
Revenue recognition for credits and breakage	4	Accounting constraints affect credit and prepayment primitives
Marketplace and take-rate models	1, 3	May require extension of ontology
Tax and invoicing semantics	3, 4	Provider-specific but policy-driven
Sales-assisted negotiation workflows	2, 4	Approval thresholds and deal desk integration
Pricing health checks	2, 5	Ongoing monitoring of margin, churn, and discount exposure
Migration tooling	3, 4	Operational safety for lifecycle transitions

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Suggested Research Artifacts Per Phase

Phase	Artifact examples
1	PricingOntology.md revisions, lifecycle playbooks, value-metric guide, scenario catalog
2	LTV spec, solver spec, simulation contract, boundary-condition schema
3	Provider abstraction spec, Stripe mapping guide, adapter interface card
4	Governance policy spec, explainability standard, experiment playbook
5	Recommendation contract, agent integration guide, evaluation rubric

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Relationship to Implementation

This repository remains research-first. Implementation repositories should consume canon outputs as:

1. Vocabulary and schema definitions from Phase 1
2. Validation and solver rules from Phase 2
3. Provider mapping specs from Phase 3
4. Governance and publish policies from Phase 4
5. Recommendation interfaces from Phase 5

See INTENT.md for the intended adaptive-pricing engine scope and success criteria.