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pricing-experiments

Design ethical A/B pricing experiments measuring conversion and cohort LTV effects.

Updated Aug 27, 2026
Search Tags:#guardrails#ecommerce#randomization#ab testing#price elasticity#cohort ltv#pricing experiments
One-click install
npx skills add https://github.com/ohsonerdy/openclaw-frontier-stack --skill pricing-experiments
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pricing-experiments
Source: https://github.com/ohsonerdy/openclaw-frontier-stack/tree/main/skills/pricing-experiments
Command: npx skills add https://github.com/ohsonerdy/openclaw-frontier-stack --skill pricing-experiments

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps ecomm operators design controlled price tests that produce defensible learning while avoiding unethical or legally risky price discrimination.

Core Features & Use Cases

  • Hypothesis-driven experiment design: forces explicit expectations for both short-term conversion impact and long-term LTV or margin uplift.
  • Data-aware baseline pulls and measurement plan: uses AOV, margin-by-product, ROAS by channel, and cohort LTV to evaluate feasibility and downstream effects.
  • Ethical randomization and durability safeguards: enforces non-protected-class randomization units (visitor/session/account), persistence rules, and guardrails like grandfathering and refund-on-discovery.
  • Operational decision framework: recommends ship/stop rules using contribution per visitor plus 90- and 180-day LTV signals, with segment-by-segment evaluation.

Quick Start

Use the pricing-experiments skill to design an A/B test for your flagship SKU price change, including a clear hypothesis, randomization unit, required guardrails, and the metrics and stop-rule needed to decide whether to ship.

Frequently Asked Questions about pricing-experiments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run ethical A/B pricing tests without legal risk?▼

Ethical A/B pricing tests require guardrails like non-protected-class randomization units, persistence rules, grandfathering, and refund-on-discovery to avoid legally risky price discrimination while measuring conversion impact.

What metrics should I track for e-commerce price elasticity experiments?▼

Track price elasticity using contribution per visitor and cohort LTV over 90 and 180 days. This metric plan captures both short-term conversion shifts and long-term margin uplift across SKUs and channel contexts.

How do I structure a hypothesis for a controlled price change test?▼

A controlled price change hypothesis needs explicit expectations for short-term conversion impact and long-term LTV or margin uplift. Include elasticity expectations alongside baseline AOV, margin-by-product, and ROAS by channel data.

What is the best randomization unit for e-commerce price testing?▼

The best randomization unit for price testing is a non-protected-class identifier like visitor, session, or account. Apply persistence rules and exclusions to ensure operational feasibility and defensible methodology.

When should I use stop rules and ship decisions in pricing experiments?▼

Use stop rules and ship decisions when contribution per visitor and 90-day or 180-day cohort LTV signals reach statistical significance. Evaluate segment-by-segment performance to determine whether to ship or stop the price change.

Can I test discounts and bundles with the same methodology as SKU price changes?▼

Yes, A/B price tests across discounts and bundles use the same guardrailed methodology as SKU price changes. Apply ethical randomization, durability safeguards, and contribution per visitor metrics to measure conversion and LTV effects.

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