ab-testing-platform-strategy

name	ab-testing-platform-strategy
description	Plan A/B testing platform strategy, architecture, and build-vs-buy decisions for product engineering teams. Use when deciding whether to build or buy an experimentation platform, scoping feature flagging, targeting, assignment, exposure logging, metrics pipelines, dashboards, governance, or evolving a simple testing setup into a durable platform.
license	MIT
compatibility	Codex, Claude Code, and other Agent Skills-compatible clients.
metadata	{"version":"0.1.0","displayName":"A/B Testing Platform Strategy","category":"Product Management","tags":"practical-ab-testing,ab-testing,experimentation,platform-engineering,product-analytics"}

A/B Testing Platform Strategy

Use this skill to decide how an organization should support A/B testing through platform choices, architecture, ownership, and incremental scope.

Source Traceability

Primary source: Practical A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from chapter 5 lines 3804-4443. Related startup and "start simple" context comes from preface lines 286-332 and chapter 2 lines 1622-1628.

Related Advanced Skills

• experimentation-strategy-roadmap: use when deciding which platform capability to prioritize across rate, quality, cost, usability, and company strategy.
• experimentation-throughput-strategy: use when the platform needs capacity visibility, isolated versus overlapping test policies, or coordination tools.
• experiment-verification-monitoring: use when the platform needs QA tooling, canaries, A/A tests, active monitoring, leakage checks, or quality metrics.
• adaptive-experimentation-strategy: use when considering sequential testing, bandits, Thompson sampling, contextual bandits, or dynamic allocation support.

Reference Routing

Need	Read
Platform concepts and components	references/core/knowledge.md
Build-vs-buy and scoping rules	references/core/rules.md
Scenario examples	references/core/examples.md
Decision workflow	workflows/decide-platform-strategy.md

Workflow

1. State the experimentation goals and current constraints.
2. Inventory required platform components.
3. Separate must-have launch capability from later platform maturity.
4. Compare build, buy, and hybrid options against team capacity and risk.
5. Plan data, assignment, exposure logging, metrics, and reporting ownership.
6. Define the smallest useful platform and the triggers for expanding it.

Output Format

# A/B Testing Platform Strategy

## Recommendation
[Build | Buy | Hybrid | Start manually] because [reason].

## Current Context
- Team:
- Product surface:
- Experiment volume:
- Data maturity:
- Engineering capacity:

## Required Capabilities
| Capability | Need Now? | Build/Buy/Manual | Owner |
|------------|-----------|------------------|-------|

## Tradeoffs
- Build advantages:
- Build risks:
- Buy advantages:
- Buy risks:
- Hybrid notes:

## Incremental Roadmap
1. Minimum viable experimentation:
2. Reliability and governance:
3. Scale and self-service:

Quality Bar

• Do not recommend building a full platform before the team has proven demand.
• Do not recommend buying without checking integration, data, and governance fit.
• Keep data and exposure logging first-class; a platform without trustworthy measurement creates false confidence.
• Treat platform scope as evolutionary, not all-or-nothing.