Experimentation Strategy Roadmap

Experimentation Strategy Roadmap

Use this skill to decide which experimentation capability deserves investment next. It helps balance experimentation rate, quality, cost, usability, process, infrastructure, and company strategy when adopting advanced techniques.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 1 on rate, quality, and cost; and Chapter 9 on optimization strategies, process improvements, infrastructure, company goals, user needs, robustness, cost versus quality, and evaluating new strategies.

Related skills:

• ab-testing-platform-strategy for platform architecture and build/buy scope.
• experimentation-throughput-strategy for increasing experiment rate.
• experiment-verification-monitoring for quality investments.
• experiment-sensitivity-optimization, ml-experiment-evaluation, and adaptive-experimentation-strategy for advanced technique candidates.

Reference Routing

Workflow

1. State the company's strategic goals and current experimentation constraints.
2. Classify candidate work as optimization strategy, process improvement, infrastructure enhancement, or a combination.
3. Score each candidate against rate, quality, cost, usability, robustness, and adoption effort.
4. Prototype advanced strategies before platformizing them.
5. Sequence the roadmap so tools, processes, and education support each other.
6. Define success metrics for the platform itself.

Output Format

# Experimentation Strategy Roadmap

## Strategic Context
[Company/product goals and experimentation constraints.]

## Recommendation
[Top roadmap priority and why.]

## Candidate Initiatives
| Initiative | Type | Rate | Quality | Cost | Usability | Effort |
|------------|------|------|---------|------|-----------|--------|

## Roadmap
1. [Now]
2. [Next]
3. [Later]

## Adoption Plan
- Tooling:
- Process:
- Education:
- Success metrics:

Quality Bar

• Do not platformize an advanced method before proving it in real use cases.
• Do not optimize for experimentation rate while ignoring trust and quality.
• Do not build tools without updating process and education.
• Do not adopt complexity that product teams cannot understand or operate.