croexperimentsanalyticsconversion

CRO Experiment Backlog

Prioritize conversion experiments with evidence-based hypotheses and implementation-ready specs.

Context

Use this to convert unstructured idea lists into a shippable experimentation roadmap.

Procedure

  1. Compile experiment ideas from analytics and qualitative inputs.
  2. Write hypotheses in “If we [change], then [metric] improves because [reason].”
  3. Estimate impact, confidence, and effort scores.
  4. Rank backlog and identify dependencies.
  5. Define instrumentation and success/failure criteria.

Output Format

# Experiment Backlog

| Experiment | Hypothesis | Metric | Impact (1-5) | Confidence (1-5) | Effort (1-5) | ICE | Owner | Status |
| ---------- | ---------- | ------ | ------------ | ---------------- | ------------ | --- | ----- | ------ |
|            |            |        |              |                  |              |     |       |        |

## Top 3 Next Experiments

1.
2.
3.

## Instrumentation Notes

- Event names:
- Segment filters:
- Success threshold:

QA Rubric (scored)

  • Hypothesis quality (0-5): testable and causal.
  • Prioritization quality (0-5): scoring reflects constraints and upside.
  • Measurement quality (0-5): success criteria are clear.
  • Delivery readiness (0-5): owner and dependencies are defined.

Examples (good/bad)

  • Good: “Changing CTA copy targets activation rate with defined lift threshold and event tracking.”
  • Bad: “Try a redesign and see what happens.”

Variants

  • Startup speed variant: weekly 1-week tests.
  • Enterprise variant: risk-checked experiments with legal review.