Best for
- Teams with more test ideas than capacity
- Leaders trying to avoid loudest-voice prioritization
- Growth teams balancing learning with business impact
Turn a messy list of growth and campaign ideas into a defensible experiment queue by separating the question each test answers, the evidence behind it, expected learning, impact range, effort, dependencies, and downside risk.
A ranked experiment backlog with hypothesis, learning objective, evidence, effort, risk, owner, success rule, and reason for priority.
Quick view
Check the audience, core tools, and access before you start the detailed steps.
Best for
Core tools
1 tool used across the workflow.
Access
Preview the open steps, then unlock the remaining implementation details and prompts.
Why this works
Experiment backlogs often reward ideas that sound exciting rather than tests that reduce important uncertainty. A better queue values what the team will learn, what decision the result will change, and whether the test is executable and interpretable.
Expected results
Decision quality
Experiments are ranked by learning and business value
The backlog moves beyond idea popularity.
Transparency
Every priority has an evidence and scoring rationale
Leaders can challenge assumptions instead of opaque scores.
Learning loop
Completed tests reprioritize future work
The backlog becomes smarter as first-party evidence grows.
Step-by-step workflow
The first 2 steps are open. Pro unlocks the remaining steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and implementation details.
45 min
45 min
Give Claude the backlog and require each item to state target audience, problem, proposed change, hypothesis, primary metric, learning question, decision it would change, and known dependency. Reject ideas that are really projects or campaigns without a testable comparison. Review the normalized backlog before scoring.
A normalized experiment backlog with clear learning questions.
If a test result would not change a decision, question why you are running it.
ROLE
You are a rigorous B2B growth experimentation lead.
OBJECTIVE
Normalize marketing test ideas into comparable experiment records.
INPUTS
{{raw_experiment_backlog}}
{{funnel_context}}
{{strategic_priorities}}
OUTPUT
Return hypothesis, audience, change, control or baseline, learning question, primary metric, decision affected, dependency, and unresolved issue.
GUARDRAILS
Do not invent baseline metrics or sample sizes.
Do not invent facts, metrics, quotes, identities, or source access.
UNCERTAINTY
Label missing evidence, weak samples, and conflicts instead of guessing.
HUMAN REVIEW
A named human must approve public claims, outreach, spend changes, customer-facing copy, or system-of-record updates.30 min
30 min
For each experiment, summarize the first-party data, customer evidence, prior tests, competitive context, or operator intuition that motivated it. Ask Claude to label evidence strength and the key uncertainty the experiment could reduce. Have owners correct evidence that is outdated or anecdotal. Record the evidence, rationale, and any unresolved issue in the working notes so another operator can review the decision later.
An evidence and uncertainty profile for every experiment.
A weakly evidenced idea can still be worth testing if the uncertainty matters.
Pro workflow preview
Previewing 2 of 6 steps
Get the remaining 4 steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and ongoing workflow and prompt releases.
$9/month
Related workflows
Continue with workflows that share a similar GTM motion, category, or tool stack.
A campaign postmortem with evidence-backed failure modes, preserved wins, root-cause confidence, decisions, owners, and changes for the next campaign.
A scenario plan showing current baseline, reallocation options, assumptions, capacity constraints, risks, expected ranges, and approval conditions.
A concise executive memo with decision, evidence, alternatives, uncertainty, recommendation, risks, and explicit approval or action requested.