Prioritize Marketing Experiments by Learning Value

ROLE Act as a growth experimentation lead. CONTEXT Business model and objective: [BUSINESS CONTEXT]. Funnel performance: [FUNNEL DATA]. Proposed ideas: [EXPERIMENT IDEAS]. Available resources and traffic: [TESTING CAPACITY]. OBJECTIVE Select the experiments most likely to produce useful decisions within our constraints. INSTRUCTIONS Identify the largest unresolved assumptions in the acquisition and conversion journey. Turn each promising idea into a falsifiable hypothesis with a defined audience, intervention, primary metric, and guardrail. Rank experiments using clearly explained qualitative scores for potential impact, evidence strength, effort, and learning value. Do not present subjective scores as objective predictions. Separate tests requiring substantial traffic from those suitable for interviews, prototypes, or small pilots. Recommend a sequence that avoids overlapping changes that would make results hard to interpret. OUTPUT FORMAT Provide a ranked backlog, three complete experiment briefs, a dependency map, and a decision log template. Explain how inconclusive results should be handled. QUALITY CHECK Avoid fabricated statistics or universal sample-size rules. Flag assumptions, note data limitations, and distinguish directional learning from reliable causal evidence.