creative-testing

name	creative-testing
description	Design, produce, and evaluate disciplined social creative tests for marketers. Use when the user asks for A/B tests, hook tests, creative variants, message experiments, format tests, offer or CTA tests, an experimentation roadmap, ways to improve a campaign systematically, or draft variants whose results can produce a reusable marketing learning.

Creative Testing

Create tests that isolate a meaningful decision and produce learning the team can reuse—not a pile of unrelated variants.

Guardrails

• Do not claim statistical significance from ordinary organic social comparisons, small samples, unequal delivery, or platform-reported totals without a valid experiment design.
• Hold audience, offer, placement, timing, and CTA constant when testing a creative variable unless one of those is the declared variable.
• Preserve factual claims and required disclaimers across variants. Never make a test “stronger” by inventing proof or urgency.
• Image or video generation spends credits. Confirm ambiguous generation and avoid generating variants that do not test a defined hypothesis.
• Create variants as drafts and require explicit approval before scheduling or queueing.
• Do not automatically declare a winner from the highest raw engagement count; match the decision metric to the objective.

Workflow

1. Define the business decision: what choice will change if this test succeeds? Resolve objective, audience, offer, channel/placement, conversion path, current control, constraints, and available volume.
2. When a baseline exists, call social_getSocialMediaAccounts, then retrieve relevant aggregated, range, and post analytics. Distinguish observed patterns from hypotheses.
3. Write one falsifiable hypothesis: changing X for Y audience/context should improve Z metric because reason.
4. Select one primary variable: hook, promise framing, proof type, visual treatment, opening frame, format, CTA language, creator/brand voice, or offer framing. Use references/experiment-design.md to control confounds.
5. Define the control and two to four purposeful variants. Each variant must express a distinct strategic alternative, not superficial synonym changes.
6. Choose a primary decision metric and guardrails before production. Examples: qualified reach/video hold for attention, saves or substantive engagement for utility, clicks/leads/bookings for response, and negative feedback for audience cost.
7. Produce a test matrix with invariant elements, variable, hypothesis, assets, account/placement, run window, minimum practical evidence, and decision rule.
8. If new creative is authorized, use $generate-image or $generate-video with reusable asset storage. Keep composition, product, and brand constants unless visual treatment is the tested variable.
9. Create each execution with social_createSocialMediaPost and action: "draft", using required platform settings. Never publish one variant early and call it a fair comparison.
10. After the run, use $social-performance-analyst to compare results. Record result, confidence/limitations, learning, next decision, and follow-up test. Retain a control until a challenger wins under a credible comparison.

Experimentation Standard

• Prioritize high-leverage uncertainty. Test the promise or proof before button color, emoji, or trivial copy edits.
• Separate exploration from validation. Early tests can identify promising territories; later tests should isolate and confirm the driver.
• Build variants from different audience tensions or persuasion mechanisms, not random creativity.
• Evaluate platform delivery effects, audience overlap, spend, timing, and sample imbalance before attributing performance to creative.
• Stop tests that create brand, legal, reputational, or customer-experience risk regardless of short-term metrics.
• Turn each result into a reusable rule with scope: what worked, for whom, where, under what conditions, and what remains unknown.

Output

Lead with the decision and hypothesis. Then show the controlled matrix, draft/asset status, measurement and stopping rules, validity risks, and the learning record the team should complete after results arrive.