Skill detail
ad-creative-fatigue
Decides whether a running ad creative is genuinely wearing out or a confounder explains the decline, returning a verdict with confidence and the highest-return remedy.
Why we recommend it: Diagnoses creative wear-out on running ads including Meta, covering creative and measurement facets of Meta Ads.
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SKILL.md
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--- name: ad-creative-fatigue description: "Decide whether a running ad creative is genuinely wearing out, or whether a confounder - budget change, learning-phase reset, audience saturation, auction CPM inflation, seasonality, tracking breakage - explains the decline, and return a verdict with confidence plus the highest-return remedy per unit of effort. Use whenever the user mentions creative or ad fatigue, wear-out, climbing frequency, dropping CTR, a rising CPA on an older ad, when to refresh creative, or whether to kill an ad - even if they never say 'fatigue'. Covers B2B and B2C across search, social, video, and native. Ends at the verdict: replacement creative belongs to mbfinotti/advertising-skills@ad-copy-variants and mbfinotti/advertising-skills@ugc-ad-scripts." license: MIT metadata: author: Maya-Beth Finotti version: "1.1.4" --- # Creative Fatigue Read a creative's performance-over-time data and decide whether it is genuinely wearing out, how confident that call is, and what to do about it. The core discipline is refusal: many things can cause a declining metric, and creative wear-out is only one of them. Most refresh decisions are made too early, off one metric, against no baseline. This skill runs a differential diagnosis in a fixed order, confounder screen before the fatigue call, never after: 1. Baseline 2. Confounder screen 3. Decay measurement 4. Confidence gate 5. Verdict Two further tensions stay live throughout: - Fatigue is often hook fatigue rather than whole-ad fatigue (Ben Heath). - Some ads improve with repeated exposure, the "wear-in" effect Les Binet describes in brand advertising, though Meta's 2023 research found no wear-in for direct-response objectives - wear-in is a real verdict only where the objective is brand, not DR. There are no universal thresholds here: every trigger is derived from the creative's own trailing baseline and the account's own history. Named practitioner numbers appear only as attributed starting reference points. This skill ends at the verdict and the recommended action. - Producing the replacement asset belongs to `mbfinotti/advertising-skills@ad-copy-variants`, `mbfinotti/advertising-skills@ugc-ad-scripts`, and `mbfinotti/advertising-skills@ad-creative-brief`. - Sample-sizing a pre-launch test belongs to `mbfinotti/advertising-skills@ad-creative-test-plan`. - Scoring a video opening before launch belongs to `mbfinotti/advertising-skills@ad-hook-analyzer` (here, hook rate is only read as a decay signal on ads already running). - When the confounder screen shows the problem is not the creative at all (structure, targeting, tracking), hand off to `mbfinotti/advertising-skills@ad-account-diagnostic`. - Retargeting-sequence and frequency-cap architecture design is `mbfinotti/advertising-skills@retargeting-funnel`. - Scaling and pacing decisions after a win are `mbfinotti/advertising-skills@paid-media-scaling` and `mbfinotti/advertising-skills@ad-budget-pacing`. ## Interview Ask before diagnosing anything. One question per message; offer multiple-choice answers where possible; skip anything already answered or visible in supplied data. - Which platform(s) is the creative running on? (Meta / Google Ads - search, Performance Max, or YouTube / LinkedIn / TikTok / other) - B2B or B2C/ecommerce? - Funnel stage: cold prospecting or retargeting/warm? (Tolerated exposure differs sharply between the two.) - What can you actually export, and at what granularity: per-creative per-day, per-creative weekly aggregate, or only ad-set/campaign level? Per-creative per-day is the working assumption; anything coarser weakens every step downstream. - Daily spend and conversion volume on the creative in question? (Decides whether the confidence gate can clear at all.) - Rough audience size for the ad set, and is the audience list-based/ABM or broad? - How many creatives are live in the same ad set? (Sibling mix changes what "losing spend share" means.) - When was the ad, or its pareRead the full source on GitHub (opens external page)