Made for Content Creators
Diagnose Performance Without Chasing Vanity Metrics
Also useful for: people in Creators
Review each content asset against its intended job, separate useful signals from impressive-looking noise, diagnose likely bottlenecks, and build a focused experiment plan for your next cycle.
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Use this when
Use this after a campaign, launch, or publishing cycle when your metrics point in different directions, your strongest-looking content isn't producing business results, or you need to decide what to test next.
You'll need
- Review period
- Content inventory and results
- Intended content jobs
- Available benchmarks
- Business context
- Tracking limits
- Next-cycle capacity
- Review decision
You'll get
- Every reviewed asset a primary job assigned
- It with a defensible reference compared
- Facts from hypotheses separated
- The main performance bottleneck identified
- Only the experiments you can run cleanly next cycle chosen You also have success thresholds, review dates, and decision rules for those experiments.
By the end of this playbook
You will have:
- Every reviewed asset a primary job assigned
- It with a defensible reference compared
- Facts from hypotheses separated
- The main performance bottleneck identified
- Only the experiments you can run cleanly next cycle chosen You also have success thresholds, review dates, and decision rules for those experiments.
Sample
This is sample data, not your prompt. Pro unlocks this higher-stakes Playbook. On Free Playbooks you can still copy the assembled prompt into any AI.
Sample input
Review period: April 1 through June 30, with purchases counted through July 14. Inventory: 18 short tutorial videos, 6 opinion videos, 5 newsletters, 2 case studies, and 1 product comparison guide. Tutorials produced 420,000 total views but few profile visits. Opinion videos had lower reach but generated most replies and newsletter sign-ups. The comparison guide received 1,900 visits, 310 trial clicks, and 27 attributed workshop purchases. Jobs: Tutorials were for reach, opinion videos and case studies were for trust, newsletters were for conversion, and the comparison guide was for revenue. Benchmarks: Prior median short-video completion rate was 31 percent. Prior newsletter click rate was 3.8 percent. The launch target was 150 qualified product-page visits. Tracking limits: Podcast traffic was untagged, view definitions differ by platform, and direct purchases can't always be connected to a social post. Capacity: 3 experiments. Decision: Choose what to test next.
Sample assembled prompt
Primary diagnosis: - Reach isn't... Tutorials created substantial exposure, but weak... - Trust content appears more commercially... Opinion videos generated stronger replies and... - The comparison guide is the... Attribution remains incomplete for direct sales. Decisions: - Continue: Comparison-led content and... - Investigate: Tutorial audience quality, profile... - Stop: Using total tutorial views... - Test: A tighter tutorial-to-newsletter transition... Ranked experiments: 1. Tutorial call-to-action alignment test. Keep topic, format, length, and distribution... Compare the current general call to... Primary metric: qualified sign-ups per 1,000... Decision rule: adopt only if the... 2. Opinion-video proof test. Compare a claim-led version with a... Primary metric: qualified landing-page visits. Supporting metric: saves and substantive replies. 3. Comparison-guide distribution test. Keep the guide unchanged and test... Primary metric: qualified visits. Lagging metric: attributed purchases within 14...