Marketing analytics
Full-stack Claude Code pack for marketers: FTC claim-substantiation, CAN-SPAM, deliverability, privacy & no-fabricated-metrics guardrails + skills for positioning, ad copy, landing pages, email, SEO, A/B tests & analytics, loops & review agents. Not legal advice.
npx -y skills add m-binimran/marketing-pack --skill marketing-analyticsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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What its author says it does
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Turn marketing metrics into insights and next actions - funnel, channel, campaign, and cohort analysis - using real data only. Use when reviewing performance or building a report.
SKILL.md
1.4 KB, as published. Nobody here has run it
marketing-analytics
Numbers into decisions. Read the right metric for the goal; never invent a figure.
Read the right metric per goal
- Acquisition: traffic by channel, CTR, CPC, CPL/CPA, ROAS.
- Conversion: landing/page conversion rate, funnel drop-off, form completion.
- Email: delivery, open, click, unsubscribe, conversion.
- Retention/value: repeat rate, LTV, churn, cohort behaviour.
Process
- Use the real data (supplied or via a connector - GA4, ad platforms, ESP). Don't fabricate or guess benchmarks.
- Find the story: what's working (scale it), where the funnel leaks (fix it), what underperforms (cut it). Compare to the baseline/goal, not vibes.
- Attribute honestly - note attribution model limits; don't over-credit a channel.
- Recommend: 3 concrete next actions + one experiment (
ab-test).
Output
- A short report: top performers + why, the biggest leak, 3 actions, 1 experiment - every number real and sourced.
Guardrails
- No fabricated metrics or benchmarks (
truth-telling); mark missing data[verify]. - Don't over-read small samples or short windows; state the limitation.