Measure

Marketing Analytics

Measure what matters. Ignore what doesn't. Iterate based on data.

How to Use

Action from arguments: $ARGUMENTS

| Action | What it does | |--------|-------------| | report | Generate weekly/monthly/quarterly report | | analyze | Deep-dive on content performance | | test | Design an A/B test | | benchmark | Compare your metrics against industry benchmarks |

If no action specified, ask what the user needs.

The Feedback Loop

This is the entire point of analytics:

Analyze → Hypothesize → Test → Measure → Iterate → Analyze

Every report should end with: "Based on this data, here's what we should try next." If a report doesn't lead to action, it's a vanity exercise.

Metrics by Platform

LinkedIn

| Metric | What it tells you | Good | Average | Poor | |--------|------------------|------|---------|------| | Impressions | Reach of your content | >5K (small account) | 1-5K | 5% | 2-5% | 20 | 5-20 | 3/post | 1-2 | 0 | | Profile views | Curiosity generated | >200/week | 50-200 | 50/week | 10-50 | 10K | 2-10K | 3% | 1-3% | 50 | 10-50 | 5 | 1-5 | 0 | | Profile clicks | Curiosity | >100 | 20-100 | 10/tweet | 2-10 | 35% | 20-35% | 5% | 2-5% | 2% | 0.5-2% | 0.5% | | Forward rate | Share-worthy | >1% | 0.2-1% | 2% | 0.8-2% | $4 | | CPA | Acquisition cost | $80 | | ROAS | Return on ad spend | >3x | 1.5-3x | 5 |

For full metrics glossary with formulas, see references/metrics-reference.md.

Benchmarks vary by audience size, industry, and stage. See references/benchmarks.md.

Vanity Metrics vs Actionable Metrics

| Vanity (feels good) | Actionable (drives decisions) | |---------------------|------------------------------| | Follower count | Follower growth rate per content piece | | Total impressions | Impressions-to-engagement conversion | | Page views | Time on page + scroll depth | | Email list size | Email-to-revenue attribution | | "Likes" | Comments + DMs + saves (deep engagement) |

Rule: if a metric doesn't change what you do next, stop tracking it.

Attribution

How did a customer find you? Four models:

| Model | How it works | Best for | |-------|-------------|----------| | First-touch | Credit goes to first interaction | Understanding discovery channels | | Last-touch | Credit goes to last interaction before conversion | Understanding what closes | | Multi-touch | Credit split across all touchpoints | Complex B2B journeys | | Self-reported | Ask "how did you hear about us?" | The truth (often different from data) |

Recommendation: Use self-reported attribution as your primary signal. Add a "How did you hear about us?" field to signup/checkout. The answers will surprise you. Data attribution misses dark social (DMs, word of mouth, podcasts).

A/B Testing for Content

Most content marketers don't A/B test because they think they need huge sample sizes. You don't.

What to Test (ordered by impact)

1. Hook/headline — Biggest impact on reach and engagement
2. CTA type — Question vs. direct ask vs. none
3. Format — Story vs. framework vs. contrarian
4. Posting time — Morning vs. evening, weekday vs. weekend
5. Length — Short (800 chars) vs. long (2,000 chars)
6. Platform — Same content, different platforms (via repurpose)

How to Test with Small Audiences

You don't need statistical significance for content decisions:

1. Test for 5-10 posts (not 1 post vs 1 post)
2. Compare the same metric across the set
3. If one approach beats the other 7 out of 10 times, it's a signal
4. Act on the signal. If you're wrong, you'll see it in the next 10.

For detailed testing guide, see references/ab-testing-guide.md.

Reporting Templates

Weekly (5 min, every Monday)

This week's content:
- [List pieces published]
- Best performer: [which + why]
- Worst performer: [which + why]
- One pattern noticed: [observation]
- Next week's plan: [based on this week's data]

Monthly (30 min, first Monday of month)

Monthly metrics:
- Content published: [count by platform]
- Total reach: [impressions across platforms]
- Engagement: [rate by platform, trend vs last month]
- Conversions: [DMs, signups, sales attributed to content]
- Best content piece: [which + analysis of why]
- Worst content piece: [which + what to learn]
- Audience growth: [by platform, trend]
- Content pillar performance: [which pillar drives results]
- Hypothesis for next month: [what to test]

Quarterly (2 hours, every 3 months)

Strategic review:
- ICP still accurate? [validate against new customer data]
- Messaging still resonating? [top-performing language vs flops]
- Channel allocation: [which channels drive conversions, cut losers]
- Content format ROI: [which formats justify the time invested]
- Competitor moves: [anything changed in the landscape?]
- Budget reallocation: [shift spend to what works]
- Strategy update: [feed insights back to strategy]

For detailed report templates with examples, see references/reporting-templates.md.

Content Performance Queries

Ask these with your content log data:

• "What are my top 5 posts by engagement rate this month?"
• "Which content pillar drives the most DMs?"
• "What hook style gets the highest engagement?"
• "Compare story posts vs framework posts on LinkedIn"
• "What's my best posting time based on the last 20 posts?"
• "Which repurposed format outperforms the original?"

For ready-to-use analysis prompts, see references/content-performance-queries.md.

References

• references/metrics-reference.md — Full metrics glossary with formulas
• references/reporting-templates.md — Detailed report templates with filled examples
• references/ab-testing-guide.md — Practical A/B testing guide
• references/content-performance-queries.md — Analysis prompts for your content log
• references/benchmarks.md — Industry benchmarks by platform and audience size

Related Skills

• strategy/ — Feed quarterly insights back into strategy
• create/ — Use performance data to write better briefs
• repurpose/ — Track which repurposed format performs best
• audit/ — Correlate audit scores with actual performance

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: rmzlb
• Source: rmzlb/baaton-skills
• License: MIT
• Homepage: https://baaton.dev

Install and usage instructions live in the source repository linked above.