LinkedIn Analytics — describe honestly, then refuse to over-conclude

The characteristic sentence of LinkedIn analytics is "carousels do 3x better for me", built on four posts. With engagement as heavy-tailed as it is, four posts will show a 3x difference between almost any two groups you care to define. These three scripts stop that sentence becoming a strategy.

Your own data only. Nothing is fetched; scraping post or profile data is prohibited by User Agreement §8.2 and none of this analysis needs it.

Workflow

1. Get the export. LinkedIn Analytics → Post impressions → Export, or Settings → Data privacy → Get a copy of your data. CSV and JSON both work.

2. Describe it. Exit 0 analysed / 2 below the 10-post floor, descriptive only / 3 unusable. Reports median and MAD rather than mean and standard deviation — one breakout post makes a mean describe a distribution none of your posts belong to — plus Tukey percentile bands and a 1.5×IQR breakout threshold, so "this did well" has a number behind it.

python3 scripts/post_performance_analyzer.py --input posts.csv --csv --output human

3. Test the pattern they think they see.

python3 scripts/pattern_miner.py --input posts.json --output human

Exit 0 something survived / 2 nothing survived / 3 under 10 posts. Four gates: 5 posts in and 5 out; a 15% relative difference in medians; beating 90% of 2,000 seeded label shuffles; and a multiple-comparisons accounting of how many candidates would pass on noise alone.

"Nothing survived" is the most common honest answer and it is a real finding. Report it as one. Do not soften it into a hedge that reads like a conclusion.

4. Turn a survivor into a test.

python3 scripts/experiment_planner.py --hypothesis "..." --variable "..." \
  --cv 0.45 --effect 0.30 --posts-per-week 2 --max-weeks 12 --output human

CV comes from step 2: 1.4826 * MAD / median. Exit 0 feasible / 2 too long, with the minimum detectable effect in their window / 3 refused. It will frequently say the test needs more posts than a quarter allows — that is the honest answer, and more useful than a confident conclusion from retrospective data.

Rules

• Under 10 posts, describe; do not conclude. Say so plainly.
• A pattern in past posts is a hypothesis. Retrospective data is confounded — you made carousels when you had structured material, on topics you knew best, in weeks you had time. No statistics on the same data removes that.
• Never benchmark against someone else's numbers. Different denominator, different audience, usually a vendor's sample.
• Follower count is not a success metric. Track inbound conversations, specific references, invitations — the Tier 1 metrics you count by hand.
• Report the confidence level. LinkedIn-official 🟢, third-party study 🟡, folklore 🔴.
• One good post is not evidence. It is the most common cause of a strategy change and the least informative event available.

Scripts

References and assets

• references/linkedin_metrics_canon.md — what each number is, what it is not, and which three tiers to track (7 sources)

• references/evidence_thresholds.md — the four gates, forking paths, and the uncomfortable arithmetic of LinkedIn A/B tests (7 sources)

• assets/example_post_export.csv — a 12-post export in the expected shape

• assets/measurement_log_template.md — the Tier 1 outcome log you keep by hand

Distinct from

• marketing-skill/social-media-analyzer — cross-platform brand campaign reporting. This is one person's own LinkedIn export, with refusals attached.
• linkedin-strategy — decides what to do next. This says what happened.
• product-team/experiment-designer — product A/B tests with real traffic; here n is posts, and usually too small.

Version: 1.0.0