Analytics that matter: separating signal from vanity

Vanity metrics feel good in meetings and rarely change behavior. Follower counts, raw page views, and “impressions” without context are easy to game and hard to tie to revenue or retention. Pick a small set of metrics that reflect your model: activation, repeat purchase, expansion, or time-to-value.

Funnels leak; the question is where. If you measure only the top and bottom, you optimize blindly. Instrument steps that map to user decisions—signup, first success, payment, renewal—then review weekly, not hourly.

A/B tests need hypotheses and sample size. Peeking at results early produces false positives. If you cannot run clean tests, prefer before/after with clear documentation of what else changed in the market.

Collect only what you need. Aggregated analytics often suffice for product decisions; identifiable trails require stronger governance and consent.

Review dashboards monthly: remove one chart that nobody acts on. If a metric has no owner, it is decoration.

When you launch a feature, predefine success metrics and failure thresholds. Otherwise you will argue outcomes from anecdotes.

Correlate marketing spend with qualified pipeline, not only clicks. Attribution is imperfect; directionally correct beats pretending precision.

Document data definitions. “Active user” should mean the same thing in analytics, sales, and exec reviews.