Key takeaways
- A pillar page on analytics: how to build measurement that drives decisions, survives attribution limits, and connects to LTV and real outcomes
Table of Contents
Analytics fails when it becomes a reporting theater.
Good analytics is decision infrastructure: inputs, definitions, and feedback loops that let a team learn faster than competitors.
The minimum viable measurement model
- One north-star outcome (revenue, retention, qualified pipeline).
- 3-5 supporting metrics that you can actually influence.
- Clear definitions (what counts, what does not).
- A weekly cadence that leads to actions.
Attribution is limited (plan around it)
Attribution is useful, but it is not truth.
Cookies die, platforms hide data, users switch devices. The solution is not a better model. The solution is humility + redundant signals.
Supporting essay:
Connect SEO to outcomes without lying
SEO outcomes are delayed and multi-step. You can still measure:
- indexing and coverage,
- impressions by topic cluster,
- assisted conversions,
- and branded search growth.
If you only measure last-click, you will underinvest in compounding channels.
Build a clean data pipeline
A good pipeline is boring:
- stable UTM rules,
- consistent event naming,
- clean source of truth,
- and a small number of dashboards.
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