Guidebook 7 of 8
Measure What Matters
Some numbers go up and prove nothing. Here is how to tell whether any of this is working, in plain language and without a data team.
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What’s inside
8 guides, in reading order.
Is It Actually Working?
Some numbers go up and prove nothing. How to tell real effect from noise: the decision test, the one question that separates lift from luck, and the few numbers worth watching.
Vanity vs Real Metrics
Some numbers go up, feel good, and change nothing. The sorting test in depth: what makes a metric real, why vanity numbers survive, and the practice of demoting them.
What to Actually Measure
The index card: five numbers a busy practice can keep honestly — enquiries, booked share, source, days-to-paid, repeat-and-referral share — and the monthly fifteen minutes that reads them.
Attribution Is Mostly Guesswork
Why 'this channel drove 14 enquiries' is a guess wearing a report's clothes: the last-click problem, the multi-touch reality, and how to hold attribution claims without being fooled.
Would It Have Happened Anyway?
The single most useful question in marketing: incrementality in plain language — the difference between what happened and what would have happened anyway is the only thing you ever buy.
Testing Without a Data Team
The holdout method: a real experiment any practice can run with a calendar and honesty. How to pause, what to watch, how long to wait, and reading the result without fooling yourself.
Why Your Numbers Don't Match Your Sales
The dashboard says growth; the bank account shrugs. The usual causes of the drift, in the order to check them — counting differences, timing lags, funnel gaps, and phantom wins.
Did AI Send That Customer?
The newest blind spot: clients who arrive from an assistant's answer leave almost no trace. Seeing AI-referred work — imperfectly but usefully — before your dashboards can.
Every guide in it is free to read on this site. The guidebook is the same words, laid out to read in one sitting or to send to someone.