Domain 1 of 6

Choosing What to Measure

The first decision, made before any tooling. What product analytics is for, the kinds of number a product produces, the north star metric and the frameworks around it, and how a measure is derived from a strategy rather than from whatever the tool reports by default.

Concepts

~15%

Of the exam

Practice questions

Concepts in this domain

01Product analyticsWhat product analytics is, how it differs from business intelligence and from user research, the event as the unit everything else is counted from, and the single test that separates a practice from a wall of dashboards.02Product metricsThe four kinds of number a product produces, what each one is good for and how each one misleads, a worked case where the mean and the median disagree by the width of the whole population, and the property that separates a measure a team can act on from one that only ever rises.03The north star metricOne measure of the value a product delivers, the input measures beneath it that a team can move directly, the four properties a candidate has to pass before anybody commits to it, and the parts of a business a single number will never describe.04Product metric frameworksAARRR from Dave McClure in 2007 and HEART from Google's research team in 2010, what each one was built to solve, the Goals Signals Metrics process that sits underneath HEART, and the honest criticism of both alongside the north star.05Choosing a product metricDeriving a measure from the claim a product strategy makes, the goal, driver and guardrail roles a measure can play, the five tests a candidate has to pass before a team commits a quarter to it, and the two failure modes that leave a team steering by a number it cannot use.

Try a question from this domain

A train operator's finance team reports that season ticket revenue fell four per cent in March. The product team is asked what to do about it. What can product analytics contribute that the revenue figure cannot?

AAn account from season ticket holders of why they stopped renewing, gathered from the people who left.
BA more accurate revenue figure, broken down by route and by ticket type.
CA count of what people did inside the product, such as the share of renewal reminders that led to a renewal being started in the app, and the date that share moved.
DA forecast of what season ticket revenue will do over the rest of the year.

14 questions on this domain.

One per page, with a worked explanation.

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