Data Analytics

Data analytics that measures outcomes, not activity

Analytics is the step between having data and doing something different because of it. That sounds obvious, but a lot of analytics work stops at description: sessions, clicks, open rates, row counts. Those are activity measures. They tell you something happened. They rarely tell you whether it helped.

This section covers measurement plans, marketing analytics, customer analytics, operational analytics and predictive analytics, always tied back to a business decision. We look at how to pick the handful of metrics that track outcomes, how to spot when a correlation is being sold as a cause, and how to report uncertainty instead of hiding it. Expect worked examples with the arithmetic shown, and plain notes on where each number comes from.

Coming up here: the mistakes teams make most often with data analytics, and a first-month plan for anyone starting from a spreadsheet and a CRM export.