This module shows how to measure whether the visitors a channel brings actually return over time, rather than judging channels only by how many people they deliver. You will explore why returning visitors tend to cost far less than new ones, how a cohort table groups people by when they first arrived and tracks their return week by week, and how to break that table down by acquisition channel and switch between counting any return and counting only returns that end in a purchase. These skills let you compare channels by the loyalty and revenue they produce, using a view that a standard traffic report cannot provide.

This module shows how to make business-specific details visible in Google Analytics reports by registering them as custom definitions. You will explore why custom information a site collects stays hidden until it is registered, how the scope of a definition — whether it describes a single action or a person — changes the report it produces, and how to register text labels, numeric measures, and metrics calculated from existing figures within a property you control. These skills let you set up the fields needed to answer questions that the standard, built-in reports were never designed to address.

This module shows how to locate the exact point where an online store loses shoppers during checkout, rather than relying on a single overall conversion rate that reveals only that a problem exists. You will explore why the purchase process behaves as a funnel with predictable drop-off points, how Google Analytics records shopping as a chain of named events, and how to build a step-by-step checkout funnel, split it by device, and identify where losses are heaviest and for whom. These skills turn a vague sense that "sales are low" into a specific, assignable finding about a particular step and a particular type of visitor.

This module shows how to turn recent analytics history into a defensible estimate of near-future traffic and revenue, using free tools. You will explore why most marketing decisions already rest on an unstated forecast and how a baseline, a target, and a scenario differ, how any metric can be separated into trend, seasonality, and random variation, and how to project a trend line into a spreadsheet, state its likely error, and use built-in predictive metrics to identify visitors at risk of not returning. These skills let you offer a forecast expressed as a range with a stated margin, rather than a single confident guess.

This module shows how to set up automatic monitoring in Google Analytics so problems surface on their own, without depending on anyone remembering to check the reports. You will explore why manual daily checks tend to lapse over time, how alerts, scheduled reports, and dashboard deliveries each serve a different monitoring need, and how to build a custom alert that emails you when a metric behaves unusually, choosing a threshold that fires rarely enough to stay trustworthy. These skills give you a monitoring setup that catches genuine issues promptly while avoiding the alert fatigue that makes over-eager notifications useless.