Sessions are pretty arbitrarily defined, all too easily inflated, and far more complex than most realise. It’s possible for apparent step-changes in Google Analytics reports to have little real-world relevance, and common for reports to show numerous mysterious and apparently inexplicable landing pages and traffic sources. It is therefore essential for Google Analytics users to understand what they’re actually talking about when they reference a session, and that’s what this post is all about.
How to track offline interactions using the Universal Analytics Measurement Protocol – what can and cannot be done with Measurement Protocol, and how to deploy it in practice. Primarily this will be focused on the use case of lead tracking, but the lessons here apply to any application of Measurement Protocol.
A walkthrough of the most common statistical pitfalls that marketers encounter in CRO testing.
Statistical forecasting is a powerful tool that’s been used at Distilled for a while, both by consultants when analysing client data and by our in-house monitoring tool that alerts us to problems with client sites. In this post, I’m publicly launching a free forecasting tool that I spoke about last week at BrightonSEO, and explaining how to make best use of it.
In this post I’ll pull apart four of the most commonly used metrics in Google Analytics, how they are collected, and why they are so easily misinterpreted.
Back in December 2013, I wrote a tutorial post showing how to find basic analytics data in Omniture, and explaining differences in terminology between Omniture and Google Analytics. Both platforms have seen some changes since then, so this refresh restores the guide to its original usefulness.
Out of the box, Google Analytics handles being deployed across multiple domains or subdomains extremely poorly. This quick easy post covers what you should be doing and how to do it.