This is my 2nd Whiteboard Friday appearance, but actually the one I recorded first. It’s about a pet peeve of mine, or at least something that seems to come up *constantly* in SEO work – the surprisingly finicky definitions in Google Analytics.
I’d long noticed on sites with multiple analytics setups, that traffic levels could differ even on unfiltered views. In this post, I tried to dig into the patterns in that data.
A bit of a rant about some of my biggest pet peeves in interpreting analytics, rank tracking or ranking factor study data.
Following on from some of my recent content and research around the importance of brand awareness for SEO, the next question should be how we can measure it with the same level of accuracy that we’ve become used to for other digital marketing KPIs. This post suggests a variety of ways to get started.
Hidden quirks, misleading metrics and tips and tricks for Google Analytics.
A self-referral in Google Analytics is a session where the source is your own site. This is often ignored or considered innocuous, but it represents something very wrong with the sessions it represents.
Don’t let data sampling lead you astray. Learn when sampling happens in GA, how accurate it is, and what you can do about it.
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.
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.