Tom Capper's Posts



How to Diagnose and Fix a Self-Referral in Google Analytics

A self-referral in Google Analytics is a session where the source is your own site. For example, on Distilled.net in September 2015, we had a bunch of sessions show up in Google Analytics like this:

This is often ignored or considered innocuous, but it represents something very wrong with the sessions it represents. It doesn’t make sense for a user to have arrived on your site via your site - there must have been an original source, and we want to include every subsequent hit after that original landing as part of one big session, providing the user doesn’t leave and come back via some other channel, or go inactive for a long period of time (neither of which would appear as self-referrals).

Back in June, I wrote a post discussing the various ways in which Google Analytics can split sessions, with highly misleading results, and self-referrals are often a symptom of this.

Continue reading >>

When is a Session not a Session?

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.

And yet, we put a lot of stock in these concepts - businesses are sold on how many visits their site received in a year. We optimise for conversion rate, a metric calculated using session count. SEOs, ad agencies, consultants and marketing managers can all have targets of a growth in organic sessions. Distilled’s own creative pieces are often judged by clients in terms of how many visits they received. 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.

Continue reading >>

A Comprehensive Guide to Tracking Offline Interactions in Google Analytics using the Measurement Protocol

The trouble with web analytics is the possibility of it telling you lots about your website but nothing about your business. A browser is not the same thing as a customer, and yet we forget this in the data that we use to optimize our marketing efforts. Using Google Analytics, part of the solution to this problem is User ID, which allows us to track users as they move between multiple browsers, as long as they log in along the way. However, a lot of the most important interactions in a customer’s journey might not take place in a browser at all - instead they’ll take place in a shop, or over the phone, at an event, or in the customer’s inbox. In these cases, it might be that you can draw together these interactions with your existing Google Analytics data using Measurement Protocol.

Continue reading >>

Statistical Forecasting for SEO & Analytics (and a Free Tool!)

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.

Using the tool

You can access the tool at distilled.net/forecaster. It utilises the CausalImpact R package which you can read about in this paper published by Google if you’re so inclined, but don’t worry if you’re not - the precise purpose of the tool is to make these methods accessible.

Continue reading >>

Quick Fix for Referral Spam in Google Analytics

There’s been a lot of talk recently about referral spam and how it’s ruining everyone’s analytics data. While this isn’t cause for panic, it is very annoying, and depending on the size of your site it could be having a very meaningful effect on your data.

Most solutions I’ve seen talked about so far involve maintaining a list of spammy domains, which seems impractical at best. In this post I’ll outline two filters which in most cases should exclude the vast majority of referral spam  and require zero maintenance. Lastly, if you want to go the extra mile and filter out those last few spammy sessions, I’ll outline a low-maintanance option for that, too.

Background

Firstly, if all this is news to you, check the referring domains report in your Google Analytics account and see if it contains any of these:

GA referral spam domains

Continue reading >>

< older posts