Analytics

Have you ever stopped to think about what the chain of events was that led you to a particular decision? Maybe not, but in web analytics it is something that should be considered. After all, there is something counter-intuitive about analytics tools such as Google Analytics, which require us to think in terms of clicks and recorded events that occur on the website, during the visit.

Thankfully, we have evolved as a species, and we no longer place too much emphasis on last click attribution. In short, last click attribution is a model where success of a particular goal is attributed to the conditions underlying the active session. So if a person visits a page via bookmark and downloads a PDF file on that page, the direct visit channel (which covers, by default, bookmarked visits) is the only one which receives the kudos for the file download. Nothing is attributed to other channels, which might have exposed the user to the brand, the product, and the file itself. Ridiculous, right? R.I.P. last click attribution!

(Last updated March 2014) This post is the final chapter of a trilogy. The ultimate refinement, if you will. It all started with my foray into the murky waters of context, when I tested how weather data could be used to provide extra information about site visits. When I wrote that post, I had two trepidations: 1) does sending the API call with every single page view affect site performance negatively, and 2) does forcing the page view call to wait for the API call to complete affect the quality of visit metrics.

There is a new version of this post for GTM V2 here.

While going over my previous post about using weather conditions to segment data in Google Analytics, I started thinking about performance issues. Since I’m using a visit-scope custom dimension, it seems futile to have it send the weather details with every single page load. The odds of the weather changing drastically during one visit are slim (unless you live in the UK), and I have yet to come up with a good reason to change my on-site behavior because the weather changed from a drizzle to a downpour.

(Last updated June 2014) Google Analytics provides us with a nifty way of tracking social interactions. With a simple plugin, you can track how many +1s and Likes your pages accumulate.

This guide shows you how to activate social interaction tagging with Google Tag Manager and Universal Analytics. The instructions are for Facebook Likes, Google+ +1s (now deprecated since Google Analytics tracks +1s automatically), Twitter Tweets and Pinterest Pins.

Note that if you use a third-party API (e.g. AddThis) these tricks probably won’t work. You’ll need to use the API functions themselves to enable tracking.

UPDATE 9 Oct 2013 - This works in Google Spreadsheets now as well! Feel free to make a copy of the sheet and work with the code. It’s still just a prototype, but I’m happy to see data bouncing from spreadsheets to Google Analytics.

View the Google Spreadsheet here

ORIGINAL POST Inspired by Daniel Waisberg’s excellent post detailing the use of Google Web Forms to send data to your Analytics account, I decided to create something similar.

I’ve had such a blast in the Google+ Google Analytics community. Not only are the people super-duper-nice, but I have the wonderful opportunity of helping people with their Google Analytics problems without feeling obligated to invoice them or ask for compensation for my troubles. I do it because I love to help, because I feel like I have a lot to share with the community, and because I’ve always believed that the more knowledge you share the more you accumulate.

There is a new version of this post for GTM V2 here.

The Google Analytics Summit came and went, and thanks to the Live Stream, everyone could participate. We were treated to a rapid-fire selection of Google Analytics’ new features, and this post sheds light on one of these in particular: automated event tracking in Google Tag Manager.

Auto-event tracking introduces a nice feature, which does what tag managers ought to do: it provides functionality without HTML template editing. This isn’t always a good thing, since automation is usually generic and only works for a couple of viable scenarios, but especially for these generic use cases, this new feature is a great addition to GTM’s already impressive feature list.

So here we are again. Universal Analytics and Google Tag Manager, the dynamic duo, ready to strike again.

First, remember to check my previous two tips for UA and GTM use in custom scenarios:

• Weather as a custom dimension
• Tracking page load time

In this post, I visit the idea of adjusted bounce rate, which I came across a year ago in the Google Analytics blog.

Adjusted bounce rate basically refers to tweaking the traditional bounce rate collection method (single engagement hits / total visits) so that visits which only included a single page view would not count towards a bounce, as long as they met some qualitative requirements.

In Google Analytics, you can monitor your site speed and get a decent overview of what pages are contributing positively and negatively to site speed. The problem with page load time metric is that it’s an average based on a sample. You can modify the sample rate with setSiteSpeedSampleRate(), but for me that’s not bloody well good enough.

(UPDATE 28.3.2014: This post is still valid, but an implementation with User Timings is a much smarter way to measure actual page load time. I’ll be doing a short update at some point in the near future.)