Data Understanding

On-site Versus Off-site Web Analytics – 2. How They Work

This is Part TWO of a three post series. In this post I discuss the different methodologies for collecting web data – on-site versus off-site analytics tools – and how they work.

8 Recommendations For Choosing A Tool: on-site versus off-site analytics

This is Part THREE of a three post series. In this post I discuss how to keep your sanity when trying to compare numbers from your different tools.

Define: Digital Analytics

This question came form Manu Jeevan as part of his interview for the Big Data Made Simple blog. It made me think about something that has been troubling me for some time.

Not Provided: Now impacting 80-90% of organic traffic

I have tracked this issue since the beginning – i.e. plotting the percentage of organic traffic impacted by not provided. Google has now applied this to pretty much ALL visitors using Google organic search.

10 Micro Goals for Tracking Content Engagement

Assuming you have no other “macro” drivers on your site – for example, no e-commerce facility, lead generation request from, store finder information, or advertisement click-throughs – how can you measure content engagement?

Google Content Experiments – A good or bad feature?

I came across this quote: [Google Analytics] “Content Experiments sucks and I will never use it for any of my clients….run away“. My initial response: Whether you like a G product or not, to say that Google’s stats methods are unreliable is silly and lacks credibility. I am no fan of the Google Analytics Content Experiments, but I put my views into context here.

Calculating your REAL ROI for AdWords

The default Return on Investment (ROI) displayed in Google Analytics is misleading:
1: It combines revenue form your transactions and goals. That can lead to double counting e.g. if add-to-cart is also a monetised goal.
2: GA has no idea about what profit margins you operate under – how can it? Google therefore has to assume that *ALL* revenue is 100% profit.

Multi-Channel Attribution Modelling – don’t write off the default models

Avinash Kaushik is a great measurement thought provoker and I always come away from his posts challenged and simulated. The following post from him – Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models – is a great reference read, though I disagree on a couple of items. Here is my input.