Noise or Music?

This definitive whitepaper sets out the critical requirements you need to have in place for your website (or app) to be consent compliant in a GDPR world. Backed by formal legal expertise.

Getting a website’s privacy and consent process done right is difficult. In fact, my research shows 98% of websites get consent wrong. In this post I walk through the 3 fundamentals that must be done correctly.

Cross domain tracking has always been difficult to get right in Google Analytics – and it still remains a challenge. This audit guide will help you get on top of it.

The discussion centres around whether we should aim to be data-driven or data-informed people.

Excluding or filtering internal i.e. staff visits from Google Analytics is an interesting challenge – it’s not as straightforward as you may first think. This is because there is no obvious way to identify a staff visitor… Hence this detailed post showing a possible alternative.

In this article I discuss why applying a filter is flawed and suggest a quick fix that may help you overcome its limitations. However the smarter and more robust way is to use a visitor label – a.k.a Custom Dimension.

Since GTM introduced an automatic way of tracking scroll depth in Google Analytics, there has been a proliferation of this tracking. In this post I explain why you should avoid such “noise”, avoid the cost implications (would you pay $1000/month for this?) and provide alternative (better!) ways to track engagement!

According to the ICO: analytics is not defined as necessary or covered under legitimate interest. That means the vast majority of websites are breaking the law!

But how seriously do we need to take this?

These are my best tools, or “add-ons” I use when working with Google Analytics and Google Tag Manager. They focus on helping you get you get on top of and maintaining your data quality. I use all of them regularly (listed in no specific order).

From the recent Forrester Prediction 2019 report:

57% of global data and analytics decision makers are still at the early stages of their insights-driven business. Only 8% demonstrate advanced insights-driven competencies.

Meaning, that despite the hype of recent years around machine learning and artificial intelligence, even the biggest companies struggle to extract value from their data at scale and drive impactful business results.

Mulling over this insight, I wanted to add my thoughts for why organisations are in such a low state of analytics maturity. Based on working with senior managers in large brand-leading organisations, these are my top 3 reasons:

Part of my job involves the audit of Google Analytics setups in order to establish the quality of the data collected. This video story brings together some of the extraordinary findings of my work. Its a study of 75 enterprise websites using Google Analytics. I describe the audit methodology and display the results in a visual scorecard format.

The results are somewhat surprising (and depressing) in that they show the general poor quality of data that organisations are working with – an average Quality Index score of only 35.7 out of 100. One surprising metric is that 1 in 5 websites had a PII issue i.e. were collecting personal information into Google Analytics reports…