Today I’m going to show you how to harness data on the real-world usage of your website to shed light on what’s breaking and where.
Last week, Google announced that in July 2018 it would make another major stride towards the complete normalisation of HTTPS encryption. Version 68 of the Chrome browser will be the first to explicitly mark all HTTP pages (i.e. every URL served over the legacy protocol) as “not secure”. Operating a secure checkout on a predominantly insecure site is no longer a viable option.
While moving to HTTPS is easier and cheaper than ever before, it is nevertheless vital that any protocol migrations be carried out carefully and with SEO oversight. The onus is on you to ensure a smooth transition, and one of the most common roadblocks is mixed content.
When it comes to direct traffic in Analytics, there are two deeply entrenched misconceptions.
The first is that it’s caused almost exclusively by users typing an address into their browser (or clicking on a bookmark). The second is that it’s a Bad Thing, not because it has any overt negative impact on your site’s performance, but rather because it’s somehow immune to further analysis. The prevailing attitude amongst digital marketers is that direct traffic is an unavoidable inconvenience; as a result, discussion of direct is typically limited to ways of attributing it to other channels, or side-stepping the issues associated with it.
Last week, the Google Tag Manager team launched the Element Visibility trigger. If you’re not excited by this, you should be. In this short post I outline how to use and configure this trigger and its associated new built-in variable types, and offer a few tips for how to derive actionable insight from element visibility tracking.
In the three and a half years since I launched this website, a lot has changed. Since I last wrote, I’ve refactored my CSS, switched hosting provider, changed my development toolset, migrated to HTTPS, moved to GitHub, and lots more. In fact, until recently, the only thing that hadn’t changed was my approach to actually adding content: while on other projects I’ve experimented with a variety of CMS and frameworks, this website has remained firmly hand-coded.
This post sets out to solve a very specific problem: namely, how to balance the competing demands of a platform-agnostic Data Layer and the Enhanced Ecommerce plugin for Google Analytics. This might sound niche, but you’d be surprised how many large ecommerce websites using Google Tag Manager eventually run into this challenge.
Data-driven marketing means understanding what works. This article explores the ways in which the Custom Dimensions feature can supercharge your Google Analytics reporting setup with actionable insight into the ROI of your marketing activities. I run through several practical examples before diving into the various options for implementation.
Aside from the duplication issues which are inherent to URL parameters, UTM tracking is a pain for marketing teams to implement and maintain. I’ve recently experimented with an alternative campaign tagging method; by combining hash fragments with GTM lookup tables, you can retain many of the benefits of UTM parameters while negating (most of) their drawbacks.