Analytics – what we use and why we use them

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If you’ve arrived here via a link on one of the websites we manage – please, be assured that whilst we study websites and the way people interact with them we’re very much focused on how well the websites work and how people interact with them generally; we’re not looking at what individuals do on them.

If you find anything at all unsettling about any way we use analytics please let us know – we really want to provide business owners with the best information we can but we don’t want anybody to feel that we’ve stepped over into intrusive territory.

As GA3 nears its end of life and website owners and marketers face the prospect of losing access to historic analytics – from a practical point of view, we thought it might be useful to share why, what and how we use analytics.

The transition from GA3 to GA4 is not as easy as one might hope and if you’re intent on tracking conversions then it involves using Google Tag Manager – and that’s not very user-friendly either.

If you’re using Google ads extensively then you’ll:
  1. Probably have to transition to GA4 as it’s the only way you’ll be able to feed conversion data back into Google ads
  2. Probably want to look at Clicky Analytics as well because it’s a good way to monitor and reduce Click Fraud

Background: we provide insights on analytics for around 60 websites with a collective traffic of around 2 million visitors a year. Some of these are eCommerce and others are intended to generate enquires and/or bookings.

For practicality, we tend to use the following on all the websites we make or manage:
  • Google Analytics (GA3)
  • Clicky analytics
  • Metrica analytics
  • Microsoft Clarity

In the past we’ve also used:

  • Matomo – whilst we applaud the aims, we found it to be a bit of an inode hog and took up too much space on the server; we also didn’t find the data particularly accurate compared to other software
  • Crazy Egg – it’s been a few years since we last used it so it may have changed but we found the impact on loading time a little too much – we felt it was affecting what we were trying to measure
  • HubSpot – we understand it works for many people but it didn’t work well for us.

As in many things whether one software is ‘better’ than another is subjective and what is ‘best’ for one thing or person is not the best for another.

Because of the number of websites we run analytics on it’s not cost-effective to have any analytics that are priced on a per website basis.

Why do we augment GA3 with other analytics

Two reasons:

  • accuracy and
  • simplicity.
1. Accuracy – Google analytics has always had an Achille’s Heel or two.

One is sample rates and the other is the inability to give accurate times for eventless visits – the latter makes GA’s bounce rate a particularly misinterpreted metric.

Explaining the problem as simply as possible:

  • many website visits are one-page, actionless visits – visitor arrived > visitor looked > visitor left without clicking on anything whether the visit took 5 seconds or 5 minutes GA records it as a bounce
  • understanding these short, single-page visits is very useful for anybody with a website
2) Simplicity – it’s a faff!

If you’ve been through the faff of setting up Google Tag Manager and Google Analytics … to record clicks on tel links or mailto links as conversions you’ll know it’s way more involved than it could/should be – Tags, Triggers and Variables are fine if you’re a developer or tech savvy marketer but for most website owners it’s a faff too far.

Before we move on to other analytics packages it’s worth looking at one more aspect of Google Analytics that makes it less useful when you’re looking for deeper, actionable insights – the averages it provides are mean ie the average of all visits (aka something that rarely, if ever happens); other analytics gives mode averages – what happens most of the time – this again, is way more useful if you’re looking for what could be improved on a website.

Clicky analytics

We started using Clicky around a decade ago – it’s incredibly simple and has a similar approach to GA but it’s a little more trustworthy in terms of data.

We continue to use it for three things:

  1. a cleaner alternative to Google Analytics – out of the box it filters much of the spam that still afflicts GA
  2. a more accurate source of engagement data – Clicky by default pings at 30 seconds so we know how many are still on site after that
  3. a quick, simple way to monitor and reduce Google ads Click Fraud – if you’re running ads you’ll understand that a percentage of clicks will be fraudulent – the extent can vary on your industry and location but it appears that is some – let’s say estate agents in London – clicking on competitors adverts every day over a morning coffee is a popular sport!

How to spot/stop click fraud suing Clicky – each month click on Visitors > Most active and then right click and open in new tab on the top 10 to 20 visitors of the last 28 days – then have a look at each tab – you’ll quickly see whether any IP addresses have been clicking on ads repeatedly.

You can then block that/those IP addresses from seeing ads in Google ads (Campaign Settings > additional > Block IP) – you didn’t expect Google to make it easy for you to prevent click fraud did you?
Setting up goals/conversions is a 2 minute job – no tags, triggers or general faff required?

Metrica

This is the analytics we rely on most heavily as it’s particularly good. Follow us to learn more …

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