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Preparing for Google Analytics 4.

In March, Google dropped a data bombshell by announcing it will be removing all previous analytic formats such as Universal Analytics (UA). From June 2023, for most organisations, the new Google Analytics 4 (GA4) will be the only viable interface with which to continue collecting data.

To rub salt into the wound, Google then followed up by announcing it will be deleting all data predating GA4 from Google Analytics in January 2024. 

Focusing on the more immediate issue, we’re now less than 12 months away from when Universal Analytics will stop recording data. The clock is ticking.

At this stage we are advising to get GA4 up and running asap and also keep it running in parallel with Universal Analytics. This way there will be plenty of comparable data between the two, before UA is turned off.

Have you started preparing for the move? Is Google Analytics 4 still fairly new to you? In this blog, we’re unpacking exactly what GA4 is, how it differs from previous interfaces, and our tips to successfully make the move without jeopardising any data during the migration process.

A little side note: this article covers setting up from a more advanced, technical perspective. This will most likely beneficial for those in roles that involve managing websites, analytics such as analysts and web managers.

What is Google Analytics 4?

Interestingly, GA4 isn’t a completely new interface but a newer iteration of what was initially called ‘Google Analytics web+app’. The drive behind this new interface originated from the need for Data Analysts and Digital Marketers to view data in one central port.

Users typically move between apps and websites as they interact with various brand touchpoints, meaning data is created across many different mediums which can be difficult to capture in its entirety, hence the need for a more holistic interface.

With a growing focus on privacy in the industry, Google sought to develop more complex systems for ‘cookieless’ measurement, and behavioural and conversion modelling. 

In Google’s own words, “GA4 is designed for the future of measurement”.

 

How is this different from previous versions?

  • Events 

This ‘app data’ focus really separates the way GA4 works from ‘Universal Analytics’ (UA). At its core Google Analytics collected data through sessions and pageviews. But apps don’t have pages, and people use them in very different ways to a typical ‘session’ on a website. 

So this is where the key difference comes in, GA4 records everything as an event. Event-based tracking allows for greater insights to be derived about users and their interactions. Admittedly, as experienced users of Universal Analytics, we’ve found this to be the hardest part to adjust to due to familiarity. 

The move to event-based tracking allows GA to automatically track the majority of engagement events marketers have been used to manually setting up themselves. Now with the click of a button, marketers can automatically track ‘automatic enhancements’ such as: scroll tracking, outbound links, site search tracking, video engagements and file downloads.

  • Goals are No More 

In what feels like a move to better align language, ‘Goals’ are no more in GA4, ‘Conversions’ will  replace them. The process for making conversions has also been simplified in comparison to how you would have previously setup a ‘Goal’. Now you will easily be able to turn an event into a conversion, without having to remember the exact ‘event label’ and ‘event category’ you have used! The move to event-based tracking does mean that destination url goals will be confined to history, and not make the port into conversions. These goals will need to be switched over to events when ported across into your new property. 

  • A New Interface 

The new analytics format also brings with it a new User Interface (UI). This replaces the old interface  more visually inline with some of Google’s other products. This feels like an underlying theme in the more ‘front-end’ heavy changes you will experience with GA4, bringing one of Google’s old products inline with its growing product range.

There are changes to the default data retention period, shortening from effectively infinite retention to 2 months by default. This only affects user-level data (associated with cookies and advertising identifiers) so won’t impact basic reports, but will limit data reporting for any custom reports in the ‘Explore’ section. This change will likely see a reasonable difference when comparing repeat visitor reports between the two analytics types. Something to keep an eye out for.

GA4 also sees the ‘views’ function being removed. At a property level, you now add each website and app as a data stream. All settings that you would previously have set at a view level are now either property-level (IP filtering, conversions etc.) or view report-level filtering (domains etc.).

How to make the move:

Over time you will want to develop your use of more specific GA4 features but in the meantime the priority should be getting data collected, and in a way which is readily usable for fellow members of the marketing team and your organisation.

Here we detail our process that we have been using with our clients. But it is worth noting that our process includes two assumptions:

  • You have an existing Google Analytics account (using Google Analytics Universal Analytics).
  • You are using Google Tag Manager on your website to trigger your Google Analytics.

On this basis we have a 5 step process:

  1. Audit your existing Google Analytics data and goal setup
    1. You have the opportunity to start from scratch without any legacy issues. So this means you can leave old views and goals behind.
    2. When auditing the goals our checks cover 4 key elements:
      1. Is it still relevant to you?
      2. Is it working correctly?
      3. Is it recording data?
      4. Is it transferable to GA4?
    3. Get a second opinion. Before you decide to leave a goal behind, just make sure no one else is currently using this in their reporting.

This gives you a thorough understanding of what your data recording situation is and the scale of work needed for your migration to GA4.

  1. Now you can create your new GA4 property
    1. Google Analytics has a great wizard to help you (at a top-level) create a new GA4 property from your existing Universal Analytics property.
    2. Create new data streams for each website or app that you will be using the new property for. You will find, for each data stream you get a new set of pretty useful settings, as well as extra reporting uses.
    3. Make sure you match up some key settings for each data stream such as IP filters, with your corresponding settings in UA. So you can keep the data as actionable as possible without diluting with internal traffic sources (like employee site visits). 
    4. This is also the place to enable automatic enhancements. Which we would definitely recommend doing (for beneficial reasons mentioned above in the events section)
  2. Then head on over to Google Tag Manager, and enable your new GA4 configuration tag.
    1. Copy across your new measurement ID and enable this tag to fire a pageview.
    2. Then utilise the same triggers as you currently use on your existing Universal Analytics pageview tag.
      1. Do make sure to use the exact same triggers as you currently use, including any connected to your cookie control management.
    3. Data streams can take 24 hours to start showing data coming in so you will need to wait a day (or two) to check the data is coming in accurately.
  3. Now that data is coming into the property, head over to the events report and check how many of your old goals are being automatically reported by GA4’s new events report.
    1. For any events which are not yet being pulled through, you will need to create new GA4 Event tag in Google Tag Manager.
    2. For the new GA4 tags, you just need to mirror the existing UA tags, but with the new GA4 Events tags. This means, utilising the same triggers (including any cookie consent requirements).
    3. We always recommend previewing and testing those events before publishing the tags on site – just in case.
    4. Then as before, wait around a day to see if that date is now pulling into the event report.

Once your old goals are pulling into the event report it’s time to ‘upgrade’ some of those organisationally important ones into conversions.

If you are currently using ecommerce tracking through Google Tag Manager then you may be able to port across to GA4 with limited technical support. GA4 ecommerce utilises events too, in specific, any event which is named ‘purchase’ GA4 will deem as Ecommerce and pull that data into its ecommerce report.

GA4 does come with more sophisticated ecommerce tracking, as standard it is similar to UA’s enhanced ecommerce. Currently you can’t utilise GA4 ecommerce (fully) and UA ecommerce, at the same time and we definitely wouldn’t suggest binning your UA ecommerce yet. As a compromise, you can gain the same data as standard GA4 ecommerce into GA4 through a couple of custom variables. Truthfully this will be enough for most people, for now.

  1. GA4 in tag manager utilises the ‘data layer’s’ “purchase event” push. So you will need to adjust your trigger to utilise a ‘purchase’ event.
  2. You then need to create some new custom variables in order to translate your existing datalayer into the relevant information for GA4. Then pull them into a new GA4 tag, so they look like this:

 

A snapshot of configuration on Google Analytics 4

  • In our experience the currency code is necessary to feed that data into the GA4, although (at the time of writing) this is currently missing from Google’s own support pages. In our tests, without it the value data fails to push to GA4.
  • As always, you should then go into preview mode and run a test donation to check that the new ‘parameter names’ contain the relevant information. 

This should cover the essentials for you right now. As you go about the port to GA4 we recommend utilising preview mode on Google Tag Manager as much as possible, to ensure that you can see any issues prior to publishing.

If you do encounter any issues or want to talk through getting some support on your migration to GA4 drop us a message, we’d be happy to answer any questions, chat through the process, or see how else we can help.

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    Cross Device Remarketing & Tracking In Google

    Cross Device Tracking

    Why is Cross Device Tracking So Important?

    Well right now you’ll be reading this blog on a phone, tablet or desktop computer. Research from GlobalWebIndex shows that, on average, the typical digital consumer owns 3.64 connected devices and the average British household now owns 7.4 internet gadgets, as well as a YouGov survey showing that 11% of households owning three or more tablets. With devices now outnumbering people, accurately tracking across all them has never been more important!

    Research from Go-Gulf shows that 80% of consumers bounce between gadgets and cross device tracking is a way of identifying these users across multiple devices. In Google, this means that if someone is logged into their Google account across their phone, tablet and laptop, they can be tracked across all of them.

    74% of marketers said matching customers across multiple devices was one of their top priorities. We agree as it is important to know the step-by-step journey of a single user in order to get a more complete picture of their whole user journey and online persona. This can help you to optimise accordingly and improve your marketing strategy.

    More specifically, it allows you to identify users who have visited your site on one device and then retarget them on different one, as well as allowing a more encompassing ad strategy that follows users across devices, targeting them with the most relevant ad on the most appropriate gadget for that user or ad content.

    From May 2017, “remarketing Audiences created in Google Analytics will be enhanced to automatically take advantage of new cross device remarketing functionality now available in AdWords and DoubleClick. This will allow you to reach your customers across devices when using Google Analytics Audiences… With cross device remarketing in AdWords and DoubleClick, if someone visits your website on one device, you can now reach them with more relevant ads when they search or browse on another device”

    This is great news as it’s estimated that 40% of online transactions involve multiple devices along the way, and so being able to remarket specific ads to an individual user across their mobiles, tablets and laptops, and  build a marketing strategy around these users and their behaviour is really valuable.

    For example, a user might start their journey by visiting your site on their mobile and viewing a product, you can then remarket them with a relevant ad for that product on their desktop during their lunch break, and then remarket to them again on their tablet in the evening if they have bought that product with an ad about a complimentary product. This strategy works well on Facebook but is only now available for Google now that cross device remarketing is available.

    Since 67% of people have used multiple devices sequentially to shop online and 25% of all cross device transactions completed on desktop started on a smartphone, being able to track and adapt marketing strategies for this kind of behaviour can increase conversions, helping you to better optimise campaigns and retarget more effectively in the future.

    Cross device tracking and remarketing isn’t only important for advertisers. 87% of consumers see value in being recognised with personalised experience across all devices and I have to agree. Being able to serve even more relevant ads can only help create a more seamless experience for consumers.

    Also, the opportunity to reach the right users is bigger as you can target them on different devices at the right time. If there is no cross device remarketing and someone originally visits your site on desktop but then only uses their mobile in the evening, your ad wouldn’t be shown to them. But this problem is now reduced.

    It also limits overlap and overserving of ads as one person (logged into their google account across all their devices) is now considered one unique user, rather than potentially two or more depending on whether they are on their mobile, tablet or desktop. This means if someone visits your site on their mobile, desktop and tablet over the week, your remarketing ads won’t bombard them on all devices constantly, thinking they are 3 separate users who have visited your site. It will know they are one user  and so you can cap how many times they are shown your ad.

    There is a downside to cross device remarketing as it only works for those logged into their Google account across their devices. Therefore, anyone without a Google Account, or not logged in, cannot be tracked.

    In this way, Facebook certainly keeps the advantage as people tend to remain logged in when using social media, making cross device tracking and remarketing more effective. However, since Google claim that approximately 60% of people are logged in when browsing, and with an estimated 1 billion Gmail users alone worldwide, there is still a considerable number of people being tracked and cross device remarketing is a step in the right direction for Google.

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      Google Analytics Home: The Good, The Bad, and The Ugly

      New Google Analytics Home Page

      Google Analytics Home

       

      Google announced in a blog post last month that they were implementing a new landing page for Analytics accounts, called Home, that would be released globally at a later date. That date, it turns out, was this week, and we can now see what the newest addition to Analytics can do.

       

      Analytics Home Page

       

      What you will be greeted by when you enter the new Home

       

      Those of you who read my last blog on the new AdWords interface will likely recognise some similarities to its overview tab, with visualisations such as graphs and heatmaps summarising the reports that already existed within Analytics. Much like the AdWords update there is no new data here, it is instead an attempt to make that data as easy and intuitive to interpret as possible. So the real question is, do they succeed?

       

       

      The Good – Simple, Intuitive Data At A Glance

       

      The attempt to make the new Analytics Home intuitive begins before you even look at any graphs. Each visualisation is given context using a question above it, such as “How do you acquire users?” or “When do your users visit?”. This is a small addition to the interface, but it means that once you begin looking at the associated graph or table you already know what the data is going to be telling you. This saves you time you would otherwise use working this out. Similarly, below any visualisations summarising a report there is a deep link to the report itself, so if you see a dramatic drop in users you can instantly drill down into the user report to try and understand why.

       

      You then reach the visualisations themselves. The Home will adapt to the features you have implemented on your account. So if you have Ecommerce set up, for example, a new snippet will appear summarising your transactions. The form the visualisations take is varied, from line charts to heatmaps, and some provide intuitive insight into the data, such as the source/medium graph.

       

       

      Referral Sources in Analytics Home

       

      The same graph can display channels and referral sources

       

      The graph not only provides you with a quick ranking of your acquisition channels, which can be read at a glance, but also tracks the user trends in the last seven days. It is easy to read, and yet can give you some key insights to drive your marketing strategy. I would like to see day names displayed alongside the dates to avoid any confusion over weekly trends such as the weekend slump, but apart from that this is data visualisation done right.

       

       

      The Bad – Too Much Information

       

      However, not all the visualisations are as easy to read as the source/medium graph. In particular, I found the retention rate snippet particularly confusing. Its strange axes confused me initially, with weeks seemingly on both axes, along with percentage values and user dimensions. Even once I had worked that out, the heatmap made it seem like percentage of users had increased in week three for one of the data sets, which, it turned out, was not the case.

       

      Week Data in Analytics Home

      What is Week 2? Why are All Users dates? All questions I asked myself when I saw this.

       

      The Ugly – Fifty Shades of Blue

       

      An issue I have with all the visualisations is the colour scheme. I understand the need to stay on brand, but the differing shades of blue are too similar, and makes it far too easy to confuse dimensions on graphs. For example, the reason for the confusion on the heatmap previously was an optical illusion, caused by the shades of blue. Google’s logo on its own contains three more colours, so why can’t we see some of those here?

       

      Some snippets also seem to forgo the visualisation of data entirely. For example, the pageview snippet simply consists of a list of pages, with their view tallies. This feels out of place among the pie charts and segmented bars of the other snippets. Compare this to the keyword, or new search term snippet in the AdWords interface, which provide a visual approach to a list of data such as this. I would have much preferred one of these implementations, especially since it provides much more meaningful information at a glance.

       

      Keyword Data in Analytics Home

      The subtle heatmapping in the new AdWords keyword snippet (right) makes the chart much more intuitive than the new Analytics page snippet (left)

       

      In spite of some snippets falling short, and the colour scheme having a frustrating lack of variety, the new Analytics Home is a welcome addition to the system. It is perfect for those who have 5 minutes to look over an account, and want the top line stats presented to them in a way which communicates the trends in the data. It is also a good lesson in data visualisation, and its most important rule: Simplicity is key, no matter how complex the source material may be.

       

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        Google Tag Manager: The Case of Missing Containers

        Missing Google Tag Manager Data 2017

        Missing GTM Data

         

        Yesterday afternoon (23rd May 2017) Google Tag Manager suffered a serious issue. A large number of containers (including some of our clients) were mysteriously deleted by a blank user.

        We noticed this by chance, not through a notification from Google. The deletions also appeared to occur at random worldwide, and we advise you login to your account as soon as possible to check if your account has been affected.

         

        Google Tag Manager Change Log

         

        In cases where containers were deleted the tag manager account remained in place as normal, only the container was gone. Using the Google Tag Assistant extension for Chrome we could confirm that the containers had been completely deleted, and not just turned invisible.

        There are many users on the Google Product Forums who have this issue, with the longest thread filled with concerned users.

        While the issue seems to have been resolved, we strongly recommend making a backup of your container immediately in case the deletions occur again. To do this, login to your tag manager account and select admin from the menu at the top of the screen. From here, select ‘Export Container’, select the latest version and download the file. Be sure to do this for each container if you have more than one.

         

         

        Google have acknowledged these issues on their status page, but have not provided any meaningful detail as of yet, which we find slightly concerning. They have however, said they do not believe it is a security breach.

        Affected containers have been restored overnight, and although they weren’t immediately visible in the accounts, all accounts do appear to have returned to normal. It is however, worth keeping an eye on your account, and making sure the data recorded in Analytics seems accurate.

        The most immediate problem this issue has caused is the loss of user data being recorded. The most common use of Tag Manager is to record all visits to a website. In affected accounts, this means no visitor data was recorded for much of yesterday afternoon.

         

         

        While the loss of pageviews is not ideal, a far more serious problem is that many of these account would have been set up to record Ecommerce/conversion data, which will have been lost in the same way.

        In our case, we run PPC campaigns for some clients who have been affected. We now have no Analytics data for yesterday afternoon for these campaigns, so it is impossible to determine key metrics such as conversions, bounce rate and time on site.

        We eagerly await news from Google about this issue how and why it occurred. Until then, we are simply keeping a close eye on our Tag Manger accounts, to ensure all seems well.

         

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          Don’t Use UTM Tracking Internally – Use Event Tracking Instead

          utm-vs-event-tracking

          UTM vs. Event Tracking

           

          Tracking interactions are an important part of any digital marketing strategy, but it is important to implement to record this in the best way possible, without affecting your other data.

          UTM tracking and Event tracking are two way to measure interactions, but it is crucial you use them in the correct situations.

          For regular digital marketing updates and news announcements, why not stay follow us on Twitter or sign up to our monthly newsletter.

           

          What is UTM tracking?

          UTM tracking is brilliant at measuring external campaigns.  UTM tracking simply involves adding a number of parameters, to a url. When the link is clicked, Analytics receives this and interprets the url to determine many things, including, most importantly, the source of the click (eg. Google, Facebook etc.).

          For example, if we created a post on LinkedIn for one of our events, we could use the following url:

          www.upriseup.co.uk?utm_source=LinkedIn&utm_campaign=Digital%20Marketing%20for%20Charities&utm_medium=referral

          We can then easily filter our data in Google Analytics to only see people who have arrived at our site having clicked that particular link.

          There are five different utm parameters available to use:

          • Source – This identifies the source of the session (eg. Google or Facebook)
          • Medium – Identifies the marketing medium (eg. cpc or email)
          • Name – Campaign name or specific promotion (eg. January Sale)
          • Term – Should be used to identify the keywords used
          • Content – Used to differentiate ads (eg. when A/B testing)

           

          Google do provide a helpful url builder for this purpose, which given the desired UTM parameters, will provide the campaign url to use.

          UTM tracking could be used for:

          • PPC Advertising
          • Display/Banner Advertising
          • Email Marketing
          • Social Media Advertising

           

          Helpfully, plenty of services (including Google Ads & Bing Ads) have an option that will auto-tag all urls, which means no extra work for you!

          However, tracking internal interaction is a different matter. Examples of the type of interaction we may want to record on our site are:

          • Button Clicks
          • Video Plays
          • Form Completions
          • PDF Downloads

           

          However, there is a major issue here. When you track an interaction (eg. a button click) using UTM tags, you will lose the original source of the session when the user clicks on the button.

          The button click will overwrite the original inbound traffic source and replace it with the new source that was set in the UTM parameters. This also means that sessions will be double counted.

          For example, if someone enters your site after clicking an ad on Google, their source will be ‘Google’. If there is a button on the landing page with UTM tracking set up with a source of ‘Button’, then when the user clicks on the button, the source will change from ‘Google’ to ‘Button’.

          Everything the user then does on the site will now have a source of ‘Button’, and we will not be able to tell what the original source was. Clearly, this means the data will now be incorrectly attributed, which makes it very hard to evaluate the performance of each acquisition channel.

          For this reason, we strongly recommend that UTM tracking tags should never be used on any interaction within your own site.

          Instead, we would advise using events to track internal interactions.

          Events are extremely simple to implement using Google Tag Manager. Google Tag Manager is a free and incredibly easy way to manage adding small snippets of code to a site, and is something we would recommend installing to anyone who is not currently using it.

          In Google Tag Manager, we can give each event its own Event Tracking Parameters to distinguish them from each other. These parameters are Category, Action, Label & Value. It doesn’t matter how you complete these fields, but it is important to keep a strict naming convention across your site.

          We suggest these parameters are used as follows:

          • Category – Should be used to group interaction you want to track (eg. PDF Downloads)
          • Action – This should be what the user does to trigger the event (eg. click)
          • Label – The most specific identifier of the event (eg. An Evening of Ecommerce)
          • Value – This should be used when an event has a monetary value. Otherwise it can be left blank.

           

          I’ve included a portion of a tag we use on our site to track PDF downloads, which is the same as the examples given above.

           

           

          Once live, these events can then be handily viewed in Analytics by navigating to Behaviour > Events. However, a much better way of viewing these event completions is to create goals measuring the completion of specific events. Fortunately, Analytics makes creating goals extremely easy. The example below is the goal created to count the number of PDF downloads on our site. We simply configure the goal to trigger when an event Category equals ‘PDF Download’ and the action equals ‘click’.

           

           

          These goal completions can then be viewed in Analytics under Conversions > Goals. As mentioned earlier, the major advantage of this is that the original source of the user remains unchanged. This means that we can tell exactly where a user has come from, whereas the use of UTM parameters does not allow this.

          The easy creation of goals using event can be considered a bonus!

          If you have any questions about the ways of tracking on Google Analytics or want to know how we can help, please feel free to contact us. We’d love to hear from you.

           

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