Google Analytics DIY – Replicating 360 Functionality in GA

Some of you out there might be lucky enough to have the budget to afford Google Analytics 360 with all its bells and whistles. But, for those of you who maybe aren’t ready to invest the big bucks quite yet, this article should hopefully give you a few tips and tricks that will allow you to go away and get some of those paid for features without having to spend a spenny.

The majority of clients that we work with do not have the paid for version of Google Analytics so there are a number of processes we’ve refined over the years that allow our clients to get the most out of their free analytics packages.

What are you buying with 360?

Firstly, what are you actually paying for when you invest in Google Analytics 360? Well, as you might image there are a number of excellent features: 

• Big Query integration*
• Salesforce integration*
• Advances analysis
• Access to raw data
• Advanced funnel reporting
• Attribution modeling
• Increased views
• Larger data quotas
• Additional custom dimensions*
• Fresher data
• Unsampled reports*

These are all great features, however in this article we are going to be looking at the points that are highlighted in the above list and how we can get those in the free version of the platform.

Creative thinking

Google Analytics gives us a set of tools or reports that allow us to look at our data in a certain way. Whilst this is obviously the most straightforward way of using the platform, with a little creative thinking around these tools we can create bigger and better uses for them where we can get even more insights out of our data. So let’s look at a few DIY feature jobs that we can undertake.

Unsampled reports

The first DIY project we are going to look at is unsampled reports. Many of us have probably faced the situation where we go to overlay a segment or a secondary dimension onto a report and all of a sudden a yellow tick appears in the top left corner telling us that our report is now being sampled.

There are 2 ways around this. Firstly, if we spend £120k on Google Analytics 360, we will get a button attached to all reports that allows us to create an unsampled report. Alternatively however, there are a number of free products out there which utilise the Google Analytics API and come with a feature called minimise sampling. Examples include:

• Analytics Edge for Excel
• Supermetrics for Google Sheets/Data Studio

To understand how these products work, we need to first look at why Google Analytics samples data in the first place. There are a number of factors involved in the rate of sampling, including the type or quality of dimensions, however a big factor is the amount of data being processed.

The amount of data in a report has a very linear relationship with the time span of the report – the greater the date range the more data in the report and therefore the greater the sampling levels.

What the aforementioned minimising sampling feature does is, if you are running a report over a 10 day period, it will instead run 10 reports in 1 day increments and stitch them together. Each of these 1 day reports will have either a significantly reduced sampling rate or no sampling at all due to their reduced size.

This is a really easy way to reduce the size of the sampling and very simple to implement.

A note of GA4:

It’s worth noting that these DIY projects are focused around Universal Analytics, however, Google Analytics have recently released a new version of the platform called GA4 which has a very different engine under the hood.

So what does sampling look like in the new GA4 platform? Well the short answer is that it’s still a thing. It works slightly differently, but fundamentally you have a maximum limit of 10m hits per query – above this sampling will occur. 

It’s early days for the platform so we will see how some of the third party platforms discussed previously adapt to these limits to offer us options around minimisation of sampling.

CRM integration

Any company that generates leads online and converts them offline will have encountered the problem at some point of connecting up historic customer browsing data like pages visited and channel attribution with lead information like quality and revenue generated. 

Google Analytics 360 solves this problem by introducing a native connector for SFDC that allows you to feed information from this particular CRM through to Google Analytics. This is a huge benefit as it means you can start to optimise your campaigns towards quality leads rather than just form submissions.

It is however perfectly possible to build this connection yourself by utilising one key element – the User/Client ID. This is the ID that Google Analytics stores in a cookie and uses to identify unique visitors. If you capture this ID when a form is submitted you can store this as a field in your CRM. 

The next stage involves leveraging Google Analytics’ measurement protocol to start sending CRM data back into Google Analytics. If you send this data along with the Client/User ID then Google will automatically connect this up with any historic campaign/browsing behavior within GA.

This means you can start to feed data like offline revenue and lead status into Google Analytics (and therefore Google Ads also) and start to optimise campaigns around metrics that really count.

S.A.M Technology

If you’re paying for GA 360 or building this yourself sounds like a bit more effort than you’re looking for, then help is at hand. We have built a piece of software that connects a large range of CRMs (not just SFDC) to Google Analytics. We call this software S.A.M (Systematic Attribution of Marketing) Technology. If you are interested in optimising your campaigns towards the metrics that generate real value for your business then please do get in touch.

A note of GA4:

Native CRM connections are still not possible with GA4, so this is still a problem that we will need to overcome. However, there is a native connection with Big query included which gives us the option of merging our CRM data via this platform also.

Big Query integration

Big Query is Google’s database platform. It’s likely to become more and more prominent in the world of analytics so I would definitely advise people to get familiar with it. Having your data stored in a customisable database as opposed to Google Analytics has a number of different advantages including:

• Faster integration with DataStudio 
• Predictive modeling 
• Merging data
• No sampling

The basic idea of how this works is that you define the fields you want to extract via the reporting API, then upload that data to Big Query. The nuances of how this works are obviously a bit more complex but fuzzy labs do an excellent job of explaining how this works in the link below: https://github.com/fuzzylabs/google-analytics-big-query-importer

A note of GA4:

GA4 is a game changer in terms of the use of  Big Query. It comes with a native connector which makes it very easy to import all your GA data into Big Query. If you are looking at getting your GA data into Big Query going forwards then this is definitely the way to go.

Additional Custom Dimensions

Our final DIY project is custom dimensions. For anyone that wants a refresher on how they work there’s a link here: https://support.google.com/analytics/answer/2709828?hl=en . 

They are extremely powerful and allow us to enrich our analysis with custom dimensions and metrics that are specific to how our website works. The trouble is we only get 20 of them so there is often a decision to be made in terms of what we capture and what we don’t.

Unless of course we rethink how we report on them. The custom dimensions we want to focus on for this DIY project are ones with low cardinality (i.e. there are few variations). For instance, let’s focus on the scenario of when a logged in user where the dimension can be either logged in or not logged in. When there is a dimension with low cardinality, we can look to instead create a segment for each outcome and thereby free up a custom dimension.

As an example here therefore, if we wanted a session level custom dimension for logged in and not logged in users, we can instead look to create an event that fires when someone logs in. Next, we create a session level segment for sessions that include a logged in event and a second segment that don’t include a session level event.

If your dimensions are slightly more complex then you might also want to ensure they haven’t changed within a session (to replicate the behavior of custom dimensions). 

To do this you can use the sequence options within segments. An example here being account types. Someone might go from a free account to a paid account within a session. You might want to report on just sessions with free accounts created. 

In this case therefore, we need to exclude anyone that went on to create a paid account. We would therefore want to create a sessions level custom dimension where a free account event existed but exclude sessions where someone went onto create a paid account.

A note of GA4:

GA4 completely rewrites the way that custom dimensions work. You now have 100 custom dimensions and metrics and 25 user properties that work in a similar way to custom dimensions. Gone however are session level custom dimensions!

Conclusion

So what are we to take from all this? Essentially Google Analytics gives you a set of tools and tells you how you are meant to use them. But with a small amount of DIY and creative thinking we can adapt those tools to create even greater levels of insight.