Categories
Google Analytics

GA4: Migration Plan – The Helicopter Method

I know, you know the drill, but worth repeating anyway. In October 2020 Google announced GA4 as the new default property for tracking website visitors. The announcement was huge, it is the biggest thing to happen to analytics in nearly a decade.  If you want to get a quick summary of GA4 pop over here

Google is working hard on its PR machine, with key messages around dual tagging and all the updates it is making to GA4. This is leading to lots of questions about GA4, from ‘what is it?’ which then quickly leads to  ‘what should I do?’

Over the last year, along with the juggle of homeschooling, covid isolations and lockdowns, I have been working through GA audits and analytics training. GA4 is fast becoming the bulk of my work. People are asking questions and want to know how to get started. 

That is the big question isn’t it? Do you ignore it? Do you set it up? How deep do you need to go on your learning journey to get to grips with it? What are other people doing? Are they making the jump to migrate and dual tag? 

This post is breaking down my process: The Helicopter Method ©.  

Personally, and I hope you feel the same way, I prefer to break down tasks into smaller, bit size chunks. Working in phases or sprints helps me maintain a rhythm in my work, and stops me getting overwhelmed at a massive to-do list. 

Introducing The Helicopter Method © 

Why have I called this The Helicopter Method ©? Good question. To help explain how we got to this 4th version of GA, and to help people understand the work ahead of us, I have likened analytics to methods of transportation.

Urchin to GA4

If we think about analytics as a mode of transport then the first analytics Google brought out was in 2005, Google bought Urchin Analytics and ‘Google Analytics’ was born. In the ‘analytics as a mode of transport’ story, Urchin would have been a bike, our 1st version of Google Analytics: GA1.

Roll on 2007 and we got a makeover and some new features, we said hello to Classic analytics (GA2). Then 2012 rolled round and Google made some massive changes, and we got Universal Analytics. This is the Car of analytics, our GA3. 

Apart from the enterprise 360 product offering around 2016, we have been using the same version of analytics for nearly a decade. Which is a very long time in tech years. 

GA4 is the 4th version of GA and it is nothing like a car, this upgrade is so different, this vehicle is not even on the road anymore. It is a helicopter, and we all need to learn how to fly one! We all need to learn how to fly a GA4 “helicopter”. 

how to approach ga4

How to get your GA4 Licence 

Like any licence there are 2 parts, theory, and practical. 

The Helicopter Method © is your navigation plan which is broken down into phases that have 2 parts to each. The thinking, theory, learning side, and the doing, practical element of a setup. 

How and when you get started with GA4 is up to you. This change is going to happen, and change is hard.  

If you are in the research phase of GA4 and are feeling a little overwhelmed, that is normal, find some comfort in knowing that everyone, and I mean everyone, is going to have to make the switch at some point.

I have been working with clients who have been dual tagging and getting Phase 1 at a minimum completed. Then working towards Phase 2 and 3, and using the insights and reports alongside GA3 (Universal Analytics). By all means, if you want to go all in on GA4, you do you. Equally, if you are in the camp of ‘I am not touching GA4’  it is a free world after all, then my question to you is ‘Which analytics platform are you planning to migrate to?’ Either way, analytics is having a shake up and we are going to be using a new platform. Whichever you use, there is work ahead to get your analytics house in order. 

GA4 Migration Plan phase 1

Phase 1 : Dual Tag Basic Configuration

The key objective of this phase is to activate and set up the basic configuration required for GA4 to start to collect baseline data. Ideally you will also setup your BigQuery project which will only collect data the day you setup and link to your GA4 account. 

At this early stage, it is an opportunity for you to understand the difference between UA and GA4 in terms of the data model and how events work. You may need the support of your agencies or your IT department to set up your configuration tag in your GTM container, and edit your Property settings at Admin level. 

Keep an eye on the GA PR machine, they will announce roll outs and features and decide if the new features should be included in your roadmap.

GA4 Migration Plan theory and practical phase 1

Theory: 

-Understand the data model in GA4

-Understand the Events Concept

-Learn about the UI and how the reports are different

TIP: Use the Google Analytics Demo Account to familiarise yourself with the new UI. 

Practical: 

-Create GA4 Property 

-Setup Data Stream 

-Setup GA4 config Tag 

-Amend Property Settings 

-Create BigQuery Project and Link to GA4 Property 

-Introduce Metrics from GA4 into UA reports eg User metrics 

OUTPUT: 

At the end of this phase you should be collecting data in a GA4 data stream.

This is a baseline setup alongside an active BigQuery Project to collect data. 

TIP: BigQuery will only start to collect data the day you link up the property. Ideally you should set this up at the start of your Dual Tag Configuration.If you are new to BigQuery, the best way to describe it is to think of BigQuery as the black box to your helicopter.

GA4 Migration Plan phase 2

Phase 2 : Customise your setup 

This phase will move you from the core baseline setup that you get with GA4 to a more customised approach, so you are tracking things bespoke to your website and business model. When you deploy the GA4 configuration tag there are some events that are tracked automatically, and depending on your Property settings, some additional ‘enhanced’ events will also be tracked for you.  However there are likely going to be cases where you need additional events to be in the GA4 report that you do not have with the core configuration. For example, form submissions, or ecommerce. 

Review your current UA setup and audit the events and conversions you are currently tracking. From this review, it is a chance to see what you would keep, what you want to improve, and what you would like to delete and remove. This activity will help you understand which settings, events, and conversions from Universal Analytics you would like to migrate over to GA4. Doing this step will stop bad settings and bad data being migrated over to your new GA4 Data Stream.

GA4 Migration Plan theory and practical phase 2

Theory 

-Review your UA setup.  Audit events and conversion setup. Identify what you would like to edit, add, remove.

-Map out your UA tracking to your GA4 events, this will help your migration. GA4 is not a carbon copy, not all dimensions and metrics have the same event name and event parameter.

-Plan which events you want to publish in your reports. By default only your event name is published in your UI reports. All event parameters will show up in Real Time reports and will all flow to BigQuery. If you want to use your parameters for analysis, you need to publish them. You do this in Custom Definitions. You can have 50 Custom Dimensions and 50 Custom Metrics published. Enhanced Measurement events are available by default so they will not count against your quota. 

-Start to create your measurement strategy documentation. Who will own the documents, where will they live, how will they be communicated throughout the organization?

TIP: Keep in mind that Google Analytics already has event names and parameters which should be used before creating your own event names. Before you brief a new event, check that the name does not exist in Automatically Collected Events, Enhanced Measurement or  Recommended Events provided by Google.

Practical 

-Migrate your UA > GA4 Events. Build these events using the GA4 Event Tag in GTM 

-Create Custom Events within the UI to use in reports 

-Publish Events (Custom Definitions) so parameters are sent to UI reports 

-Create Conversions using Event Name and the option of bespoke Conversions using existing events.

OUTPUT: 

At the end of this step, in addition to your basic configuration tracking, you will also have the specific events that complement your measurement strategy and marketing plan. You have custom events created and published for use in the UI and have your conversions setup for your main KPI’s.

GA4 Migration Plan phase 3

Phase 3 : Compare and Review Reports

As you now have core configuration data flowing into your account, and your own events and conversion data setup, you can start to look at building some reports that meet your marketing questions. 

This step is all focused on getting the most out of the data you are receiving with GA4 and use it alongside your UA reporting. 

There are reports in GA4 that either only exist in Analytics 360, or are new and are not even available in the paid version. You should take advantage of those reports. 

TIP: Get familiar with Analysis Hub, this is where you will build your reports. In particular, try building funnels, trended funnels and path analysis. Use these reports alongside your UA reports to get more insights on your strategies.

GA4 Migration Plan theory and practical phase 3

Theory 

-Learn how to use the UI in GA4 start with Standard Reports 

-Learn how to build reports in the analysis hub 

-Learn how to compare reports and build segments 

-Brainstorm how you could use reports in marketing strategy eg path analysis, funnels with time etc. 

Practical 

-Build and share funnels and reports 

-Setup custom insights (like Alerts)

 TIP: Consider using the Data Studio connector for GA4 to pull any metrics or insights alongside your existing UA reports. Start to introduce reports and insights now as you build up use cases and confidence with the platform. 

GA4 Migration Plan phase 4

Phase 4 : Enhance Setup

By now, you should be confident with the standard reports and be using the Analysis Hub to create your bespoke reports, funnels, path analysis. You should have some specific segments and audiences for detailed analysis of users. You are getting ready to brief your agency or team on the creatives for your remarketing lists. You are also finding which events, parameters and custom definitions are needed for your analysis. 

This is the stage where you enhance your setup, adjust your settings, and brief in new events to close any blind spots. Thanks to the flexibility of GA4 you can toggle conversions on and off, and if you meet your collection limits, you can delete and archive to free up new slots.

GA4 Migration Plan theory and practical phase 4

Theory 

-Brainstorm use cases for advanced features like temporary exclusion from remarketing lists. 

-Review data and identify how you could edit/ adjust events to bring clarity to your analysis

-Start to internally brief stakeholders with reports from UA and reports from GA4  

TIP: Keep an eye on the GA PR machine, they will announce roll outs and features and decide if the new features should be included in your roadmap.

Practical 

-Build core reports for analysis and use for reporting (alongside UA reports)
Build segments based on actions

-Complete any ecom setup or monetisation report features eg subscription revenue

-Create Audiences (option to include time sequences)  

TIP: Provide a timeline for your sole use of GA4 for analysis in your reports to stakeholders.

OUTPUT: 

At this stage, you are making full use of the new and enhanced features GA4 has to offer. You and your team are getting ready to use GA4 as the primary reporting source and UA as a backup.

ga4 migration phase 5 big query

Phase 5 : BigQuery

Think of BigQuery as the black box to your helicopter. It is going to store all your rawdata, everything.  BigQuery will become your new best friend for capturing the data you are interested in, but also for joining additional data that you were not able to send with your implementation.

It is well worth setting up your BigQuery and GA4 account as soon as you can (Phase 1) as there are no billing charges associated with exporting data from GA4 to BigQuery. 

Even if you think you may not be ready to use BigQuery – yet– I would still advocate setting up and linking your GA4 account to your BigQuery account as it will only start to record data in your little black box the day you set it up.  

The data retention in GA4 is a max of 14 months (GA4 360 has the option of up to 50 months). So you are going to have to use BigQuery at some point (especially if you are on the free GA4) to do your historical data analysis, think year 2021 vs 2020, 2019 etc, so setting up now will help calm a future headache! 

GA4 Migration Plan theory and practical phase 5

Theory 

-Understand how BigQuery works and start to look at the interface and data in the warehouse ‘black box’ 

-Identify what you need in Analysis hub UI and what is needed in BigQuery

-Work with agencies / IT to scope out use cases for BigQuery and your marketing eg predictive analysis for paid media, analysis of buyer patterns  

Practical 

-Setup queries and export data for reporting.

TIP: There is a Google Analytics sample dataset for BigQuery that you can use . Once you have access to the dataset you can run queries  for the period of 1-Aug-2016 to 1-Aug-2017.

There is of course a LOT to get your head around with this new analytics platform. This road map is intended to help you plan your migration and give you some focus points in phases. 

This is a huge opportunity to develop and refine your measurement strategy. Define what works for you now, and what you need for later. Yes, this is going to take investment.

 You will need to dedicate time and resources to set up GA4 and train your staff on how to use it. Doing it now, over the coming months will make the adjustment easier. 

And remember, we are all on this journey, everyone is going to have to do this work, so go at your own pace. 

You got this! 

If you found this useful, drop me a comment below or share this post. And a happy easter egg of sorts for getting all the down to the end of this post *highfive* here is the link for a PDF copy of this GA4 Migration Plan from The Coloring in Department

The Coloring in Department offers Google Analytics training and consulting. If you would like to chat about helping your company get to grips with GA4 just drop me a note here or DM me on Twitter or Linkedin.  

Categories
Google Analytics

What is GA4 ?

If you’ve found this blog, chances are you are looking for some answers. You have questions. Lots of questions! Like… What is GA4? What is the fuss all about? What does it do? How should I approach it? What do I need to do?

Well dear reader, this post aims to answer some of those questions.   

You probably saw the announcement from Google about this shift to a ‘new analytics’ in October 2020. You have also, no doubt, noticed the little arrow in your property settings calling for you to ‘upgrade’. You may have even clicked that button, but it opened more threads of questions than you thought. 

This post aims to give you a short summary of how GA4 came to existence, using the analogy of methods of transportation (it will make sense I promise). There are some really cool opportunities with GA4, so I am going to share the top highlights for me so far. 

We will talk about the key differences between GA Universal Analytics and GA4, this will help to set your expectations and help to navigate the new version of GA. And, if you stick with me until the end, we will finish up with some steps on how to approach moving from Universal Analytics to GA4. 

Ready? 

Lets begin. 

Analytics Timeline

Humour me, and think of analytics as a method of transportation. This analogy is going to help explain how we got to where we are today, and show the work we have ahead of us. 

If analytics was a mode of transportation, then Urchin, when it delighted us back in 2005 would have been a bike. We were happy enough, we could get around, but it was hard work and if the weather was bad you got soaked to the bone. 

Then around 2007 Google said to us, be gone with your bike, we have something faster, and we got an upgrade, a scooter. We were all delighted, scooting around our website data was faster, and it had an engine! But that only got us so far. We wanted more.  

Google listened, and around 2012, GA got an upgrade and Google presented us with the keys to our analytics car. This is our Universal Analytics. We all know how the car works, either from just sitting as a passenger or as the driver. Some of us have a better serviced car than others, some have a sports car (hi GA360), either way, we are all familiar with the car.   

Then, October 2020 rolls around, and GA announces GA4. The 4th version of their analytics programme, and, despite being called an ‘upgrade’ it is a helicopter! 

You see, whilst UA and GA4 share similarities, think cars/ helicopter, they both have doors,seats, seatbelts, a dashboard and whatnot, but, GA4 runs on a totally different model to UA. 

Hard truth, our UA car is going to be 10 years old next year, which in tech land, is rather old. For a number of reasons, this 4th version was overdue. Google Search Console and Google Ads have all had quite significant changes over the years, and now it’s GA’s turn. 

Google is going all in on investing into their GA4 helicopter, which means no more investment on our UA car. This means we are going to slowly start to notice the car being a bit jumpy, and slow to start, it won’t be serviced anymore, it is going to start to break down. We all need to skill up, we all need to learn how to fly a helicopter. 

oh my new ga4 features

Oh, my, new features. 

Obviously measuring your marketing and website is important,and adopting GA4 early will give an advantage. But let’s face it, no one likes change. Change is hard. If you are in the research phase of GA4 and are feeling a little overwhelmed, that is normal, find some comfort in knowing that everyone, and I mean everyone, is going to have to make the switch at some point. 

Whilst change is hard, change also brings new opportunities. 

On that note, let me share some things to get excited about, which I hope answered the question “what is all the fuss about”.

Let’s dive in 🙂  

1- BigQuery

BigQuery GA4

One main advantage of GA4 is that all users of the product will have access to a BigQuery streaming export. This differs from Google Analytics today, where only Google Analytics 360 customers are able to view hit-level data via BigQuery.

If you are new to BigQuery, the best way to describe it is to think of BigQuery as the black box to your helicopter. It is going to store all your rawdata, everything.  BigQuery will become your new best friend for capturing the data you are interested in, but also for joining additional data that you were not able to send with your implementation.

It is well worth setting up your BigQuery and GA4 account as soon as you can as there are no billing charges associated with exporting data from GA4 to BigQuery. They let you export a free instance to a BigQuery sandbox, if you exceed the sandbox limits, you will need to pay for the usage (queries and data storage) the cost for doing this for most sites is minimal. You can find out more about BigQuery here, and if you are worried about BigQuery costs you can control them. 

My personal take on this is that it is a really good thing. At some point, everyone I have worked with gets to a point where they need to get data out of GA to do more with it, but the barrier was always having to pay for GA 360 to get what you need. 

For those of you who may not be ready to use BigQuery – yet- I would still advocate setting up and linking your GA4 account to your Big Query account as it will only start to record data in your little black box the day you set it up. 

If the thought of working with BigQuery freaks you out, follow Team Simmer run by Mari and Simo Ahava and sign up for their newsletters. It has been my go-to for BigQuery learning.

2- Funnels 

Who doesn’t like a funnel? 

In our Universal Analytics Car, if you have a Destination URL for a conversion goal , and the URL has a few steps that the user has to take before they reach it by way of additional website pages, then you can set up a Funnel in your Goal settings. Doing this would give you a shiny new Funnel Visualization report, to the destination Goal that you may want to create. The Funnel Visualization report is a good example that shows where people are dropping off, and you can see which pages they drop off and leave to. However, You can’t segment it. Boo. 

If you set up ecommerce tracking, and followed a similar pattern (configuring your ecom settings with your conversion steps) then you got a similar report in Conversions >Ecommerce> Checkout Behavior. which you can segment, yay! However, you can’t see the pages they have left you for.

Although I always wanted more out of them they were very useful to visually see user behaviour. If you wanted to do anything more, you needed to pay for GA 360. 

Now, with GA4, we get the funnels that you used to only get if you stumped up the cash for GA360- this is a massive win for us small businesses that don’t have a 6 figure analytics budget.  I am excited about this because all the really good funnels were always in 360. 

funnels in ga4

With GA4 you can create retroactive funnels, yes that is correct, it will apply your funnel details to your historical data.And as much as bar charts are fabulous, if you wanted to change the funnel to something that reduces the cognitive load, you can now change the visualization. Think how you would benefit from a funnel that is applied retroactively to your data, and you can look at it as a line graph to help you spot trends and changes. Erm yes please GA. 

My final-wiggle-in-my-seat-excited-about -funnels came when I was playing around with them and spotted the elapsed time feature. Now, I have hated the time concept of UA for ages, it is just not that helpful the way it is calculated,basically if you go on page A and then page B it can do basic math to see how long it takes from page A to B to give you time on page. Which is fine, but what if people do not go to another page?! 

GA4 have changed their concept of elapsed time, and will tell you exactly what time passed between steps (seconds, minutes,days) and you can apply this to your funnels so you will know exactly how long it takes your website visitors to complete each step. 

3- Path Analysis 

Did you ever look at the flow reports in UA and think, oh they look cool. But then found them really hard to work with and get any insights from? Well, GA4 has given us something called Path Analysis.

path analysis ga4

You pick an end point, purchase your goods, fill out a form, subscribe to your newsletter, whatever you want. Using this end point, GA4 will show you all the steps working backwards from that end point. You get to see all the steps/ paths your visitors did on the run up to doing the end point. 

This for me is brilliant, I have tried in the past to work with flow reports, but they were too rigid, and normally sampled to hell. I then tried to build sequential segments and use data to work out the steps. This takes out the guesswork, and you can add segments to this report.

Think about the use cases for this? Blending funnels with elapsed time you can see how long it takes people to do the thing you want them to do. Take that end point and see all the steps that lead them to the final point. Then add a segment to see how different cohorts behave. Ah-mazing! 

4- 30 Goals (and they are flexible!) 

With UA, we had 20 goals, and if you have ever built a goal, you will share the same frustrations as I had when you realized you can’t delete goals, you could only edit them. 

GA4 gives you 30 goals, and trust me, you can use them all up, and will want to use them all up when you see what you can build in GA4. 

Firstly, the Goals in GA4 can be toggled on or off, and you can archive them if you do not need them anymore. The best part for me is the sheer flexibility and potential of these conversions. 

One example of this flexibility is the ability to create a conversion from a segment, or from a blend of events that you have been collecting. 

flexible goals in ga4

Let’s say you are an ecommerce site, and you have a goal for ‘bought the product’. 

Well, you can go deeper than that now, you can use a Recency Frequency Value proposition model to work out your good, bad, and best customers. 

If your average order value is say £50, you could work out that someone who is a really good customer has lots of visits, reads all your blog content, and spends on average £150. You can build a conversion for your big spenders. Or you want to create a goal based on a particular category or product which you are focusing on. Build that goal! 

Let’s say you are a SaaS site, you could build segments to show people who are warm prospects e.g. they visit your site monthly, watch your free content and have a free trial setup. Verus your mega hot customer, finished the free trial, on the paid plan and referred a friend. Or you could build a goal based on the type of SaaS product they bought. 

5- Google Signals 

Most UA accounts that have Google Ads setup, usually have Google Signals enabled. GA4 has the same sync, but it is using it in a slightly different way to UA.  Google signals is Google’s identity graph, and it takes session data from websites and apps that Google associated with users who have signed into and opted in/ turned on Ads Personalisation. 

Used with GA4 in addition to powering your cross device reports,  it will be used to help fill in more information about your website visitors. This can be used to power your remarketing and ad personalisation.  

6 – Remarketing Lists now with temporary exclude rules! 

When I think of remarketing, my mind wanders to the bad remarketing. You know, you go to a website for a hot second, and they follow you around the web. 

It could be so much better, and GA4 is giving you more flexibility in how you approach remarketing. 

temporary exclude remarketing lists

I am all for opted-in, well put together , relevant remarketing. There is a new feature in GA4 where you can build an audience and define a rule to temporarily exclude users for a set amount of time. 

For example, being a coffee addict, I go to a website and buy a bag of coffee beans to use at home. Typical use of the coffee beans would see me wanting to buy a new bag in 4 weeks time. That website could build an audience that is set to exclude me from seeing their remarketing for a month, and then when that time is up, I am eligible to see the ads again. 

Key Differences between UA and GA4 

Now we know where we are in terms of the analytics journey, using my analogy of modes of transportation. We covered some of the new features, and I hope you got excited about some of the things we can do in GA4, maybe even sparked some ideas on how to use it for your business. Now, let’s talk about the key differences between our UA car and GA4 helicopter. 

Data Model Universal Analytics 

Our data model in UA is a hit based model and runs on the basis of a user (someone who has visited your website), and their sessions (how many visits your user makes to your site). Say you have one user who visits your website three times over a period of time – this would be counted as three sessions. When a user pops up on your site and their visit is recorded as one or more sessions, anything they interact with will trigger something to fire in the code, and this will be recorded as a hit, an interaction. For example, when a specific page was loaded, video played, pdf downloaded etc. 

data model ga4

GA4 is the result of Firebase, Google’s analytics product for tracking apps. Web+ App which was rebranded to GA4, adopted the firebase model which was all event based. Therefore Google is moving away from the hit based data model and moving towards this User/Event data model. 

Event structure UA

In Universal Analytics we have a data model that is built on User’s, Session’s and Hit’s, we also have Events. The structure for Events which you would find in your Behaviour reports follow the rules for Category, Action and Label. The Category is your broad bucket eg Video. The Action describes the ‘doing’ i.e what is the action that we are tracking eg played video, and the Label helps to provide context to the ‘thing they just did’ eg the name of the video that was just played.  

event structure ga4

As our data model for GA4 is all User and Event based, everything is an event. Like, everything. This for me is going to take some rewiring/ recalibrating, as I have used GA UA for sooooo long, it is a shift for my brain to go pageview (which was a hit) to pageview (now an event). Although, like most things, when you start working on it, you do get used to it. 

We now have Event Names and Event Parameters. This is a big difference as the Category, Action, Label hierarchy has gone, and with an event based data model, everything is captured as an event. 

Account structure

Your account structure in UA as you know is built on Account, Properties, and Views.  The account structure in GA4 is Account, Property and within your Property you can define a Data Stream, which you can think of as an equivalent for a reporting view. 

account structure ga4

Reports UI 

The reporting interface is completely new as well. Remember, we are moving from our Universal Analytics ‘car’ where our reports looked like Audience, Acquisition, Behaviour, Conversion. Well, you ain’t driving a car, you are flying a helicopter now. 

The User Interface for GA4 is different, don’t expect it to be a clone of UA. 

You have a number of Standard Reports in Lifecycle. 

  • Acquisition = how did your visitors find your site? 
  • Engagement = what did they do on your website?
  • Monetization= did they make you any money? 
  • Retention= do they come back? 

There are User reports under this that show Demographics and Tech data (similar to what we would get in UA Audience Reports). There is an Event section that will show all the Conversions you have set up (remember you get 30 now!) and a list of all the Events you have running on your GA4 collection and configuration. 

Then you will notice a section called Explore, this holds the Analysis Hub. This is where you can use all the event data you are collecting to build those funnels and path analysis reports that we went through at the start of this post.

user interface ga4

What do you do now? 

Bottom line, we are all going to need to skill up. You can’t just walk up to a helicopter, sit in the seat and fly to the shops. You are going to need to get a licence to fly. As with any licence you have a blend of theory and practice. 

how to approach ga4

Theory : we are all going to have to learn how this data model works, and how to plan strategically for your next measurement strategy phase.  

Practical: once you know how it works and what you need to do, time to roll up your sleeves and start to ‘do it’, make the properties, edit the configuration,  build the events, create the conversions. 

How and when you do this, well that is up to you. This change is going to happen, and change is hard. 

So, work at your own pace, and my advice is to work in phases. 

I have a GA4 migration roadmap called ‘The Helicopter Method’  that sets out a number of Theory and Practical items in set phases. This is a quick one liner on each phase.

Phase 1 is around setting up your Dual Tagging. Doing the basic configuration so you are collecting data and have some historical data to work with. 

Phase 2 would focus on customising your setup, adding more events to fit your business needs. 

Phase 3 is to start to use the reports in GA4 to review and compare between your UA reports

Phase 4 you can now start to enhance your setup, build your temp exclusion lists, build your segments and insights.

Phase 5 is when you are ready to dip your toe in the black box of data, BigQuery. 

I have been encouraging my students and clients to start to focus on Phase 1.  Learn the basics and get the core configuration done so you have something recording data in the background, even if it is just the automatically collected events that you get when you set up your GA4 config tag. And hey, you have already made a start by finding and reading this post. 

You may also want to start to edge in a few key metrics in your current reports to help with the shift. For example, your current website data reports, maybe add in the User metric instead of focusing just on Sessions? 

Start to look at the Demo Account. This is a sandbox account, where you can see the Google Merchandise GA4 setup.  

One thing is for sure, we are all going on a journey. 

 

If you found this post useful and want to hear about GA4 content from us, then sign up to our newsletter below.

The Coloring in Department offers Google Analytics training and consulting. If you would like to chat about helping your company get to grips with GA4 just drop me a note here or DM me on Twitter or Linkedin.  

Categories
Google Analytics

What is Data Import?

Data Import: What it is and how can you use it?

Data Import is underused, again in our humble opinion. If the previous lesson was to get you to think about creating new dimensions and metrics, Data Import lets you upload data from external sources and combine it with data you collect. Naturally, we have a handy explainer on data import in Google Analytics. This post will explore the concept and give you some ideas for the use cases. 

First.. You need a ‘key’ to ‘lock’ the data together.  You don’t have to create Custom Dimensions and Metrics in order to use Data Import. If it already exists, you just need to work out what data you are going to Import. Data is usually uploaded into your GA account with a formatted CSV file. You can also look at pushing data into GA using the Analytics Management API, but this will take some additional help from your dev team. 

A really good use case for data import, and to really highlight the use-cases for this feature, is cost data for marketing programs that are not part of the Google party.  If you have linked your Google Ads account to your Google Analytics property, it will push in additional data into your acquisition reports, things like the cost of that traffic, the Impressions, and the click-through rate.  But what if you use in Bing or another search engine that offers PPC as a channel? 

Well, you could create a dataset and import that data into Google Analytics which means that when you’re looking at your data you see a better apple for apple comparison.  

Another example, we worked with a business that specialized in outdoor activities (for those of you that take pleasure in the great outdoors). A causal factor that really impacted sales was the weather.  So, we imported the weather reports. When we did our analysis for their marketing campaigns which were sent to the board. It helped to show why, at times, marketing campaigns were really good but if the weather was really bad sales went down. 

Now since the arrival of Google Data Studio, you may not need to import data into Google Analytics.  You may want to look into something called ‘data blending’. This is where Google Data Studio will link up with another data source. This could be a Google Sheet or an Excel document and you can blend that data together.  This would be the go-to option if you knew a causal factor, or it wasn’t necessary for you to import it directly into Google Analytics to get the information you needed.

So, it’s really thinking about information that sits in different data pots that would be really useful for you to see it together in Google Analytics to help you understand causality,  and to get a better insight into how your marketing campaigns are working, your content, and how well this is being received by your visitors.

Data Types for Data Import 

When you are looking at Data Import, there are three types of data that you can import. 

1- Hit-Data Import 

Lets you send hit data directly into Analytics. This is an alternative way to get data into your account outside of using the tracking code, or Measurement Protocol. You would use this for Refund Data. If you want to import your internal E-Commerce reporting with GA so you can see Refund Data, this is an option for you.  

2- Extended-data Import  

This lets you upload data that has already been collected, processed or being processed for your reporting views. You may need to create a Custom Dimension or Metric for this to happen. 

  • User Data—No Personal Identifiable information here, but if you have user metadata, from your CRM or equivalent data pot you can load this in. Think about things like loyalty rating, lifetime customer value, Monthly Recurring Revenue, Churn Rate. 
  • Campaign Data—can be used to expand and reuse your existing non-Google campaign codes by importing ad campaign-related dimensions, such as source, new campaign classifications, or variations of the campaign.
  • Geographical Data—create custom geographical regions, that are better aligned with your business’ organization. Think about businesses that want to move out from GA’s settings for say, the USA as a whole. You may wish to put in your own Sales Regions to help analysis 
  • Content Data—who wrote the content, when was it published, what type of article, did it have a video, how many words, etc. 
  • Product Data—this uses the SKU as a key and you need to have Enhanced E-Commerce for this to work. Use it to gain better-merchandising insights by importing product metadata, such as size, color, style, or other product-related dimensions.
  • Custom Data—basically anything that doesn’t fit in the above 🙂 

3- Summary- Data Import 

This lets you import metrics into the reporting views that have already processed your data. You use this option for Cost Data. So anything that you pay for outside of the Google Ecosystem. 

Think about the non-Google costs for marketing campaigns, ad network clicks, cost, and impression data to gain a more complete picture of your ad spend. Twitter Ads, Bing Ads, Facebook Ads, can all be pushed here. 

You can create up to 100 data sets. That doesn’t mean you can only load up data 100 times. It just means you can create 100 data sets to load. 

So, if we were to create data sets for our non-Google marketing I would create: 

1- One set for Twitter Ads

2- One set for Bing Ads 

For your GA audit, have a look at your Property> Data Import to see if you have anything in there already. If you see a use for this feature, write it in your measurement plan. 
Did this content tickle your fancy? Well, we have something we think your brain will love. Our online Google Analytics course is packed with everything you need to understand how GA works. So, if you have been using it for a while, but feel like you are not making the most out of its potential. Well, walk this way.

Categories
Google Analytics

Content-Marketing

Think of Google Analytics as a bunch of instruments. Sometimes you try and compose something and it sounds, well, awful.

Get it right on the other hand, and you’ll find yourself with a song that gets everyone moving, and gets you a record deal – ‘Oh, hi marketing budget!’

This webinar will show you how to use Google Analytics to pump out the best tracks and avoid the one-hit wonders.

You’ll learn how to report on content across the customer journey, as well as make sure that its t impact, (and of course you!), get the much-needed credit.

We have spent years and years learning how Google Analytics works. It is not always that intuitive to use, but this is why we are here to help.

If you want some additional notes to read based on the content from this webinar, well you are in luck. We have a nice blog post diving into the tracks of this webinar. Head this way to read about the content marketing mixed tape for Google Analytics. 

And if this got you thinking 🤔 ‘I am totally up for building my knowledge on Google Analytics’ then you should totally check out our Online Google Analytics Course. We dive into all the topics from this webinar. And by dive in we are talking about the nitty-gritty, no-nonsense, how it works, how to do it…you get the picture…

Plus enough editable templates to shake a measurement stick at #winning!

Categories
Google Analytics

How does ecommerce tracking work?

How does Ecommerce Tracking work in Google Analytics?

If you have a website where you are selling stuff. As in, I would have the ability to go to your website, add to cart, give you my credit card details and pay you for the order. Then you need to set up ECommerce Tracking.

You may think that this has something to do with Goals, and it kinda does. It’s quite conversion orientated, to show that your website traffic equals dollar-dollar bills. If you have a thank you page for your orders (as in a page you get to which only happens if you complete the sale (e.g. /thanks-for-shopping) which is highly probable, then you will, of course, want to create a goal to show that people converted. Which is obviously very useful in itself.

But, if you sell lots of things, like a clothes shop, or software as a service with bespoke product options, you’ll need more than just a ding-ding of the bell to say you made a sale. You need and dare I say it, you should want to know more.

We have a super resource for you to download (for free) which is a handy explainer that summarises this blog post. Head over here for the ecommerce tracking goodies.

You want to record and report on the transaction total (so how much cash did you make on that sale). You want to know which products you sold. If there was any tax added to the sale. You would want to know the name of the product. So, yeah. More than you get from just the thank-you-page-we-have-your-money destination goal.

Hopefully, you’re sold on the idea that you should be getting this extra information inside your analytics.

Spoiler.

You are going to need to work with your developers to make the magic happen. Which is going to require some work.

First things first, let’s understand the concepts and the process to make it happen.

There are two types of E-Commerce tracking that you can use.

Basic Ecommerce Tracking

Basic Tracking works by adding additional code to your thank-you pages that confirm a sale. There will be code on that page that will push data into your Google Analytics Account. There are two types of data types that are used, which are:

‘Transaction Data’, which is as it sounds, the revenue, shipping, tax data. And ‘Item Data’, which is data about the items you are selling, be it services or products. So, think about the name and price of the items you are selling, the SKU code (that means stock keeping unit, which is used in managing inventory). Then you have things like the item category for example Dresses, Shoes, the quantity (how many you sold) and the transaction ID. All useful information, as a start, we think.

Enhanced Ecommerce Tracking

Enhanced ECommerce needs a bit more work from you so that you get the lovely data into your Google Analytics Account. As a side note, depending on what you sell and how much money you make, you may decide to start out with Basic ECommerce. As you build and expand your business, move up a level to Enhanced ECommerce. It all depends on your business model, marketing strategy and budget.

Ok, so back to Enhanced ECommerce. Unlike Basic tracking, which happens on the thank-you page, for a completed order – here, there’s additional code that needs to go on other pages of your website, like the product pages and checkout steps.

Enhanced ECommerce gives you more than you get from Basic, unsurprisingly, and it has five data types which as you will see can overlap between each other.

‘Impression Data’ gives you insights regarding how the product was Viewed on your website. Things like the brand that is associated with the product,  variants of the product, for example, black dress, white dress, the position the product was in if it was in a list or collection.

‘Product Data gives information on individual products. So, things like the brand name associated with the product, and if say a coupon code was used for the product purchase.

‘Promotion Data passes information about any promotions you have that were viewed by customers. Things like the creative, did they see a promotional banner and click to the product page.

‘Action Data’  is the bottom line stuff, all the ECommerce action data = money data stuff.  This is where you can send the revenue data to Google Analytics if any tax was associated with the transaction or shipping costs. It also has the ability to track the specific steps in the checkout process, which is very handy when you want to see if you have a leaky bucket. Do people get to the last page and just leave? This would give you at least, a starting point to investigate what’s going on with your checkout flow and ideas on how to fix it, so you make more money.

‘Product and Promotion Actions’ helps you to interpret product and promotional data that you send to GA. Like people adding products to shopping carts, or removing them, if they initiate the checkout process, that kind of thing.

All sounds wonderful doesn’t?

Now, how do you get the goods into Google Analytics?

Setting up E-Commerce for Google Analytics

Short answer – ask your developers. This also happens to be the lazy, kind of useless answer.

The real answer, working with your developers. You need to map out a clear plan, which will vary depending on how your website is built. We’re after all our own individual and special snowflakes, so the implementation can vary from one ECommerce setup to the next. It really should, if we are honest.

The first thing to check in your Google Analytics Admin audit is to check your Conversions> ECommerce reports. Is there anything there?

If you don’t find anything in the reports but you think you had the ECommerce data added to your website by a developer, the next check is to go to your View settings and for each View you have, check if the ECommerce toggle is turned on. Is it?

This may seem really bloody obvious, but I have done many audits that HAD the data on the website, but nobody knew to go to View>Settings and turn this toggle ‘Enable E-Commerce’ to ‘On’. So they both had their data and did not have the data. Fun times.

And as you know all too well now dear readers, Google Analytics will not go back in time and re-process your data. I had a client that missed out on two whole years of data because someone didn’t turn this toggle on. And, I’ve seen an Account where one View had Basic turned on, and the other reporting View had both Basic and Enhanced ECommerce turned on. It happens.

For some of you, you either have it set up (thank the analytics gods) and you just need to check if you have or need the Enhanced option.

Some of you, just need to turn on the toggle to receive this information in your Account.

For the rest of you.

You need to get this specced up, and briefed in, with your technical dev-people.

There are a few ways to do this. How it’s done for you will depend on your ecommerce platform, your shopping system as it were.

If you are using something like Shopify, or WooCommerce, they have plug-ins to help setup ECommerce within Google Analytics. So, go check out what types of integrations you have with your shopping system provider and how they might work with Google Analytics.

After that, you can either manually do it, as in manually tag your site. Admittedly, almost no one really does now because it ain’t 2005 anymore. Instead the common, the go-to option, is to use Google Tag Manager (hello again GTM).

Google Tag Manager will help you populate the data layer with ECommerce transaction and product variables (those data types we just chatted about) that one would have on their thanks-we-have-your-order page.

Or, if you are going into the Enhanced ECommerce route, populate the data layer with data for the product and checkout pages on your website.

A few things may be going through your head right now, as they went through mine when I first learned about ECommerce tracking.

One thought was, why can’t I just pop the code from the Google developer help pages onto each page, like adding the Google Analytics tracking code? Well, each sale is unique to the user, so the data is dynamic, as in, it is unique to the user. Plus, your business is unique too, so you need to tell our lovely GA computer program which data types it should be looking for, for example, what your brand names are for a product, because, how would it know?

What is a data layer?

The next thought, for me anyway, was “what is a data layer?” 

Good question.

So, let’s just chat about the Data Layer.

If you Google ‘Data Layer’ you get an answer like this “A data layer is a JavaScript object that is used to pass information from your website to your Tag Manager container.” 

So, think of the data layer like an intermediary. You pop your data into the data layer, and it keeps it nice and safe, to then pass on to your website, which has other things linking to it, like your Google Analytics javascript.

Provided you have sent your data to the intermediary, and that data is written in the language of the platform it’s passing the information onto, it works. Oh, and don’t forget about turning the toggle ON to receive the ECommerce data.

Your Ecommerce Tracking Plan

So your plan is, as mentioned, to check if you have ECommerce data in your Conversion reports first.

Then head to your View settings, and make sure you have turned on the toggle to receive the data. If you have more than one View, check each View to make sure.

  1. Sign in to Google Analytics
  2. Click Admin, and navigate to the View you want
  3. In the View column, click E-Commerce Settings
  4. Set Enable ECommerce to ON
  5. Click Next step
  6. Click Submit

Check your Ecommerce platform to see if there are any plug-ins that will help with the heavy lifting.

If your shopping system has no plugin, or you have a bespoke shopping system on your website, you need to brief your development team.

You will need to get the ECommerce Plugin setup on your site, which is referenced in the Google Analytics Developer Guide. This is not the same as say a Shopify plug-in. This is a Google thing, and in their words:

To reduce the size of the analytics.js library, ECommerce tracking is not provided in the default library. Instead, it is provided as a plugin module that must be loaded before being used.

To load the E-Commerce plugin, use the following command:

ga(‘require’, ‘E-Commerce’);

This command must occur after you create your tracker object and before you use any of the E-Commerce specific functionality.

Once loaded, a couple of new commands specific to E-Commerce tracking will be added to the default tracker.”

There are two types of ECommerce Plug-ins for you to use, one for Basic ECommerce tracking and another for Enhanced Tracking.  If you are bumping yourself up from Basic ECommerce to Enhanced you need to get your dev team to migrate your plugin from the Basic plugin to the Enhanced plugin, as they can’t work together.

Basic ECommerce Tracking

For Basic ECommerce to work on your site, you need to populate the data layer with the following data variables, and they need to be triggered on your “thank-you-confirmation” page.

The data variables that are required in order for it to work are:

  • transactionID
  • transactionTotal

Optional data variables are:

  • transactionShipping
  • transactionTax
  • transactionProducts

Although it’s optional, most ECommerce sites would add the transactionProducts to the list, as it gives you richer information. Using transationProducts means you have to use these variables:

  • Name
  • SKU
  • Price
  • Quantity

There is an additional optional data type too:

  • Category

This one is used a little bit less often.

These transaction tags will then show up in your Conversions reports. See this example from an Account that is just using Basic ECommerce. We can drill into the product performance to see out of all the products we sell, which ones have been sold on the website in a given period of time. How many were sold, and our product revenue.

Sales Performance is a report that shows how much you made in a given day. And remember here guys, it is going to pull in data as per your time zone for that View.

The Transaction report will populate all the Transaction IDs which will link up to your shopping system. Time to purchase shows the number of days between a user purchase and the campaign referral.

This is very valuable information.

Enhanced ECommerce Tracking  

If you want to level up from there, this is going to take a lot more work from your developers. All five data types we have mentioned work together to give you so much more information, and a totally different report in Conversions> E-Commerce.

Now, I am not going to go through all the data types in detail, as that would probably send you to sleep. You can have a look at them all here. What I will say, is that there are a set number of requirements that I would have as my go-to ECommerce enhancements.

  • Clicks on a product link
  • Viewing product details
  • Impressions and clicks of internal promotions
  • Adding / removing a product from a shopping cart
  • Initiating the checkout process for a product
  • Purchases and refunds

In addition, I would want to have my ECommerce funnel mapped out so I can drill down into the ECommerce data further, by looking at the Shopping and Checkout Behaviour reports. Now, just to note, this is turned on at View level, where you type in the name of the steps into your funnel set up.

These have to match the name you have given in your enhanced ECommerce settings. So please, please, sit down with your dev team and agree on user-friendly, idiot-proof names, as they are going to show up in your Account.

This example from the Google Demo Account is a good example of clean, user-friendly labeling.

  • Billing and Shipping
  • Payment
  • Review

Things to Keep in Mind 

I have done a lot of GA audits and when it came to E-Commerce companies, they always asked why there was a data discrepancy.

The first thing I do is an audit of their Admin settings. Common culprits are things like having staging sites active, but not filtered out so you get the staging/test data. View settings with the wrong timezone, not having the correct Account structure can all make the data murky. If you have ECommerce set up correctly, there will always be some gaps.

Why? Well, Google Analytics was created to track what your website visitors are doing on your website. It was not designed as a customer relationship management database or a shopping accounts system. Let’s be fair.

Which means, it will tell you how many people bought a particular product, the transaction ID, all that glorious stuff. However, it will not tell you if someone who bought the clothes then decided to send them back to you in the post because they didn’t quite look right on them. Or if they buy the hotel room, and then cancel the order. All that refund data, yeah, that will not appear in the Account.

Although, there are some workarounds to fix this. It is possible to link up CRM and Sales systems to push data back into Google Analytics so you get a better picture with Custom Dimensions and Metrics. 

Like what you read here? We have a whopper of a Google Analytics Course in which we cover your Admin Settings, so you can find out how to clean up your data ecosystem. And our Advanced Analytics module in our Google Analytics Course talks through the workarounds we just mentioned. You should check it out.😉.

For students on our Google Analytics Course,  you get access to all our editable templates. In particular, we have one for Ecommerce Tracking and Briefing 👊. 

This template has been created to bridge a conversation with what you, the marketer or the business owner wants to have in GA. This process to get ecommerce data into Google Analytics could be much smoother  IF marketing and development work together. And that needs a good brief. With clear outcomes.

So use this template to get everyone on the same page, agree on timelines and what needs to happen in order for the ecommerce tracking magic to happen.

Go check out our Google Analytics Course details here! You know you want a peek.

Categories
Google Analytics

Reporting Tutorial

The aim of the Google Analytics, game, of course, is to see insights and make changes and recommendations to your website and marketing.

Those recommendations need to be both justified, reported on, and most importantly, correct.

I think we can all agree that reporting on your work is incredibly important for your future success. But sometimes, the reports we try and build, well, suck.

We know they can suck, because we have spent years training, teaching and troubleshooting Google Analytics, and are happy to share some of the things we have learned along that journey.

In our first webinar (*throws confetti*)  we covered a few key themes – which you can rewatch here, if you love a good webinar.

Give it a watch to find out how you can get to grips with reporting so that you can create and present a story with your data. In the webinar, we talked about how to plan your reports so you display data in a more straightforward, and more meaningful way.

We also explained why some numbers make your head hurt, and how Google defines certain metrics, so you can make sense of the data too.

And who doesn’t want to be an analytics badass?

We finished our webinar with some sweet advanced tips and techniques to build reports awesome reports in Google Data Studio.

As you are here, you should check out the free ‘Report like a boss’ explainer that you can download for free. Or this cool guide around the difference between a session and a user-based segment, and if you want a handy explainer on segments, we’ve got your back with a little guide on how to use segments in Google Analytics’.

If you liked the section in the webinar about where we talk about linking multichannel funnels assisted conversion data into Data Studio, yeah we wrote some stuff on that as well.

Not forgetting that if you like this sort of stuff, and want to get better at it, we have whole modules in our Online Google Analytics Course on your Admin Setup, Goals, Assisted Conversions, Building Reports. In fact, the course is 10 hours of content, across 17 modules, with a whole bunch of word and excel templates, all for $199.

Even still though, you can lose a lot of time trying to build your dashboards, can’t you? I’ve spent months, maybe even years, ok maybe not years. But, I’ve wasted time over my career building dashboards and worst-case scenarios, and nobody ever reads them. If you listen quietly you can hear the sound of tiny violins.

Aiden has a fun story about this actually, he went to a meeting with a large global agency group, by all accounts a fabulous building, but it had broken air conditioning. Sat in a stuffy room someone said ‘oh just prop the window open with this book.” 

Aiden asked what it was – because he’s nosy. The guy said our client analytics reports. We just print it off every month and send it to them because it’s something tangible that they can look at, but nobody ever reads. It’s fine, prop away!

You hear that…that was the sound of my jaw-dropping.

What’s the point of doing that? What a waste.

So, let’s dive into a process to avoid the data vomit and take a moment to plan how you’re going to show your work in a way that will identify insights and answers to questions you have about your marketing and website performance. So, people might actually look at your hard work, and celebrate you accordingly.

We want to help you get to grips with a process that we use so that you can create and present stories with your data.

We start with a set of questions.

What’s the point of the report?

Your goal here is to make decisions based on the report. Not report for the sake of reporting.  Tempting as it may be to throw everything together in a dashboard form. Try to remember, what is the whole point of the report? Of course, a big no-no is to pull data together, to make your work feel tangible, and to show that “stuff happened.”

So, what is it that you are trying to say?

What questions are you trying to find an answer to in your business, or for your client?

Is the point of the report to show how well your marketing budget is being spent? How well your campaigns are doing? How much money did the site make this month, and, are we improving from previous months? Etc.

Who’s getting the report?

Just like the idea of having personas. Old skool.

Think about the idea of having reporting personas.

Who’s getting the report? What is it that they want? This will give you an idea of how much information to give those people – and allow you to highlight the metrics that matter to them.

For example, the CFO of a SaaS business may want very high-level metrics; that focus more on the finance side of things. Cost per visitor, cost per acquisition, ROI, churn rate – that kind of thing.

Whereas your VP of Marketing many want to know how many free trials were registered on the website, how many people are upgrading to premium products, that kind of thing – because those metrics are more aligned with their projects and workstreams.

What are the key messages? 

You want the reader to look at the report and walk away with your key message. Whilst, of course, making sure that they have the right message too, which is significantly easier when you know who you are reporting to and what the point of the report is in the first place.

How can you make it easy for them to get that message?

User Experience 101. Don’t confuse people, plan how you’re going to show your work, your data, in a way that is as smooth as silk and as easy to read as your A,B, Cs. As a result, those insights and answers will be easier to see and action. It is, after all, part of your job to communicate your findings.

The person looking at the report should very quickly get to their destination, their ‘aha’ moment. To get them there, think about how you can visualize the data in a very simple way, don’t make them think and ask questions, or be unclear about what they are looking at.

Select the right visual style for your message and make each data point clear – by giving each metric or section a heading in plain language.

Never assume that the person knows what they are looking at! Hint: they don’t.

Wireframe the Report 

The next step, once you have answered these questions, is not to dive right in and create a dashboard. I’m sure many of you have spent hours, days, weeks, and for some of you, months, creating reporting dashboards. Only to find that they weren’t correct, or someone wanted different information. Or worst crime of all, people didn’t even bother to look at them! That my friends, used to really upset me!

Creating reports can take time, and let’s face it, it’s not exactly the most exciting job in the world – sitting building dashboards.

So, save yourself time and future amends on your wondrous creation by creating a wireframe of your reports, get sign off, then build it. You may have used wireframes to sketch out landing pages, websites, or emails, but imagine how you can use this technique to build out a dashboard.

You can use good old pen and paper, or if you feel like it, use a tool like Figma to create a wireframe of your report.

Let’s walk through our process and see what we come up with for our wireframe.

Q: What’s the point of the report?

A: You want to isolate organic traffic from the website to see how your SEO strategy is working for you.

Q: Who is getting the report?

A: The CEO who has signed off on the budget.

Q: What are the key messages? 

A: Which Search Engines are we getting traction with? E.g. Google, Bing, Yahoo. How many users are coming from organic search, which pages they visited, and if they converted or not.

Q: How can we make this easy? 

Assuming you are going to build this report in Google Data Studio. Provide headers above each metric and data item. Display the search engines in a pie chart format, for any goals, provide the data as a % point AND the real number of conversions, use a bar chart to show the pages users visit, or heat maps. Add a calendar icon so the reader can change the dates, and an option to switch between reporting views.

Once your wireframe is signed off, then you build it! You are done.

Found this interesting? Enjoyed the webinar? You should totally check out what is in our GA course! Head this way, my analytics friend!

Categories
Google Analytics

Advanced Segments: Conditional and Sequences

Why you should use Segments in Google Analytics

Looking at all of your Google Analytics data at once sucks like a sour lemon. If you don’t segment the data, you will never find the good, the bad, or the wonderful. Plus, we all know that customers take different journeys to, and on, your awesome website. So, it’s great to know how your marketing channels are performing, and how to find your best customers, as well as how to enhance channels and help those customers that are struggling. 

Ideally, you’d want to use Google Analytics to find answers to your question like an easy-peel clementine, so let’s dive into what you can do with these little gems. 

In particular, if you use the Advanced Segment options, Conditions and Sequences, you can really power up your work.

This post dives into those Advanced Segments. 

If you want to learn more about what Segments are, we have a handy, free PDF explainer ‘How To Use Segments’

Segments in Google Analytics from The Coloring In Department

Google Analytics reports are all built on the basis of Dimensions and Metrics. Segments are looking at your data set to find a match for the Dimensions and Metrics you want to focus on. 

You can create user Segments on the following terms: 

Demographics 

You can look at your data – and dive into people, places, ages and language. 

Technology 

Isolate users or sessions by device, so things like mobile/tablet/desktop. The browser they are using or the size of screen resolution. 

Behaviour

This is all down to recency and frequency, so the number of sessions. The days since their last session, how many transactions they had.

Date of First Session 

Create a Segment based on cohorts, that match a definition, for instance, the dates between their first and last visit to your website. 

Traffic Sources 

How do your users find you? Which campaigns, channels, mediums, sources, paid keywords etc.

Enhanced Ecommerce 

Segment your users by their shopping actions. Revenue per user, the product they bought, the brand they purchased etc.

Advanced Segments: Conditions and Sequences 

These options allow you to really build out and power up your work. This function gives you more flexibility, as you can layer Dimensions and Metrics, and mix things up. For example, you can create a Segment for Traffic Sources, but if you wanted to look at a Traffic source AND the page a user viewed you would need to use an Advanced Segment to do that.

They both work on the basis of you defining what the dimensions and metrics are; and you can opt to include or exclude dimensions and metrics. Then define what the conditions are, so which dimensions and metric values are we looking for, and then, if the next part of the Segment is to be included as an and/or.

Please note – And / Or statements behave differently. You are putting together two or more Dimensions and Metrics. You use AND to say that both conditions are required, the OR means that only one condition must be met.

Condition Based Segments

This is where you are building a set of conditions. You can pick what you want, provided that the dimension, metrics and their values actually exist in your data already. Segment your users and/or their sessions according to single or multiple-session conditions.

Sequence Based Segments 

This gives you the same flexibility as Conditional Segments, the difference here is that you are segmenting your users and/or their sessions according to sequential conditions. Specific steps in a journey rather than conditional ones.

You do this by  Filtering> Include or Exclude> Sessions or Users> then define the Sequence Start > Any User Interaction or First User Interaction.  Then you put in your Dimensions or Metrics and assign a value for each step.  That value uses basic math symbols (greater than, less than, etc) you also get the option to use And/Or statements to further qualify this.

You need to make sure that you select the correct type of Advanced segment as they behave a little differently overall.

Scope of Segments 

We’ve talked about getting the scope of the segment correct, but the difference between a user based segment and session-based segment are equally – super important. 

In your Condition and Sequence Segments, you can filter between Sessions and Users. As you might expect, this has an impact on your data, even more so when you are using the And / Or statements.

Let me explain.

If you create a Condition Segment that is Session based on Source = organic and, say for example Event = info pack downloaded and Event = Free resources sign up. This will only pull in an answer if these two criteria happened within the same session.

But what if the traffic from Organic doesn’t always assist in these events happening? You would need to swap this out to a User based segment.

Hold up, when we do this, look what happens to our data. Zero ? That can’t be right surely? There must be users that came from organic and downloaded content to fire our event tracking. What’s going on?

Well, if you change it to a User based in this Condition Segment, here’s the kink, it will only pull the data if the conditions happened within the same Hit.

That is down to the criteria box. This Segment works on what is in the box specifically.

So you need to change this by adding a new box (we will demo this in our Google Analytics Course).

Basically, if you want to see two conditions for a user based segment but not the same hit, then you have to split the box to capture the two respective criteria.

Now, to further emphasis the Sequences point. Here you can change the filter to have Session or User. But – for each of the steps, GA will look at each step as a single hit. This can be quite handy when you want to look back over a 90 day period (which is the limit for User Segments). When you build a session based sequence segment, it will give you the number of users too.

Found this post interesting?

Cool.

We have a whole module on Segments in our Online Google Analytics Course.

You’ll work through all the types of Segments available to you, and understand the pros and cons of Segmentation in general, as well as some issues that come along with using it.

And, as no one likes a blank sheet of paper, we’ll walk through a process to work out the types of Segments you may want to consider for your business.

You know you want to have a look 👀

Head this way my measurement loving friend.

Categories
Google Analytics

Attribution Across the Customer Journey

Right, as marketing folk we -know- that our customers take their own unique journey, and their intent at each stage of their journey will change –only– when they are ready to move to the next stage in the relationship.

The problem is that reporting and Return On Investment (ROI) for our marketing skews to the last channel, campaign or page that scored the goal. So that interaction, ends up getting all the credit. Let’s call them your quarterback, see every high school drama, ever made.

Lately, we have been doing some talks that dived into how you can use Google Analytics (GA) to measure user intent and the effectiveness of your campaigns at each stage of the journey, as well as highlighting some freaky little quirks within GA that may have cost you dearly.

What does attribution and the customer journey have to do with each other? 

When we talk about attribution, remember it’s just a fancy way of saying who gets the credit. 

Who do we attribute success to?

We need to make sure that when we’re reporting on how our marketing channels are working for us, that we don’t make bad or biased decisions on that data. By that I mean just focusing on the superstar channels, that score most of the goals. *cue shameless plug to our Attribution Explainer*

Using Google Analytics to measure your marketing across the customer journey. from The Coloring In Department

Now, let’s imagine you have just scored a Goal on your website. High Five! That could be a physical sale, or someone filling in a form. Either way, someone did the thing you need them to do – in order for your business to survive. Always a good thing!

So, who gets the credit for scoring the Goal?

The answer will depend on the attribution model you are using. Or rather, which model Google Analytics is using in your reporting.

Now, in our hypothetical example, in our lovely picture. If this were a typical customer journey, the Acquisition reports in Google Analytics will give all of the credit to the Last Non Direct channel. Which is fine, but it does make the other channels look a bit useless when you read the reports.

Goals! 

It may seem obvious, but you can’t do any attribution if you don’t have any goals setup. 

Something we like to do here at The Coloring in Department is build dashboards in Data Studio. Now, provided you have more than 1 goal (you can have 20!), you can then select a mix of macro and micro conversions, as well as a heat map to help visualize the conversion rates across the goals that you have set up. If you list the sources, you then get to see how well your marketing is doing to drive your conversions. It also helps to show the role of that channel for you.

If you do this, your reports begin to paint a visual picture about the role your marketing channels plays in contributing to the growth of your business. And, it helps to manage the expectations and roles of your marketing.

For example, social media may be amazing at getting people to watch a video, and sending a newsletter will get them to sign up to a free trial. Don’t judge the success of all channels in the same light, they may not be the closer, that may not be the role of that channel.

Attribution Reports 

When you are in Google Analytics you get some really useful information, like which channels assisted in conversions, what the value of these assisted conversion is, how long it takes people to covert, and top path to purchase reports.

I like these reports because they help level the score card. We want to be a little fairer, a little more balanced, when reporting on our marketing channels.

The Assisted Conversions report in particular is going to show you how your marketing channels are assisting in driving conversions for you. This report became available, circa 2011, because people were making bad decisions on Google Ads. I remember being in a meeting with a client who said that, when they looked at the acquisition report, that paid search only accounted for two conversions, and that it cost them about £500 in Paid Search spend.

So, they were going to get rid of it. But – when they had a look at the assisted conversions report, it showed that the paid search channel actually assisted in 98 sales. So, that meant if they had closed off the budget of £500, assuming that it is only contributing to two sales, they would have actually been saying bye bye to 98 sales. Paid Search for them was a channel that started the journey, it was brand awareness, an exposure channel.

So, it’s a great little report, but how do we get these metrics and dimensions into a Google Data Studio report.

Now, for a slight challenge with Data Studio…

Data Studio (and I do love it very much) does not let you pull in my favorite reports, which are found in Multi-Channel Funnel reporting. There is a work around though. You need to use Google Sheets.

In this example, we have the top half of the report being pulled in from Google Analytics. Number of users, sessions, revenue. Lovely. We then have the Default Channel Grouping (DCG) listed with revenue next to it. Remember, the DCG is going to give 100% credit to the last non direct channel.

We want to make the report a little balanced. Using Google Sheets with the Google Analytics Add-On we can pull in the assisted conversions for our marketing channels. Yippie. Let’s talk through the steps on how to implement this, shall we?

What you need:

You need to fire up Google Sheets and get the Google Analytics Add On. Once you have this loaded, you can select from the top navigation in Google Sheets <Add-Ons> and then select ‘Create New Report’. Give your report a name and use the dropdown to select the GA Account, Property, and View that you want to use. Keep the Metrics, Dimensions and Segments empty . Click on ‘Create Report’.

You end up with a sheet that looks like this.

You still need to do some tinkering here to get our Google Sheet to do what we want.

For some reason rows 13-17 are squished together, so you need to click on the arrows to expand the row. In row 14 where it says ‘Report Type’ you need to type in mcf. This is super important. I banged my head against the table for ages trying to work out how to pull this in Google Sheets. You must change the ‘Report Type’ to ‘mcf’ in order to pull in the MCF API which is different to the core reporting API, which powers the Acquisition Reports.

Now, we need to type in the MCF Metrics and Dimensions in row 6 and 7. These are not the same as the typical GA Metrics and Dimensions, we need to specifically use the MCF Dimensions and Metrics Reference. 

When you know which MCF Dimensions and Metrics you want, type them into row 6 and 7. You can add more than one Dimensions and Metrics to each row. In this example we have 3 Metrics, Total Conversions, Assisted Conversions and Conversion Value. In the Dimensions row we have the Basic Channel Grouping and Day.

When you are happy with your selection, head to the top of your Google Sheet and click Add-On. Select Google Analytics > Run Report.

You should get a message that looks like this.

Once it has run the report you will find your data inside a new tab.

To get it inside Google Data Studio, log in, and select Google Sheets, which is already listed as a connector, and select the name of the Google Sheet file that has your MCF data in it. Then, work as normal in Data Studio and create your tables, charts etc. whatever takes your fancy.

If you enjoyed this post, you should check out our GA online course. We have a whole module dedicated to getting your attribution grove on!

Categories
Google Analytics

How to use GA to Measure and Improve Content Marketing Effectiveness

The Google Analytics Mixed Tape of Content Reporting Happiness

Do you remember the feeling of getting a mixed tape (playlist for those of you who are too young to remember a cassette tape)? 

How did you feel when you got one? You play the tracks and you are reminded of songs you love, and you got the opportunity to discover new songs, without having to do much work. Personally, I loved the feeling of getting a mixed tape. Someone making something for you was special.

They were special because they were a total ball ache to make. The time it took to make a mixed tape needs to be remembered and cherished. It was utterly painstaking to create a tape, blood, sweat, tears, and cramp in your hands from hitting pause so you can move between songs. 

You needed to have a mood in mind, you thought about the soundtrack, the order, so you could express something and hope that the recipient of your tape would feel the same thing.

The Google Analytics Mixed Tape of Content Reporting Happiness from The Coloring In Department

So, this, ladies and gentleman is my mixed tape for you on Google Analytics, bet none of you ever thought you would get a mixed tape about Google Analytics did you? First time for everything and all that.  It is based on my Learn Inbound talk I presented on Friday 16th August 2019.

The sentiment and patience are all in here. I have spent years and years learning how GA works. It is a ballache to use sometimes, and yes, blood, sweat, tears, and cramp in my hand clicking away at my laptop, lost down the rabbit hole of reporting is real.

So here we go, a mixed bag of things you may have forgotten you can do in GA, some new ways to look at this Javascript based tool. And, as each song belongs to its own album of sorts, I hope to spur your curiosity to check out the album behind the report.

Let’s get started!

Come Together: The Beatles

 

Now, investing in content, good content, is worth it, but how can you use Google Analytics to report on content?

Content can be your blog posts, articles, and case studies. Maybe you have started a podcast or hosting some webinars. Or perhaps you have focused on optimizing your content ala conversion copywriting style.

Before you dive into your analytics to see what your content is doing for you. You need to take a step back and see how it all comes together a 100-foot view kinda thing.

Map Out Your Content

Starting with your typical customer journey.

1 Pain or Problem Aware: This is where you have a visitor who is aware of a problem but they have not found a solution yet.

2 Solution Aware: They are well aware of the pain or problem and they have discovered that solutions exist for them.

3 Product Aware: They know that you are one of the products in the solutions to their pain.

4 Most Aware: They know you are the best solution with a product or service for their pain.

Then look at your keyword modifiers. A modifier is a word that in combination with your core keyword creates your long-tail strategy. In this use case, we look at grouping them by their stage of intent. This is a tactic that can be used for both SEO and Paid Media. Typically, at the start of the journey, the keywords are non-brand. People don’t know what they don’t know. So the intent is around questions. The who, what, why, when, etc. Move to the middle of the journey and they are a mix of brand and non-brand as they become exposed to solutions and products. Right at the end. Well, it all brand my friend, they know your name now.

You can apply the same thinking to your content types. There are different types of content that will be more receptive to your prospects and website visitors as they move down their customer journey. Think about it, you have to be quite invested in a company to dedicate time to download and read a large report.

We recommend that you map out your content types to the customer journey and investigate your current ecosystem, as this will have an impact on how you need to approach your GA setup.

For example, if your webinars are hosted on a 3rd party site, you may need to look at UTM tracking and possibly cross-domain tracking. If you have video on your website, you will need to set up event tracking so you know if someone played the video or not.

In the past, we have done this activity using a spreadsheet similar to the one pictured. It provided a high-level overview of the content we were creating and what we needed to do to track it all correctly.

Eye of the Tiger, Survivor

 

All websites, no matter how large or small, need to have some way of measuring whether or not we are all doing a good job, or not. Goals are used in Google Analyticsas a way to find this out because you really need to have a way to measure effectiveness.

You need to know how well your marketing on one hand, and website content on another – is actually contributing towards making your visitors convert, and convert – we mean doing the thing you need them to do so you’re still in business tomorrow.

Hello Macro and Micro Goals!

Macro are the main key performance indicators for your business, AKA, if this doesn’t happen your business will go bust!

Micro are the small interactions of people actually moving towards what you want them to do. There is value here; don’t throw this data away!

You can have a whopping twenty Goals on your site, yet, more often than not, we have found that Goals aren’t set up in Google Analytics, or if they are, there are only a small few, and they have been focused on the big hitter elements.

You should be thinking about your Goals from a Macro and Micro point of view at all times.

There is so much value, in our humble opinion, when it comes to tracking your Micro Goals. To put it another way…let’s say you just focused on the one big Macro Goal, revenue.

Let’s say then, that your Macro Goal converts at 2%.  Are you going to throw away the data and insights of the 98% of your visitors that didn’t give you money today?No, you totally aren’t.

When you start to dig into this, you will most likely notice that your Micro conversions are going to look very similar to your content production.

So, go audit how many Goals you have, and see if you are indeed tracking everything that you should be.

We recommend reporting on both Micro and Macro Goals. It will give you a richer story when you are reporting on what your users are doing on your website and how well your marketing flywheel is working for you.

Like this use case (see picture below) we reported in Data Studio a mix of Micro and Macro goals. Notice how one of the Goals is for content that was downloaded. We used heat maps to help visualize conversion rates across these goals and listed the sources of traffic to see how well our marketing was doing to drive conversions.

Never Enough, The Greatest Show

 

Your tracking your Micro and Macro Goals ✔️

You have setup a few swanky dashboards for conversions. Some of which include your awesome content. ✔️

However, you may think your reports show all the credit for your conversions correctly, there is a ‘however’ in here. That ‘however’ been on how GA allocates credit.

Which depends on a few things, such as with the attribution models that Google Analytics uses within reports.  Before your eyes roll into the back of your head in boredom at the thought of attribution.

When we talk about attribution, remember it’s just a fancy way of saying who gets the credit.

Who do we attribute success to?

We all know that our website visitors are people, and these people have a customer journey. We know that they touch multiple channels before they convert. So we need to make sure that when we’re reporting on how our marketing channels are working for us, that we don’t make bad or biased decisions on that data. By that I mean just focusing on the superstar channels, that score most of the goals.

Which channels are exposing customers to our brand, which channels are helping the top of the funnel?

Which channels are supporting us and driving consideration? The channels helping people work through the middle of the funnel.

Which channels and the closers, ding ding bam – the conversion happened.

Hello Multi-Channel-Attribution > Assisted Conversion Report! This report is going to show you how your marketing channels are assisting in converting for you.

In addition to looking at your marketing channels, which is very useful, you can change the dimensions in the report from channels to landing pages.

At the top of the report, you will see ‘Primary Dimension:” move over to ‘Other’ and click on it.

You then need to type in the dimension called “Landing Page URL”

This will give you a report showing how your website pages (that host your super content) are helping assist in conversions. You can look at conversions and assisted conversions for All Goals, or drill down to a specific Goal in your reporting view.

You can also repeat the process with the Multi-Channel-Funnel> Top Conversion Report, by changing the primary dimension to Landing Page URL, you get to see the top conversion path by website pages.

This will help you get an idea of how people are moving through your website.

Umbrella, Rihanna

 

My last song ladies and gentleman, is for a feature in GA that in my humble opinion doesn’t get enough attention.  It is one of my faves.

Let’s set the scene.

It is a sunny day, you are logged into Google Analytics and pumped full of excitement to see how well your content is doing.

You head over to Behaviour> Site Content> All Pages report and that smile on your face suddenly turns into a frown, you poor marketing soul. Because you notice on the bottom right that you have oodles of pages, indeed tons of pages, to work through.  The pain is real.

All you wanted is to shuffle through the report and see what your top pages are, which is really easy to do.

But, how can you work out how many blog pages are being viewed?

Or product pages?

Or if you have multiple brands, which are getting the lions share of page Views?

Could you find this out without having to manually count, or worse, export and try some complex excel workaround?

Now, imagine if you could organize these website pages, grouped under nice neat umbrellas?

Wouldn’t that be smashingly good?

Well, you can do this with Content Groupings, and you can have five of these per View.

As the Google Demo Account has created them, we can see an example in action. If you click on ‘none’ (found where you see Primary Dimension just above the table) you will get to see that there are three content groupings out of a possible five. If you select one of them, let’s look at Product Categories, you will see something like this:

It works at the View level, so you have to create these for every reporting View that you have, and it works by setting up rules, like anything else. As you can imagine, as its Google Analytics here, they work the day you create them.

You have 3 options to create Content Groupings.

Rule Set 

The Rule Set option is the quickest and easiest of the three options to create a content grouping. All you really need to know is what the page URL, Page Title or Screen Name is. The other routes require some development work, but this one is surprisingly easy to create, the hard part is working out what goes into each group.

Assign content via extraction

If you have a larger site, with a slightly more complex URL structure then you will need to use this option, which will require some Regex work.

You would follow a similar pattern from our Rule Set, but select Add Extraction as your option, give the content rule a name, and then select either Page URL, Page Title or Screen Name.

Assign Content via the Tracking Code 

In this option, you will need to work with your developers, as they are going to need to modify your tracking code.  You would select Enable Tracking Code, keep the toggle turned ‘on,’ and then select the index number and modify your javascript tracking code to include one of the following snippets. You need to make this code modification to each page you want to include in a Content Group.

What should you create? 

Start by thinking about what it is you want to achieve. We always like to use one of our five to group a companies website structure. That way, you can look at the All Pages report and use the Content Grouping to see how many page views are going to your homepage, product pages, blog pages, campaign pages, contact pages, etc. After your website structure, you should think about other use cases. Like pages defined by stages of awareness, or by content type.

The Google Demo Account has three content groupings, as we’ve just seen. One to look at brands (Google, Youtube, Android). One to focus on Product (Apparel, Bags, Electronics etc). The last one used to define clothing by Gender (Mens, Womens).

They are worth the investment. Not only will it help you navigate your Behaviour> All Pages reports, but they have other use cases within Google Analytics. You can apply a Content Grouping as a Secondary Dimension. You can also use them to create Segments.

Imagine having a Content Grouping for your content types, and you wanted to know more about people that say, look at your audio website pages. You could create a GA Segment and dive into your Audience Reports to know more about their demographics, and Acquisition Reports to find out what channels are driving them to your website. You could use these insights to improve the targeting of your campaigns focused on content.

Now, as I mentioned before, they work the day you create them, but there is a workaround.

Hello, Data Studio you total babe! The picture below is from a report that shows Organic traffic, but to help bring our long Behavior> All Pages report into a more digestible format, we used something called Case Statements that allowed us to look historically at content by stages of awareness.

If you have gone through the process of working out what your URL structure is for your Content Groupings, then Case Statements will likely follow a similar set of rules.

A Case Statement is a set of logical rules that you create, so you can categorize data in a certain way.

In our example we are essentially saying, when a page has  ‘case study’ in the url then please put in a data set that is called ‘Product Aware’.

It really is a lot of fun to play with this, especially in Data Studio as you are manipulating data that has already been collected, so you can make a mistake and it is ok.

I encourage you to explore how you can use Data Studio to show how well your content works for you. Here is one I made with Sam Marsden from Deep Crawl. 

This is an example of a data mashup with did using Content Grouping (aka Case Statements) in Data Studio blending GA data, and data from a crawl using Deep Crawl. I will write up a post about this in more detail soon, watch this space!

The aim was to show all the website traffic from Organic  (medium = organic) and include some goals to give a high-level view of what SEO is doing for traffic and conversions.

Now I don’t know about you, but sometimes when I have done a crawl, I just sit there and look at all the data and I think to myself. Where do I start? One way is to split the site into our 4 stages of awareness.

This allowed us to look for opportunities to improve website performance at each stage of awareness.  Are there pages that are alive on the website but not indexed. Do you have pages that are indexed but the impressions are high but no clicks? Is that down to the SERP (hi featured snippet pain aware content) or is it down to missing metadata? Could you improve the performance of the content, speed, URL fetch time, etc

And that is it my friends.

If you want more, ‘cos there is always more…head over this way to look over our Google Analytics course. We cover everything, and we mean everything. Go have a little look 😉 

Categories
Google Analytics

What Filters should I use for Google Analytics?

Filters in Google Analytics have quite a bit of power behind them, and because with great power comes great responsibility, they should always have strategic reasoning behind their use. We touched on this when talking about The House Model© previously. We also naturally, of course, have a free PDF download for you on filters in Google Analytics.

How do filters work in GA?

You add your UA-property code onto your website for Google Analytics to start collecting data and based on your configuration (aka your admin settings) it will process that data and pop it into a reporting view.

Filters essentially tweak the data so you see what you want to see inside your reporting views, and remove things that you might not. Filters lurch into action between the configuration and processing stage. They take out the stuff you don’t want or include the bits you do. So, as you can imagine, they can be quite powerful. No one wants the grind in their coffee, or staff in the sessions, as that would be a terrible experience.

Step 1 Collection Google uses a little bit of javascript, the proverbial cookie with your coffee, to collect data about your users and their behavior.

Step 2 Configuration Google takes a look at what you are after, and all the data it can get and pings it to the Google servers – accounting for all the toggles and things that you’ve been tinkering with in your admin settings.

Step 3 Filter This is a part of your configuration that Google pays particular attention to in settings.

Step 4 Processing Google processes the data, aggregates it, performs a bunch of calculations, and passes it on to reporting.

Step 5 Reporting Google passes the report to your desk for your attention – good times!

Examples of Filters

Common examples for filters in Google Analytics are removing things like staff. If you have a static IP address and lots of staff all firing up your website every day, they will show up as users and sessions, which is going to mess up your data, big time! Google Analytics will calculate your conversion rates based on sessions to the website, if those sessions are your staff, you may be diluting your conversion rates.

Another example is to create filters to zoom in on users and sessions from a particular country. We use that here at The Coloring in Department, we have a view to show all of our data, and then we have views to show traffic just from the UK, just for the USA and just for Australia, etc.

Aside from removing staff and looking at a country, it can be hard to know what kinds of filters to apply. Where do you start?

What filters should you have in Google Analytics?

Here at The Coloring in Department, we think a great place to start is by grouping a set of filters together that have common themes. We use 3 themes for our filters so that you can see the right people, the right content, and improve the reporting readability. 

With this in mind, let’s jump into these themes and some examples of the filters within them that you would want as a baseline foundation. We will also give a nod so to speak as to the type of filter you would need. However, we won’t get that down and gritty in the details for this particular blog post.

Goes without saying, when you create any filters, do them in your TEST view 1st, check they work, then move them over to your Reporting views!

Theme 1: See the right people 

I can not stress this enough, you want to see the right people in your data, you do not want to dilute those conversion rates and over-inflate your user and session counts.

Filter: Exclude Staff 

Purpose: You don’t want staff showing up as visitors, because they are not your visitors! This filter works really well for big companies where you know the static IP address or range of IP addresses.

Example Filter Type:  Predefined: Exclude traffic from the IP address that equals. Or for those of you that have a range of IP addresses, you will need to use a Custom filter to exclude traffic from a Filter Field = IP Address and put in the filter pattern (you will need regex here).

Filter: Exclude Dev Sites and Staging Areas 

Purpose: When you are working in your development or staging environments, sometimes, if you forget to put a filter in place to exclude this traffic, you can head into trouble – as you think you have visitors looking at product pages, or completing a checkout, but the money doesn’t end up in your pocket.

By all means, set up a View for Development and Staging sites so you can see how it works and if the tracking is correct, just make sure you set up a filter to not include traffic from your development environments.

Example Filter Type: Predefined, Include (or Exclude depending on the View) traffic, from the hostname, that is equal to the name of the domain.

Filter: Only See a Particular Country/City 

Purpose:  If your business is working in different markets, you may want to create a view and add a filter to just see users from particular countries or cities. This is particularly handy as your view settings have the option to select a currency and timezone. So, you could have a filter for a view just for the USA and have the time-zone and currency to match. Easier to watch the dollars roll in.

Example Filter Type: Custom, include, a city that is equal to for example London

Theme 2: See the right website content 

Once you have the right users and sessions being counted correctly in GA, you move on to getting the right type of content into your account. We are mostly talking here about getting your Behaviour reports (aka what do people do when they are on your website) tidy, so these filters can help you view your website content in a cleaner light.

Filter: Show Full Hostname 

Purpose:  If you have cross-domain tracking setup across a few domains/subdomains, this filter is an absolute MUST. Let’s say you are using cross-domain traffic across shop.website.com, website.com, and account.website.com. In your Behaviour > All Pages report, GA will show the URI (which is the bit after your domain).

This means that for this example, all three sites homepage will show up as / and all the pages will be /pagename etc. Adding this filter would show the FULL URL so you can see correctly what traffic is from shop.website.com etc

Example Filter Type: Custom, Advanced filter, this is a tad more complicated as it uses Regex which has a habit of melting my face. But if you want to try it, you need to request to change the Field A > Extract A hostname (.*) and Field B > Extract B (.*)  and Output to > Constructor Request URL $A1$B1 end this filter by ticking on the box Field Aa Required, and Override Output Field.

Filter:  Include Directory 

Purpose:  You can then build filters to just show traffic from a directory. For example, you have a blog and you just want to see what is going on there, you could add a filter to just show traffic from the blog.

You could also do this if you have a site that is using multiple languages eg website.com/fr/ for French pages, website.com/es/ for Spanish pages, etc You can build a view with the settings set to that country, and then a filter to show traffic from the directories/blog that matches the country.

Example Filter Type: Custom, Include, Request URI and add the filter pattern for your directory, which may need some regex to work for example ^/(fr)/

Theme 3: Improve reporting readability 

This final example here, on filters, that you should have in your filter playbook is based on making the reports easier to read, and making sure you don’t fragment your data. Which is surprisingly easy to do.

Filter:  Append a Trailing Slash 

Purpose:  Once upon a time, if you didn’t type or hadn’t clicked on a link that was exactly correct, it would say “page not found.” I’m talking about how you can type or link to website.com/blog/ and if someone used website.com/blog you still get to the same page.

Problem is, Google Analytics is case sensitive and will record the URL as a hit within a users session. So, you end up with content reports and you have fragmented your data. Having a filter to add a trailing slash across all your URLs means you have a tidy All Pages report and you don’t accidentally undervalue pages on your website.

Example Filter Type: Custom, Advanced, and like our show full hostname filter, you are going to look for Field A > Extract A Request URI and use this Regex string  ^(/[a-zA-Z0-9/_\-]*[^/])$

Then look for Output to > Constructor Request URI $A1/ and again end this filter by ticking on the box Field Aa Required, and Override Output Field.

Filter:  Rewrite Dimensions 

Purpose:  The final one for this theme is readability. Sometimes, the way our websites are built can make our job hard. Let’s say you have a contact form that comes into your website data as ‘ht_form_9785672-contact’ and maybe you have more than one of these… well wouldn’t it be nice to have a filter that would change this to something like “Sales Contact Form”, yeah, you guys have guessed it, there is a filter that can tidy that up for you!

Example Filter Type: Custom, Search and Replace, and for this example, you could select Request URI and then tell GA what the search string you are looking for (your search) is, eg /ht_form_9785672-contact/ you may need Regex (another reason you do this in your test view to check it works). Last part of the filter you just add the name you want to replace the search string with, for example, ‘Sales Contact Form’

Quick Recap on Filters: 

  • Filters take about 24 hours to work on your account, so when you apply them to your test view, you should ideally check in a day later. I would like to have at least 7 days worth of data to look over and check before I call it either way. You may need more time to check how they are working, depending on what your normal traffic looks like.
  • One filter input is the output of the next filter, so your filter order can make a big difference to your data.
  • Filters are assigned to your View, but if you edit a filter at View level, it will impact any other View that has that filter added to it. This is because filters sit in a big bulk at the Account Level, and thinking about our House Model © anything you do to your roof is going to impact the floor and all windows associated with it.
  • Google Analytics is case sensitive, so even if you think “I don’t think I need any filters” that is probably not true, if you have, say, a few URLs that are a mix of lower case and upper case, they will show up in your reports as different pages, and therefore you have fragmented your data, not ideal.

Found this post interesting?

Cool.

We have a whole module on Filters in our Online Google Analytics Course.

You’ll work through all the types of Filters you should use, how to create them, as well as some issues that come along with using it. All revealed in the course.

And, as no one likes a blank sheet of paper, we’ll walk through a process to work out the types of Filters you may want to consider for your business. We have also thrown in the typical regex formula for Filters you may want to add to your Views.

You know you want to have a look 👀

Head this way my measurement loving friend.