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’.
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:
You can look at your data – and dive into people, places, ages and language.
Isolate users or sessions by device, so things like mobile/tablet/desktop. The browser they are using or the size of screen resolution.
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.
How do your users find you? Which campaigns, channels, mediums, sources, paid keywords etc.
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.
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?
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.
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*
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.
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.
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!
Hack the Stack – Five Ways to Automate Like A Human
I recently had the privilege of taking to the stage at the inimitable Turing Fest to talk about this very subject, and, given the number as well as epic nature of the questions, I thought I’d write the talk up. Save the tennis elbow on the notes front.
To cut a long story short, everyone has a different perception of paid media, and even more so of its bigger, scarier, better sibling, programmatic marketing. We are all guilty of occasionally doing marketing 1.0 in a 3.0 world, a little bit like sticking a five-year-old in a Ferrari. Not that technology in this case should be ignored, like a Ferrari, it should be aspired to.
To drive this estimable vehicle however, we need to be smarter than we’ve been, because if we are not careful we’ll be using new technology in old ways. I hate the word integration, and just because the whole team is using the stack, doesn’t mean they are working together. Brand messaging is one size, repetitive and stuck at the top of the funnel.
Performance messaging is locked into an eternal spiral of “buy now” and “join us” garbage. Basically, everybody is not at their best.
The talk explored how the factors of programmable advertising (automation, data, integration & scale) come together, and hopefully don’t blow your brand and budget to pieces. Maybe then we’d even start to move past leveraging the buying technology and start serving the right messages, in the right way, to the right people.
To a degree though, as much as short talks allow, this is an oversimplification of sorts. If it were easy, we’d in fact be doing it. The fear factor of new, unknown, sometimes impenetrable tech options does, and should, bring pause. Equally, the speed at which is changes take place can be extremely daunting. The panic is inherently warranted but isn’t really that productive.
Productive is driving towards efficiency at speed. Leveraging automation in advertising, both buying and serving, using the audience data available to the best of your ability and measuring the living hell out of the lot. Admittedly, we say we are doing this already, but I have to just drop a little note here and say – we aren’t.
The truth is, if we are advertising centric, we can hit our short terms goals with relevant (hopefully) advertising. We rarely satisfy human needs, we “add value,” sure – but we also get in the way, a lot. On top of that, we have one team force feeding relatively irrelevant advertising to people, or worse, just doing “performance” and stalking people around the moment of purchase. Let’s stop before we drop. Stop and think. A few tips on the thinking front.
In advertising, as we know, the audience is god – and we are tired, and sometimes lazy. That means, we mostly want to identify who they are and what they want, as well as give it to them as quickly as possible. Now, we can do this, between our own data (1st party), media platforms, like Google (2nd & 3rd party), and good other fashioned third-party data providers, from Experian, for instance. Practically, that might look like a few things:
Remarketing – around some kind of behavior e.g. shopping cart abandonment
Affinity – around some kind of interest e.g. snowboarding
Environmental – around some kind of context e.g. a warm day
Don’t get me wrong, getting the right audience from the black box of the stack is hard work, but you’ll know you are nearly there when they start to nail the KPIs you’ve set for yourself.
Great, found them, got them – but what to say to them? We tend to, on the whole, communicate messages that we want to communicate, not those messages that the audience actually want to hear. We need to thing sequentially here, in our story at least – it helps to answer these questions in constructing linear messaging:
What will you say that grabs their attention in the first instance?
How can you inspire them to want to know more, or better, provide more?
What can you say to turn that intent into an action of some sort?
How can you create loyalty, and repeat purchase?
How can you turn them into advocates?
Lead them down the digital garden path, as it were.
If I’m honest, people get lost here, big time lost – as there are up to a thousand different major ad units at this point in time. The media agency knows some of them from planning, the creative agency only knows what they are told to work with, and the brand – knows very little of anything beyond the position. When’s the last time you sat down and looked at the new, cutting edge formats, I’m going with never.
There isn’t a right or wrong format for you though, there is for a campaign, a customer, and an outcome however. To be both genuinely impactful, as well as helpful, is the name of the game here – and answering these questions might help:
Intent – does this creative match the user’s expectation
Digital First – would this be possible in TV or print world?
Brand Links – could I swap out your brand for a competitor, and no-one would notice
Useful– is this creative genuinely useful, interesting, or informative?
If it’s no to any of those, start again.
Listen, some of the greatest campaigns in the world managed to be all things to all people – but none of them lacked for measurement. You can just pay the money, spray ads everywhere, and pray to gods (old and new) that it all worked. You need to not just measure and track your work, but also validate the effectiveness of your media and creative, which the questions here might help you with.
The People Who Matter
To make all of the above work, we need to work together. Now it’s not my intent to harp on about collaboration here, but it is my intent to encourage you to think about the different skills that make programmatic pop – the business folks, the data nerds, and the creative people. Now, more often than not, these people speak totally different languages, totally. So, you need to hunt out those who can bridge the gap, the integrators, the godsends, the magicians. Magicians make the magic happen.
In the End
If we don’t genuinely appreciate that we have new capacities technologically, new ability and skills, as people – and a whole ton of news processes that we need to get working – we might as well fill a trash can with money, put it on the boardroom table – and light it on fire. We are trying to be environmentally friendly now, sustainable even – don’t burn your budget.
Think fast, think smart, think together – and everything is going to be ok.
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.
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.
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.
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.
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
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 😉
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 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.
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?
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.
This guest blog post comes from Kayleigh Alexandra at MicroStartups, which is a resource for solopreneurs, startups, and small businesses. Read on to find out why they believe CMOs need to be trained in tech in order to lead.
The Chief Marketing Officer (or CMO) of today’s world has a very challenging position. While there are more viable possibilities, tools and opportunities for marketing than ever before, this level of choice isn’t always straightforward. With each new option comes new demands, fresh industry terms, and distinct dangers -for instance, just as social media has the power to massively promote, it also has the power to rapidly destroy a brand’s reputation.
And while it’s certainly vital for a CMO to have impressive marketing savvy in the traditional sense (knowing the target audience, understanding how people think, and being able to balance a professional image with a certain degree of transparency and accessibility), it’s also important that they have a strong grasp of technological matters. Without it, they simply cannot productively lead marketing efforts that look to the future.
In this article, we’re going to go into some more detail about why every modern CMO needs a solid level of technical comprehension to lead the way for their company. Let’s get to it.
You can’t Optimize What You Don’t Understand
Since this is the biggest reason, it makes sense to start with it: as the CMO, your job is to bring together all the skills of the marketing team for all the facets of the marketing campaign – everything high-level should meet your approval before proceeding. And if you don’t know anything about tech, then you can’t usefully comment on tech-based marketing.
Given how much of today’s marketing is digital (online or offline), this is a huge problem. When someone on your team pitches a PPC campaign on a new social media network that’s gathering some buzz, you’re going to struggle to meaningfully assess the risk/reward ratio (you need to understand the basic digital marketing metrics). That will leave you far more likely to sign off on bad ideas and reject good ones.
Realistically, the best you can hope for in such a situation is that your staff are aware of your knowledge gaps and take steps to mitigate the damage they cause. This is likely to involve numerous simplifying presentations and a lot of coaching, particularly if you’re dealing with something complex like high-level funnel analysis (once a rarity, but now a core part of everyday business).
But if that’s the case, then not only will you essentially be delegating large chunks of your role, but you will also be requiring employees to spend time and resources helping you that they should be using elsewhere. When the CMO is effectively serving as the biggest client, the business isn’t long for this world.
Managerial Figures are Increasingly Exposed
Back in the pre-internet era, or even in the early days of online growth, there remained a significant disconnect between the public face of the company and the executive staff tier. Upper management could pull strings from behind the scenes and (for the most part) be ignored by the customers – but that’s much less likely today.
Why? Largely because the rise of social media has conditioned us to expect people to have personal brands. Any company that wants to appear relatable must make an effort to be active on social media, and not just through posting business rhetoric but also through demonstrating some element of personality.
Today, a CMO shying away from Twitter and Facebook might be perceived as cold and indifferent, so they need to get involved – and when they do, they’ll inevitably have their knowledge tested by the public. It’s fair to say that it looks bad for a company when one of its top executives seems behind the times.
In fact, even if you do manage to get away with avoiding social media, your online activity (and activity in general) is still readily exposed through basic investigation. You can admit some degree of ignorance in a private conversation only to later discover that it somehow leaked out. The higher your profile becomes, the more scrutiny you’ll be subjected to.
It’s Important to Lead by Example
I talked about a CMO being assisted by their staff, but that isn’t always possible. Particularly in an older company that’s still slowly undergoing modernization, it’s possible to have a major dearth of digital skills in the marketing team. Can those skills be picked up on the job? Absolutely, but they need to be properly valued and incentivized, and neither one of those things will happen if there isn’t a high-level executive ready to fight that battle.
This isn’t to say that you’re unlikely to see low-level employees aspiring to expand their skills and embrace new technologies, of course, because that’s not the case. Instead, the issue is that employees at that level are conditioned to take their cues from management, and if they’re not told that they should be developing their tech skills, they’re going to assume that the company as a whole has decided that they’re not sufficiently valuable to acquire.
Think about the extent to which a good CMO must challenge their team by assigning them new projects to work on and encouraging both personal and group improvement. To further collaboration, you’ll need to set team projects: working together, you could write a digital magazine, or program a chatbot script or you might build a joint venture as a way of learning about ecommerce, picking up coding skills and perhaps making some money along the way.
Accordingly, the fastest way to spread tech skills throughout a business is to have someone in a position of influence acting as a digital evangelist of sorts. Whenever someone new joins the company, perhaps as an apprentice with no tech skills whatever, it will be their influence that steers their learning. Have a tech-savvy CMO in place and you’ll find that their knowledge will slowly filter throughout the tiers below them.
The possibilities of MarTech are astounding, and it’s a field rife with industry terms that are absolutely worth learning — but if the CMO gives the impression that such things aren’t worth their time, it will set a very bad example for everyone else.
Classic offline marketing is never going to disappear entirely, but it’s ever becoming further enhanced with technology. Anyone with behind-the-times tech skills is only going to last so long in a position of influence before they take the company down with them.
MicroStartups is an all-in-one resource for solopreneurs, startups, and small businesses. We love telling the world about hard-working entrepreneurs. Head to our blog for marketing insights from leading experts or follow us on Twitter for more inspiring startup stories @getmicrostarted.
How much time do you invest in setting up a CRM, SEO or social Media Strategy? Loads right? So why wouldn’t you spend the same amount of time getting your house in order in Google Analytics?
We all want to be data driven marketers, but you need to build a solid foundation first! Only then will you will find the road to success. We like to think or Google as a blueprint to a house, you still need to bring in a decorating team to make it look pretty and design it to the way you want it to be for your business.
Check out our three part video series that we did for SEMrush below.
Session One: Delve into why the default settings in Google Analytics are never okay for your business. If you don’t learn how to configure your account properly then you are left with a, data rubbish in = data rubbish out kinda situation, leading to lost opportunities and poor business choices!
Series Two: So that you can keep doing super smashing marketing wizardry, we’ll teach you exactly how to track your campaigns and traffic so you know what’s working, what’s not and use this information to justify your budget and resources to the big old boss.
Series Three: Do you know what users are doing on your site? No? Well to find out what’s really going on, you’re going to want to get your teeth stuck into something called Event Tracking. In the last of this three part series we’ll show you how to find out what pages people are looking at on your website, setting up events and goals, and we’ll also share a case study of a company that had no event tracking, what they did with it and how it improved the bottom line.
Segments in Google Analytics are vital if you want to get better answers to your analytics questions. If you don’t use segments, well you should, because looking at all of your Google Analytics data at once sucks like a sour lemon.
Segments, can be super sweet and juicy, but there are a few pips that you’ll want to avoid, the kind of pips that get lodge in your reporting throat and choke you on your own “insights.”
One of those pips, is knowing the difference between user and session segments.
Let’s use a hypothetical example to help explain the difference.
You work for a shop that sells pens, and you have a marketing budget that you spend to drive traffic to your website, with the aim of selling your really nice pens.
Imagine that you are doing a bunch of SEO, Email Campaigns and some PPC. This is working a treat in driving traffic to your website, and when people arrive at your website they interact with something, hopefully anyway. They read your blog, look at your product pages, watch the videos, read the reviews, add to cart, then part with their cash and buy the pen.
Let’s imagine for a moment that the customer, let’s call him Tim, is looking for a nice bit of stationery. Tim is one user and within our journey example here, he has four sessions, as well as-as interactions with your website (playing video, adding to cart, which needs Event Tracking setup btw).
Tim types into Google ‘what is a fountain pen’ and he watches a video on your website.
You send a newsletter to Tim about how ace your pens are and he clicks through and checks out the product as well as reads the reviews.
Tim goes off to chat with his sister about the type of pen he is going to buy, and knowing the brand, types in the company brand name and clicks on a PPC ad, he ads to cart, but the phone rings and he forgets to complete the transaction.
Tim sits down later in the day and remembers he needs to buy the pen, he types the URL directly into her browser and buys the pen. Happy days.
Show me the money!
Now then, your boss has walked in and says to you; “can you tell me how SEO is working to drive conversions, and based on that budget you spent on videos, do those people that watch the videos spend more money?”
This is where segments are going to help you, however, you need to know the difference between user and session-based segments, otherwise, you could end up with low inaccurate data and insights that lead to bad decisions. So, on to the distinction then.
User and Session Segments
A user segment will look at all session that belongs to that user and will show you all the interactions that they had on the site. With user-based segments, you can apply a maximum date range of 90 days to your reports. It’ll capture all of the sessions, involving that user, in that time frame.
A session segment will look at a session that matches your requirements, and therefore will ignore all the other sessions belonging to that user that do not match. If you add a conversion to the segment it will include the final session that the user converted, and will ignore the rest for reporting purposes.
Let’s say I start with a basic segment to show me everyone coming from Organic sources, and I select the ‘Filter Users’ option, it will show me all users and their sessions with interactions to my site that came from Organic.
Now, your boss again, asks you if the video is making a difference to the bottom line. To answer this you could build an Advanced Segment, in effect allowing you to ‘layer’ on different conditions as necessary. If you selected ‘Sessions’ as the filter, using our basic example of Tim going about his pen buying business, the video is going to look like it didn’t do much for you – as it is looking for a session where a video played and they bought the pen. And no such session might exist.
If however, you changed this to ‘User’ it would pick it up – and you demonstrate that value you’ve been asked for.
It can get a bit blurry here depending on which filter you are using, and on that note, a little tip. I’ve used system segments for ages, as these are the pre-baked into the tool, thereby saving you time building, and I assumed, quite wrongly, that all the system segments where session segments.
They are not, they’re a mix of users or sessions, depending on which ones you select.
System Segments that are user based are the following:
Made a purchase
Performed Site Search
System Segments that are session based are the following:
Mobile and Tablet Traffic
Now, off you pop, go have a play with segments, and remember to check if the segment is set to User or Session.
If you want to know more about this wonderful part of GA, we have a whole module all on Segments in our online course. This module also comes with a super useful template to brainstorm and plan your segments so you get those juicy insights. Go have a look at all our online course has to offer, head this way.
We did a webinar for SEMrush on Mistakes in Your Google Analytics Setup That Can Trash Your Data. If you missed it, do not worry, the webinar is ready and waiting for you to watch, and the nice folks at SEMrush did a write up of the transcripts they wrote out (which is super helpful). Slides can be found here, and of course, if you want any templates we reference, head your good self over here.
Aiden: Welcome back and happy new year. Jill and I are back now for series two, with SEMrush of course, around all things analytics once again.
We’re going to be talking about how Google Analytics can turn into a little bit of what we would call a dumpster fire if you don’t set it up properly. We’re talking about the human mistakes that make this particular tool, from a technological perspective, not work all that well.
A massive hello to all of you. Now, Jill, you’ve seen, over the hundreds of audits that have kind of crossed our various different desks, mistakes ad infinitum at Google Analytics. I think we agreed that there was one account that we’ve seen that was nearly correct but wasn’t quite. As for the rest, not so much. Can you tell us a bit about that?
Jill: Yeah. With Google Analytics, because it’s a computer program, you can’t always assume that the people that created the account for you have done it correctly, or they understood your business well enough to track the things that matter.
When we’ve done audits, when we’ve trained people in Google Analytics, the same mistakes happen, and that’s the reason for this particular webinar.
Aiden: I’m going to ask the lovely Jill to kick off her extremely interesting talk about some of those mistakes and indeed what you can do about them. Jill, quick, take it away.
Jill: Thank you very much. Okay, let’s do the screen share.
How Mistakes in Google Analytics Setups Can Ruin Your Data
Right. Mistakes in your GA that can trash your data.
Now, why should you care about your GA setup? I’m going to tell you a bit of a story. Some of you may know this story already. The reason that we called our company The Coloring-In Department, is because people had used that almost as a slur, as a mean thing to say to me when I was in the office.
When the budget was getting cut, or things were being moved, my department, my budget was the first one to go, and I’d have people say to me, “Well, we know marketing does stuff, but we don’t always know what the impact is.”
That’s when Google Analytics can really help you, because you’re reporting to people, and you don’t want them shouting at you saying, “I gave you this money and what did I get from it?” You want to be able to turn around and give them a number and show them some data.
Going back to my favorite quote from our data scientist, “Without data, you are just another person with an opinion.” You want to make sure that you can validate what you’re doing. On that thought, you also need to make sure that the data that you are providing is correct, because otherwise you will lose your credibility and people will shout at you, and nobody wants that.
Crap in, crap out. One of the things with any program, including Google Analytics, is that it’s as good as the settings that you have. If you have a bad configuration, if you haven’t turned a certain toggle on, then the data that’s inside those reports, those reporting views that you’re using to give information to your boss, your board, your client, whatever. You’ve got to make sure that the data that you’re presenting is actually valid data.
This messy door, this crap that can be in the back of your admin settings, I’m talking about this, the account settings. You arrive here by looking at the little left-hand cog, which will show your analytics account setup.
Accounts, Properties, and Views in Google Analytics
One of the mistakes that we’re going to talk about, is actually getting your house in order to make sure that you have the right account, properties, and views. We did this in the first webinar in series one, where we talked about analytics being a little bit like a house. You have your account level, and I want you to think of this as the roof of your website, your little house that you have. You’ll get given a UA number, so hypothetically UA1234.
You then have a property, and I want you to think of these properties as a floor, an actual living floor that your visitors are going to come in and sit down on your sofa and do stuff, like buy stuff from you, send a lead form, whatever. You’ve got different reporting windows, which are your view settings. Then to make those windows work you use filters, which is like adding drapes and blinds.
If you have a basic website, so you’ve got like our website, thecoloringindepartment.com. If you just have a single website, you have essentially a bungalow. You’d have a roof setting, UA12345. You have a property, so it’ll be UA12345-1. You should have four windows, four reporting views.
Google recommends that you have a raw window, so if I have a look through this window I see absolutely everything. You have a test view, this is where you play around and check if something’s going to work or not. You check a filter. You check a goal. You check your settings and make sure that you don’t flatline your data. Then when you’re happy with it, you then create your reporting view, which is the one that you go in and use to validate your decisions and your strategies.
Now anything that we do to our settings if I start messing around with my roof if I whack a massive hole in it, it’s going to impact the floors beneath it. If I do something to a window, by adding a filter, it only changes the data to that particular window. You’ve got to make sure you go into all of your settings and audit them, and not assume that if you do something at one end of the house that something’s going to magically happen at the other.
Importance of Cross-domain Tracking
Now if you have a multiple brand scenario, so you have different websites that sit independently from each other. You have a website, maybe you have a separate business, maybe you have an app, but they all sit separate to each other, you’ve essentially got a flat or a multi-story building. They all sit under the same roof, so the company name I’m going to make up a company named JillLikesGin.com.
I’ve got several floors for different things that I’m selling because I’ve decided I don’t want to do gin anymore. I want to sell vodka, or I want to sell ice cream or whatever. If they sit separately to each other, they’re separate floors with their own little reporting views, and I can see what is going on for those particular properties.
Now the mistake that I see more commonly these days is something where you have multiple domains for a website, but they’ve built as if they were a flat. I see this a lot in eCommerce sites at the moment. Their domains will change, so you might have website.com where you’re sending all of your beautiful visitors to go and have a look at your products and services.
They decide they want to buy it, so they click on a button and they go to shop.website.com. The website still looks the same. The design still looks the same, but the URL has changed. Then when I’ve decided that I’m going to buy from the shop and I’m going to create an account, another domain pops up called account.coloringin, or whatever it is that you’ve got.
Now, the mistake that can really ruin your data is if you set up a multi-domain website where the ecosystem is actually within the same business, and you’ve set them up as separate floors, you are unable then to track the customer journey. Because when I log into my analytics, like a floor, an actual physical floor, I’m on floor four of our building, and Aiden’s on floor seven. I can’t see what’s going on on the floors above me. I can only see what’s going on on this particular floor that I’m on at the moment.
If you have a goal for somebody creating an account, or somebody buying something, if I’m in the reporting view for coloringindepartment.com, but the goal happened on a different floor and you haven’t tied that all together with something called cross-domain tracking, then you are not able to build your goals. You’re not able to see the journey. You can’t see what marketing brought people into that particular sale, so everybody’s having a bit of a hard time.
The first thing that you need to think about when it comes to your analytics setup… double check that your roof and your floor and your windows are correct. Double check that if you do have an ecosystem where you’ve got several domains but they all really need to be packed into one, have you done cross-domain tracking so that you’re essentially treating all of these different domains as one.
Making Sure You Set Goals For Your GA Reporting Views
The next mistake that we see a lot, which seems really obvious but they’re interestingly absent in so, so many audits that I’ve done, is not having any goals on any of your reporting views. Sometimes when I’ve done an audit where there are several windows, there are goals on one, and not on the other. I’ve had so many people come back to me going, “Oh, I thought if I created it in this view they would be created magically in the other.” I’m like, no, it’s a computer program. You’re going to have to go in and build that goal another time. If you’ve got 17 views, then you build that goal 17 times.
For any business, you want to be tracking a significant profitable customer interaction. Now, that can be I signed up to your newsletter, I scrolled down to the bottom of the page, I submitted a contact form, I bought the damn product.
Whatever it is that you’re doing, you have 20 goals available to you per reporting view. 20. What we normally see are either one or two big goals, so these are your macro goals. Think of these as, if these don’t happen, then I’m out of a business. Then your micro goals are the smaller meaningful interactions as people edge ever so close to doing what you want them to do.
Imagine having 20 goals, and being able to understand the role of a particular marketing channel. You might see that Facebook is really good at getting people to watch a video, but they’re not going to convert. Like that was the early awareness stage of the journey, but email was really good at getting them to start a free trial.
By doing this you’re able to give the managed expectations of your marketing channels and how they’re actually performing and the role that they have. Not every single channel in every single campaign is going to be the person that scores the goal, but they have a role. If you have 20 goals, your micro and macro conversions, it gets a lot easier to actually present that information to people.
Verify Your Filters in Test View First
Now onto one of my favorite topics, filters. When we are talking about our reporting views, as I mentioned, we have our little windows where I can look in and see what all of my lovely users are doing and if they’re engaging with my content, if they are buying stuff. Filters have probably been one of the main culprits of the data just being god awful because they are quite powerful. If you haven’t done it in a test view, and you haven’t checked out that they were working, it is very easy to flatline your data.
I’m going to give you some examples of where this has happened. This is an example of a reporting view that was created. Now, this view was meant to only show people from the United States of America. Now, when I went into the reporting view, and I went into audience, I think we can all see quite quickly here, because of the gorgeous heat map, it’s not just showing people from the United States of America.
This company was saying, “Right, we had 52,000 users in America this month.” That’s wrong because actually, only 48% of this data was from America, the rest came from different countries. When they said to me, “Jill, we have a problem with our data. There’s a data discrepancy. We don’t understand what’s gone wrong.” I was like, let’s have a look at those filters. Let’s see what’s happened.
Essentially they had the right idea but the wrong filter. Now if they did this in the test view, they would have gone, “Hang on a second there Bob. You can see that the traffic is clearly not just showing from a particular country. Let’s go back and see what we’ve done and make some changes.” They didn’t do that. They didn’t do any of the test views, which would have fixed this particular obvious problem.
Another tip here, you have to write the country exactly as it shows in the Google Admin settings. If they put in France with a capital F, then you have to put the filter pattern that exactly matches what is in the Google Analytics settings. Because this is a computer program, there is no empathy here. They won’t look at this and go, “Oh you put the USA there. Did you mean the United States?” No. It’s going to just flatline your data and say, “Computer says no.” You’ve got to have a look at your filters and double check that it’s giving you the information that you want.
That was a nice easy one to get us started. The next set of filter mistakes will show you how you can have real errors in your revenue or your page views, or just generally the data’s not making any sense.
Exclude Staging Environments From Filters
The first mistake that I see with filters is to not exclude your staging environments. This happens quite a lot actually. There was one audit I did for a global company last year, where they had 20 different staging domains, and they didn’t put a filter to say, “Exclude staging.website.com, dev.website.com.”
You’ve got to remember when it comes to conversion rates that Google is calculating those conversion rates based on your sessions. If some of the sessions are your dev teams, or your agencies working on stuff, you’re diluting your conversion rate. You might be getting kicked for a conversion rate that looks really bad, and in truth, it’s actually not bad.
You can still, for the reverse, have a view that says, “Only include traffic from staging.website.com.” You can see what’s going on, on that staging. That’s fine, but you do not want to include this in your reports, because it’s just going to give you those data discrepancies.
If you have got any staging environments, you want to go into your view settings for each view that you want to do this in. Do it in your test view first, check that it works. You want to go in and say, “Hey Google, please exclude the data from dev.whatever the website name is, or staging.websitename.” Do it in your test view, check that it works, and then roll it over into the reporting views. Then the data is going to be a little bit cleaner, and life will be a little bit better as a result.
Exclude Query Parameters from Filters
Another problem with filters can be down to your content report. When we are going into our behavior reports and we want to understand what people are doing, we can go into our all pages report. This was from a client that we did some work for, and they said, “Our content is amazing. We have 11,416 page views. Go, team.” We went, “That’s fabulous. That’s wonderful.” They didn’t set up their site search correctly, in terms of adding a filter to exclude the query path.
Now you may or may not know this, but Google will record the URL at the time of a user’s session. For this particular website, they had a lot of people using their site search, and I mean a lot. Every time somebody went onto the little search bar and said, “I am looking for insert keyword.” Whatever the keyword is for the content that they were looking, that URL, where it pulls in the query parameters included in that particular search string, gets recorded as a page in your analytics.
When we added a filter to exclude query parameters, those 11,000 and odd pages actually got stripped down to about 3000 pages. That was an uncomfortable conversation to have with them, to say, “Actually people aren’t reading your content. They’re not looking at your pages. You have a usability problem. People can’t find the information, and they are resulting in your search features to try and help them find that information.”
Why You Need Event Tracking
One of the other mistakes that we’ve seen in accounts is not using event tracking. Now event tracking, for me, once you’ve got your house set up and you’re tracking things correctly in your acquisition reports, is to actually understand what people are doing on your website.
Do you know what people are actually doing on your website? Who’s scrolling down the page? Who’s clicking on images? Who’s printing a page, or downloading a PDF, or playing a video, or clicking on an email address?
This does not come pre-baked into Google Analytics. The reason why it doesn’t come pre-baked in is because you’ve got like a million, million, million websites using analytics. Google doesn’t know what you want to track. They don’t know what you want to call it, so you have to do this. You have to set up events.
Now the mistake that I’m going to focus on here is just getting the brief wrong from the offset. You need to think about the things that you want to track, and you have to talk to the computer program the way that it wants to be talked to. They want to have category, actions, and labels. Think of your categories as big, broad buckets for you to organize the things that people could be doing on your website. The action describes the doing, what is it they actually did. The label is going to further describe that action.
You’ve got to go into your event tracking, which will be found in your behavior reports, and I want you to audit what you currently have. This is how not to do it. I did an audit for an eCommerce company, and they said, “We’ve got event tracking, Jill.” I was like, “Fabulous. Let’s have a look at what you’ve got.”
We had one bucket, one bucket, for eCommerce. I’m like, all right, let’s have a look what’s in here. Two actions associated with a category, non-interaction, and interaction. I was like, okay, I’m going to open door number two, to see what the interactions were labeled, and they were all labeled to not set. That doesn’t help anybody. You’ve basically just wasted a load of opportunity here.
When you go in and audit your analytics, which is the fix, you have to go through your pages, your home page, your money pages. Your money pages being the pages that you want people to actually do something. Fill out a form, buy the shoes, buy the bottle of gin, book the holiday, whatever.
You want to go through it and make sure that you have independent categories, and that the actions associated with those categories don’t overlap. This is one of the issues that I’ve seen with event tracking. I might have somebody have an action, so this is the doing, and they’ve called it click. That’s associated with one or more categories. When you look into the report, you’ve overinflated a potential action because you’ve named three different things the same thing, if that makes sense. It’s just really heartbreaking when that happens.
You want to make sure that you don’t duplicate any of the naming conventions for your categories, actions, and labels. Because when it comes to building segments, or it comes to digging into this data if I’ve got three different separate categories. Let’s say it was playing a video, or downloading a PDF, or clicking on an email address, and all the actions for those categories were labeled as a click. When I go into my analytics and say, “Hey, show me all the people that watched the video.” It’s going to count all the other things as well, and that’s going to give you a false number, which you don’t want to have.
Building Remarketing Lists from Segments
Now a tip here, because I’m aware that we’re speaking to people that are going to be using a tool like SEMrush for their ICO and their PPC. If you have event tracking on your Google Analytics account, which I strongly suggest you do, because you’re going to need them to build goals, you need them to build segments. I want you to start building remarketing lists based off of what people nearly did.
Imagine having events firing correctly where somebody goes on a webpage, scrolls down the page, downloads a PDF, plays a video, half filled out the form and added to the basket but didn’t buy. Build a segment, and if you’ve linked up your AdWords to your Google Analytics, and again we go through this in the first webinar that we did in the series, then you can click on that little button called actions and build an audience.
That’s going to build a remarketing list that will sit in your AdWords, which means you can remarket to the people that nearly bought. They would be my first go to people that I would do any remarketing on. Then you can drill down further as you so wish. There’s a nice little bonus point for you there.
As you can see with a lot of these mistakes in analytics, it’s just down to not fully understanding the implications of the features that you have. Namely, a bad account set up in the first instance. You don’t have the right reporting views. You have a flaw, or several flaws, for different sub-domains when you should have done the cross-domain tracking. You don’t test anything, so you don’t see any filters that are making mistakes. If you are doing filters, you want to make sure that you’re at the very least removing your staff. You want to remove any staging environments. If you’re going to be looking at particular markets, then you want to make sure that you have the correct filter to isolate and only see those particular users.
Usefulness of Custom Dimensions & Metrics
I now want to touch on a mistake which is just a mistake by not using them. Custom dimensions, and metrics, and data import. My main point here is that your business is very unique. The insights that you want are also very unique.
Now, creating a custom dimension in your property settings will take you minutes. It will take dev a lot longer to actually get this working for you. The thought process, of thinking about what else can help my data analysis?
Let’s go through these one by one. In Google Analytics there is no dimension for a refund. If you are selling stuff, you can go, right, this month I sold 20 pairs of shoes and I made 1000 pounds. If somebody then decides that they don’t want to keep the shoes anymore and they send them back to your company, Google Analytics doesn’t know that that happened.
For me, I want to understand, how much did I sell? Then what got refunded? Then hopefully tie this together so I can understand the marketing channels. The way that we’re going to stitch this together is with your eCommerce data and your product data. Provided you’ve set that up correctly, you can stitch that together by creating a custom dimension and a metric where you can punch in that refund data. You can either load this up manually through your property settings, or you can just pump it in through your API. When you’ve got this, it’s so, so cool.
When we are looking at things like content, so if you are writing a lot of content, then it would be useful to know if I’ve got a lot of people on staff writing, who gets the sale? Looking at things like the author, because that doesn’t exist as a dimension in analytics. I can tie that to the page URL, so say, “Hey Google, this is the URL, and this is the person that wrote it, and this is the category.”
For anything, if you can build segments, you want to build a segment. You might say, “Show me everybody where the monthly recurring revenue is like 200 pounds.” If your average order value is like 50, who are your best users? Show me all of my users that are the B2B, or the B2C if you’re a two-sided marketplace. You’re able to split out that data. You’re able to split out those different users.
Again, as I mentioned, if you can build a segment, you can build a remarketing list. You can say, “Hey, show me all the users that are the buyers.” They haven’t bought anything for a while, so let’s send them a message around the web, and follow them, and give them a little message to say, “Hey, remember us? Come back.”
Tackling Spam & Undesirable Traffic in GA
Aiden: Jill, there seems to be a lot of chatter around, well actually how do we exclude bots? Is this an issue? What about spam? What if we’re getting traffic from sources that perhaps are undesirable? Things that we might have disallowed, say for instance in search console, are still coming into our Google Analytics reports. How might we address these two relatively synergistic issues, in your opinion?
Jill: Yeah. That’s a very good question. Spam, we’ll tackle that one first. Fake websites sending you traffic, but they’re not actually real visitors. Those visitors are showing up for sessions, and with them showing up for sessions they’re screwing with your data. If you go into your acquisition reports, and you drill into the referrals, so websites sending you traffic. Then I add a secondary dimension to say source, so where does the link live? You can start to find particular users and visitors where the bounce rate is 100% and time on site is set to zero. You’ll see things like freesocialsharebuttons.com. You start to pick out common culprits that are basically spamming your site.
If I find a couple of different domains that are rubbish, I will go through the process again, where I’ll go, “Right, let’s have a look at all of these different links.” I add a secondary dimension to show the hostname.
If you find that you’ve got several domains that are all spam, but there’s a mother ship where the host is faketraffic.abc.com, I will build a filter to say, “Exclude all traffic from the hostname, the mothership.”
Also, in your view settings, there is a little tick box that doesn’t get ticked by default, which is exclude all known bots and spiders. You want to make sure in your view settings that you tick that little bad boy to make sure that it excludes Google’s kind of nice list.
Aiden: A couple of other things came up. Cross-domain tracking is a bit of a repetitive one. That one came in on Twitter actually.
Cross-domain tracking, thinking about connecting up our various different mother ships, so to speak. Are there issues with that that we need to be aware of? Is the UA code involved in some way? What would you say in a nutshell in making sure cross-domain tracking is set up properly?
Jill: You need to understand which domains do you want to track as a whole ecosystem. We’ll take our example. We’ve got Coloring In Department, and then when you do our online courses it’ll be like teach.coloringin. We want to stitch those two websites together essentially, those two different domains. I want to see the full ecosystem.
Instead of having several floors, where I’ve got different properties with different property numbers, I’m going to say, “Hey Google, we’re going to treat each of these different floors as if it’s one really big floor.” I’m going to use the UA1234-1 on Coloring In Department and teach.coloringindepartment.
If you know that you’ve got an ecosystem that needs to be tracked, then cross-domain tracking is what you need to have set up.
Aiden: Thank you, Jill. Thank you, everybody, for watching this wonderful webinar on all things GA and mistakes there. Hopefully, you are less terrified, or you feel a little bit more able to troubleshoot maybe some of your own account issues or challenges that you might have.
We’re obviously giving a nod to Seth Godin’s reference to people being part of tribes of like minded people, all connected with shared goals and interests. The first step before you dive right into your strategy is to create your persona for your ideal customers. Find the hooks, the pain points, the problems, that they’ll have and think about how your product or service is going to solve that problem for them. Basically, be empathic, do the human thing.
Understand their social activities and footprints
Once you have identified your personas and segments you need to have a better understand of what they are actually doing online, if you’ve identified the main social media platforms that your target audiences are using, how are they using those platforms? Does your target audience just simply maintain a profile on network sites? Do they read a lot of content, maybe sometimes repost or share? Do they actively post reviews? Are they doing the content creation?
Having an understanding of what your audience is doing on social media will help shape the content that you need to create and publish. For example; if your audience are mainly spectators, then running a lot of competitions where you’re asking people to load up images or interact may not be the right path. Be commonsensical, it’s surprisingly not that common.
Taking the time to understand the conversation landscape
When you know where your tribe hang out and you have a confident indication about which social media channels they are using, and how they are using them – you need to start listening! I do mean listening, I don’t mean reporting on all those retweets.
If you jump right in and start talking at people with no thought to what content will hook or engage them you are simply shouting at people who will ignore you and won’t be afraid to publicly tell you that you’ve missed the mark.
Equally, this is the time that you want to find groups and see what the hot topics are, do your homework and find out who the influencers are, who do you need to engage with, and don’t forget to listen to what your competition is saying on these channels. Nothing wrong with a bit of borrowed genius.
An overlooked and inordinately simple tip at this stage, is to use Google Trends to map out the hooks and pain points you identified in your personas and listening activity and plot out a calendar and/or topics for your social media conversations.
If you have social monitoring tools (free or paid) set that up now and monitor the conversations based on your target keywords, brand names, and the issues/ paid points your target audience are interested in, discussing and so on. Keep those ears to the ground.
Key performance indicators measuring your efforts
You need to measure your efforts. Social media can and should be measured and have key performance indicators which demonstrate social media impact on the business and its bottom line.
Social media can have a bad reputation about being fluffy and not an overly magical value driver for a business- but it can, and should, be measured. In any event, you must demonstrate to your boss, or your investors, or whoever, that your marketing efforts on social media can, and will make a difference to the bottom line.
The only way you can do that is to measure it with the metrics available to you. Both the challenge and the opportunity here is that there are literally hundreds of metrics that you can measure. Start by focusing on the ones that matter for your business, how does social media impact the bottom line? Avoid blowing your trumpet too hard about how many thousands of followers you have on social media, instead tie this into the bigger picture in a meaningful way, how does social support your business objectives?
A very useful thing to do here, and one often neglected, is to get familiar with Google Analytics (GA) and the attribution models therein. The Acquisition report in GA works on the last click model, which means social media can sometimes look a little bit rubbish, as it isn’t necessarily getting its due credit. We have a handy guide on Tracking campaigns in Google Analytics and an explainer on how Attribution works, you should check them out in the resources section.
Identify resources technology and time to implement
Social media might look really easy to some but it is a large group of marketing channels in their own right, and you are going to need to invest in it, both time and budget. Social media platforms may be ‘free’ to set up, but many companies fail in their social media strategy by underestimating the resources needed overall.
Do you have a dedicated person who is going to monitor and reply to your tweets, who is going to write your blog posts? What’s the process if someone posts something bad? What would your social media policy be? Do members of the team need additional training and development? Will you outsource some campaigns to agencies?
Discover and engage with influencers
Your social media efforts will be improved if you are partnering and working with influencers in your space. You should have an idea of who to work with and follow, or collaborate with from the listening part of this social media journey.
Use comments and conversation to improve your reputation and brand image. Inspire posts, and create an opportunity to deliver your content and amplify this through influencers. If you have listened well, and invested time in this, it will come across as genuine and not shouting on your soapbox. I’m under no illusion that influencer marketing is considerably more nuanced than this, but you have to walk before you can run.
Set smart goals for your business
Why are you doing this? What, at the end of the day, is the whole point of this social media strategy? Is it for awareness, sales, loyalty, retention?
Google Analytics provides a number of reports to help you analyse how much your social media efforts are impacting your goals and bottom line. They are not necessarily off the shelf and ready to go however.