As you know, I run regular Google Analytics courses. But sometimes people want some tailored training instead. I also do that.
Tailored training follows my usual training philosophy:
What's different is that it's designed specifically for you.
Does tailored training sound appealing? Get in touch.
If you're in a country with privacy laws about cookies, then you may worry about Google Analytics. I'm based in the UK, and I was worried about Google Analytics.
One way to address Google Analytics' (GA's) use of cookies is to install a consent tool on your website (the grander offerings are often labelled 'cookie management platforms'). The idea is that the user says 'yes' or 'no' to the cookies used for GA.
Here's an example from the University of Plymouth in the UK:
Inclusion isn't a recommendation. These four have different offerings and prices. You should test a consent tool works before you put it live.
CIVIC's Cookie Control is used by the ICO (the UK privacy regulator). When I first installed a cookie consent tool, that provided extra reassurance to me. I'd be less cautious these days.
Some examples of Cookie Control:
I like the design of Usercentrics' Cookiebot. Maybe that's just me.
Some examples of Cookiebot in the wild:
I've seen CookieYes less than the others on this list. Perhaps it's a newer contender?
Some examples of CookieYes in action:
OneTrust are the most common example I've seen for medium-sized and large organisations.
At the time of writing, the digital agency Torchbox have said that a number of their clients have been hit with a big increase in charging. You may want to check their blog post before considering OneTrust.
Some examples of this consent tool:
There you have it: four cookie consent tools you could use to deploy Google Analytics. There are numerous others, but these are the most common that I've seen.
Privacy part 1 - where the data goes in Google Analytics 4
Can Google Analytics help us measure user satisfaction?
I'll cut to the chase, because time is short. Here's what to do.
I'll explain more later.
Let them know what's going on. You want them on your side; making demands can backfire in this situation. I'd say something like:
"I'm getting lots of traffic today. Is there anything you can do to keep the website stable until this passes?"
If you have a relationship with the source of the traffic, it can help to get in touch with them. When things went viral at the University of Oxford, the traffic was coming from another department's website - the Wildlife Conservation Research Unit. When we contacted them they explained the connection with the Jimmy Kimmel Show.
We'll come back to this later on.
If you work for an organisation you need to let your colleagues know at some point. This gives and also takes away. Other people can spot opportunities that come with a traffic surge. But, as other people learn of events they will come to you to ask questions. The extra interruptions can cause your practical actions to grind to a halt.
My advice is this: tell someone else, then ask them to handle communications with the rest of the organisation.
If you have clear answers to steps 3 and 4, now is the time to make the most of this event. Think of the mindset of these people - what are they after? Is there anything you can promote that they'd be interested in? You could edit the page to link to that.
Sometimes traffic comes into a very old page. Check: is it up-to-date and accurate? I've seen traffic peaks hit pages more than 10 years old.
Your website might go down at any moment. If it does, what would users see? Now is a good time to optimise that message. You can help visitors fulfil their tasks in other ways.
For example, at Oxford we provided a phone number people could call to make donations. Our website did go down, and we still received gifts.
These sort of events are wild. It's useful to take a short break to let the adrenaline subside and marshal your thoughts. You'll make better decisions afterwards.
When you go viral, it's a shock. It happens so rarely you're not clear what to do. Hopefully this post will help you make the most of the moment.
Go easy on yourself. An event like this causes us to make hurried decisions. It's understandable if we make some mistakes.
If you glance through the Acquisition Report of Google Analytics your eye might be caught by the phrase 'organic social'. What, pray tell, does that mean?
Users who came from Organic Social sources are those from typical posts on social media channels.
Imagine the following Google Analytics scenario.
Cambridge City Council are thinking about reorganising the way they work. They want to ask residents for their views. They create a survey on their website and post about it on X, Facebook and Instagram.
Daniel sees the post on X. He clicks through to the survey, but then gets distracted and never fills it in.
Next month a member of Cambridge City Council staff checks their Google Analytics (GA) account. Daniel's visit is listed in the Acquisition Report, on the Organic Social row.
As with the Organic Search example, when you see 'organic' think natural, or normal. And social refers to social media, such as X, Facebook, LinkedIn.
This is not paid traffic: in the example above, Daniel didn't click on an advert on X.
One word of caution... as discussed in a previous post, occasionally traffic from social media gets categorised in GA in the Referral row.
What does 'Email' mean in Google Analytics, and why are those numbers so small?
The danger of the Realtime Overview
Where do people come from? That's a key question when exploring the performance of your website.
In earlier posts I've explained the organic search and email channels on Google Analytics' Acquisition Report. Today, I want to unpack another channel: Referral.
So, in the eyes of Google Analytics, what are referrals?
The Acquisition Report breaks things down by sessions and by users. For brevity I'm only going to talk about sessions in the explanation that follows. All principles discussed will apply to users as well.
Google Analytics lists a session as a referral when the user has come from another website.
An example always helps, I think. Here goes...
Trey lives in Bournemouth in the UK. He saw an article on a local news website about a local company that had raised £10m for a charity called Water Aid. Trey followed a link in the article that led him to the Water Aid website. He spent some time exploring the work of the charity.
Next month a Water Aid staff member looked at Google Analytics (GA) for their website. In the Acquisition Report GA listed 3,000 sessions in the Referral Channel. One of those was Trey's visit.
Simple, right?
Not so fast.
If we look more closely we discover that the Referral Channel can also mean some other things.
The numbers for referrals also include sessions that come from AI chatbots, such as:
That feels different to me. When I'm interacting with an AI chatbot I tend to forget I'm on a website.
I think AI traffic is important to watch, because of predictions large language models will change the way people look for information. I think it's a good idea to customise the channel group in GA to list AI traffic sources separately from referrals.
I've seen some traffic from Bluesky and Threads logged in the Referral Channel of GA rather than the Organic Social channel. It's not all traffic from those social networks, just a portion.
In the case of Bluesky, the misplaced traffic has the following source dimension:
go.bsky.app
In the case of Threads, the traffic has this source dimension:
l.threads.com
These dimensions may provide a way to adjust the channel grouping and get more accurate data.
So, what does Organic Search mean?
What does 'Email' mean in Google Analytics, and why are those numbers so small?
How does Google Analytics affect the privacy of your audience? That's a good question to ask, not least because there may be legal implications to the answer.
In the first part of this series I looked at where the data on audience members goes. In this part I look at something more basic: what the data is.
As before there is a distinction between what Google know about a member of your audience, and what they let you know about that person. In this case I'm focusing on the latter, because it's hard to know the former (that is true of many companies, not just Google).
Audience privacy all depends on how Google Analytics is set up.
You know the most about website visitors if you've enabled Google Signals in your Google Analytics (GA) settings. In that case GA will pull info about the audience from the Google accounts they use for their Android phones, their Gmail, their Google Docs, etc. But this only happens if they are logged in at the time of visiting your website, and using the same device.
Of course, a website visitor may use an iPhone, or a Yahoo email address or Microsoft Word. They may not even have a Google account. In that case, turning on Google Signals will not reveal any more information about them.
When Google Signals is turned on, you see this information about your audience:
For quieter websites, thresholding may hide this data about some audience members. I haven't done any testing around that functionality, so I'm unclear how effective it is.
If you don't enable Google Signals, you'll find the fields listed above are empty in GA:
Have you enabled Granular location and device data collection in the GA property? If so, then GA will store the city of website visitors. They label this 'city', and it can be that. But, it can also be a much smaller entity. For example, I've seen a UK village listed which has a population of 6,000.
So, where a user lives affects how much privacy they are afforded by Google Analytics. Or does it? I say that because city seems to correspond to the location given by the Internet Service Provider (ISP) of the audience member. I've seen ISPs describe location accurately. I've also seen them give a location 30 miles away from the actual location of the user.
If neither of the above settings is enabled, then Google will show this information about visitors to your website:
It might be possible to learn about a website visitor from their actions. A website visitor who visits a page designed for gambling addicts may be a gambling addict. Or they might just be interested in the subject.
You might have shared a page address with only a small group of people, and it may not be possible to get there without having the page address. In that case you would know that website visitors are one of that small group.
In most cases it's not possible for you to identify an audience member. However, if a website has a low level of traffic it is possible to make an educated guess in combination with other information.
Here's an example: imagine Murali is someone you met at an event last month. He said he was from Market Harborough in Leicestershire, UK. You check your stats this month and you see that you've had a visitor from Market Harborough. Is that the same person?
If your website is quiet - say you get 100 visitors a month and only 5 are from the UK - then it's very likely to be the same person. But if you get 10,000 visitors a month from the UK, then you couldn't say that.
Either way you could never prove it was Murali who visited.
Privacy part 1 - where the data goes in Google Analytics 4
Can Google Analytics give an early warning of going viral?
I have a love/hate relationship with the real-time data in Google Analytics.
I love the immediacy: people are here right now! This person from France just arrived on the website. Here is someone else: they're from the US. That user is sticking around a while. Do they like what they see?
Also, the Realtime Overview report is great for checking an account is working at its basic level: visit your website, head over to Google Analytics, are you counted?
Sadly, my brain tells me that my love of real-time data is superficial. The reason? The numbers draw me in too close: I lose perspective.
I know one website that gets around 800 active users per day during the week. I kept a close watch on it during a 24-hour period last week. For the Active users in the last 30 minutes measure, the highest number I saw was 33, while the lowest was 14.
That exposes a problem: when I look at the Realtime Overview, am I seeing my website at its busiest moment, or its quietest moment? I just don't know.
Moreover, while it's great to see the last 30 minutes, there's a measurement gap here. What happened 44 minutes ago? Or 3 hours back? That data is hidden away until tomorrow arrives.
When I experienced a viral moment at the University of Oxford, I vividly remember watching the real-time number ebb and flow. It was a fruitless exercise: I had nothing to compare it with.
What does 'Email' mean in Google Analytics, and why are those numbers so small?
Should I care about average engagement time?
Some years ago I found myself in the middle of a viral moment. It was a wild ride, involving a lion, Jimmy Kimmel and a world-famous university.
I worked for the University of Oxford at the time. My department, the main fundraising office, ran a donation platform. Most of the colleges and departments had pages on the website to accept gifts.
I happened to share an office with the staff who did the admin that accompanied donations. One afternoon, by pure chance, I happened to overhear two of them discussing how busy things were that day. My week rapidly unravelled.
It turns out that the illegal hunting of a lion had made the news around the globe. But this lion, known as Cecil, had long been monitored by the Conservation Unit at the University of Oxford. The head of their department somehow got invited onto the Jimmy Kimmel show to talk about the event.
Lots of viewers were moved by the segment and wanted to give. And our donation platform was their destination. That day we had a 7,000% increase in the number of donations. It was a wild time, as we tried to keep the website up and make the most of the viral moment.
As I reflect on that time, I think - how could I get an early warning of this sort of event? Time is so critical when you have a large traffic surge. You might only have minutes before your website goes under. In this case I knew quickly because I happened to share an office with some staff from finance.
Could Google Analytics help me with this?
The best way I have found is to use Custom Insights. Sadly, there isn't enough space to provide a full guide to the feature here. Here is Google's information on Custom insights.
For this particular purpose, the key is to set the evaluation frequency to Hourly. My tests have found that there's a sizeable delay for Daily frequency: email notifications come through 11-28 hours after the end of the day. That's far too long for this sort of situation.
By contrast, a Custom Insight based on Hourly frequency usually delivers results an hour after the time period concerned. For example, an insight from the period 9-10am on a particular day gets delivered by email at 11am that day.
What metric do you use for the insight? In a viral situation I'm most concerned about the amount of work for the server. Will it fail? I think the best metric for this situation is Views. If you are cloud hosted, and are confident in your host's ability to scale up, then you may pick a metric based on maximising impact.
Hourly traffic is unusual: it doesn't show up anywhere in Google Analytics. So I had to experiment to set this up. To begin with I wanted to trigger the insight frequently, so I picked a low value for a test: 50 views an hour. Then I waited for the first notification. That came, so I was comfortable the mechanism was working.
Over a period of a week I then boosted the trigger value to: 100, 200, 300 views. I kept going until I got to a level that wasn't reached with typical traffic fluctuations.
The currently value should give me a useful early warning for a viral surge. What would I do then? Probably all of these things:
If you're paying close attention, you'll notice that I said 'usually'. Why was that? Well, my experiments have found that Hourly Custom Insights aren't triggered in a portion of cases - about 7%. I guess one has to see this approach as a helpful aid rather than a fool proof system.
Can you see a better way to do this in Google Analytics, or another package? Do let me know via Blue Sky or Threads.
Quick! 9 things to do when your website goes viral
Can Google Analytics help us measure user satisfaction?
Email traffic is confusing in Google Analytics.
I suspect it's because there are both a variety of email providers and a variety of mechanisms for accessing that email. Whatever the reason, it's normal to be confused by the way email traffic is presented, or by the fact that said traffic is missing from a report.
Let's start with the Acquisition -> Overview screen in Google Analytics. Acquisition is all about how people come to your website (or app). On this page there may be rows labelled Email.
Here's an example:
This screen should be simple. But no. [sigh]
These tables show channels in a particular grouping, designed for ease-of-use. The people (or sessions- there are two tables) in the Email channels are those that Google Analytics (GA) knows come via email. And it transpires that GA understands little about the people on that journey.
The Email Channel relates to people who come to your website via an email sent via email marketing software that has been set up correctly.
That's a narrow definition, which is why the numbers in this row are sometimes disappointing and occasionally absent.
There are a few different situations to consider. All of these scenarios use email. Most do not provide data in the Email Channel by default.
Let's imagine a fictional university called the University of Berkshire. Unsurprisingly, the University of Berkshire have a website. They've installed GA to track its usage.
Monica is thinking about going to an open day at the University of Berkshire. She thinks her friend Simon will also be interested. She emails him a link to the open day info on the University's website.
Simon opens the link in Monica's email and visits the University of Berkshire website.
In the GA account of the University, where was Simon's visit listed?
Not in the Email Channel of the Acquisition Overview, sadly. In this case Simon's visit was listed in the Direct Channel.
The University of Berkshire employ a communications officer called Fatema. Fatema emailed everyone who has registered for the open day. She used Mailchimp, the University's email marketing software, for the task.
Rebecca received the email Fatema sent in her Gmail account. She read it on the App on her phone and clicked on the link about accessibility considerations. How was Rebecca's visit recorded in GA?
In this case her visit was counted in the Direct Channel in the Acquisition Overview.
Note: this is usually the case, but not always. Unfortunately, the factors in email journeys throw up some quirks from time-to-time.
A few months later Fatema has learned more about connecting Mailchimp to GA. She set up Mailchimp to connect to the University of Berkshire's GA account.
She has just sent out an email asking for feedback about the open days. The email she sent linked to a feedback form on the University's website.
Rebecca opened the email and clicked through to the feedback form. How was Rebecca's visit recorded in GA?
In this case Rebecca's visit was counted in the Email Channel on the Acquisition Report. Hurray!
Also, in GA's Explorations section, Email was stored in any medium dimensions, for example the Session Medium Dimension. That means Fatema could set up an exploration to track the impact of her emails.
Erick is a business consultant. He runs a Substack email newsletter about marketing. He was impressed with a study from the University of Berkshire's Business School. He included a link to it in the latest issue of his newsletter.
Robert is a subscriber to Erick's newsletter. He read the latest issue and clicked through to learn about the study.
How was Robert's visit to the University website recorded in GA?
The answer: it appears in the Email Channel. The reason is that Substack connects with GA, even when the two accounts don't have the same owner. Not all email marketing tools do that, but Substack do.
This is why many people have a low number of acquisitions via email in GA: because their email marketing package isn't connected up. You can have a healthy email marketing campaign that converts well, and gives a low number in the Email channel.
It's not a disaster - email marketing software will give you analytics data, after all. But it does make it more difficult to compare the performance of the different channels you use for promotion.
We should also remember that some of our audience are emailing each other about our website/app. That positive activity currently goes untracked in GA.
So, what does Organic Search mean?
Should I care about average engagement time?
Hi, I'm James.
I run regular Google Analytics training courses. My approach is based on listening and candour. I work hard to understand the needs of delegates, and I'm open about the strengths and weaknesses of Google Analytics (GA).
I've had a long career in digital, which has included working for the BBC and the University of Oxford.
I choose a low-carbon approach to business. What does that mean?
Can I help you learn about Google Analytics? Take a look at my courses page, or contact me.
Can we talk about organic search? It's a term that appears in the acquisition area of Google Analytics. It sounds like a sign you see in the supermarket, which is disconcerting.
Organic Search means people, or sessions, that come from search engines, but not from the sponsored links on those search engines.
Let me give you an example.
Imagine Katy used Google to search for the term web design agency london (I know London should have a capital letter, but how many people do that when they Google?).
Katy got a page of results like this:
Imagine Katy skipped past the sponsored links and started perusing the other search results. She liked the look of this one:
Katy clicked the link, and it took her to the website for Bond Media.
Bond Media were running Google Analytics on their website. Later that month one of their team looked at the Acquisition Report. It looked something like this:
Katy's session was counted in the Organic Search channel. That's because she came to the Bond Media website via a search engine, and not via one of the sponsored links. If one of Bond Media's sponsored links had brought her here, then she would have been counted in the Paid Search row instead.
Here's one way to remember this: when we see the term 'organic' think 'natural' or 'normal' - as nature intended. People arriving via organic search come via the normal way search engines work.
I should mention a caveat: all of this depends on how well Google Analytics interprets incoming website traffic. It's possible that some combinations of user device, browser and search engine don't get assigned to the Organic Search channel and end up listed in a different one.
Hey Google Analytics, when you refer to a referral, what do you mean?
Should I care about average engagement time?
We don't talk enough about the seasons.
When you’ve been in this game for a while you notice that the calendar has significance. In my experience in the charity sector, our websites were always quiet in August. I guess the audiences were mostly based in the UK, where August was the big school holiday period.
I once worked on a website for a US-based university fundraising operation. Surprisingly, their peak-time was December. You see, you get tax relief on donations in the US. And, if I recall correctly, the tax year ends in December. So, every December was peak-donation time, which meant peak-website-traffic-time.
Regardless of your organisation there will be monthly and seasonal fluctuations in traffic. For that reason, it’s common practice to compare with the same month in the previous year. For example, how did website traffic in August this year compare to August last year?
Be careful about diagnosing a new trend without a prior year of comparison data. How do you know it isn't a seasonal fluctuation?
A colleague once pointed out something interesting: the impact of the length of the month on our reporting. You see, most years February has 28 days. So, most years February will get 10% less traffic than January by default. In the same way March will receive 11% more traffic than February by default.
I hate to think of the times I flagged a good result in March without considering the impact of the short month in February. An 11% jump is eye-catching.
Are there any aspects of the calendar I've missed? Let me know on Threads or Bluesky.
The danger of the Realtime Overview
Privacy part 1 - where the data goes in Google Analytics 4
I had a hit!
But I didn’t.
Or, maybe I did. Let me explain...
A blog I own had a spike in traffic. It happened the day I launched a new post.
Here’s how the stats looked in Google Analytics:
These numbers are an under-count, as usual. That’s because some users don’t agree to the use of cookies, and so don’t get counted.
Still, jumping from 11 daily active users to 561 daily active users is good, right? I launched a new page, promoted it on X, and the traffic rolled in. What’s not to like?
Firstly, my social media stats were weaker than for previous posts. The post on X had 60 click-throughs by the end of Saturday. The post on Threads, my backup promotion channel, drew a paltry 3 click-throughs. I’m experienced enough to know it’s mostly your promotion than brings you traffic. If my promotion wasn't performing, why was there a spike in traffic?
Sometimes people share your material somewhere hidden from you: a private Facebook group, an email list, a Telegram channel. Perhaps that happened in this case.
But there are other factors that raise suspicions.
If I focus on the day of the spike and look at the engagement stats, I notice that these 600 users racked up 19,000 page views! That’s wild. A quick calculation shows that would be 34 pageviews per active user. It’s just about possible if every visitor read every single page on that blog. But who does that?
Moreover, when I look at the Pages and Screens Report things get wilder still. According to Google Analytics, these 19,000 views occurred on the home page. So now, you’re asking me to believe that each visitor sat there and refreshed the page 34 times. What's more, my promotion on X (and Threads) didn’t link to the home page, it pointed straight to the new post.
The traffic was spread evenly across numerous countries, which adds credibility to it. Also, the reading times for these ‘users’ were high: 9m 57s was the average engagement time per active user.
But the technology stats were off. I looked at Active Users by Operating System, found under Reports -> User -> Tech -> Overview. On the day of the spike I found the following:
If we add up these numbers, we get 2,233 active users. That’s rather different from the 560 users recorded elsewhere in Google Analytics.
I find the even split deeply suspicious. For a website aimed at a general audience (as opposed to one aimed at Mac users), I’d expect Windows to be way out in the lead, and Linux to be behind the others.
I’m calling it: this is a fake event.
I wonder what the point is. Unlike other Google Analytics spam I’ve seen before, there’s no referring website being promoted. Maybe it’s a spam-bot that’s malfunctioned, or has been partially blocked by Google.
Google Analytics throws lots of metrics your way. If you're looking at how engaged your users are, it's easy to latch on to average engagement time per active user. It's the first number in the Engagement Report, after all.
Is it a good metric to track? Or is it prone to distortion?
Imagine you have a content website - like BBC News - and you provide stories for your audience. If your content is good they will read more, would you agree? And if it's boring, or confusing, or broken, they will spend less time there, right?
That's a good general rule.
Let me give a caveat. Imagine your aim is for someone to understand your message. That might be an aim for a public service website, like the NHS portal in the UK, or a local council website such as Liverpool City Council. A long-winded, badly-worded page would hinder that goal. Confusingly, a long-winded, badly-worded page might also have a longer average engagement time in Google Analytics (GA).
Imagine you work for a cancer charity and you're trying to raise money. You run an email marketing campaign. The emails lead supporters through to a particular website page to give money. But imagine that the donation form is actually stored on a different website such as the Charities Aid Foundation.
In this context success is measured in the number and quantity of donations. That money helps the charity invest in cancer research, and provide support to those affected.
These would be the steps in the user journey:
I'm James, a Google Analytics consultant in the UK.
I can help you make the most of Google Analytics.
All industries need to take action on Climate Change. That applies as much to the digital sector as it does to the building and travel sectors.
For that reason, I've worked hard to make this website as sustainable as possible.
The Website Carbon Calculator graded my efforts as:
A, or 0.3g of CO2/view
(This test was run on the home page on 18th September 2024)
I'm proud of this, and also appreciate the picture is nuanced. It's possible to have a low-carbon website, whilst also working in an office powered by fossil fuels and flying every month on business trips.
I chose to use a small number of images. I don't use images for decorative purposes, have no logo, and have few images in the website template.
I worked hard to compress the images that are used. Every image on the website is less than 50k in size, and most are less than 30k. This is achieved by using the WebP image format rather than PNG or JPEG, and by limiting image dimensions. I was surprised that WebP images were more efficient than PNG, given most of my images contain a small number of colours. But they were.
I picked a design that uses native fonts. That means when you visit the website, energy isn't expended downloading a new font in order to display the page. I'd prefer to use a stronger typeface, to be honest.
I selected a colour-scheme that uses a lot of black. If visitors are using a device with an OLED screen, that saves energy. For different screens this has no impact.
I chose a host that uses renewable energy for power. This is the biggest risk area - it's possible that the host, Google, aren't being honest about this.
Most of these principles were learned from Tom Greenwood's excellent book Sustainable Web Design.
This is a complex area to consider. When you visit a website energy is used by the server that hosts the website, by your device, and also by all the switches and devices data passes through along the way. I can choose a host that uses renewable energy, but I can't control the energy sources for the other parties involved.
As in other areas of sustainable living, one makes trade-offs. I'm a specialist in Google Analytics so I chose to install that on the website. That choice adds to the data expended when a page is viewed. It also adds to the energy Google uses to store that data on their servers. I made some sacrifices with regard to design to compensate for that.
My contact form is built with, and hosted by, Zapier. It's the quietest part of the website, but for completeness I should look at how Zapier power their hosting.
The website is maintained using the Blogger CMS. I've read it would be more sustainable to use a static site generator. That's next on my list of low-carbon actions.