Can Google Analytics help us measure user satisfaction?

Do people like my website? Is it valuable? I ask these questions regularly, and I imagine they're on the mind of many website owners. 

Sometimes I think about this because I want to serve my audience well. Other times, I ask those questions because I need a website to be trusted in order for it to fulfil its purpose.  

Either way, user satisfaction is on my agenda. Can Google Analytics help me to measure it?

Here are some metrics that may provide the solution:

Average engagement time per active user

I think if someone appreciates a website they spend longer on it. And so average engagement time per active user seems a logical choice.

The number is unaffected by seasonal changes, making it a good choice if you don't have enough past-data for year-on-year analysis.

Take care though: as discussed previously, average engagement time per active user can mislead you if your website is focussed on quick interactions, or if your calls-to-action lead off-site.

This metric is found in Google Analytics (GA) in Reports -> Engagement -> Overview

Returning users

This measure is the number of people who come back a second time in the specified date range. Be aware that if you expand the date range in GA then returning users grows for two reasons: 

• you're catching multiple sessions from new users
• you're catching additional sessions from existing users, which redefines them as 'returning'.  

I like returning users because it's simple: we all understand the concept of repeat customers.

It does have drawbacks though. One is that GA only gives this number rounded to the nearest 100. That might frustrate you if you run a small website. 

Another drawback of returning users is the link to the date range. Imagine you have a website where you publish new content once a month. Many users will get used to the pace, and will only visit once a month. If you check this metric once a month those people will never get counted as returning users. 

Perhaps the answer is to use this measure with an eye to the content schedule of your website. Report on it for a time period significantly greater than the interval between changes. In the example above, maybe check returning users once a quarter.

Bear in mind that returning users does fluctuate with the time of year. 

This metric is found in Reports -> Retention

Active users

I sometimes forget that the most prominent metric in GA, active users, has audience satisfaction built-in. 

Google defines active users as users who:

visit for more than 10 seconds

or

view 2 or more pages

or

trigger a conversion event

or

make a first visit

Now, a person who stays for over 10 seconds, or visits several pages, is indicating some degree of satisfaction with the website. They certainly didn't "come, see, and puke" as Avinash Kaushik used to say. 

When we measure active users we're measuring some element of user satisfaction. So should we just track that number? I'm uneasy with that idea. I like my metrics to be more targeted. 

WAU/MAU

Catchy name, eh? The longer version is: Weekly Active Users / Monthly Active Users. It's expressed as a percentage. This metric is about how many people who visit each month also visit each week.

I find WAU/MAU a bit complicated, which puts me off using it. I don't want to have to remind myself what a metric means each time I check my analytics.

I'm sceptical of the value of this metric because it hinges so much on the frequency with which you update your website. You might get a low number because your website changes once a month.

As in the case of returning users, you'll find WAU/MAU does vary with the time of year.

This measure is found in Reports -> Engagement -> Overview

Websites designed for infrequent use

Sometimes people don't return to a website because the driver for visits occurs rarely. An example of this is the UK website for passport applications. However brilliant the user experience is, most people visit once every 5 or 10 years. 

Another example of a rarely-used website would be one that sells new cars. Do many customers buy a new car every quarter?

A context like these would significantly change the metrics you choose for user satisfaction.

Final thoughts

So, what do I use? I like returning users, for its simplicity.

Do you have views on the metrics I mentioned? Tell me more on Bluesky or Threads. 

More Google Analytics posts

Can Google Analytics give an early warning of going viral?

We don't talk enough about the seasons

Hey Google Analytics, when you refer to a referral, what do you mean?

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.

The quick answer

Google Analytics lists a session as a referral when the user has come from another website.

The detailed answer

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. 

Traffic from AI

The numbers for referrals also include sessions that come from AI chatbots, such as:

• ChatGPT
• Claude
• Copilot
• Google Gemini
• Perplexity

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.

 

Some traffic from social media

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.

More on Google Analytics

So, what does Organic Search mean?

What does 'Email' mean in Google Analytics, and why are those numbers so small?

Privacy part 2 - what personal data is stored in Google Analytics?

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.

Google Signals

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:

• Age 
• Gender
• Interests - for example: 'Food & Dining/Cooking Enthusiasts/Aspiring Chefs'

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:

Granular location

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.

Data stored as standard

If neither of the above settings is enabled, then Google will show this information about visitors to your website:

• Region (for example Florida )
• Country 
• Language

Data inferred from user actions

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. 

Can you identify a website visitor?

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.   

More Google Analytics posts

Privacy part 1 - where the data goes in Google Analytics 4

Can Google Analytics give an early warning of going viral?