Analytics Unpacked

Tuesday, April 1, 2025

The danger of the Realtime Overview

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.

Tuesday, February 18, 2025

Can Google Analytics give an early warning of going viral?

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 central fundraising office, ran a central 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, I often 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 guide to Custom insights.

For this particular purpose, the key was 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 currently 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 my test-site: 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:

Go to the Realtime reports to identify some details: Where is the traffic going? Which part of the world is it coming from?
Warn my hosting provider and ask if they can increase the resources to the server.
Prepare my outage pages for the worst. Can I point to an alternative location, such as a social media post, that will help the users complete their task if the website fails?

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.

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Tuesday, January 14, 2025

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

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 via each provider. 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 (GA). Acquisition is all about how people come to your website (or app). On this page there may be several rows labelled Email.

Here's an example:

This screen should be simple. But no. [sigh]

The people (or sessions- there are two tables) counted in these rows are those that Google Analytics knows come via email. And it transpires that Google Analytics understands little about the people on that journey by default.

The quick answer

The Email rows relate to people who come to your website via links in 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.

The detailed answer

There are a few different situations to consider. All of these scenarios use email. Most do not provide data in the Email rows by default.

Let's imagine a fictional university called the University of Berkshire. Unsurprisingly, the University of Berkshire have a website. They've installed Google Analytics (GA) to track its usage.

Scenario 1 - people email a link to other people

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 rows of the Acquisition Overview, sadly. In this case Simon's visit was listed in the Direct rows.

Scenario 2 - organisation emails a link to people

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 rows in the Acquisition Overview.

Note: this is usually the case, but not always. Unfortunately, the varied factors in email journeys throw up some quirks from time-to-time.

Scenario 3 - organisation emails a link to people - and have configured things correctly in advance

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 rows 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 Explorations to track the impact of her emails.

Scenario 4 - a different org emails a link to people - and have configured things correctly in advance

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 rows. 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.

In conclusion

This is why many people have a low number of acquisitions via email: because their email marketing package isn't connected to GA. You can have a healthy email marketing campaign that converts well, and a low number in the Email rows of the Acquisition section of GA.

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.