Answering questions with Google Analytics

In this step we will answer a number of questions about the users of the test site in Week 1.

  • how many of us visited our website last week?
  • what was the bounce rate?
  • where do we come from? Which countries and cities?
  • what is the usual language of our users?
  • how many used notebooks, tablets or mobile phones?
  • what was the gender split for our users?
  • how did visitors use our simple 3-page website?

Answers to the Questions

This page was updated at 22:15 on Monday 22nd July 2019.

The answers to the questions appear below.

Visitors to our website 14-21-July-2019

How many of us visited our website last week?

The graph above shows that 293 users visited our test site in Week 1 with 385 sessions between them.

What was the bounce rate?

The above graph also shows that our bounce rate, the number of users visiting just one page, was 11.95%.

Where do we come from? Which countries and cities?

Figures 2 and 3 of the previous step show which countries we came from. Google analytics also reports on the cities our test site visitors came from in Week 1:

Figure 1. Cities of origin of visitors to our test site, 14-21 July 2019 Cities of origin of visitors to our test site

The city data is less accurate than the country data because Google tracks its users by the internet nodes they connect to. This varies according to the internet routing algorithms, e.g. recently Google reported me as coming from Bristol, which is about 100 miles away.

What is the usual language of our users?

Figure 2 shows the languages of our users were mostly variants of English and other European languages. The exceptions are Russian (ru-ru), Portuguese as used in Brazil (pt-br), Chinese (zh-cn), Spanish as used in Latin America and the Caribbean (es-419), Turkish (tu-tu), Arabic (ar-ae, ar-eg, ar-bh), Indonesian (id-id), Vietnamese (vi-vn), Japanese (ja), and Turkish (tu-tu).

Figure 2. User Languages of visitors to our test site ,14-21 July 2019

User languages

How many used notebooks, tablets or mobile phones?

Figure 3 shows the devices used to access our website in Week 1 – 75.8% Desktop, 19.0% Mobile and 5.2% Tablet. Figure 3 also shows that Chrome is overwhelmingly the most used browser to access our site.

Figure 3. Devices and browsers of our test site users, 14-21 July 2019  Devices and browsers of users visiting our test site

What was the gender split for our users?

Figure 4 shows our gender split - 46.7% Female and 53.3% Male. Note that Google Analytics only knows the gender of 46.08% of the users. Google collects data such as gender and age from many sources, e.g. when registering gmail accounts and using other social media. Not everyone gives this information.

Figure 4 also shows the age distribution of our test site visitors. Almost half were 25 to 34 years old, as might be expected of a FutureLearn audience. Google Analytics based these statistics on 40.96% of our users.

Figure 4. Gender split and age profile of our visitors, 14-21 July 2019.

 Gender split and age profile of our visitors

How did visitors use our simple 3-page website?

Figure 5 shows the pageviews for the pages of our test website: https://cs-dc.uk/flga_1.html had 917 pageviews, https://cs-dc.uk/flga_2.html had 770 pageviews, https://cs-dc.uk/flga_3.html had 548 pageviews. This tells us that not everyone visits our third page. If this were a commercial website this could be useful to know, especially if the third page has important information that we want our users to see.

Figure 5. Pageviews for the test site, 14-21 July 2019. Pageviews for the test site

Figure 6 gives a graphical display of how users flowed through the website, i.e. how they moved from page to page. To get a version that enables you to see the details use control-click on the link under the image. This shows that most users entered the site on the first page, moved on to the second pages, and then moved on to the third page. From there almost equal numbers of visitors went back to the second page or directly to the first page.

Figure 6. User flows through our test website, 14-21 July 2019

graphic showing users flows through the web pages of our test site (Control-click here for an enlarged image in an new window)

This example illustrates the limitations of displaying data on small screens such as phones and notebooks. Professional data scientists often have large screens that can display a lot of data. Even so a user’s eyesight may prevent them from reading small text or seeing colours (1 in 12 men and 1 in 200 women have some form of colour blindness). Displaying data in accessible ways is an important consideration in data science.

How does Google Analytics know so much about the users of our test website? The answer is that when signing up for web services such as gmail or Facebook users often give information about themselves such as interests, occupation, age and gender. Frequently the provider of the service will create a file called a ‘Cookie’ on the user’s device that stores this information locally. Google Analytics accesses these cookies as ‘secondary data’, which can be used to build statistics for the population of users. So, for example, Google Analytics is able to produce the pie chart showing showing users’ gender. The accuracy of this is discussed in Step 2.7.

What do you think?

Are you impressed by the information that Google Analytics can give on the use of websites? Are you surprised at how much personal information Google knows about us? Do you find the Google Analytics graphic displays helpful? Let us know what you think.

Share this article:

This article is from the free online course:

Introduction to Data Science with Google Analytics: Bridging Business and Technical Experts

UNESCO UNITWIN Complex Systems Digital Campus