You might be familiar with the “Users by time of day” report in Google Analytics home screen. The report shows a heatmap of when your users visit your website. Darker shades represent times of day with more visits and lighter shades show when there were fewer visits.
Here is what it looks like:
In this post, you’ll find out why this report is helpful, how to replicate it in Data Studio and how to improve on it to get even more insights.
This is the report you’ll be able to create in Data Studio using my step-by-step guide. I’ve used Google Merchandise Store as my data source – it’s available for free to anyone and it’s great for creating report templates to share! Follow this link to view the dashboard. You can copy it into your account and apply to your own data in just a few clicks.
Below is what the dashboard looks like – naturally you can customise it however you like.
What insight can we get from the Users by Time of Day report?
The report is useful for a number of reasons. Primarily, you can get to know your users’ weekly rhythm – either to gain new insight or to confirm or debunk assumptions. Websites targeted at children are usually quiet at night, and business-to-business content mostly attracts traffic during office hours.
These rhythms could change as work and life become more and more blended. You might see business users browsing on weekends and ecommerce shoppers visiting during business hours.
The data can add to your insight about your users’ lifestyle, working style, social life and types of interests.
If you have a B2B website, you might see that your site visitors are more likely to visit during standard office working hours. However, you may also see some additional patterns in terms of specific times and days of the week that are more likely to bring shoppers onto your site.
By adding extra features to the report, you can map different behaviours and how they affect one another – more about that later on in the post.
A note about Google Analytics time zones…
Before I jump into how to replicate the Users by time of day report in Data Studio, it’s important to note how Google Analytics handles time zones. The platform lets you define a time zone in your View settings. According to Google support, the setting is “The country or territory and the time zone you want to use as the day boundary for your reports, regardless of where the data originates. For example, if you choose United States, Los Angeles Time, then the beginning and end of each day is calculated based on Los Angeles Time, even if the hit comes from New York, London, or Moscow.”
If your business spans multiple countries, states or regions, you may be interested in your users’ local time. One way to do it is to configure a Custom Dimension that captures your visitor’s local hour of day.
Next, I’ll go through the set up of all the charts – if you’re not interested in all the technical bits, skip here.
How to configure the Popular Days chart
The first chart in the series is a pie chart that shows the breakdown of the most popular days of the week.
Here are the set up details:
- Dimension: Day of Week Name
- Metric: Sessions
- Sort: Sessions (Descending)
When it comes to Style, I recommend changing Label drop-down to “label”. This will mean that each slice will say what day of the week it represents. How you visualise your pie chart is completely up to you though!
Well, that was easy!
How to configure the Popular Times chart
Next, let’s move onto the popular times of day chart. It also uses the pie chart template. I decided to make it into a doughnut using the slider function under Style – you can do the same or stick with the pizza style. You may also adjust the number of slices you display – with up to 20 available. I went for 14.
The data set up details are as follows:
- Dimension: Hour
- Metric: Sessions
- Sort: Sessions (Descending)
How to configure the users by time of day table
Let’s move on to the exciting part (yes – heat maps are exciting – can you tell I’m a geek?).
I used a Pivot table to achieve the effect.
Under Row dimension, select Hour – same as I used for the Pie chart. This time though, I needed to display hours as numbers in the 24 hour format so that I could sort them correctly from the beginning to the end of the day. Click the little pencil next to the Dimension and change format to Numeric > Number.
Next, move onto the Column dimension. You’ll also need to sort days of the week in an alphabetical order. In Data Studio, you can do it by adding a number before each day of the week. To do that, I created a calculated field. Under Column dimension click Add dimension. Next, click CREATE FIELD. Under Name, put in “Day of the Week” and copy the formula below to add numbers to the original day names:
CASE WHEN Day of Week Name = "Monday" THEN "1 Monday" WHEN Day of Week Name = "Tuesday" THEN "2 Tuesday " WHEN Day of Week Name = "Wednesday" THEN "3 Wednesday " WHEN Day of Week Name = "Thursday" THEN "4 Thursday " WHEN Day of Week Name = "Friday" THEN "5 Friday " WHEN Day of Week Name = "Saturday" THEN "6 Saturday " WHEN Day of Week Name = "Sunday" THEN "7 Sunday " END
If you’re in the United States, Canada or Australia, you might think that the week start on a Sunday… Well, it doesn’t. But if you want to change the numbers, feel free to do that, I won’t get upset.
Next, select the Metric as Sessions.
You should see the following settings in front of you:
Next, make sure you configure your sorting settings. Row #1 is sorted by Hour of Day Ascending and Column #1 is sorted by Day of the Week Ascending.
Finally, convert your Pivot table to a heatmap. Play around with style settings until you’re happy with the final effect.
Ta-dah! Your heatmap is ready.
How else can you use this report?
The data about what times of day are the busiest is already very insightful. You can schedule your emails and social activities to align with these times. After all, you know that your visitors are more likely to consume your content at those times.
To get even more actionable information, it helps to explore other aspects of the data. Why not replace sessions data with conversion rates, number of transactions, average session duration or another engagement metric. This could help you establish the times that your users are most likely to take action.
Rather than replacing the data, you could add this as an additional metric to see the trends side by side. From the chart below, you can see that users like to browse on weekdays during the day but are more likely to make a purchase weekday evenings and weekends. To get to the bottom of this behaviour, you could explore differences between segments of users to see how different consumer types behave.
The heatmap report can help you uncover some insightful trends in your traffic and engagement that can lead to bigger findings about your users.
If you downloaded the free dashboard, leave a comment – I’d love to know your thoughts! How have you used your chart?