There are many different ways to tackle the challenge of building a traffic dashboard. In this post I'm going to cover four different methods from the easiest to the most complex.
For the remainder of this post I'm going to assume you use Google Analytics to track the traffic to your website. If you use a solution like Segment, Adobe Analytics, Piwik etc then method #4 is your best bet.
Method #1 - Build a custom traffic dashboard in Google Analytics
Google Analytics has it's own custom dashboard feature where users can build custom dashboards quickly, easily and for free.
These dashboards can hold one or more custom widgets. Each dashboard can hold up to 12 widgets. A widget can be a single value, table, pie chart, etc.
Below is an example of a KPI dashboard I built to track the performance of this blog.
This approach is by far the quickest and easiest and what most businesses should start with.
To build a custom dashboard in Google Analytics follow the steps below.
- Log into your Google Analytics account.
- Click on "Customization" in the left hand menu and then on "Dashboards".
- Click on the "Create" button in the middle of the screen.
- Give your dashboard a name and choose either "Blank Canvas" or "Starter Dashboard". I suggest "Blank Canvas".
- Click on the "+ Add Widget" button to add a widget.
Pros of building a custom dashboard in Google Analytics
- Quick and easy
- Numerous widget types
Cons of building a custom dashboard in Google Analytics
- No way to add dynamic filters or advanced selectors to the dashboard
- Limited to 12 widgets per dashboard
- Limited segmentation options
- No way to group data by moving windows, running sums etc
Even though this method limits the value you can get from your traffic data, it is a great place to start, and can be highly effective at keeping your marketing team focused.
Method #2 - Build a traffic dashboard in a 3rd-party plug-and-play dashboard solution
There are a number of solutions out there that allow you to build custom dashboards using multiple data sources.
In these tools, you connect to the relevant data source, choose the metric you care about, the widget type, and the tool does the rest.
Pros of building a traffic dashboard in a 3rd party plug-and-play dashboard solution
- Display metrics from all your marketing data sources in one dashboard
- Multiple widget and customization options
- Alerts, "send to Slack", and other useful extras
Cons of building a traffic dashboard in a 3rd party plug-and-play dashboard solution
- Not free (relatively cheap at $30 - $200 per month for solutions in this category)
- More customizable than Google Analytics dashboard but still has significant limitations
Method #3 - Build a traffic dashboard in an advanced data visualization solution
If you're fortunate enough to have an analyst on staff who knows how to use one of the many advanced reporting solutions then method #3 becomes an option.
There are a number of advanced data visualization solutions on the market today. The top solutions which come to mind are Tableau, PowerBI, Looker and Google Data Studio.
I would usually place Google Data Studio in its own category, but I'm including it here because it still requires certain skills to use it correctly. It's a free solution which makes it very appealing to companies on a budget.
Most of the solutions in this category will have a native connector to Google Analytics which will allow you to pull in specific metrics and dimensions in a tabular format. This allows you to access the raw data underlying the reports you see in Google Analytics. This is a big shift from the previous methods which don't expose you to the underlying data.
One of the ways I analyze Google Analytics data is by using Tableau's window_sum function and grouping the data by default channel grouping, traffic source and even custom dimensions like landing page category.
The image below shows an example of such a report.
Pros of building a traffic dashboard in an advanced data visualization solution
- Highly customizable
- More visualization types
- Easier to tell a story with the data (i.e using the story dashboard type in Tableau)
- Allows you to leverage functions like window_sum, running_sum, min, max, etc
Cons of building a traffic dashboard in an advanced data visualization solution
- Requires technical know-how
- Not free
- Not as quick as methods 1 and 2
- Certain limitations since you're connecting to Google Analytics via a native connector
Method #4 - ETL the traffic data into a database and then build a traffic dashboard in an advanced data visualization solution
The most advanced method for creating a traffic dashboard is similar to method #3 but with one key difference.
In method #3 you'll be connecting directly to Google Analytics from the data visualization solution via a native connector. This approach will serve 95% of the needs of most businesses but if you want to take things to the next level then you'll need to use method #4.
Method #4 involves moving the data out of Google Analytics as an initial step. Once the data is out of the source, you have a lot more options when it comes to manipulating and enriching the data.
Moving the data out of Google Analytics can be done in many different ways, but for the purpose of this post I want to try and keep things as simple as I can.
The tool I use to move data out of Google Analytics and into databases is Stitch. I've been using Stitch for years and it works like a charm.
Another tool to consider is Supermetrics which has a few different destination options from Google Sheets to BigQuery.
Once you've set up a connection in Stitch, and your raw traffic data has been moved into your database of choice, you can start building your dashboard in Tableau, PowerBI, or whichever data visualization solution you use.
Below are some of the biggest advantages for going with method #4 instead of the previous methods.
Method #4 allows you to get around the API limits
When connecting directly to Google Analytics via a native connector in a tool such as Tableau, you run into some frustrating limitations. The most substantial limitation is in the number of measures and dimensions you can pull in a single connection.
Since Stitch allows you to create multiple connections you can get around this issue with a bit of work. The idea is to pull in all the data you need by creating multiple connections to Google Analytics and pushing all the data into a single database.
For example, in order to build the traffic reports in my eCom reporting dashboard (screenshot below) I need two connections, one to bring in session and pageview data, and a second to bring in the shopping stages data.
Once the data is pushed into a dashboard you can create blends and unions and then leverage the more robust data set in the data visualization tool.
Method #4 allows you to blend the traffic data with external data sources
Another powerful advantage of method #4 is that it allows you to blend your traffic data with external data sources.
A great use case is to create a Google Sheet which lists your landing pages and blog posts and to tag each with useful "meta" data like the author of the post, publication date, word count, version number etc.
Since you're working with raw data you can easily connect multiple data sources together and enrich your data. I use this method for analyzing the performance of my older posts compared to my new posts.
Pros of building a traffic dashboard using raw data and an advanced data visualization solution
- The most customizable approach
- Allows you to get around certain API limits
- Allows you to blend your traffic data with external data sources
- Allows you to run back-end processes on your traffic data (i.e cronjob + Python scripts to check thresholds)
Cons of building a traffic dashboard using raw data and an advanced data visualization solution
- Requires technical know-how
- Requires a small budget (minimum of +-$2,000 a year)
In this post I've covered four different methods for creating a traffic dashboard. The first two methods are by far the easiest and will be good enough for most early stage online companies.
Methods 3 and 4 require some technical know-how and are therefore only relevant for companies which really want to invest in business intelligence and have some budget to work with.
Which method are you going to go with? Let me know in the comments section below.