Can I measure that? 4 cases of specialized data you can send to Google Analytics
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As browser technology is rapidly advancing we are introduced to a lot of different ways we can interact with the them. There are a lot of new and old APIs that allow us to collect more sophisticated and detailed data about our user base. Let’s see some real world use cases where we track some more “specialized” data and how this data can help us extract better insights for our businesses.
NOTE: Users are reaching our services through many devices and browsers and not all of them support these APIs. This is completely fine as long as we know that our data refers to a part of our user base. Furthermore, the data collected by your tracking implementation, are extremely dependent on the privacy laws that apply.
1. Battery Consumption
One of the most discussed issues that modern devices are wrestling with, is battery life. As JavaScript frameworks are heavily used for modern web applications, downloading, parsing and executing those scripts, battery drain is something to be really considerate about. This entails that for new features or any kind of development effort on our application, we should keep in mind to opt for as little battery strain as possible. The first thing we can do, is collect some data of the current battery consumption through the different pages/screens and widgets that users prefer using the most and are critical on our applications functionality.
Example Case: Cryptocurrency Exchange Rates Dashboard
Let’s assume that we are currently developing a tracking implementation for our highly interactive dashboard that hosts the exchange rates. In our website there are many components that generate dynamic information for the visitor and report the most important market metrics in a dashboard-like component. By measuring just how much battery is consumed and if it has an effect on the usage of our application, we can recognize areas for improvement on the user experience we deliver. Below is a snippet that can help us track battery usage information, using the Battery Manager interface.
Having collected the data and formulating fitting questions such as:
- Does power levels affect the time a user spends on my website?
- Does it affect in any way our conversion rate on mobile devices?
We can then start by planning our A/B testing strategy through Google Optimize and identify which components drain the most battery life and which platforms, browsers or devices are hurt the most.
2. Page Visibility
Another very interesting metric we can capture is Page Visibility. In a typical browsing session, we usually open a lot of tabs and it is not uncommon to close them before we even navigate to them. With the standard Google Analytics tracking code, whenever a page is loaded, a page view is sent, regardless if the tab is active or not. This does not reflect the real attention that our content gets from the users and can skew metrics such as conversion rate on these pages.
Example Case: Blogging Platform
For this particular case we will discuss this blogging platform, medium.com. Medium is hosting a lot of content and users can express themselves, their opinions and experiences through it. From the articles you wanted to read today, how many remained in an inactive tab for quite some time?
By counting views on articles that were not even seen, we are sure to misjudge the article’s real performance. This can be solved with tracking pageviews whenever the tab has become active, a kind of a ‘true view’ we can say. The Page Visibility API can help with that.
3. Network information
Using the Network Information API we can get the information about the users network attributes. What type of connection is the most used? Is there a correlation between mobile data usage and bounce rates? Is there any possible issue if the connection is not stable enough?
Example Case: Real-time Bidding Application
In this example we are responsible for analyzing a web application where users interact with each other and with the application through auctions. By tracking the user’s network information we can understand and optimize their experience according to their needs and status. Are they connected usually using WiFi or Cellular Network Data? Do we need to do something to reduce the amount of necessary network payloads?
If the bids are late, then the service will be unreliable and we are going to lose many clients hence money. So we have to take actions. Below is a snippet sample showing a way to collect these important attributes from the Network Information API and send it to Google Analytics so that we can spot possible issues.
4. Geolocation on the web
Last but not least is the ability to get access to geolocation coordinates on the web using the Geolocation API. It is important to note that, in order to track and send geolocation statistics to Google Analytics, the application must ask for user’s permission (which we can also track).
Example Case: Online Food Delivery Service
A great case where tracking geolocation makes sense is Online Food Delivery Services. Using browser’s Geolocation API to track customer’s location, we can really enhance our service with personalized offers (e.g offer a discount to customers that would like to take away their order from the closest branches instead of using delivery) or even promote stores that are closer to them and can provide the fastest delivery. A way to track geolocation and make this data available on Google Analytics Platform is shown below.
Conclusion
These are only a small set of examples showing how to track all these “specialized” data and what we can possibly do with them to better understand our users. Do you track any sophisticated data in your service and in what way do they help your service? Go on and give us your own experience in the comments.
P.S: Track everything you need for your kind of service but be sure not to move your focus away from the simple and important stuff. Complicated processes can sometimes mask a critical insight.
