-> Analytics data is used across the web for businesses to understand their user's behavior, inform future development choices, find issues, and run targeted advertising.
Resources
Why might someone want analytics data?
Page Analytics Data
One example of analytics data is for page analytics. This can be useful to companies to get data on who is visiting which pages and for how long. It also might include data about where the user came from.
Page analytics data is usually tracked by a service like Google Analytics or Segment. These services work with companies and websites to track all types of data on a user and what they do on a page. There are even some data tracking services with "Session Replay" features, where you can see exactly what users do on a website, from where their mouse is, to where they click and scroll around.
In the above data from the Semrush resource, you can see the top visited websites in the world. This data comes with other page analytics data including the pages viewed per visit, and the bounce rate. Pages viewed per visit is great information to have to understand how long people are staying on the site, and how much people are engaging with the site. Bounce rate is the rate at which people leave pages without interacting.
This data can be useful for companies for a variety of reasons, including figuring out areas for improvement and for targeted advertising, which are described later on this webpage, and on the resources above.
User Analytics Data
Another example of analytics data is user analytics data, which might include informattion about a user's device, habits, or activities. It might also include content that they've interacted with, or people who they follow.
What can you get from analytics data?
Analytics data can be useful for many things, but one specific area is to improve a product. Let's look at some imaginary data for a website, which shows user time per page on several different pages.
Identify Areas to Improve & Areas that are working well
When we visualize the data as a chart, we can now see time spent on each page. For some pages, we want more time to be spent - like on the feed page. We want users to be engaging with their feed as much as possible. However, for other pages, like the login page, it should be as seamless as possible and take a small amount of time for the user. The fact that the average login time in this dataset is so high means that there might be an issue with the login page.
Target Specific Users for advertising
We can also break down the data user-by-user, which might help to target specific users for advertising. For example, here, we can see that User 8 spends a lot of time on the site, indicating that they might be a good target to pay for the "pro" plan, so we might advertise that to them or send them a marketing email. However, User 7 spends much less time on the site, so we may send them an email about "more ways to engage" or try to encourage them to spend more time on the site.