Data Analysis – Step 3 of Web Analytics Process

Reading time: 5 minutes

We have seen last week with our article “Data Collection – Step 2 of Web Analytics Process” how to collect data. We’ve learnt that some standards dimensions and statistics are automatically calculated and displayed by your web analytics tool provider. We have also seen that some more specific datas need a manual implementation on your application or website to be read and displayed by your web analytics tool provider. Today, we are going to focus on the next step, the Data Analysis. This post will focus on the meaning of the standard figures displayed by your web analytics tool, and also on the display and analysis of the KPIs you’ve supposed to have built on the step 1 (see our blog post “Data Measurement” – Step 1 of Web Analytics Process”)

Basic figures and what they mean

There are a lot of standard and important figures that are automatically calculated and provided by your web analytics tool provider (like Google Analytics). During the Data Analysis step, it is very important to take a look at those basic figures because they are directly related to your website or application performance. Here is a list of the most important ones, and what do they mean:

Bounce Rate. The Bounce rate is the ratio of people leaving your website without doing any action on your website or application. This ratio is very important, because, if it is too high ( over 90%) it means that the people going on your website or application do not have any interest in what you offer. This could mean that the message you used to attract them doesn’t really reflect your service. On the contrary, a low bounce rate, means that you targeted the good audience, and that they found your website related to what they were looking for.

Page Views. Anytime a user visits a page on your website, you get one page view. You can then see which pages have been the most visited, and where your users are going. This is a very important metric because following your website purpose, it can have different impact. For example, if you are selling one product, you want the users to go buy the product straight away, but not visiting pages that will make them lose their focus on the purchasing.

Session Time. In Data Analysis, the session time is very important. But once again, following your website objective, a high session time average can be good or bad. It can be good if you provide content to your users (Blog, videos, etc), but it is not good if your website sells a single product. In this last case, you want the users to purchase the product as fast as possible, in order to reduce the chances of having users losing their focus on something else.

Display and Analysis of your KPIs

We have seen two weeks ago, in the article “Data Measurement – Step 1 of Web Analytics Process”, that it is important to build KPIs in order to measure the achievement of your goals. Well, now is the time to find a way to display your KPIs, and analyse them. There are many tools on the web (tools such as Google Data Studio) that can help you create a dashboard where you can visualize all the ratios and numbers you want to display. It is very important that you build these dashboards in accordance with the KPIs you’ve built earlier. They will help you have a day to day understanding of your performance, and will also inform you if you’re facing any unpredicted issues.

Last, your website or application is unique. Which means that it is useless to compare your KPIs with the ones of another website or application. The best way to track your performance, is by comparing different periods. For example, by comparing the datas of the last 7 days, with the ones of the 7 days before.

Now that we can have a clear visualisation of our data, and that we can understand what are our users doing on our website or application, we can start to interpret those data, and take actions. We will see next week how we can turn the information we’re displaying right now on our dashboard, into effective insights.

Sources:


Stay updated! Join our newsletter for webanalysis tips