In this article, I want to talk to you about a fundamental topic for any digital project: web analytics. Understanding and taking advantage of the data we generate has become crucial. In this post, we will detail what web analytics is, what it measures, and how it works, as well as some practical examples to facilitate your understanding.
What Is Web Analytics?
Web analytics refers to the process of collecting, measuring, analyzing, and displaying data related to user activity on a website or mobile application, for subsequent decision-making.
In other words, it involves understanding how visitors interact with a site or application, to improve user experience, increase conversion, and optimize overall project performance.
Instead of using intuition, we rely on measurements, so it is important to use specific tools and techniques to collect data, such as tracking tags, cookies, and tracking pixels. These mechanisms allow us to track and record user activity, such as pages visited, time spent, actions taken, conversions, how many people have visited our site again, and more.
This data is then processed and analyzed to extract relevant and valuable information that can support strategic decision-making. That is, it is about avoiding the “I believe that…”, and replacing it with “we have observed that…”.
What Does Web Analytics Measure?
Web analytics measures a wide variety of metrics and KPIs (Key Performance Indicators) to evaluate the performance of a website or application.
Some of the most common metrics are the following:
- The number of visits
- Page views
- bounce rate
- Dwell time
- Conversions
- Conversion funnels (for example, in which phase of the purchasing process we lose customers)
- Origin of traffic (such as socio-demographic data)
For example, the bounce rate tells us what percentage of users leave the site after visiting a single page, which can be an indication that something is not working correctly. Let’s take the case of a person who enters the eCommerce category, but leaves without adding any product to the cart. From here, we will have to plan a strategy that encourages conversions.
In addition to these basic metrics, web analytics also allows us to perform more advanced analysis, such as tracking specific events, analyzing user segments, and measuring the ROI (Return on Investment) of our marketing actions.
However, you must always keep one thing in mind, and that is that data that does not help us improve is noise.
We cannot expect to measure absolutely everything, even when those metrics will not influence us at all in decision-making. If we measure something, it must be because it will help us improve business performance.
How Does Web Analytics Work?
Web analytics works through a process that consists of several stages
- Define what data we want to measure and what objectives we pursue.
- Implementation of data analysis tools.
- Compilation of relevant data.
- Analysis of the collected data.
- Visualization of the results clearly and understandably.
- Data-informed decision making.
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Next, we will go on to detail each of the steps of the process.
Define what data and objectives we pursue
Before starting to collect and analyze data, it is important to be clear about the objectives of your digital project.
Based on these objectives, define which metrics are relevant to evaluate your success, such as conversions, time on site, bounce rates, etc. because everything else will not be relevant for subsequent analysis in decision-making.
Implementation of data analysis tools
As we have mentioned before, analysis tools, such as Google Analytics, are implemented first. These tools allow you to collect data on user activity.
It is important to correctly configure tracking tags and events to collect relevant information accurately, as well as establish what we need to measure. We insist that there is no point in measuring everything, and it will only cause chaos.
Collection of useful data
Once the web analytics tool has been configured, it begins to collect data on user interaction with the website or app.
This includes information such as pages visited, actions taken, or conversions, which are stored in a database and prepared for analysis, which will be the next point.
Analysis of collected data
Data analysis is a key stage in web analytics. In this step, it is time to apply analysis techniques and tools to discover patterns, trends, and relationships in the data. In the case of massive data processing, we would be talking about Big Data.
For this analysis, techniques such as user segmentation, conversion funnel analysis, and cohort analysis are used to obtain valuable information about user behavior and the effectiveness of the implemented strategies.
Data visualization
Once we finish identifying the insights or keys to the analysis, it is important to visualize the data clearly and understandably. This is achieved by creating reports and graphs that present data in a visual and easy-to-interpret manner.
These reports allow decision-makers to get a clear view of the performance of the site or app and take appropriate measures to improve, which would be the last point of web analytics.
Examples Of Web Analytics
Now that we’ve seen what web analytics is, what it measures, and how it works, it’s time to look at some practical examples of how it’s applied in the real world.
Let’s imagine that we own an online store and we want to improve the conversion rate of our site. Using web analytics, we can analyze the conversion funnel, identify shopping cart abandonment points, and perform A/B testing to test which version of the website would perform best.
Another example would be the analysis of the origin of the traffic. If we have a blog and we want to increase the number of visits, we can use web analytics to identify which traffic sources are generating more visits and which are less effective (Google, email marketing, Instagram, Facebook, Twitter…), as well as what types of articles usually bring us a greater amount of traffic.
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These are just two examples of how web analytics can help us understand and optimize our online performance, but we can go much further. The key is knowing what data we should measure, and how to treat it for strategic decision making.
Finally, if you want to learn more about Big Data and data analysis, we recommend taking our Master’s in Web and Digital Analytics, where you will delve into tools and processes to make better strategic decisions.