In this article, I want to talk to you about a fundamental topic for any digital project: that mesh analysis.
And it’s critical to understand and use the data we generate. In this post we explain in detail what web analytics is, what it measures and how it works, giving some practical examples to make it easier to understand.
What is web analytics?
Web analytics is the process of Collection, measurement, analysis and display of data related to user activity on a website or mobile applicationfor later decision making.
In other words, it’s about understanding how visitors interact with a website or an application with the aim of improving the user experience, increasing conversions and optimizing the overall performance of the project.
Instead of using our intuition, we rely on measurements, so it’s important to do this Using specific data collection tools and techniquessuch as tracking tags, cookies and tracking pixels. These mechanisms allow us to track and record user activity, e.g. B. pages visited, time spent, actions taken, conversions, how many people returned to our website and more.
After, This data is processed and analyzed to extract relevant and valuable information that can support strategic decision-making. That is, it’s about avoiding the “I believe that…” and replacing it with the “We have observed that…”.
What does web analytics measure?
Web Analytics measures a variety of metrics and KPIs (Key Performance Indicators) to evaluate the performance of a website or application.
Some the most common metrics are the following:
- The number of visits
- pageviews
- bounce rate
- dwell time
- conversions
- Conversion funnel (e.g. at which stage of the buying process do we lose customers)
- Traffic origin (e.g. socio-demographic data)
For example, bounce rate tells us what percentage of users abandon the website after visiting a single page, which can be an indication that something is not working properly. Let’s take the case of a person who enters the category of an e-commerce but exits without adding any product to the cart. From here we need to plan a strategy that will drive conversions.
In addition to these basic metrics, web analytics also allows us to do this Conduct an advanced analysisB. Monitoring specific events, analyzing user segments and measuring the ROI (return on investment) of our marketing efforts.
However, it is important that you always keep one thing in mind, and that is Data that doesn’t help us improve is noise.
We can’t pretend to measure absolutely everything, even if these metrics don’t influence our decision-making at all. When we measure something, it’s because it helps us improve business performance.
How does web analytics work?
The web analysis takes place via a process consisting of the following: different stages:
- Define what data we want to measure and what goals we are pursuing.
- Implementation of data analysis tools.
- compilation of relevant data.
- Analysis of the collected data.
- Visualization of the results clear and understandable.
- Data-based decision making.
Next, we will detail each step of the process.
Define which data and goals we track
Before you start collecting and analyzing data, it’s important to be clear about this What are the goals of your digital project?.
Define based on those goals which key figures are relevant for assessing your successsuch as conversions, time spent on the website, bounce rates, etc. because everything else will not be relevant for further analysis when making decisions.
Implementation of data analysis tools
As we mentioned, first of all Analysis tools are implemented, like Google Analytics. These tools allow you to collect data about user activity.
It is important to properly configure tracking tags and events to accurately capture and pinpoint the relevant information What do we have to measure?. We insist that there is no point in measuring everything and that it will only cause chaos.
Collection of useful data
As soon as the web analysis tool is configured, you can start Collect data about how users interact with the web or app.
This includes information such as pages visited, actions taken or conversions that are stored in a database and prepared for analysis, which will be the next point.
Analysis of the collected data
And the fact is that analyzing the data is an important step in web analytics. In this step, it is time to apply analysis techniques and tools Discover patterns, trends and relationships in data. In the case of massive data processing, we would speak of 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’re done identifying the insights or keys to analysis, it’s important Visualize data clearly and understandably. This is achieved by creating reports and charts that present data in a visual and easy-to-understand way.
These reports enable decision makers Get a clear view of website or app performance and take appropriate measures to improve it, which would be the final point of web analysis.
Decision-making based on the reports submitted
As a good data analyst, you should use the analysis presented to support your decisions. Avoid basing your decisions solely on intuition or assumptions, as this would contradict the objective pursued with this discipline.
Instead of this, Use objective data to support your arguments and make sure you make informed decisions and above all keep this in mind Web analytics is a continuous process.
Continue to regularly monitor your metrics and data, make adjustments as needed, and stay abreast of trends and changes in user behavior.
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 we own an online store and want to improve the conversion rate of our website. Web analytics allow us to analyze the conversion funnel, identify cart abandonment points, and run A/B tests to test which version of the site performs best.
Another example would be traffic origin analysis. If we have a blog and we want to increase the number of visits, web analytics allows us to identify which traffic sources generate more visits and which are less effective (Google, email marketing, Instagram, Facebook, Twitter…). as well as what type of articles typically report more traffic.
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 to know what data we should measure and how to use it for strategic decision-making.
Finally, if you want to learn more about big data and data analysis, we recommend our Master in Web and Digital Analysisand where you delve into tools and processes to make better strategic decisions.
And you, what do you like best about web analytics? What benefit do you give him? We read you in the comments!
Podcaster at Campamento Web, the most listened to Spanish language SEO talk and news show for more than 5 years. Also creator of Clave Podcast, the podcasting portal that trains you to be a podcaster. Graduated from Loyola University in Communications and is an SEO consultant, but is currently more focused on his own communications and web positioning projects.
