Making the most of data with Web Analytics

If you are a website owner or an online entrepreneur, understanding how to use Web Analytics can make the difference between online success and failure. Let’s see exactly what Web Analytics are, why they are important, and how to use them to make informed decisions.

What is Web Analytics?

In simple terms, Web Analytics is the study of data generated by online activity. This data includes information such as the number of site visitors, pages visited, time spent on the site, conversions, and much more. But what are they for?

Understanding the audience: by analyzing the data, you can get a clear view of who your visitors are, what their interests are, and how they interact with your website.
Optimizing site performance: using data to identify weaknesses in your website, you can make targeted improvements to increase effectiveness and ease of use.
Measuring success: by monitoring key metrics like conversion rate and average time spent on the site, you can evaluate the effectiveness of your marketing strategies and make changes when necessary.
Making informed decisions: by relying on data rather than intuition, you can make smarter, more targeted decisions to improve the overall performance of your site.

What are the three types of Analytics?

There are three distinct types of Analytics, each with a specific purpose and can be used to answer different business questions and problems.

Descriptive Analytics: this type of analytics focuses on describing what happened in the past. It uses historical data to understand past events and identify patterns or trends. Descriptive analytics answers questions like “What happened?” and “What is the current situation?”.
Predictive Analytics: this model of analytics relies on analyzing historical data to make predictions about the future. Using statistical models and machine learning algorithms, predictive analytics aims to anticipate what events or outcomes might occur based on current data. It answers questions like “What might happen?” and “What are the possible future trends?”.
Prescriptive Analytics: this type of analysis goes beyond simple prediction and offers recommendations on what actions to take to influence future outcomes. Using predictive models and optimized algorithms, prescriptive analytics suggests the best actions to take to achieve specific goals. It answers questions like “What should we do?” and “What are the recommended actions to achieve the desired results?”.

What indicators are used in Web Analytics?

Indicators used in Web Analytics are divided into different categories based on the specific goals of the website or online activities. Here are some of the most common indicators.

Site Traffic Metrics

- Visits: the total number of site visits.
- Impressions: the number of times the site link is shown to a user in SERP.
- CTR (click-through rate): the percentage of clicks relative to the number of times the item appeared in the SERP. The higher the CTR, the better.
- Unique users: the total number of distinct visitors during a specific period.
- Pageviews: the total number of pages viewed during visits.
- Bounce rate: the percentage of visits where the user leaves the site after viewing only one page.
- Average session duration: the average time users spend on the site during a visit.

Traffic Source Metrics

- Organic traffic: the number of visits from search engines without passing through redirects or paid campaigns.
- Direct traffic: the number of visits where the user directly typed the site URL into the browser.
- Referral traffic: the number of visits from links on other websites.
- Social traffic: the number of visits from social media platforms.

User Behavior Metrics

- Entry page: the first page visited during a session.
- Exit page: the last page visited during a session.
- Navigation path: the path followed by users through the website.
- Events: specific actions taken by users, such as clicking on links, downloading files, or interacting with videos.

Conversion Metrics

- Goals completed: specific actions taken by users on the website, such as purchasing a product, filling out a form, or subscribing to a newsletter. The website owner chooses the type of goal to set.
- Conversion rate: the percentage of users who complete a conversion goal.
- Conversion value: the economic value associated with a conversion, such as the average value of an online order.

These are just some of the most common indicators used in Web Analytics. It is important to select the most relevant metrics based on the specific goals and strategies of the website or online activity. Typically, a proper analysis includes a combination of different metrics, and it is important to know how to read and analyze them correctly.

What is the most used tool for Web Analytics?

Currently, one of the most used tools for Web Analytics is Google Analytics, a free web analytics platform provided by Google, which offers a wide range of tools and features to monitor and analyze website traffic. You can proceed with:

Traffic monitoring;
User behavior analysis;
Conversion tracking;
Custom reporting.

Google Analytics integrates perfectly with other Google tools and services, such as Google Ads and Google Search Console, allowing for a more comprehensive view of website performance and online marketing activities.

The transition from Universal Analytics to Google Analytics 4 (GA4)

In 2020, Google announced the launch of a new version of Analytics, called Google Analytics 4 (GA4). Subsequently, it specified that the previous version, Universal Analytics, would stop processing new data on July 1, 2023, for most users and October 1, 2023, for Universal Analytics 360 users. This transition has represented a significant shift for Google Analytics users, as GA4 introduces new features and adopts a different measurement model compared to Universal Analytics.

Greater focus on the customer: Google Analytics 4 shifts the focus from simple web traffic analysis to understanding individual user behavior across different devices and platforms. This approach provides a more complete and integrated view of customers and their engagement with the brand.
Events and conversion models: one of the main innovations of GA4 is the introduction of events as a key measurement unit. Events allow you to track specific actions taken by users on the website or app, enabling more advanced customization of reports and a more detailed understanding of user behavior.
More options for data integration: Google Analytics 4 offers greater flexibility in integrating data from different sources, including apps, web, and offline. This allows for a more comprehensive view of customers and their interactions with the brand across all channels.
Redefining the reporting model: GA4 introduces a new reporting model based on events and custom parameters rather than page views. This allows for greater flexibility in creating custom reports and better adaptability to the specific needs of the business.
Improvements in artificial intelligence and machine learning: Google Analytics 4 leverages advanced artificial intelligence and machine learning technologies to provide more relevant insights and recommended actions based on the data collected. This enables a deeper understanding of user behavior patterns and optimization opportunities.

Once you switch to Google Analytics 4 (GA4), the data collected from Universal Analytics will no longer be updated but will remain accessible for consultation. This is a brief period in which you will need to download your historical data to avoid losing it, which can be done manually, through GA4 reports, or with third-party tools like Supermetrics or GA3 Exporter.

Bring your website to success with Web Analytics

Understanding how to use the collected data can help you improve user experience, increase conversions, and achieve your business goals. Start making the most of data analysis to bring your website to success. Still need to migrate your data from GAU to GA4? Contact us, and we can assist you with the transition.