Machine Learning for Web Analytics

Machine learning for web analytics
Machine learning for web analytics

Machine learning is a set of methods that can learn and predict with data sets and labels in the database or by grouping new data. According to the definition made by Arthur Samuel in 1959, it is a “computer system capable of learning without programming.

Machine learning, developed as a sub-branch for the development of artificial intelligence, uses various algorithm models in order to reach the targeted information in the fastest way. These algorithms can be used to analyze, classify and estimate large data sets.

Basically, two different types of machine learning are classified in two different ways: Supervised Learning and Unsupervised Learning.

What Is Supervised Learning?

Supervised learning consists of algorithms that make predictions based on what they have learned and the labels they have. In this structure, the system first learns with training data. He then makes interpretations on new data in accordance with what he has learned. For example, if we want an algorithm to recognize cat photos, we need to use training data that includes a group of cat photos.

What Is Unsupervised Learning?

Unsupervised learning consists of algorithms that group previously detected linkages by examining incoming data without obtaining training data and labeling. This method is mainly used in cases where future data is not predictable or tagged.

Using Machine Learning in Web Analysis

In order to provide better user experience, we use a variety of tools to analyze our users’ experiences and develop new strategies. These tools allow us to obtain different variables such as the total time spent by our visitors, the pages they visit, the densities of their cursors, and their demographic information.

Good web analysis means a good data analysis. This large amount of data should be processed by good data analysis. Data analysis is one of the necessary processes we will use to improve the user experience.

Machine learning helps us to interpret all the results we have achieved with data analysis and to make predictions for the future. In addition to processing very large data, making predictions based on the data obtained will be a difficult and very long process with the human factor. Machine learning can be used to achieve faster and more accurate results.

We find examples where machine learning gives successful results in web analytics and user experience improvements. One of these is the possibility to add subtexts to the images. Visual recognition technology now has the possibility to describe a visual in all its details. This provides a successful SEO solution for platforms that are heavily used visually. It also improves screen readability for the visually impaired. There are also examples of machine learning that summarizes the contents.

It can also produce machine learning solutions for error detection and quick solutions. The site can detect errors by scanning all pages regularly and can produce instant solutions.

Machine learning develops rapidly and takes its place in almost every stage of business processes. It seems to be our most important help in providing more effective and faster solutions to problems in the future.

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