Application of Machine Learning for Target Audience Clustering in Web Applications

In this study, a machine learning method was applied for clustering data on the target audience of e-commerce web applications. Machine learning is a powerful analytical tool that enables the automatic identification of patterns in large datasets, improving the accuracy of user behavior prediction. Key interaction metrics with web applications were selected, including bounce rate, session duration, and conversion rate. The input data were normalized. To ensure proper normalization and the correct operation of machine learning algorithms, a method was used to scale values within the range from zero to one. The optimal number of clusters was determined using the "elbow" method, which analyzes the relationship between the number of clusters and the within-cluster sum of squared distances. The k-means method was applied to analyze behavioral parameters, minimizing the sum of squared distances between data points and cluster centroids using the Euclidean metric. The results were visualized using a three-dimensional plot, representing the distribution of clusters based on the analyzed parameters. The clustering results identified four groups of users with different interaction characteristics with the web resource. Users in the first cluster exhibited low engagement, short session durations, and high bounce rates, indicating insufficient content relevance. The second cluster demonstrated prolonged interaction with the web resource, but the high bounce rate may suggest navigation difficulties. The third cluster was characterized by a high conversion rate with moderate session duration, indicating an efficient user experience. The last cluster had the lowest bounce rate and the highest conversion rate, reflecting a strong alignment between content and user needs. The practical significance of the obtained results lies in the possibility of applying clustering methods to adapt UX/UI solutions, optimize content, and enhance conversion rates. The proposed approach can be utilized in e-commerce, digital marketing, and web analytics to improve user interaction strategies.