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Identifying the Intents Behind Website Visits by Employing Unsupervised Machine Learning Models.

Judah Soobramoney¹, Retius Chifurira¹, Temesgen Zewotir¹

¹School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.

Annals of Data Science

|August 26, 2025

Summary

This summary is machine-generated.

This study used unsupervised machine learning to analyze website visitor data, identifying five key user intents. Hierarchical clustering proved most effective for understanding online visit motivations.

Keywords:

Cubic clustering criteria Dbscan Dendrogram Google analytics tracking Hierarchical clustering K-means Online website visits Silhouette’s coefficient Unsupervised machine learning

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Area of Science:

Data Science
Machine Learning
Consumer Behavior Analysis

Background:

Increasing digitization necessitates corporate understanding of website usage.
Analyzing complex, high-volume website data to understand consumer behavior is challenging.

Purpose of the Study:

To apply unsupervised machine learning models to identify visitor intentions on a corporate website.
To extract actionable insights from complex website visit data.

Main Methods:

Utilized Google Analytics data from a corporate informative website.
Employed k-means, hierarchical, and DBSCAN unsupervised machine learning models.
Evaluated model performance based on cluster homogeneity and size.

Main Results:

All three models identified five distinct visitor intents: "accidentals", "drop-offs", "engrossed", "get-in-touch", and "seekers".
Hierarchical clustering demonstrated superior performance in balancing cluster homogeneity and size.

Conclusions:

Unsupervised machine learning effectively identifies user intents from website data.
Hierarchical clustering offers a robust method for analyzing visitor motivations and optimizing corporate web strategies.