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

With digitisation globally on the rise, corporates are compelled to better understand the usage of their websites. In doing so, corporates will be empowered to better understand consumers, and make necessary adjustments to ultimately improve the corporate's stance in the competitive global landscape of this modern age. However, the online website visit data has proven to be highly complex, big in data volume, and highly transactional with users expressing unique behaviours. Thus, extracting insight can be a complex problem to solve. This study aimed to employ unsupervised machine learning models to identify the intentions behind the visits on the observed website. The data studied was sourced from the Google Analytics tracking tool that was deployed on a corporate informative website. The study employed a k-means, hierarchical and dbscan unsupervised machine learning models to understand the intents behind visitors on the studied website. All three models detected five major intents that were expressed within the observed data. The intents identified were labelled as "accidentals", "drop-offs", "engrossed", "get-in-touch" and "seekers". On the observed data, all three unsupervised machine learning methods have performed well. However, in the context of the study, which investigated the intents that drove online visits, the hierarchical clustering method yielded superior results by maintaining the best balance between cluster homogeneity (stronger silhouette coefficients) and cluster size.