Abstract
Introduction: Web analytics is the process of examining websites to uncover patterns, correlations, trends, insights, and other useful information that can be utilized to optimize web usage and improve the quality of the website.
Methods: This research proffers an approach which associates the website assessment with user satisfaction and acceptance. The proposed WQA (Website Quality Analytic) Model considers websites from seven domains, and using 13 UX- based quality attributes, evaluates the quality of websites in each domain. The quality assessment is automated using supervised learning models to predict good, average, and bad websites.
Results: The real-time dataset of website domains was assessed and websites were predicted as good, average, and bad, using the algorithms.
Discussion: A Website quality model essentially consists of a set of criteria used to determine if a website reaches certain levels of fineness. User Experience (UX) directly measures the quality of site interactions and is an indirect representative of site success and customer conversions. That is, a bad UX bounces away visitors to seek a more reliable website. Every single second, a user spends on a website is directly attributable to the usability of a good UX. Hence, the evaluation of the quality of websites is essential to determine user acceptance; that is, the users are the parameter measured for the success of the site.
Conclusion: The feature (attribute)-based predictive model for quality analytics is empirically analyzed for five classification algorithms. A qualitative analysis of the domain-wise classification of websites is also presented.
Keywords: Website, UX, usability, quality, classifier, supervised learning.
Graphical Abstract
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