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An Empirical Study of Predictive Model for Website Quality Analytics Using Dataset of Different Domains of Websites



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.



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.



The real-time dataset of website domains was assessed and websites were predicted as
good, average, and bad, using the algorithms.



 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.



 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.
