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Computerised recommendations on e-transaction finalisation by means of machine learning

Germanas Budnikas ()

Statistics in Transition new series, 2015, vol. 16, issue 2, 309-322

Abstract: Nowadays a vast majority of businesses are supported or executed online. Website-to-user interaction is extremely important and user browsing activity on a website is becoming important to analyse. This paper is devoted to the research on user online behaviour and making computerised advices. Several problems and their solutions are discussed: to know user behaviour online pattern with respect to business objectives and estimate a possible highest impact on user online activity. The approach suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naïve Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at. The technique is illustrated by an example.

Keywords: online behaviour; Google Analytics; Naïve Bayes classifier; artificial neural network (search for similar items in EconPapers)
Date: 2015
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Handle: RePEc:csb:stintr:v:16:y:2015:i:2:p:309-322