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.
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Budnikas,G.(2015).COMPUTERISED RECOMMENDATIONS ON E-TRANSACTION FINALISATION BY MEANS OF MACHINE LEARNING.Statistics in Transition New Series, 16(2), 309-322.doi:10.21307/stattrans-2015-017.
Budnikas,Germanas."COMPUTERISED RECOMMENDATIONS ON E-TRANSACTION FINALISATION BY MEANS OF MACHINE LEARNING"Statistics in Transition New Series 16,no.2(2015):309-322doi:10.21307/stattrans-2015-017.
Budnikas,Germanas."COMPUTERISED RECOMMENDATIONS ON E-TRANSACTION FINALISATION BY MEANS OF MACHINE LEARNING"Statistics in Transition New Series 16.no.2(2015):309-322doi:10.21307/stattrans-2015-017.
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