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
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://index.stat.gov.pl/repec/files/csb/stintr/csb_stintr_v16_2015_i2_n10.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:csb:stintr:v:16:y:2015:i:2:p:309-322

Access Statistics for this article

Statistics in Transition new series is currently edited by Włodzimierz Okrasa

More articles in Statistics in Transition new series from Główny Urząd Statystyczny (Polska) Contact information at EDIRC.
Bibliographic data for series maintained by Beata Witek ( this e-mail address is bad, please contact ).