COMPUTERISED RECOMMENDATIONS ON E-TRANSACTION FINALISATION BY MEANS OF MACHINE LEARNING

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability

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ISSN: 1234-7655
eISSN: 2450-0291

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VOLUME 16 , ISSUE 2 (June 2015) > List of articles

COMPUTERISED RECOMMENDATIONS ON E-TRANSACTION FINALISATION BY MEANS OF MACHINE LEARNING

Germanas Budnikas *

Keywords : online behaviour, Google Analytics, Naïve Bayes classifier, artificial neural network.

Citation Information : Statistics in Transition New Series. Volume 16, Issue 2, Pages 309-322, DOI: https://doi.org/10.21307/stattrans-2015-017

License : (CC BY 4.0)

Published Online: 31-October-2017

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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.

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REFERENCES

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