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Research at St Andrews

Machine learning based prediction of consumer purchasing decisions: the evidence and its significance

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Author(s)

Saavi Stubseid, Ognjen Arandelovic

School/Research organisations

Abstract

Every day consumers make decisions on whether or not to buy a product. In some cases the decision is based solely on price but in many instances the purchasing decision is more complex, and many more factors might be considered before the final commitment is made. In an effort to make purchasing more likely, in addition to considering the asking price, companies frequently introduce additional elements to the offer which are aimed at increasing the perceived value of the purchase. The goal of the present work is to examine using data driven machine learning, whether specific objective and readily measurable factors influence customers’ decisions. These factors inevitably vary to a degree from consumer to consumer so a combination of external factors, combined with the details processed at the time the price of a product is learnt, form a set of independent variables that contextualize purchasing behaviour. Using a large real world data set (which will be made public following the publication of this work), we present a series of experiments, analyse and compare the performances of different machine learning techniques, and discuss the significance of the findings in the context of public policy and consumer education.
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Details

Original languageEnglish
Title of host publicationProceedings AI and Marketing Science workshop at AAAI-2018
Number of pages7
StatePublished - 2 Feb 2018
EventThirty-Second AAAI Conference on Artificial Intelligence - Hilton New Orleans Riverside, New Orleans, United States
Duration: 2 Feb 20187 Feb 2018
Conference number: 32
https://aaai.org/Conferences/AAAI-18/

Conference

ConferenceThirty-Second AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-18
CountryUnited States
CityNew Orleans
Period2/02/187/02/18
Internet address

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