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

Saavi Stubseid, Ognjen Arandelovic

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


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.
Original languageEnglish
Title of host publicationProceedings AI and Marketing Science workshop at AAAI-2018
Number of pages7
Publication statusPublished - 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


ConferenceThirty-Second AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-18
Country/TerritoryUnited States
CityNew Orleans
Internet address


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