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Modeling retail browsing sessions and wearables data

Medellin-Gasque, Rolando and Nordmark, Henrik and Mullen, Anthony and Citi, Luca and Perperoglou, Aris and Lausen, KB 'Modeling retail browsing sessions and wearables data.' Archives of Data Science. ISSN 2363-9881 (Submitted)

Medellin-Gasque-et-al.pdf - Submitted Version

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The advent of wearable non-invasive sensors for the consumer market has made it cost-effective to conduct studies that integrate physiological measures such as heart rate into data analysis research. In this paper we investigate the predictive value of heart rate measurements from a commercial wrist wearable device in the context of e-commerce. We look into a dataset comprised of browser-logs and wearables data from 28 individuals in a field experiment over a period of ten days. We are particularly interested in finding predictors for starting a retail session, such as the heart rate at the beginning of a web browsing session. We describe preprocessing tasks applied to the dataset and logistic regression and survival analysis models to retrieve the probability of starting a retail browsing session. Preliminary results show that heart rate has a significant predictive value on starting a retail session if we consider increased and decreased heart rate individual values and the time of day.

Item Type: Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 30 Nov 2018 15:34
Last Modified: 15 Jan 2022 01:23

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