Toward Personalized Online Shopping: Predicting Personality Traits Based on Online Shopping Behavior

32 Pages Posted: 24 Jun 2019

See all articles by Daniel Ringbeck

Daniel Ringbeck

WHU - Otto Beisheim School of Management - Production Management Department

Dominic Seeberger

WHU - Otto Beisheim School of Management

Arnd Huchzermeier

WHU - Otto Beisheim School of Management

Date Written: June 18, 2019

Abstract

Consumer's personality traits have a strong influence on their shopping behavior. Hence, e-tailers, rather than merely targeting broad consumer segments, should tailor their shop to those personality traits. However, there is no guidance on how e-tailers can assess a consumer's personality without relying on self-reported data. This study shows how consumers' personality traits can be predicted solely from their online browsing behavior. In a large-scale study, we demonstrate that a machine learning algorithm can predict the personality traits Need for cognition, Need for arousal, Lay rationalism and each of the Big 5 personality traits with accuracy comparable to well-known studies relying on social media data. We also establish that our algorithm is reliable in its predicted probabilities and is capable of making predictions of multiple personality traits in real time. Our research shows that e-tailers can quickly determine a consumer's personality traits and then dynamically adjust their online shop accordingly.

Keywords: consumer behavior, machine learning, personality traits, e-commerce

Suggested Citation

Ringbeck, Daniel and Seeberger, Dominic and Huchzermeier, Arnd, Toward Personalized Online Shopping: Predicting Personality Traits Based on Online Shopping Behavior (June 18, 2019). Available at SSRN: https://ssrn.com/abstract=3406297 or http://dx.doi.org/10.2139/ssrn.3406297

Daniel Ringbeck (Contact Author)

WHU - Otto Beisheim School of Management - Production Management Department ( email )

Burgplatz 2
Vallendar, 56179
Germany

Dominic Seeberger

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany

HOME PAGE: http://www.whu.edu/prod

Arnd Huchzermeier

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany
+49-261-6509380 (Phone)
+49-261-6509389 (Fax)

HOME PAGE: http://www.whu.edu/prod

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