A machine learning approach to identifying decision-making styles for managing customer relationships

Ana Alina Tudoran ()
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Ana Alina Tudoran: Aarhus University, Fuglesangs Alle

Electronic Markets, 2022, vol. 32, issue 1, No 20, 374 pages

Abstract: Abstract Decision-making styles have been studied in non-situational settings using the classical survey instrument. This study proposes a novel methodology for identifying decision-making styles in a real-world purchasing situation using only behavioral data and machine learning. We base our analysis on a two-week sample of 1,347,854 clickstream sessions from an e-commerce company and extract a series of parameters to infer the search goal, strategy, and decision difficulty. We implement a range of unsupervised algorithms, and we identify and validate three internally stable classes of decision-makers. One category corresponds to the classical style of satisficers; the other two subcategorize the maximisers' classical style. The customer’s entry channel preferences and movement patterns provide compelling support for the style's predictive validity. This study contributes to research and practice by proposing a new methodology to recognize the customer decision style in the e-commerce setting.

Keywords: Satisficers; Maximizers; Decision-making; Clickstreams; Machine learning; E-commerce (search for similar items in EconPapers)
JEL-codes: C38 M2 M3 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12525-021-00515-x

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