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

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  • Ana Alina Tudoran

    (Aarhus University, Fuglesangs Alle)

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.

Suggested Citation

  • Ana Alina Tudoran, 2022. "A machine learning approach to identifying decision-making styles for managing customer relationships," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 351-374, March.
    • Handle: RePEc:spr:elmark:v:32:y:2022:i:1:d:10.1007_s12525-021-00515-x
    DOI: 10.1007/s12525-021-00515-x
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    Cited by:

    1. Richter, Nicole Franziska & Tudoran, Ana Alina, 2024. "Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms," Journal of Business Research, Elsevier, vol. 173(C).
    2. Rainer Alt, 2022. "Electronic Markets on platform dualities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 1-10, March.

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    More about this item

    Keywords

    Satisficers; Maximizers; Decision-making; Clickstreams; Machine learning; E-commerce;
    All these keywords.

    JEL classification:

    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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