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.
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Notes
There are other approaches to understanding decision-making styles in the consumer behavior literature. For example, Consumer Types Inventory (CSI), created by Sprotles & Kendall (1986), identifies eight possible styles that influence behavior. However, many of these approaches lack generality. CSI was developed in the 1980s, when Internet shopping was non-existent, to assess attitudes and behaviors related to buying personal items.
Because clickstream data contain visitors at different stages of their decision-making process, within a given time frame, clickstream data include (i) complete visits (i.e., visitors who started the search and purchased during the website's observation period); (ii) visitors who momentarily explore the alternatives or are close to the decision, but their purchase occurs outside the period of observation (right-censored); (iii) visitors who momentarily purchase, but their search process commenced before the period of observation (left-censored); (iv) visitors who only explore the online environment without the intention to purchase or to purchase offline; and (v) shallow or unrelated visits (Moe, 2003; Schellong et al., 2016). In the first part of the study, we focus on complete visits to reliably determine the decision-makers' online behavior and recognize their distinguishable characteristics. In the cluster validation process, we include a sample of visitors who momentarily explore the alternatives or are close to the decision, but their purchase occurs outside the observation period (right-censored).
Unsupervised learning is a type of machine learning in which models are trained using an unlabeled dataset and can act on that data without any supervision. This technique is appropriate in our study since we do not have prior information about the customers' class. In such a case, no labels are given to the learning algorithm, leaving it on its own to find structure in its input.
The Cophenetic coefficient is the correlation between the original distance matrix between objects and the Cophenetic distance matrix based on dendrograms. The intergroup dissimilarity at which two clustered observations are initially integrated into a single cluster is described as the Cophenetic distance between two clustered observations. The clustering is well-fitting when the Cophenetic coefficient is close to 1.
Connectivity measures the extent to which neighboring observations are clustered together. The Dunn index is the ratio of the shortest distance between observations in distinct clusters to the largest cluster diameter. Silhouette considers how near the different clusters are to one another (inter-cluster separation) as well as the magnitude of the intra-cluster variations (i.e., compactness). Connectivity with a value ranging between zero and ∞ should be minimized. Dunn (values between 0 and ∞) and Silhouette (values ranging between -1 and 1) should be maximized (Brock et al., 2008).
The adjusted Rand index measures how similar two market segmentation solutions are while correcting for agreement by chance. The adjusted Rand index is 1 if two market segmentation solutions are identical, and 0 if the agreement between the two market segmentation solutions is the same as expected by chance (Dolnicar et al., 2018).
The Jaccard index has the same interpretation as the adjusted Rand index.
Gower distance (Gower, 1971) calculates the distance between records that contain combinations of logical, numerical, categorical, or text data.
SAHN often recommends a 2-cluster solution as the optimal solution because the value of any stopping rule in a tree-like structure algorithm is typically highest when moving from two to one cluster (Hair et al., 2006).
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The author gratefully thanks the anonymous company that was the source of the data used in the study, Rune Bysted and Prof. Hans Jørn Juhl (Aarhus University) for facilitating the data, Juan Lago (Lead Supply), Karin Vinding (Aarhus University), Prof. Yun Wan (Associate Editor), and the three anonymous reviewers for their helpful comments on a previous version of this manuscript.
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Tudoran, A.A. A machine learning approach to identifying decision-making styles for managing customer relationships. Electron Markets 32, 351–374 (2022). https://doi.org/10.1007/s12525-021-00515-x
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DOI: https://doi.org/10.1007/s12525-021-00515-x