Abstract
Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.
Highlights
In big data era, the research paradigms of marketing service have been greatly changed by the enormous marketing data accumulated from the internet, such as on demographics, user behavior, and social relationships
Overall, psychographic variables significantly improved the explanatory power of e-consumer preferences across most product categories we studied, whereas the improvement in predictive power was not significant
We have focused on the promising role that different psychographic segmentations play in the understanding of e-commerce consumer preferences
Summary
The research paradigms of marketing service have been greatly changed by the enormous marketing data accumulated from the internet, such as on demographics, user behavior (we will use the word “user”, “customer”, and “consumer” interchangeably), and social relationships. The predictive and explanatory power of personality traits for online user behavior remains controversial [11] This controversy motivates the investigation of other types of psychographic segmentation, such as value, in understanding user preferences, which have not been investigated. The main reason is that collecting psychographic segmentation data from e-commerce users is difficult on a large scale, typically requiring consumers to complete lengthy questionnaires Psychological variables, such as personality and value, are deeply embedded in the language that people use today [12]. By introducing psychographic-related word use behavioral evidence, followed by big data approaches, we have attempted to overcome the difficulties of obtaining e-consumer psychographics on a large scale, and have provided a promising psychographic-based consumer preference prediction method for subsequent research
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