Academic literature on the topic 'Psychographic segmentation'

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.

This study aims to analyze the effect of market segmentation strategies on purchasing decisions of Panties Pizza in Surabaya. The type of research is quantitative and SPSS 16 was employed to test the hypotheses with multiple linear regression analysis. The population in this study was the customers of Panties Pizza in Surabaya. The sampling technique used was non probability sampling with a purposive sampling approach with as many as 100 respondents who have purchased Panties Pizza. The results of this study indicated that the variables of geographical segmentation, demographic segmentation, and psychographic segmentation have a significant effect simultaneously on purchasing decisions in Panties Pizza Surabaya. The coefficient of determination is 62% and the rest was influenced by other variables not examined in this study. Geographical segmentation variables had a significant effect partially on the decision to purchase Panties Pizza in Surabaya with a significance value of 0.004 <0.05. Demographic segmentation variables had no significant effect partially on the decision to purchase Panties Pizza in Surabaya with a significance value of 0.722> 0.05. Psychographic segmentation variables had a significant effect partially on purchasing decisions of Panties Pizza in Surabaya with a significance value of 0,000 <0.05. The variables of geographical segmentation, demographic segmentation, and psychographic segmentation that possessed a significant influence psychographic segmentation with value of β = 0.644.