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Developing a personal value analysis method of social media to support customer segmentation and business model innovation
Author(s) -
Chen TsungYi,
Cheng HsiangAn,
Chen YuhMin
Publication year - 2019
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12374
Subject(s) - computer science , market segmentation , consistency (knowledge bases) , value (mathematics) , customer value , social media , business value , segmentation , feature (linguistics) , knowledge management , data mining , data science , marketing , artificial intelligence , world wide web , machine learning , business , market economy , linguistics , philosophy , hierarchy , economics , economic growth , human capital
Companies need to find out about the personal values of customers and identify customer segments before developing effective business models (BMs) or marketing strategies. Therefore, understanding the personal values of customers is critical in BM design. Traditional personal value evaluation methods are laborious and time‐consuming. In recent years, social media have become an important platform for people to share ideas and express views, making the presence of a huge amount of opinions on platforms that can cater for the demand of data for personal value analysis of customers. This study took Facebook and Instagram as the targets and developed a novel personal value forecasting method to help enterprises obtain the various personal values of customer segments automatically at a lower cost. This study adopted Schwartz's value theory as the value model and proposed a consistency model and a relativity model for weighted calculations, so as to determine the feature of a value tag. Finally, the feature selection algorithm and classification algorithm were used for judging values. In the evaluation phase of this study, 61 participants were recruited to test the proposed method. The proposed method could assist enterprises in better understanding personal value information.