Social Network and Consumer Behavior Analysis: A Case Study in the Shopping District
The increasing popularity of social networking services and the mobile devices have changed people’s life and brought the new business challenges. The goal of this study was to analyze the characteristics and purchase probability of different customers gr
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Abstract The increasing popularity of social networking services and the mobile devices have changed people’s life and brought the new business challenges. The goal of this study was to analyze the characteristics and purchase probability of different customers groups in the Shimen shopping district and extract business value. We extracted a point-earning app’s user records about the Shimen shopping district. The dates of those user records were between June 11, 2015 and July 12, 2015. Furthermore, we collected the Facebook information of the users. All statistical procedures were performed with our Customer Behavior Analysis System. The main customers in Shimen shopping district were younger groups. Different groups of users had divergent favorites because of their age and gender. The popular trend nowadays in the younger people and the reward points the shop gave may affect the conversion rate of a shop. Many people shopped at several similar stores in one day and that might mean they shopped purposefully not blindly. After customizing the point-earning app’s assignments and promotions by the analysis results, we got an up to 6% conversion rate improvement. Keywords Social network analysis marketing
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Consumer behavior analysis
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P.-L. Chen (✉) ⋅ P.-C. Yang ⋅ T. Ku Institute for Information Industry, Taipei, Taiwan, ROC e-mail: [email protected] P.-C. Yang e-mail: [email protected] T. Ku e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 N.Y. Yen and J.C. Hung (eds.), Frontier Computing, Lecture Notes in Electrical Engineering 422, DOI 10.1007/978-981-10-3187-8_84
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1 Introduction Traditional advertising and marketing used in the past focused on the non-specific customers. However, most of them have diverse interests and the effects are not significant. Target marketing makes the promotion, pricing and distribution of products or services more cost-effective. Analyzing the shopping logs to find the target customers is adopted for ages. Besides, the increasing popularity of social networking services and the mobile devices have changed people’s life. Social network analysis has become more and more important. To overcome the new business challenges, service providers have to improve their marketing strategies, analyze the existing and potential customers’ behavior, and develop the precision marketing methods. The goal is to offer the right products to the right customers at right time and right location. In the past, there were several studies focused on the analysis of customers [1– 6]. Liao et al. found the differences between heavy and non-heavy users in top video apps based on Chunghwa Telecom network connection records [5]. He et al. analyzed the trend of tweets numbers for the big three pizza chains by text mining [3]. Such analysis results can be used to design appropriate marketing plans to improve sales [7]. However, those studies used only the social network data or private customer data. In this study, we combined two types of user records, t
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