CLUSTER ANALYSIS FOR MARKETING CAMPAIGN USING SOCIAL NETWORK

Authors

  • ดารณี พิมพ์ช่างทอง คณะบริหารธุรกิจ มหาวิทยาลัยเทคโนโลยีราชมงคลธัญบุรี

Keywords:

Cluster Analysis, Social Media, Marketing Campaign

Abstract

The objectives of this research were to 1) find the most important factors influencing purchasing merchandise or using services that are advertised online, 2) identify the number of clusters and the clusters characteristics that purchase merchandise or use services after seeing online advertising in social media. The sample group was people who used to purchase merchandise or used services through online social media such as Facebook, Line, and Instagram. The questionnaires were used to collect data for 400 samples using Convenience Sampling Method. Statistics used to analyze data were percentages, frequencies, correlation, and Cross Industry Standard Process for Data Mining (CRISP – DM) using Operator k-Mean. Data were analyzed using statistical software and Rapid Miner Studio 6. The research results found that the most important factors influencing purchasing merchandise or using services that are advertised online were saving information for further consideration, the text, image, and clip advertising on social media, satisfaction with merchandise or service, and interesting price. For clusters characteristics, the findings indicated two clusters: product conscious cluster and price conscious cluster. Although these two groups had clearly different characteristics, they were similar on the influence of online advertising in saving information for further consideration and interest in the ads once seeing text, image and clip advertising on social media. Up-todate methods and technology innovation should be considered when creating ads online to attract more shoppers and create more shopper’s involvement that would lead to increased purchasing.

References

กัลยา วานิชย์บัญชา. (2552). การวิเคราะห์ข้อมูลหลายตัวแปร (พิมพ์ครั้งที่ 4). กรุงเทพฯ: จุฬาลงกรณ์ มหาวิทยาลัย.

ดนัย ปัตตพงษ.์ (2559). Statistics talk: cluster analysis, knowledge management. Retrieved from http://it.nation.ac.th/faculty/danai/showinfo_st.php

Balan, S., & Rege, J. (2017). Mining for social media: usage patterns of small business. Business Systems Research, 8(1), 43 -50.

Boonjing, V., & Pimchangthong, D. (2017). Data mining for customers’ positive reaction to advertising in social media. Proceedings of the Federated Conference on Computer Science and Information Systems, 945-948.

Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79-85.

Everitt, B., Landau S., Morven Leese M., & Stahl, D. (2011). Cluster analysis (5th ed.). UK.: John Wiley and Sons.

Han, J. & Kamber, M. (2006). Data mining concepts and techniques (2nd ed.). United States of America: Morgan Kaufmann Publishers.

Jara, A. J., Parra, M. C., & Skarmeta, A. F. (2013). Participative marketing: extending social media marketing through the identification and interaction capabilities from the Internet of things. Personal and Ubiquitous Computing, 1-15, doi: 10.1007/s00779-013-0714-7.

Rajeev, V. & Jyoti, V. (2012). The role of motivation as a moderator of the job demandburnout-performance relationship among service employees in a social marketing campaign, Decision, 39(3), 68-85.

Rouse, M. (2009). Microblogging. Retrieved from https://searchmobilecomputing.techtarget.com /definition /microblogging

Statista. (2018). Brands on social media - statistics & facts, the statistics portal. Retrieved from https://www.statista.com/topics/2057/brands-on-social-media/

Tuten, T. L., & Solomon, M. R. (2013). Social media marketing. New Jersey: Prentice Hall.

Whiting, A., & Deshpande, A. (2016). Toward greater understanding of social media marketing: a review, Journal of Applied Business and Economics, 18(4), 82-91.

Yazdanparast, A., Joseph, M., & Qureshi, A. (2015). An Investigation of facebook boredom phenomenon among college students. Young Consumers, 16(4), 468-480.

Downloads

Published

28.06.2018

How to Cite

พิมพ์ช่างทอง ด. CLUSTER ANALYSIS FOR MARKETING CAMPAIGN USING SOCIAL NETWORK. RMUTT Global Business and Economics Review, Pathum Thani, Thailand, v. 13, n. 1, p. 139–150, 2018. Disponível em: https://so03.tci-thaijo.org/index.php/RMUTT-Gber/article/view/241908. Acesso em: 19 may. 2024.

Issue

Section

Research Articles