Challenges in Using Big Data for Analyzing Consumer Behavior
Article Sidebar
Main Article Content
Abstract
This research examined the use of big data in consumer behavior by focusing on the critical local marketing strategies of local modern trade businesses and store, and how to response customer needs and customer satisfaction. Methodologies are studied on questionnaires and in-depth interviews, using both qualitative and quantitative methods, which are employed to study consumer purchasing behavior in two provinces, namely Ratchaburi and Phuket. Finding that location factor influences with consumer purchasing both type of product, quantity of purchasing, and including a high positive opinion of consumer regarding on the convenience and variety of products for shopping at the local convenience store. Data analysis on consumer purchasing can create competitive advantage in business and increase in customer satisfaction as well. It is the fact that big data are very important tool for predictive consumer behavior model in the future. It can revolutionize in the local modern trade market based on the power of data analysis.
Article Details
Copyright
Connexion: Journal of Humanities and Social Sciences has an exclusive right to publish the accepted articles in any form. However, the author retains the following rights:
1. The right to the ownership of the article;
2. The right to use all or part of the article in his/her other works;
3. The right to re-produce the article for personal use or for use in the author’s organisation, in which case the author must obtain permission from Connexion: Journal of Humanities and Social Sciences;
4. The right to make copies of all or part of the work for educational use or for the author’s use in classroom teaching; and
5. The right to include the work (both the preprinted and printed versions) in an institutional repository.
References
Ahmed, V., et al. (2017) The future of Big Data in facilities management: Opportunities and challenges, Facilities, vol. 35, no. 13/14, pp. 725-745.
Baviera-Puig, A., et al. (2016) Geo marketing models in supermarket location strategies, Journal of Business Economics and Management, vol. 17, no. 6, pp. 1205-1221.
Brinton, W. C. (1917) Graphic methods for presenting facts, New York: Engineering Magazine Company and the Engineering Management Company.
Bumblauskas, D., et al. (2017) Big data analytics: Transforming data to action, Business Management Journal, vol. 23, no. 3, pp. 703-720.
Cochran, W. G. (1977) Sampling techniques, 3rd edition, New York: John Wiley & Sons.
Chaurhuri, S., et al. (2011) An overview of business intelligence technology, Communications of the ACM, vol. 54, no. 8, pp. 88-98.
Christensen, C. (2016) What is disruptive innovation?, Available: https://www.weforum.org/agenda/ 2016/06/what-is-disruptive-innovation/ [12 January 2018]
Cliquet, G. (2006) Integration and territory coverage of the hypermarket industry in France: A relative entropy measure, International Review of Retail, Distribution and Consumer Research, vol. 8, no. 2, pp. 205-224.
Erevelles, S., et al. (2015) Bid data consumer analytics and the transformation of marketing, Journal of Business Research, vol. 69, no. 2, pp. 897-904.
Gantz, J., & Reinsel, D. (2011) Extracting value from chaos, IDC’s Digital Universe Study, Available: https://www.sciepub.com/reference/140415 [29 February 2018]
Gartner & Laney. (2011) 3Vs (volume, variety and velocity), Available: https://whatis.techtarget.com/ definition/3Vs [17 March 2017]
Grewal, D., et al. (1999) Planning merchandising decisions to account for regional and product assortment differences, Journal of Retailing, vol. 75, no. 3, pp. 405-424.
Hawkins, D. I., et al. (1998) Consumer behavior: Building marketing strategy, 7th edition, Boston: McGraw Hill.
Kaisler, S., et al. (2013) ‘Big data: Issues and challenges moving forward’, Paper presented at 2013 46th Hawaii International Conference on System Sciences, pp. 995-1004, Wailea, Maui, HI, USA.
Katal, A., et al. (2013) ‘Big data: Issues, challenges, tools and good practices’, in 2013 Sixth International Conference on Contemporary Computing (IC3), pp. 404-409, Noida, India.
Khade, A. (2016) Performing customer behavior analysis using big data analytics, Procedia Computer Science, vol. 79, pp. 986-992.
Kumar, N., & Kapoor, S. (2014) Study of consumers’ behavior for non-vegetarian products in emerging market of India, Journal of Agribusiness in Developing and Emerging Economies, vol. 4, no. 1, pp. 59-77.
Laney, D. (2001) 3D data management: Controlling data volume, velocity, and variety, Italy: Meta Group.
Latour, P., & Le Floc’h, J. (2001) Géomarketing: Principes, méthodes et applications (Principles, methods and applications), Paris: Éditions d’Organisation.
Liebowitz, J. (2013) Big data and business analytics, New York: CRC Press.
Loudon, D. L., & Della Bitta, A. J. (1993) Consumer behavior: Concepts and applications, New York: McGraw-Hill.
Monnappa, A. (2018) Data science vs. big data vs. data analytics, Available: https://www.simplilearn.com/data-science-vs-big-data-vs-data-analytics-article [28 April 2018]
Mousannif, H., et al. (2016) Big data projects: Just jump right in!, International Journal of Pervasive Computing and Communications, vol. 12, no. 2, pp. 260-288.
Positioningmag. (2018) Unlocked Thai behavior (ถอดรหัสพฤติกรรมคนไทย), Available: https://positioningmag.com/1163328 [29 April 2019] (in Thai)
Rayport, F. J. (2012) Use big data to predict your customers behaviors, Available: https://hbr.org/2012/09/use-big-data-to-predict-your-c [27 January 2018]
Retail Academy. (2013) Super convenient store, Available: https://theretailacademy.wordpress.com/ tag/super-convenience-store/ [19 April 2018]
Runyon, K. E., & Stewart, D. W. (1987) Consumer behavior and the practice of marketing, Columbus, OH: Merrill.
Sicular, S. (2013) Gartner’s big data definition consists of three parts, not to be confused with three “V” s., Available: https://www.forbes.com/forbes/welcome/?toURL=https://www.forbes.com/ sites/gartnergroup [31 January 2018]
Solomon, M. R. (2009) Consumer behavior: Buying, having, and being, 8th edition, Upper Saddle River, NJ: Pearson/Prentice Hall.
Solomon, M. R. (2013) Consumer behavior: Buying, having, and being, 10th edition, Boston: Pearson.
Stoicescu, C. (2015) Big data, the perfect instrument to study today’s consumer behavior, Database System Journal, vol. 6, no. 3, pp. 28-41.
Turban, E., et al. (2008) Business intelligence: A managerial approach, Boston, MA: Pearson Prentice Hall.
Watson, H., & Wixom, B. (2007) The current stats of business intelligence, IEEE Computer, vol. 40, no. 9, pp. 96-99.
Winslow, F. (1911) The principles of scientific management, New York: Harper & Brothers Publishers.
Yrigoyen, C. C. (2003) El geomarketing y la distribución comercial, Investigación Marketing, vol. 79, no. 1, pp. 6-14.