BIG DATA AS A TOOL FOR TUNNEL MARKETING
Аннотация
В данной работе описывается применение программного обеспечения по сбору данных в маркетинге. Также в данной работе упомянута проблема с "Туннельным видением" которая рассматривается, как очень прогрессивная, которая исходит из-за некачественной информации о клиентах, которые компании собирают на конкурентном рынке. Применение новых программных обеспечений описаны как инструменты, способные усилить операции направленные на расширение данных о клиентах, с целью применения необходимых маркетинговых действий.
Об авторах
R. Akmedov
Suleyman Demirel University
Казахстан
Казахстан
A. A. Dzhengisheva
Suleyman Demirel University
Казахстан
Казахстан
Список литературы
1. Adrian, M., Chamberlin, D. Orbitz Worldwide Uses Hadoop to Unlock the Business Value of Big Data. Gartner. – 2012. – URL: https://www.zotero.org/groups/leapforward/items/itemKey/BSHHXBHN (accessed: 06.11.2017)
2. Ajzen, I., Fishbein, M. Understanding attitudes and predicting social behavior. – NJ: Prentice-Hall, 1980.
3. Amatriain, X. Jaimes, A. Oliver, N. Pujol, J. M. «Data Mining Methods for Recommender Systems» in Recommender Systems Handbook. Kantor, Ricci, Rokach, Shapira (Eds). – Berlin: Springer, 2010.
4. Baumgartner, H., M. Steenkamp J.-B. E. Exploratory consumer buying behavior: Conceptualization and measurement // International Journal of Research in Marketing. – 1996. – № 13. – p. 121.
5. Berman, S. J. Digital transformation: opportunities to create new business models // Strategy & Leadership. – 2012. – № 40 (2). – pp. 16-24.
6. Bosomworth, D. Mobile marketing statistics [Electronic source]. – 2015. – URL: http://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/ (accessed: 06.11.2017)
7. Brosekhan, A. A., Velayutham, M., Phil, M. Consumer Buying Behaviour – A Literature Review // Journal of Business and Management. – 2003. – № 1 (1). – pp. 8-16.
8. Casaca, J. A., da Gama, A. P. Marketing in the Era of Big Data // Hum. Soc. Sci. Common Conf. – 2013. – № 5 (2). – pp. 147-160.
9. Clifford C. Japanese scientists use artificial intelligence to decode thoughts [Electronic source]. – 2018. – URL: https://www.cnbc.com/2018/01/08/japanese-scientists-use-artificial-intelligence-to-decodethoughts.html (accessed: 06.04.2018)
10. Chen, H., Chiang, R. H. L., Storey, V. C. Business Intelligence And Analytics: From Big Data To Big Impact // MIS Quarterly. – 2012. – № 36 (4). – pp. 1165-1188.
11. Choi, J, Hong, J. L., Kim, H. W. Examining the Effects of Personalized App Recommender systems on Purchase Intention: and Self Social-Interaction Perspective // Journal of Electronic Commerce Research. – 2017. – № 18 (1). – pp. 336-360.
12. Dapp, T., Heini, V. Big Data. The Untamed Force. – Frenkfurt: Deutsche Bank Research, 2014.
13. eMarketer. Big Data helps reveal consumer behavior, article retrieved [Electronic source]. – 2013. – URL: from http://www.emarketer.com/Article/BigData-Helps-Reveal-ConsumerBehavior/1010357 (accessed: 29.04.2018)
14. Erevelles, S, Fukawa, N., Swayne, L. Big Data consumer analytics and the transformation of marketing // Journal of Business Research. – 2016. – № 69 (2). –pp. 897-904.
15. Ernst and Young. Big data. Changing the way businesses compete and operate. Insights on governance risk and compliance. – 2014.
16. Fang, Z., Li, P. The Mechanism of «Big Data» Impact on Consumer Behavior // American Journal of Industrial and Business Management. – 2014. – № 4 (1). – pp. 45-50.
17. Glasgow, S., Zegler, J. Mintel looks into its crystal ball and shares top 2015 consumer behavior trends [Electronic source]. – 2014. – URL: http://www.bizjournals.com/chicago/news/2014/10/24/mintel-looks-into-itscrystal-ball-and-shares-top.html (accessed: 02.10.2015)
18. Guangting, Z., Junxuan, Z. The Study of Impact of «Big Data» to Purchasing Intention // International Journal of Business and Social Science. – 2014. – № 5 (10).
19. Halzack, S. The new shopping behavior that is creating big challenges for the retail industry [Electronic source]. – 2015. – URL: https://www.washintonpost.com/news/business/wp/2015/02/11/the-newshopping-behavior-that-is-creating-bigchallenges-for-the-retail-industry/ (accessed: 02.10.2015)
20. Ho, S. Y, Tam, K. Y, Davern, M. J. Transaction-Driven Personalization: The Moderating Effects of Personality Traits // 11th Pacific-Asia Conference on Information Systems. – 2007. – pp. 185-199.
21. Hofacker, C. F., Malthouse, E. C., Sultan, F. Big Data and consumer behavior: imminent opportunities // Journal of Consumer Marketing. – 2016. – № 33 (2). – pp. 89-97.
22. Ioanăs, E., Stoica, I. Social Media and its Impact on Consumers Behavior // International Journal of Economic Practices and Theories. – 2014. – № 4 (2).
23. Ismail, K. P., Ishak, N. Consumers Perception, Purchase Intention and Actual Purchase Behavior of Organic Food Products // Integrative Business & Economics. – 2014. – № 3 (2). – pp. 63-85.
24. Jao, J. Why big data is a must in ecommerce [Electronic source]. – 2013. – URL: http://www.bigdatalandscape.com/news/why-big-data-is-a-must-in-ecommerce (accessed: 02.03.2017)
25. Keller, K. L. Building Customer-Based Brand Equity: A Blueprint for Creating Strong Brands // Marketing Management. – 2001.
26. Kopp, M. Seizing the big data opportunity [Electronic source] // Ecommerce Times. – 2013. – URL: http://www.ecommercetimes.com/story/78390.html (accessed: 14.06.2016)
27. Kurt, M. Using Big Data and Machine Learning to enrich consumer behavior [Electronic source]. – 2015. – URL: http://www.forbes.com/sites/kurtmarko /2015/04/08/big-data-machine-learning-customerexperience/ (accessed: 14.06.2016)
28. Linzmajer, M., Schopfer, S., Keller, T., Nagengast, L., Fleisch, E., Rudolph, T. The Effects of Personalized Recommendations with Popularity Information on Sales – A Field Study in Grocery Retailing // ECIS 2015 Research-in-Progress Papers. – 2015. – № 65.
29. Li, Q., Xing, J., Liu O., Chong W. The Impact of Big Data Analytics on Customers’ Online Behaviour // Proceedings of the International Multi-Conference of Engineers and Computer Scientists. – 2017. – № 2 (7).
30. Liang, T. P., Lai, H. J., Ku, Y. C. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings // Journal of Management Information Systems. – 2007. – № 23 (3). – pp. 45-70.
31. Liebowitz, J. Big Data and Business Analytics. – New York: CRC Press, 2013.
32. Mehrabian, A., Russell, J. A. An Approach to Environmental Psychology. – Cambridge: MIT Press, 1974.
33. Miller, G. 6 ways to use «big data» to increase operating margins by 60% [Electronic source]. – 2012. – URL: http://upstreamcommerce.com/blog/2012/04/11/6-ways-big-data-increaseoperating-margins-60-part-2 (accessed: 13.06.2016)
34. Peppers, D., Rogers, M. The one-to-one future: Building relationships one customer at a time. – New York: Double Day Publications, 1997.
35. Pettey, C. Gartner Identifies the Top 10 Strategic Technologies for 2012. – 2012.
36. Petty, R. E., Cacioppo, J. T. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. – New York: Springer Verlag, 1986.
37. Pettey, C., Goasduff, L. Gartner Says Solving «Big Data» Challenge Involves More Than Just Managing Volumes of Data [Electronic source]. – 2011. – URL: http://www.gartner.com/it/page.jsp?id=1731916 (accessed: 13.06.2016)
38. Russom, P. The Three Vs of Big Data Analytics. – 2011.
39. Ryan, Kh. 3 Ways Big Data and Machine Learning Affect Consumer Behavior. – 2016. – URL: https://tech.co/3-ways-big-data-and-machine-learning-are-affecting-consumer-behavior-2016-10 (accessed: 13.06.2016)
40. Savvas, A. IBM: Businesses unable to analyse 90 percent of their data [Electronic source]. – 2011. – URL: http://www.computerworlduk.com/news/itbusiness/3313304/ibm-businesses-unableto-analyse90-percent-of-their-data/ (accessed: 09.09.2016)
41. Smolan, R., Erwitt, J. The Human Face of Big Data [Electronic source]. – 2015. – URL: http://thehumanfaceofbigdata.com/ (accessed: 09.09.2016)
42. Stoicesu, C. Big Data, the perfect instrument to study today’s consumer behavior // Database Systems Journal. – 2009. – № 6 (3).
43. William B. Mesa Marketing Revolution // Journal of Consumer Marketing. – 2009. – № 26 (2). – pp.135-135.
Рецензия
Для цитирования:
Akmedov R., Dzhengisheva A.A. BIG DATA AS A TOOL FOR TUNNEL MARKETING. Central Asian Economic Review. 2018;(3):37-51.
For citation:
Akmedov R., Dzhengisheva A.A. BIG DATA AS A TOOL FOR TUNNEL MARKETING. Central Asian Economic Review. 2018;(3):37-51.
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