Business Intelligence Technology and Marketing Strategy in the Retail Sector
DOI:
https://doi.org/10.14738/assrj.511.5438Abstract
Today’s world is undergoing unprecedented changes due to increased competition, globalization, demanding customers with diverse tastes and preferences. This growth could be attributed to the declining cost of acquiring and storing very large amounts of data arising from sources such as customer transactions in retail outlets, e-businesses, RFID tags for inventory tracking, email, and product reviews among others. Business Intelligence technology is a recent development in business decision-making, both strategic and tactical, and a key driver of profitability. Critical corporate decisions are now grounded on empirical data which constitute the basis of both descriptive and predictive analytics. The study sought to assess the influence of Business Intelligence technology on marketing strategies of large supermarkets in Nairobi. Supermarkets generate vast amounts of transactional data, which can comprise a source of value when appropriate BI technology is optimally deployed on them to extract potentially strategic information. The study targeted five large supermarkets. The study used a descriptive cross-sectional research design where a structured questionnaire was used to collect pertinent data. Data were analysed using descriptive statistics and logistic regression analysis. The study results indicated minimal use of BI technology by large supermarkets in Nairobi county hence the influence of the same on marketing strategies was not extensive. However, the study established a statistically significant influence of certain aspects of BI technology on marketing strategy of large supermarkets in Nairobi. Despite lack of access to such data, supermarkets employed other components of BI technology such as data warehousing and market basket analysis which were used to some extent for market segmentation. The study recommends that supermarkets should widen their range of BI technologies and sources of data in order to enhance their marketing strategies. The study can be replicated in other service industries.
References
Aanderud, T. & Hall, A. (2011). Building business intelligence using SAS: Content development examples. North Carolina, USA: SAS Institute Inc.
Agarwal, P. (2014). Benefits and issues surrounding data mining and its application in the retail industry. International Journal of Scientific and Research Publications, 4(7), 1-5.
Barney, J.B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Barney, J.B. (2002). Gaining and sustaining competitive advantage. (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall.
Brown, B., Schuler, S. & Sikes, J. (2012, January). Capturing the upside of technology threats. In McKinsey Center for Business Technology (Ed.), Perspectives on digital business (pp 7-17). McKinsey and Company Burton, B., Geishecker, L., Schlegel K., Hostmann, B., Austin, T., Herschel, G., Soejarto, A. & Rayner, N. (2006). Business intelligence focus shifts from tactical to strategic. Stamford, U.S.A: Gartner Research
Constantiou, I. & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30 (1), 44-57.
Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, Business Intelligence Research, 13(30), 319-340.
Drucker, P. F. (1973). Management: Tasks, responsibilities and practices. Toronto: Fitzhenry & Whiteside Ltd.
Duff & Phelps (2016). Industry insights: Food retail industry insights
Dyche (2012), in Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, April–
June(2014), 1–12, DOI: 10.1177/2053951714528481
Fotaki, G., Spruit, M., Brinkkemper, S. & Meijer, D. (n.d.). Exploring big data opportunities for Online Customer Segmentation. (Technical report) Utrecht University.
Gieskes H. (2000). Competitive intelligence at Lexis-Nexis. Competitive Intelligence Review, 11(2): 4–11. Kenya Vision 2030: The popular version.
Hosmer, D.W. (2000). Applied logistic regression. (2nded.). New York: John Wiley and Sons Institute for Business Intelligence (n.d.) Verständnis von Business Intelligence. In K a r r a s c h T. F.
Kahalekar, S.A.,& Patil, S. (2016). An analysis on predictive analytics and data mining in telecom and automobile in Maharashtra. International Journal of Advanced Computational Engineering and Networking, 4(2), 88-90.
Kaplan, R & Norton, D. (2006) Creating the strategy-focused organization. Boston, Massachusetts: Harvard Business School Press
Kar, A.K., Pani, A.K. & De, S.K. (2010). A study on using business intelligence for improving marketing efforts. Business Intelligence Journal, 3(2), 141-150.
Kenya National Bureau of Statistics (2017). Economic survey 2017.
Khan, R.A. & Quadri, S.M.K. (2012). Business intelligence: An integrated approach. Business Intelligence Journal, 5(1), 64-70.
Kothari, C.R. (2004). Research methodology: Methods and techniques. (2nd ed.). Jaipur, India: New Age International Publishers.
Kotler, P. & Armstrong, G. (2012). Principles of marketing. (14th ed.). Pearson, Prentice Hall
Kotler, P., Armstrong, G., Saunders, J. & Wong, V. (1999). Principles of marketing: Second European edition. New Jersey, USA: Prentice Hall. th
Kotler, P. (1997). Marketing management: Analysis, planning, implementation, and control. (9 Ed). Upper Saddle River: Prentice Hall.
Leavey, J. (2013, September). Social media and public policy: What is the evidence? London: Alliance for Useful Evidence
Liu, F. & Lee, H. J. (2010). Use of social network information to enhance collaborative filtering performance. Expert Systems with Applications, 37, 4772-4778, Manyika, J., Chui, M., Burghin, J., Brown, B., Dobbs, R., Roxburgh, C. & Byers, A.H. (2011, May). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute, McKinsey and Company Mbaluka, W. (2013, November). Big data management and business value in the commercial banking sector in Kenya (Master thesis).
Mithamo, M.K., Marwa, M. & Letting, N. (2015). An analysis of challenges facing major supermarkets that emanate from the changing environment: A case of Kenyan large cities. Journal of Business Management and Economics, 3(4).
Nketia, M.O. (2016). Business intelligence (BI): A system to optimize business performance. International Journal of Business & Management, 4(9), 218-224.
Porter, M.E. (1980). Competitive strategy: Techniques of analyzing industries and competitors. (26-31). New York: The Free Press
Redzepovic, D. & Theodoros, P. (2015). Big data-driven innovation: The role of big data in new product development, (Master Thesis).
Reinartz, W., Dellaert, B., Krafft, M., Kumar, V. & Varadarajan, R. (2011). Retailing innovations in a globalizing retail market environment. Journal of Retailing, 87(1), 59-66. doi:10.1016/j.jretai.2011.04.009
Ruhi, U. (2014). Social media analytics as a business intelligence practice: Current landscape & future prospects.
Journal of Internet Social Networking & Virtual Communities, 2014(2014): 1-12. doi:10.5171/2014.920553. th
Saunders, M., Lewis, P. & Thornhill, A. (2009). Research methods for business students (5 Ed.). Essex, England: Pearson Education.
Soukhoroukova, A., Spann, M. &, and Skiera, B. (2010). Sourcing, filtering, and evaluating new product ideas: An empirical exploration of the performance of idea markets, Journal of Product Innovation Management. (Pre-print)
Sudhakar, K., Prasath, S.R.G. & Kumar V.A.(n.d.). Modern marketing practice. IOSR Journal of Business and Management, 34-37
Vercellis, C. (2009). Business intelligence: Data mining and optimization for decision making. John Wiley & Sons, Ltd.
Vojtovič, S., Navickas, V. & Gruzauskas, V. (2016, June). Strategy of sustainable competitiveness: Methodology of real-time customers’ segmentation for retail shops. Journal of Security and Sustainability Issues, 5(4).
Walliman, N. (2011). Research methods: The basics. New York, USA: Routledge.
Winston, W. (2014). Marketing analytics. Indianapolis: John Wiley & Sons.
Yelkur, R. & Herbig, P. (1996). Global markets and the new product development process. Journal of Product and Brand Management, 5(6), 38-47
Zhang, X., Chen, X. & Ding, L. (2013). Self-service product innovation based on data mining technology. International Journal of Database Theory and Application, 6(5), 105-118, DOI: http://dx.doi.org/10.14257/ijdta.2013.6.5.10
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