Decision Making in Reference to Model of Marketing Predictive Analytics – Theory and Practice
Languages of publication
Purpose: The objective of this paper is to describe concepts and assumptions of predictive marketing analytics in reference to decision making. In particular, we highlight issues pertaining to the importance of data and the modern approach to data analysis and processing with the purpose of solving real marketing problems that companies encounter in business. Methodology: In this paper authors provide two study cases showing how, and to what extent predictive marketing analytics work can be useful in practice e.g., investigation of the marketing environment. The two cases are based on organizations operating mainly on Web site domain. The fi rst part of this article, begins a discussion with the explanation of a general idea of predictive marketing analytics. The second part runs through opportunities it creates for companies in the process of building strong competitive advantage in the market. The paper article ends with a brief comparison of predictive analytics versus traditional marketing-mix analysis. Findings: Analytics play an extremely important role in the current process of business management based on planning, organizing, implementing and controlling marketing activities. Predictive analytics provides the actual and current picture of the external environment. They also explain what problems are faced with the company in business activities. Analytics tailor marketing solutions to the right time and place at minimum costs. In fact they control the effi ciency and simultaneously increases the effectiveness of the firm. Practical implications: Based on the study cases comparing two enterprises carrying business activities in different areas, one can say that predictive analytics has far more been embraces extensively than classical marketing-mix analyses. The predictive approach yields greater speed of data collection and analysis, stronger predictive accuracy, better obtained competitor data, and more transparent models where one can understand the inner working of the model. Originality: Authors describe the importance of analytics which enhance the decisions that the company makes as it executes strategies and plans, so that the company can be more effective and achieve better results. The key factor that enables to execute marketing strategies accurately and build competitive advantage in the future includes predictive modeling. The ability to predict probable futures allows us to shape the future, rather than merely survive whatever it brings.
- Davenport, T.H. and Harris, J.G. (2007). competing on analytics – the new science of winning, Boston: Harvard Business School Press.
- Few, S. (2008). Predictive analytics for the eyes and mind, Statistical Discovery with SAS, white paper: 1–16.
- Kohavi, R., Rothleder, N. and Simoudis, E. (2002). Emerging Trends in Business Analytics. Communications, 4: 45–48.
- Laursen, G. and Thorlund, J. (2010). Business analytics for managers – taking business intelligence beyond reporting. New York: John Wiley and Sons.
- Leeflang, P.S.H., Wittink, D.R., Wedel, M. and Naert, P.P. (2000). Building models for marketing decisions. International series in quantitative marketing, Boston: Kluwer.
- Lilien, G.L. and Rangaswamy, A. (2004). Marketing engineering: computer-assisted marketing analysis and planning, 2nd edition. Victioria: Trafford Publishing.
- McLean, A. (2000). The predictive approach to statistics. Journal of Statistics Education, 8(3): 23–34.
- Stubbs, E. (2011). The value of business analytics – identifying the path to profitability. New York: John Wiley and Sons.
- Tarka, P. (2012). Application of predictive analytical models in marketing activities improvement. Marketing and Market, 2: 24–29.
Publication order reference