Retail revenue management: applying data-driven analytics to the merchandise line of business
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Recent advances in data collection technology and computing power yield opportunities to apply robust analytical methods to retail. Additional profitability can be obtained by leveraging data-mining techniques and optimization models to decisions that have heretofore been based heavily on experiential understanding. Scientific methods can be applied to the revenue-driving areas of merchandise such as assortment, pricing, placement, and promotion to obtain further insight and make more precise decisions.
Article History
How to Cite
Bata, S. A., Beard, J., Egri, E., & Morris, D.. (2012). Retail revenue management: applying data-driven analytics to the merchandise line of business. Journal of Business and Retail Management Research, Volume 05 Issue 2.
Citation Context
APA
Bata, S. A., Beard, J., Egri, E., & Morris, D.. (2012). Retail revenue management: applying data-driven analytics to the merchandise line of business. Journal of Business and Retail Management Research, Volume 05 Issue 2.
MLA
Bata, Seth A., et al.. "Retail revenue management: applying data-driven analytics to the merchandise line of business." Journal of Business and Retail Management Research, Volume 05 Issue 2, 2012.
Chicago
Seth A. Bata, Jonathan Beard, Erica Egri, and David Morris. "Retail revenue management: applying data-driven analytics to the merchandise line of business." Journal of Business and Retail Management Research Volume 05 Issue 2 (24 Feb 2012).
Harvard
Bata, S. A., Beard, J., Egri, E., & Morris, D. (2012) Retail revenue management: applying data-driven analytics to the merchandise line of business. Journal of Business and Retail Management Research, Volume 05 Issue 2
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