Price optimization

Price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services through different channels.[1] It is also used to determine the prices that the company determines will best meet its objectives such as maximizing operating profit.[1] The data used in price optimization can include survey data, operating costs, inventories, and historic prices & sales.[2] Price optimization practice has been implemented in industries including retail, banking, airlines, casinos, hotels, car rental, cruise lines and insurance industries.[3][4][5][6]

Overview

Price optimization utilizes data analysis to predict the behavior of potential buyers to different prices of a product or service. Depending on the type of methodology being implemented, the analysis may leverage survey data (e.g. such as in a conjoint pricing analysis[7]) or raw data (e.g. such as in a behavioral analysis leveraging 'big data' [8][9][10]). Companies use price optimization models to determine pricing structures for initial pricing, promotional pricing and discount pricing.[11]

Market simulators are often used to simulate the choices people make to predict how demand varies at different price points.[12] This data can be combined with cost and inventory levels to develop a profitable price point for that product or service.[13] This model is also used to evaluate pricing for different customer segments by simulating how targeted customers will respond to price changes with data-driven scenarios.[11]

Price optimization starts with a segmentation of customers. A seller then estimates how customers in different segments will respond to different prices offered through different channels.[14] Given this information, determining the prices that best meet corporate goals can be formulated and solved as a constrained optimization process.[1][15] The form of the optimization is determined by the underlying structure of the pricing problem.[1][15]

If capacity is constrained and perishable and customer willingness-to-pay increases over time, then the underlying problem is classified as a yield management or revenue management problem.[1][15] If capacity is constrained and perishable and customer willingness-to-pay decreases over time, then the underlying problem is one of markdown management. If capacity is not constrained and prices cannot be tailored to the characteristics of a particular customer, then the problem is one of list-pricing. If prices can be tailored to the characteristics of an arriving customer then the underlying problem is sometimes called customized pricing.[1][15]

References
  1. Phillips, Robert L. (2005). Pricing and Revenue Optimization. Stanford, CA: Stanford University Press. p. 35. ISBN 9780804746984.
  2. Alina Tugend (April 8, 2014). "As data about drivers proliferates, auto insurers look to adjust rates". The New York Times. Retrieved July 7, 2015.
  3. Alex Dietz (September 6, 2012). "Revenue management vs. price optimization:part two". SAS. Retrieved July 7, 2015.
  4. Bob Tedeschi (September 2, 2002). "Scientifically priced retail goods". The New York Times. Retrieved July 7, 2015.
  5. Anne Kadet (May 2008). "Price profiling" (PDF). The Wall Street Journal Magazine. Archived from the original (PDF) on 2015-07-07. Retrieved July 7, 2015.
  6. Kim S. Nash (April 30, 2015). "Carnival strategy chief bets that big data will optimize prices". The Wall Street Journal. Retrieved July 7, 2015.
  7. Smallwood, Richard (October 1, 1991). "Using conjoint analysis for price optimization". Quirk's Marketing Research Review. Retrieved September 27, 2018.
  8. Angelica Valentine (2015) "Control The Future of Your Business with Predictive Analytics"
  9. Leslie Scism (February 20, 2015). "Loyalty to your car insurer may cost you". The Wall Street Journal. Retrieved July 7, 2015.
  10. Perakis, Georgia (2016-07-25). "A Revolutionary Model To Optimize Promotion Pricing". Huffington Post. Retrieved 2018-10-01.
  11. "Price optimization models". Bain & Company. June 10, 2015. Retrieved July 7, 2015.
  12. "Use Discrete Choice Simulator to Launch the Right Product | Infosurv". Infosurv. 2012-08-03. Retrieved 2018-09-27.
  13. Arie Shpanya (2015) "Test Until Your Price is the Best"
  14. Arie Shpanya (2014) "There's No Such Thing As One Right Price in Retail"
  15. Özer, Özalp; Phillips, Robert (2012). Models of Demand" in The Oxford Handbook of Pricing Management. Oxford University Press. ISBN 978-0-19-954317-5.
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