Price optimization

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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]


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]


  1. ^ a b c d e f 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. ^ a b "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. ^ a b c d Özer, Özalp; Phillips, Robert (2012). Models of Demand" in The Oxford Handbook of Pricing Management. Oxford University Press. ISBN 978-0-19-954317-5.