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]


Getting Started with Conjoint Analysis by Bryan K. Orme is a practical guide on the application of discrete-choice analysis (also called trade-off analysis) in quantitative marketing research to optimize for price and other attributes of a product or service. [16]

Pricing and Revenue Optimization written by Dr. Robert L. Phillips discusses the economics behind pricing optimization, how it is used as a corporate process, its relationship to supply constraints and how it is perceived by the consumer. In the book, pricing optimization is recognized as an important application for quantitative analysis and there is increased interest in learning its techniques among different industries.[1]

Manfred Krafft and Murali K. Mantrala discuss the use of price optimization software in the retail industry and the paradigm shift from price optimization to pricing process improvement in their book Retailing in the 21st Century: Current and Future Trends. The book mentions that the research conducted on price optimization by its traditional definition is not applicable to the retail industry, thus they recommend retailers adopt a process view of pricing.[17]

In 2009, the NAW Institute for Distribution Excellence and Texas A&M University’s Industrial Distribution Program conducted a research study titled Pricing Optimization: Striking the Right Balance for Margin Advantage which investigated price optimization and best practices in wholesale distribution. The study recommended wholesalers practice complexity management to provide structure and consistency with regards to pricing in order to improve margins.[18]

Price optimization software[edit]

Software companies have developed price optimization software packages to handle complex calculations. Companies have tailored these to meet the needs of B2C organizations, such as retail, or B2B companies, such as those that do more complex quoting. Another common use of pricing software and pricing systems is for companies, both B2C and B2B, with a large number of products/articles sold in a wide range countries using different currencies and with commercial arrangements. Here, the complexity of combinations and permutations is an example of a big data solution where the seller can create central pricing strategies that then can be applied and executed across the organization. A further development of pricing software, especially in B2B companies, is to integrate this with software that configures larger, customized systems and solutions, and then also to integrate this with software that transforms the configuration and resulting price into a customer offer/quotation. The combination of configuration, pricing and quoting solutions is abbreviated to CPQ solutions.

Notable companies offering price optimization software include the following:

Product Industry
IBM Price, Promotion, Markdown Optimization Retail
Intelligence Node Retail, eCommerce, Technology
JDA Retail, Supply Chain
Nomis Solutions Consumer Finance
Oracle Retail Regular Price Optimization Retail
PROS Retail, Supply Chain, Healthcare, Insurance
Revenue Analytics Retail, Supply Chain
Revionics Predictive pricing, assortment management
Zilliant[19] Supply Chain


  1. ^ a b c d e f g 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.
  16. ^ Orme, Bryan K. (2014). Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research. Research Publishers, LLC. ISBN 978-1-60147-111-6.
  17. ^ Krafft, Manfred; Mantrala, Murali K. (2006). Retailing in the 21st Century: Current and Future Trends. Germany: Springer Berlin. ISBN 9780804746984.
  18. ^ "Pricing Optimization: Striking the right balance for margin advantage". CleanLink. October 8, 2013. Retrieved July 7, 2015.
  19. ^ Taylor Provost (March 29, 2013). "Can software take the guesswork out of pricing?". CFO. Retrieved July 7, 2015.