Finance: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]). Companies use price optimization models to determine pricing structures for initial pricing, promotional pricing and discount pricing.[10]
Market simulators are often used to simulate the choices people make to predict how demand varies at different price points.[11] This data can be combined with cost and inventory levels to develop a profitable price point for that product or service.[12] 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.[10]
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.[13] Given this information, determining the prices that best meet corporate goals can be formulated and solved as a constrained optimization process.[1][14] The form of the optimization is determined by the underlying structure of the pricing problem.[1][14]
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][14] 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][14]
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 Phillips, Robert L. (2005). Pricing and Revenue Optimization. Stanford, CA: Stanford University Press. p. 35. ISBN 9780804746984.
- ↑ Alina Tugend (April 8, 2014). "As data about drivers proliferates, auto insurers look to adjust rates". The New York Times. https://www.nytimes.com/2014/04/19/your-money/as-data-about-drivers-proliferates-auto-insurers-look-to-adjust-rates.html?_r=0.
- ↑ Alex Dietz (September 6, 2012). "Revenue management vs. price optimization:part two". SAS. http://blogs.sas.com/content/hospitality/2012/09/06/revenue-management-vs-price-optimization-part-two/.
- ↑ Bob Tedeschi (September 2, 2002). "Scientifically priced retail goods". The New York Times. https://www.nytimes.com/2002/09/02/technology/02ECOM.html.
- ↑ Anne Kadet (May 2008). "Price profiling". The Wall Street Journal Magazine. http://revenueanalytics.com/wp-content/uploads/2014/03/SmartMoneyPriceProfiling.pdf.
- ↑ Kim S. Nash (April 30, 2015). "Carnival strategy chief bets that big data will optimize prices". The Wall Street Journal. https://blogs.wsj.com/cio/2015/04/30/carnival-strategy-chief-bets-that-big-data-will-optimize-prices/.
- ↑ Smallwood, Richard (October 1, 1991). "Using conjoint analysis for price optimization". https://www.quirks.com/articles/data-use-using-conjoint-analysis-for-price-optimization.
- ↑ Leslie Scism (February 20, 2015). "Loyalty to your car insurer may cost you". The Wall Street Journal. https://blogs.wsj.com/moneybeat/2015/02/20/loyalty-to-your-car-insurer-may-cost-you/.
- ↑ Perakis, Georgia (2016-07-25). "A Revolutionary Model To Optimize Promotion Pricing" (in en-US). https://www.huffingtonpost.com/entry/a-revolutionary-model-to-optimize-promotion-pricing_us_579638bee4b0e002a313c6da.
- ↑ 10.0 10.1 "Price optimization models". Bain & Company. June 10, 2015. http://www.bain.com/publications/articles/management-tools-price-optimization-models.aspx.
- ↑ "Use Discrete Choice Simulator to Launch the Right Product | Infosurv" (in en-US). Infosurv. 2012-08-03. https://www.infosurv.com/how-a-discrete-choice-simulator-can-be-used-to-launch-the-right-product-part-2/.
- ↑ Arie Shpanya (2015) "Test Until Your Price is the Best"
- ↑ Arie Shpanya (2014) "There's No Such Thing As One Right Price in Retail"
- ↑ 14.0 14.1 14.2 14.3 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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