Dynamic pricing in retail
Developments from science and practice
Historically, it has not been unusual at all to change prices frequently over time and across customers. Actually, before John Wanamaker introduced the concept of price tags in his department store in 1861, prices could change according to numerous factors, such as product scarcity and the haggling skills of a customer—dynamic pricing avant la lettre, you could say. As a devout Christian, Wanamaker’s motivation for the introduction of price tags was that “if everyone was equal before God, then everyone should be equal before price”. Currently, this perspective is continuously being challenged by both disruptive technology startups and incumbent firms with the goal of increasing revenues—an appealing prospect to many retailers as well.
8 december 2016
What drives the price?
When adopting a dynamic pricing strategy, the most profound question a retailer should address is what underlying factors should cause prices to change. Early adopters, such as the airline industry, are characterized by having a fixed short-term capacity and relatively low variable costs. In such settings, it is the available capacity, for example, the number of seats still available on a flight, that causes prices to change throughout time. After all, the potential revenue of an empty seat on a departed flight is lost forever. For reasons very much alike, dynamic pricing has recently been adopted, for example, for the sale of ski lift tickets (by Liftopia Inc.), for tickets of the Texas Rangers (a baseball team), and food delivery (by Sprig Inc.).
A somewhat different perspective is taken in Uber’s surge pricing, in which taxi prices are increased (with respect to baseline fares) in case of surge in demand. In doing so, Uber argues that it is able to bring more taxi drivers to the streets during busy times. Thus, whereas airlines use dynamic pricing to maximize revenue from their fixed capacity, Uber uses dynamic pricing to adjust its capacity on the short term—a subtle but interesting difference. The additional revenue earned through surge pricing is merely a side-effect… according to Uber.
Dynamic pricing in retail
Given the versatility of the retail industry, a one-size-fits-all approach to dynamic pricing seems inconceivable. For example, the perishable inventories of grocery retailers or seasonal items in apparel retailing exhibit similarities with the airline industry, in a sense that lost revenue from unsold units is lost forever and short-term supply is often inflexible. Therefore, inventory levels can be used to determine prices, which makes mathematical modeling relatively straightforward, resulting in an abundance of scientific literature on optimal pricing from the field of operations research in this setting.
However, in many other cases, retailers have relatively flexible short-term supply or non-perishable inventories. Then, alternative means of dynamic pricing for retail exist that are very promising. A very prominent direction is that of personalized pricing and fits the ambition of many retailers to customize the customer experience on the individual level by utilizing customer-level data. This could range anywhere from personalized discounts to third-degree price discrimination, in which complete assortments are priced on an individual level throughout time. Alternatively, environmental factors that are likely to change customers’ willingness-to-pay for certain products, such as outside temperature, may cause prices to change. The fields of operations research and machine learning have made significant progress in algorithmically operationalizing such data-driven pricing strategies. However, presumably the most important, but challenging, condition for a successful strategy is to be able to explain and communicate it to customers in a successful manner.
Ice cream priced as airline tickets?
Despite the fact that methodological advances from the scientific community enable retailers to adopt rather disruptive pricing strategies, it will be very interesting to see how far the industry is willing to take dynamic pricing in practice. Dimensions such as customer perception, but also the competitive landscape, make the implementation complex, but rewarding when done properly. Possibly, the price of ice cream at your local grocery retailer will depend on the outside temperature in the very near future.
About the authors
Ruben van de Geer is PhD candidate at the department of mathematics and at the Amsterdam Center for Business Analytics, which is a cooperation of academia and industry in which Deloitte and de Vrije Universiteit participate. He studied ‘Econometrics and Financial Mathematics’ and ‘Operations Research and Business Econometrics’. His PhD project is joint research with Deloitte and focuses on dynamic pricing in the retail industry.
Sandjai Bhulai is full professor of Business Analytics at Vrije Universiteit Amsterdam. He studied ‘Mathematics’ and ‘Business Mathematics and Informatics’, and obtained a PhD on Markov decision processes for the control of complex, high-dimensional systems. He is co-founder of the Amsterdam Center for Business Analytics, co-founder of the postgraduate programme Business Analytics / Data Science, and also co-founder of Prompt Business Analytics.
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