Dynamic Pricing Strategies under a Finite Time Horizon Joan Morris DiMicco Amy Greenwald Pattie Maes MIT Media Laboratory Brown University MIT Media Laboratory 20 Ames St Box 1910 20 Ames St Cambridge, MA 02139 USA Providence, RI 02912 USA Cambridge, MA 02139 USA +1 617 252 1603 +1 401 863 7678 +1 617 252 1603 [email protected] [email protected] [email protected] ABSTRACT Cost has been perhaps the greatest factor precluding the In the near future, dynamic pricing will be a common competitive widespread use of dynamic pricing by ballparks, theaters, and maneuver. In this age of digital markets, sellers in electronic retail shops. In traditional markets, it is expensive to continuously marketplaces can implement automated and frequent adjustments re-price goods, but in digital markets, the costs associated with to prices and can easily imagine how this will increase their making frequent, instantaneous price changes are greatly revenue by selling to buyers "at the right time, at the right price." diminished [11]. Moreover, in markets under a finite time But at present, most sellers do not have an adequate horizon, such as ballpark and theater tickets, a clear benefit to understanding of the performance of dynamic pricing algorithms changing prices over time is the ability to clear inventory before in their marketplaces. This paper addresses this concern by the market ends. Thus, it seems likely that in the near future, analyzing the performance of two adaptive pricing algorithms. We dynamic pricing will become a common competitive maneuver. study the behavior of these algorithms within the Learning Curve A remaining obstacle that still precludes widespread dynamic Simulator, a platform for analyzing dynamic pricing strategies in pricing is the lack of understanding of the inner-workings of finite markets assuming various buyer behaviors. The goals of our dynamic pricing models. Now that sellers can easily implement research are twofold: (i) to explore the use of simulation as a tool automated algorithms that make frequent adjustments to price, to aid in the development of dynamic pricing strategies; and (ii) to how should they do so? What are the most effective dynamic explicitly identify the market conditions under which our example pricing strategies, and how should prices change with changing strategies, Goal-Directed and Derivative-Following, are market conditions? We propose that sellers analyze dynamic successful. pricing algorithms using a market simulator that is capable of simulating many different market scenarios with realistic models General Terms of buyer behavior. Using a market simulator, a ballpark could model the characteristics of its market and the behavior of the Algorithms, Measurement, Economics, Experimentation. team's fans, to develop a pricing strategy that would capture more revenue than an existing fixed-price policy. Keywords Agent simulation, dynamic pricing, electronic markets, buyer To illustrate our approach, we analyze two pricing strategies behavior, pricing strategies. within the Learning Curve Simulator, our platform for running dynamic pricing algorithms in simulated markets. Our investigation focuses on adjusting prices over time in what we call 1. INTRODUCTION “finite markets” – markets with a finite time horizon, seller Today, when a ballpark sells baseball tickets, it charges the same inventory, and buyer population. In this investigation, we are not price for the tickets throughout the season. Yet the demand for exploring price discrimination, the adjustment of prices between tickets changes over time depending on the length of time before individual buyers. Our strategies would apply to markets such as the game, the team's success over the season, and additional event tickets, airlines, hotels, perishable goods, and seasonal unpredictable factors such as the weather. In a best-case scenario, retail. The adaptive pricing strategies we present, Goal-Directed a park sells all its seats for every game at an optimal fixed ticket and Derivative-Following, demonstrate two approaches to price. In a more realistic scenario, some days the park has empty dynamic pricing within finite markets. We hope these strategies seats and on other days the park is filled with fans willing to pay will lay the groundwork for designing more complex strategies more. Nonetheless, today ballparks leave the practice of dynamic designed to be deployed in a real-world market. pricing to scalpers. 2. LEARNING CURVE SIMULATOR The Learning Curve Simulator models market scenarios, such as a Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are ballpark selling game tickets, through a rich set of inputs not made or distributed for profit or commercial advantage and that describing the market and the behavior of buyers over time. The copies bear this notice and the full citation on the first page. To copy simulator, a Java 1.3 application, accepts three categories of otherwise, or republish, to post on servers or to redistribute to lists, inputs to describe the marketplace: the Market Scenario, requires prior specific permission and/or a fee. EC’01, October 14-17, 2001, Tampa Florida, USA. Copyright 2000 ACM 1-58113-387-1/01/00010…$5.00. Simulator Inputs: Description Market Scenario: Number of Days Number of periods in the market. Each seller can change its price at the end of a day. Number of Buyers The size of the buyer population over the entire market. Number of Sellers Number of sellers. Number of Goods Initial inventory for each seller. Market Mechanism Posted-Price or First-Price Auction. See [7] for discussion of the auction implementation. Buyer Behavior: Daily Price Distribution The demand distribution of buyers on a single day. Available choices are normal distribution, positive slope, negative slope, or segmented into a high and low grouping. Price Variance Per Day The buyers’ reservation prices vary ± the variance in a single day. The variance determines the range for the daily price distribution. Percentage Comparison Shoppers The percentage of the buyer population (0-100%) who compare each seller’s offer price and purchase from the seller with the greatest % discount below its reservation price for that seller. Preference for Certain Sellers The entire buyer population can have a preference for one or more of the sellers, which is represented by a higher reservation price for that individual seller. This is a method for expressing product and seller differentiation. Lifetime Number of days a single buyer will be in market, actively looking for seller. Regardless of lifetime, once a buyer purchases, it leaves the market. Buyer Valuation over Time Over the course of the market, the buyers’ demand curve will change, and the valuation/time curve expresses how the demand will change over time. The shape of the curve can be flat, increasing, decreasing, mid-peaking, or mid-dipping over time. Minimum/Maximum Buyer The range of prices for the buyer valuation curve. These values are the minimum and maximum reservation Prices over Time prices over the market. Seller Behavior: Seller Strategies The different pricing strategies sellers use in the market, either Goal-Directed or Derivative-Following. See [7] for a discussion of other implemented strategies. Initial Prices The different prices sellers offer on the first day of the market, before adjusting price through the chosen strategy. Available Inventory per Day Amount of inventory a seller can sell in one day. This can be limited to represent shelving costs and to prevent 100% inventory sell-off in a single day. Table 1: Learning Curve Simulator Inputs the Buyer Behavior, and the Seller Behavior. Table 1 presents the over the market, with the goal of selling to the highest paying simulator inputs in each of these categories. The simulator creates buyers on each individual day. Equation 1 presents this strategy populations of buyers and sellers that search for each other on calculation. each day of the market. When a buyer and seller match on price, The GD calculation has been modified from our previous work they perform a transaction and the buyer leaves the market. At the end of each day, each seller computes its price for the next day. At [9] with the addition of a scaling factor ( scalei in Equation 1). the end of the market, the success of a pricing strategy is This scaling improves the strategy's ability to make price determined by the total revenue earned, a function of the amount adjustments at the end of the market. Previously, the strategy of inventory sold by a seller. We treat revenue, not profit, as the adjusted its price dramatically in the first third of the market but success metric because in finite markets the incremental costs of was unable to make large adjustments to price in the last days of inventory production are often minimal. More information on the the market. By incorporating in knowledge of the progress simulator's behavior and modeling can be found in [7-9]. through the market, the strategy now has the ability to make dramatic price changes during the last days, when sales are most 3. ADAPTIVE STRATEGIES important. As presented in [9] and as will be demonstrated below, the GD strategy performs best under high variance among the The Goal-Directed and Derivative-Following strategies perform buyer population and when sales are less critical during the first dynamic pricing by making incremental, exploratory adjustments days of the market. to price each day in an attempt to learn the demand in the marketplace. The Learning Curve Simulator is designed to The Derivative-Following (DF) strategy adjusts its price by accommodate any dynamic pricing strategy, so these two looking at the amount of revenue earned on the previous day as a represent our initial strategy implementations as methods for result of the previous day's price change.