Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes
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Pricing of Digital Products and Services in the Manufacturing Ecosystem
Pricing of digital products and services in the manufacturing ecosystem From cost-based to value-based pricing 02 Pricing of digital products and services in the manufacturing ecosystem | From cost-based to value-based pricing Executive summary 04 The traditional vs. digital approach to pricing 06 Pricing strategies in a digital ecosystem 12 Key considerations for strategic pricing decisions 18 Next step: How to get the pricing right? 20 Legal considerations 22 Conclusion 24 Endnotes 26 Contacts 28 03 Executive summary Digital products and services are becoming their specific role within the network in a increasingly important for discrete broader sense rather than relying solely manufacturers looking to complement on internal supply chains when it comes to their core business, stabilize profits and pricing and positioning their digital prod- generate new revenue streams. Today’s ucts and services in the market. industrial companies already generate 20–30 percent of their profits in the after This white paper addresses these chal- sales and service business, and the trend lenges, focusing on the pricing of is toward more digital solutions. To exploit digital goods and services in discrete the value derived from digitalized products manufacturing. The objective is to help and services, manufacturing companies manufacturing companies better under- need to understand what makes digital stand the pricing mechanisms in a digital products and services unique and how the ecosystem and to identify potential pricing market dynamics are changing in the new, scenarios for their digital business: platform-driven manufacturing ecosystem. 1. How do pricing mechanisms work in a Digital products and services differ digital ecosystem and why are traditional fundamentally from traditional industrial pricing approaches no longer feasible? products, mainly in terms of their cost structures and the customer's willingness 2. -
Dynamic Pricing Efficiency with Strategic Retailers and Consumers: an Analytic Analysis of Short-Term Markets Interactions
CHAIRE EUROPEAN ELECTRICITY MARKETS/WORKING PAPER #46 RETAILERS’STRATEGIES FACING DEMAND RESPONSE AND MARKETS INTERACTIONS Cédric CLASTRES and Haikel KHALFALLAH 1 RETAILERS’STRATEGIES FACING DEMAND RESPONSE AND MARKETS INTERACTIONS Cédric CLASTRES1,2 and Haikel KHALFALLAH1 Avril 2020 Abstract Demand response programmes reduce peak-load consumption and could increase off- peak demand as a load-shifting effect often exists. In this research we use a three-stage game to assess the effectiveness of dynamic pricing regarding load-shifting and its economic consequences. We consider a retailer’s strategic supplies on forward or real time markets, when demand is uncertain and with consumer disutility incurred from load-shedding or load-shifting. Our main results show that a retailer could internalize part of demand uncertainty by using both markets. A retailer raises the quantities committed to the forward market if energy prices or balancing costs are high. If the consumer suffers disutility, then the retailer contracts larger volumes on the forward market for peak periods and less off peak, due to a lower load-shifting effect and lower off-peak energy prices. Keywords: Dynamic and stochastic model, electricity markets, load-shifting, disutility. JEL codes : C61, D12, L11, L22, L94, Q41. Acknowledgments: This paper has benefited from the support of the Chaire European Electricity Markets (CEEM) of the Université Paris-Dauphine – PSL, under the aegis of the Foundation Paris-Dauphine, supported by RTE, EDF, EPEX Spot and Total Direct Energie. Disclaimer: The views and opinions expressed in this paper are those of the authors and do not necessarily reflect those of the partners of the CEEM. -
Dynamic Pricing: Building an Advantage in B2B Sales
Dynamic Pricing: Building an Advantage in B2B Sales Pricing leaders use volatility to their advantage, capturing opportunities in market fluctuations. By Ron Kermisch, David Burns and Chuck Davenport Ron Kermisch and David Burns are partners with Bain & Company’s Customer Strategy & Marketing practice. Ron is a leader of Bain’s pricing work, and David is an expert in building pricing capabilities. Chuck Davenport is an expert vice principal specializing in pricing. They are based, respectively, in Boston, Chicago and Atlanta. The authors would like to thank Nate Hamilton, a principal in Boston; Monica Oliver, a manager in Boston; and Paulina Celedon, a consultant in Atlanta, for their contributions to this work. Copyright © 2019 Bain & Company, Inc. All rights reserved. Dynamic Pricing: Building an Advantage in B2B Sales At a Glance Nimble pricing behavior from Amazon and other online sellers has raised the imperative for everyone else to develop dynamic pricing capabilities. But dynamic pricing is more than just a defensive action. Pricing leaders use volatility to their advantage, capturing opportunities in market fluctuations and forcing competitors to chase their pricing moves. Building better pricing capabilities is about more than improving processes, technology and communication. Pricing leadership requires improving your understanding of customer needs, competitors’ behavior and market economics. Dynamic pricing is not a new strategy. For decades, companies in travel and transportation have system- atically set and modified prices based on shifting market and customer factors. Anyone who buys plane tickets should be familiar with this type of dynamic pricing, but what about other industries? Does dynamic pricing have a role? Increasingly, the answer is yes. -
Congestion Pricing
Congestion Pricing A PRIMER December 2006 FEDERAL HIGHWAY ADMINISTRATION Office of Transportation Management, HOTM 400 Seventh St. SW, Room 3404 Washington, DC 20590 Publication Number: FHWA-HOP-07-074 Table of Contents I. THE CONGESTION PROBLEM .....................................................................................................................1 Costs of Congestion ........................................................................................................................................1 Alarming Trends ...............................................................................................................................................1 Causes of Congestion .....................................................................................................................................1 II. WHAT IS CONGESTION PRICING? ..............................................................................................................1 Technology for Congestion Pricing .................................................................................................................2 Variably Priced Lanes ......................................................................................................................................2 Variable Tolls on Roadways .............................................................................................................................3 Cordon Pricing .................................................................................................................................................4 -
Dynamic Pricing When Consumers Are Strategic: Analysis of a Posted Pricing Scheme
OPERATIONS RESEARCH INFORMS Vol. 00, No. 0, Xxxxx 0000, pp. 000{000 doi 10.1287/xxxx.0000.0000 issn 0030-364X j eissn 1526-5463 j 00 j 0000 j 0001 °c 0000 INFORMS Dynamic pricing when consumers are strategic: Analysis of a posted pricing scheme Sriram Dasu Marshall School of Business, University of Southern California, [email protected], Chunyang Tong Marshall School of Business, University of Southern California, [email protected], We study dynamic pricing policies for a monopolist selling perishable products over a ¯nite time horizon to buyers who are strategic. Buyers are strategic in the sense that they anticipate the ¯rm's pricing policies. We are interested in situations in which auctions are not feasible and in which it is costly to change prices. We begin by showing that unless strategic buyers expect shortages dynamic pricing will not increase revenues. We investigate two pricing schemes that we call posted and contingent pricing. In the posted pricing scheme at the beginning of the horizon the ¯rm announces a set of prices. In the contingent pricing scheme price evolution depends upon demand realization. Our focus is on the posted pricing scheme because of its ease of implementation. In equilibrium, buyers will employ a threshold policy in both pricing regimes i.e., they will buy only if their private valuations are above a particular threshold. We show that a multi-unit auction with a reservation price provides an upper bound for the expected revenues for both pricing schemes. Numerical examples suggest that a posted pricing scheme with two or three price changes is enough to achieve revenues that are close to the upper bound. -
Smart Pricing
01_013149418X_intro.qxp 2/11/10 8:37 AM Page 1 Introduction: Fingerprints of the Invisible Hand After a long season of back-breaking labor seeding, feeding, and growing a crop, a farmer would never say, “Time to harvest—let’s take it easy.” If anything, the farmer would get up even earlier and go to bed even later to make sure that every last grain was harvested. Yet supposedly sophisticated companies, run by some of the best-edu- cated people in the world, neglect what peasants have known by instinct for thousands of years. They work hard thinking about, grow- ing, and finding markets for their product but then pay scant atten- tion to the decision that determines what all that hard work yields the company: setting the price. Despite the critical function prices play in corporate profitability, we find that managers with pricing responsibilities do not usually think systematically about their pricing strategies. Most pricing deci- sion makers never look for a strategy that could yield their product’s maximum value. According to one study, only a tiny number of firms have “both a pricing strategy and research to support it.” When it comes to pricing, some estimated that only about 8% of American businesses can be considered “sophisticated players.”1 Oddly, nobody seems bothered by this state of affairs. Many exec- utives we talk to about prices say, “We don’t set prices. The market does!” As economists, we are not sure what this statement means. “Who is the market, then?” we press them. 1 01_013149418X_intro.qxp 2/11/10 8:37 AM Page 2 2 SMART PRICING To our mind, this is a reasonable question. -
Skimming Or Penetration? Strategic Dynamic Pricing for New Products
This article was downloaded by: [113.197.9.114] On: 26 March 2015, At: 16:51 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Marketing Science Publication details, including instructions for authors and subscription information: http://pubsonline.informs.org Skimming or Penetration? Strategic Dynamic Pricing for New Products Martin Spann, Marc Fischer, Gerard J. Tellis To cite this article: Martin Spann, Marc Fischer, Gerard J. Tellis (2015) Skimming or Penetration? Strategic Dynamic Pricing for New Products. Marketing Science 34(2):235-249. http://dx.doi.org/10.1287/mksc.2014.0891 Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact [email protected]. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. Copyright © 2015, INFORMS Please scroll down for article—it is on subsequent pages INFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Vol. -
Dynamic Pricing Strategies for Multi-Product Revenue Management
MANUFACTURING & SERVICE OPERATIONS MANAGEMENT informs ® Vol. 8, No. 2, Spring 2006, pp. 136–148 doi 10.1287/msom.1060.0105 issn 1523-4614 eissn 1526-5498 06 0802 0136 © 2006 INFORMS Dynamic Pricing Strategies for Multiproduct Revenue Management Problems Constantinos Maglaras Columbia Business School, Columbia University, 409 Uris Hall, 3022 Broadway, New York, New York 10027, [email protected] Joern Meissner Lancaster University Management School, Lancaster University, Room A48, Lancaster LA1 4XY, United Kingdom, [email protected] onsider a firm that owns a fixed capacity of a resource that is consumed in the production or delivery of Cmultiple products. The firm strives to maximize its total expected revenues over a finite horizon, either by choosing a dynamic pricing strategy for each product or, if prices are fixed, by selecting a dynamic rule that controls the allocation of capacity to requests for the different products. This paper shows how these well- studied revenue management problems can be reduced to a common formulation in which the firm controls the aggregate rate at which all products jointly consume resource capacity, highlighting their common structure, and in some cases leading to algorithmic simplifications through the reduction in the control dimension of the associated optimization problems. In the context of their associated deterministic (fluid) formulations, this reduction leads to a closed-form characterization of the optimal controls, and suggests several natural static and dynamic pricing heuristics. These are analyzed asymptotically and through an extensive numerical study. In the context of the former, we show that “resolving” the fluid heuristic achieves asymptotically optimal performance under fluid scaling. -
Pricing in Ride-Share Platforms: a Queueing-Theoretic Approach
Pricing in Ride-share Platforms: A Queueing-Theoretic Approach Carlos Riquelme, Siddhartha Banerjee, Ramesh Johari Department of Management Science and Engineering, Stanford University, Stanford, CA 94025 [email protected], [email protected], [email protected] We study optimal pricing strategies for ride-sharing platforms, such as Lyft, Sidecar, and Uber. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided – this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support high temporal-resolution for data collection and pricing – this requires stochastic models that capture the dynamics of drivers and passengers in the system. In this paper we build a queueing-theoretic economic model to study optimal platform pricing. In particular, we focus our attention on the value of dynamic pricing: where prices can react to instantaneous imbalances between available supply and incoming demand. We find two main results: We first show that performance (throughput and revenue) under any dynamic pricing strategy cannot exceed that under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load). This result belies the prevalence of dynamic pricing in practice. Our sec- ond result explains the apparent paradox: we show that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing. Thus dynamic pricing does not necessarily yield higher performance than static pricing – however, it lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters. Key words: Ride-Sharing, Dynamic Pricing, Matching Markets, Queueing Networks 1. -
Developing a Pricing Strategy for the Los Angeles Dodgers Denise Linda Parris
Old Dominion University ODU Digital Commons Human Movement Sciences Faculty Publications Human Movement Sciences 2012 Developing a Pricing Strategy for the Los Angeles Dodgers Denise Linda Parris Joris Drayer Stephen L. Shapiro Old Dominion University, [email protected] Follow this and additional works at: https://digitalcommons.odu.edu/hms_fac_pubs Part of the Marketing Commons, Sports Sciences Commons, and the Sports Studies Commons Repository Citation Parris, Denise Linda; Drayer, Joris; and Shapiro, Stephen L., "Developing a Pricing Strategy for the Los Angeles Dodgers" (2012). Human Movement Sciences Faculty Publications. 20. https://digitalcommons.odu.edu/hms_fac_pubs/20 Original Publication Citation Parris, D., Drayer, J., & Shapiro, S. (2012). Developing a pricing strategy for the Los Angeles Dodgers. Sport Marketing Quarterly, 21(4), 256-264. This Article is brought to you for free and open access by the Human Movement Sciences at ODU Digital Commons. It has been accepted for inclusion in Human Movement Sciences Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. Sport Marketing Quarterly, 2012, 21, 256-264, © 2012 West Virginia University Developing a Pricing Strategy for the Los Angeles Dodgers Denise Linda Parris, Joris Drayer, and Stephen L. Shapiro Denise Linda Parris, PhD, is an assistant professor of marketing in the Barney Barnett School of Business and Free Enterprise at Florida Southern College. Her research interests and consulting include pricing and marketing strategies, non- profit management, leadership, and entrepreneurship. Joris Drayer, PhD, is an assistant professor of sport and recreation management at Temple University. His research interests Case Study include ticketing and pricing strategies in both primary and secondary ticket markets, as well as consumer behavior. -
A Simulation-Based Approach to Dynamic Pricing
A Simulation-based Approach to Dynamic Pricing Joan Morris Sc.B. Applied Mathematics Brown University May 1995 Submitted to the Program in Media Arts & Sciences, School of Architecture and Planning, In Partial Fulfillment of the Requirements for the Degree of Master of Science in Media Arts & Science At the Massachusetts Institute of Technology May, 2001 @ Massachusetts Institute of Technology, 2001. All rights reserved. Author Joan Morris Program in Media Arts & Sciences \0 May 11, 2001 Certified by Pattie Maes Associate Professor of Media Arts & Sciences MIT Media Laboratory Accepted by Stephen Benton Chair, Departmental Committee on Graduate Studies Program in Media Arts & Sciences MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUN 13 2001 LIBRARIES POT Room 14-0551 77 Massachusetts Avenue Cambridge, MA 02139 MITLibraries Ph: 617.253.5668 Fax: 617.253.1690 Document Services Email: [email protected] http://Iibraries.mit.edu/docs DISCLAIMER OF QUALITY Due to the condition of the original material, there are unavoidable flaws in this reproduction. We have made every effort possible to provide you with the best copy available. If you are dissatisfied with this product and find it unusable, please contact Document Services as soon as possible. Thank you. The scanned Archival thesis contains grayscale images only. This is the best copy available. A Simulation-based Approach to Dynamic Pricing Joan Morris Sc.B. Applied Mathematics Brown University May 1995 Submitted to the Program in Media Arts & Sciences, School of Architecture and Planning, In Partial Fulfillment of the Requirements for the Degree of Master of Science in Media Arts & Science At the Massachusetts Institute of Technology May, 2001 Abstract By employing dynamic pricing, the act of changingprices over time within a marketplace, sellers have the potential to increase their revenue by selling goods to buyers "at the right time, at the right price." Software agents have been used in electronic commerce systems to assist buyers, but there is limited use of selling agents in today's markets. -
Dynamic Pricing and Matching in Ride-Sharing Dawn Woodard Senior Data Science Manager of Dynamic Pricing, Uber
Dynamic Pricing and Matching in Ride-Sharing Dawn Woodard Senior Data Science Manager of Dynamic Pricing, Uber Ride-sharing platforms like Uber, Lyft, and Ola are transforming urban mobility by connecting riders with drivers via the sharing economy. These platforms have achieved explosive growth, by dramatically improving the efficiency of matching, and by calibrating the balance of supply and demand through dynamic pricing1. We review the publicly available literature on the data-driven marketplace technologies at the core of ride-sharing technology. The dynamic adjustment of prices ensures a reliable service for riders, and incentivizes drivers to provide rides at peak times and locations2. Dynamic pricing is particulary important for ride-sharing platforms, because pricing too low creates the “wild goose chase” phenomenon: demand outstrips supply, and pickup ETAs get very long. Drivers then spend too much time en-route to pickups, so their time on-trip drops, and few rides are created. Ride requests also frequently go unfulfilled, yielding a negative experience for both riders and drivers. Dynamic pricing prevents the wild goose chase; when dynamic pricing is disallowed by regulatory authorities, the welfare-maximizing fixed price is close to the highest price from the dynamic pricing scenario, and fewer trips are generated3. The pricing and matching decisions rely on several key inputs that are estimated from data. These include predictions of demand, supply, and driving time in the road network4. For example, demand prediction is used for dynamic pricing, and can be based on historical demand patterns, real-time information, and signals like events, weather, and points of interest5.