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REVENUE MAXIMIZATION USING PRODUCT BUNDLING by Mihai View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by D-Scholarship@Pitt REVENUE MAXIMIZATION USING PRODUCT BUNDLING by Mihai Banciu B.B.A., Romanian-American University, 1999 M.B.A., James Madison University, 2002 Submitted to the Graduate Faculty of The Joseph M. Katz Graduate School of Business in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2009 UNIVERSITY OF PITTSBURGH THE JOSEPH M. KATZ GRADUATE SCHOOL OF BUSINESS This dissertation was presented by Mihai Banciu It was defended on July 29, 2009 and approved by Dissertation Chair: Prakash Mirchandani, Professor, Katz Graduate School of Business Esther Gal-Or, Professor, Katz Graduate School of Business Brady Hunsaker, Google Inc. Jerrold May, Professor, Katz Graduate School of Business R. Venkatesh, Associate Professor, Katz Graduate School of Business ii Copyright © by Mihai Banciu 2009 iii REVENUE MAXIMIZATION USING PRODUCT BUNDLING Mihai Banciu, PhD University of Pittsburgh, 2009 Product bundling is a business strategy that packages (either physically or logically), prices and sells groups of two or more distinct products or services as a single economic entity. This practice exploits variations in the reservation prices and the valuations of a bundle vis-à-vis its constituents. Bundling is an effective instrument for price discrimination, and presents opportunities for enhancing revenue without increasing resource availability. However, optimal bundling strategies are generally difficult to derive due to constraints on resource availability, product valuation and pricing relationships, the consumer purchase process, and the rapid growth of the number of possible alternatives. This dissertation investigates two different situations—vertically differentiated versus independently valued products—and develops two different approaches for revenue maximization opportunities using product bundling, when resource availability is limited. For the vertically differentiated market with two products, such as the TV market with prime time and non-prime time advertising, we derive optimal policies that dictate how the seller (that is, the broadcaster) can manage their limited advertising time inventories. We find that, unlike other markets, the revenue maximizing strategy may be to offer only the bundle, only the components, or various combinations of the bundle and the components. The optimality of these strategies critically depends on the availability of the two advertising time resources. We also show how the network should focus its programming quality improvement efforts, and investigate how the iv “value of bundling,” defined as the network’s and the advertisers’ benefit from bundling, changes as the resource availabilities change. We then propose and study a bundling model for the duopolistic situation, and extend the results from the monopolistic to the duopolistic case. For the independently valued products, we develop stochastic mathematical programming models for pricing bundles of n components. Specializing this model for two components in a deterministic setting, we derive closed-form optimal product pricing policies when the demand functions are linear. Using the intuition garnered from these analytical results, we then investigate two procedures for solving large-scale problems: a greedy heuristic, and a decomposition method. We show the effectiveness of both methods through computational experiments. v TABLE OF CONTENTS ACKNOWLEDGMENTS ........................................................................................................ XII 1.0 INTRODUCTION ........................................................................................................ 1 1.1 REVENUE MANAGEMENT AND PRODUCT BUNDLING ........................ 3 1.2 OBJECTIVES OF THIS WORK ....................................................................... 6 1.3 OVERVIEW OF CHAPTERS ......................................................................... 10 2.0 REVENUE MANAGEMENT AND PRODUCT BUNDLING .............................. 12 2.1 BUNDLING STRATEGIES ............................................................................. 16 2.2 FORECASTING ................................................................................................ 19 2.3 THE MEDIA ADVERTISING MARKET ...................................................... 21 2.4 BUNDLING IN COMPETITIVE ENVIRONMENTS .................................. 22 2.5 SUMMARY ........................................................................................................ 23 3.0 MIXED BUNDLING PRICING STRATEGIES FOR THE TV ADVERTISING MARKET..................................................................................................................................... 25 3.1 INTRODUCTION ............................................................................................. 25 3.2 THE GENERAL MIXED BUNDLING MODEL ........................................... 31 3.3 REVENUE MAXIMIZING STRATEGIES WHEN CAPACITY IS BINDING ............................................................................................................................. 39 3.3.1 Characterization of the different strategies ................................................ 42 vi 3.3.2 Optimal product prices and shadow prices ................................................. 46 3.3.3 Relative shadow prices .................................................................................. 51 3.3.4 Incentives for improving the programming quality ................................... 54 3.4 VALUE OF BUNDLING .................................................................................. 59 3.4.1 Broadcaster’s Value of Bundling ................................................................. 59 3.4.2 Advertisers’ Value of Bundling .................................................................... 65 3.4.3 Total (Social) Value of Bundling .................................................................. 70 3.5 EXTENSIONS .................................................................................................... 73 3.5.1 General density functions ............................................................................. 73 3.5.2 Bundling with unequal resource proportions ............................................. 80 3.6 CONCLUSIONS ................................................................................................ 86 4.0 COMPETITIVE ENVIRONMENT ......................................................................... 89 4.1 INTRODUCTION ............................................................................................. 89 4.2 DUOPOLY MODELS ....................................................................................... 93 4.3 EQUILIBRIUM ANALYSIS ............................................................................ 97 4.4 VALUE OF BUNDLING WITH COMPETITION...................................... 113 4.5 CONCLUSIONS AND EXTENSIONS .......................................................... 118 5.0 MIXED BUNDLING WITH INDEPENDENTLY VALUED PRODUCTS ...... 121 5.1 INTRODUCTION ........................................................................................... 121 5.2 SUBMODULAR OPTIMIZATION ............................................................... 123 5.3 THE GENERAL MIXED BUNDLING PROBLEM .................................... 125 5.4 BUNDLING WITH LINEAR DEMAND FUNCTIONS ............................. 130 5.4.1 Unconstrained model ................................................................................... 131 vii 5.4.2 Both capacity constraints binding .............................................................. 132 5.4.3 One binding constraint ................................................................................ 133 5.5 GREEDY HEURISTIC ................................................................................... 136 5.5.1 Worst-case heuristic performance ............................................................. 137 5.5.2 Computational results ................................................................................. 142 5.6 DECOMPOSITION FRAMEWORK ............................................................ 144 5.6.1 The restricted master problem ................................................................... 145 5.6.2 The separation sub-problem ....................................................................... 147 5.6.3 The pricing sub-problem............................................................................. 148 5.6.4 Computational results ................................................................................. 151 5.7 CONCLUSIONS .............................................................................................. 152 6.0 CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS .......................... 155 BIBLIOGRAPHY ..................................................................................................................... 160 viii LIST OF TABLES Table 1. Optimal prices for the ROMB_U model ......................................................................... 48 Table 2. Broadcaster’s VoB .......................................................................................................... 62 Table 3. Value of Bundling ........................................................................................................... 72 Table 4. Strategies for Different Types of Advertisers (α = 1, β = 2, γ = 2.5)
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