| Description |
1 online resource |
| Contents |
Intro -- Half Title -- Title Page -- Copyright Page -- Contents -- List of figures -- List of tables -- Preface -- Theme of this book -- Possible approaches to writing this book -- Depth of material -- Book structure -- Required background -- Caveats -- Before we start -- Acknowledgements -- Part I Background -- 1 Introduction -- 1.1 Answering the Key Business Question -- 1.1.1 Uniform pricing strategy -- 1.1.2 Price discrimination strategy -- 1.1.3 Strategy parts -- 1.2 Price effect -- 1.3 Pricing research approaches -- 1.4 Qualitative pricing research -- 1.4.1 Pharmaceuticals case study -- 1.4.2 Cost-plus pricing -- 1.4.3 Importance of qualitative information -- 1.5 Quantitative pricing research -- 1.5.1 The role of theory -- 1.5.2 The role of data and data analysis -- Revealed preference data -- Stated preference data -- 1.5.3 The role of statistical models -- Mechanistic model -- Statistical model -- 1.6 Simulators -- 1.7 Price elasticities -- 1.8 Summary -- Notes -- 2 Elasticities -- Background and concept -- 2.1 Economic concept -- 2.2 Consumer surplus -- 2.3 The market demand curve -- 2.4 Elasticity concept -- 2.5 Properties of elasticities -- 2.6 Parallel shifts in demand -- 2.7 Cross-price elasticities -- 2.7.1 Elasticity constraints -- 2.7.2 Expenditures -- 2.8 Income elasticities -- 2.9 Elasticities and time -- 2.10 Some general demand function specifications -- 2.10.1 Isoelastic demand function -- 2.10.2 Log-linear demand function -- 2.11 Qualitative statements about elasticities -- 2.12 Summary -- Notes -- 3 Elasticities -- Their use in pricing -- 3.1 The basics -- 3.2 Profit maximization -- 3.2.1 Monopoly firm profit maximization -- Myopic monopolist price -- 3.2.2 Perfectly competitive firm profit maximization -- 3.3 Pricing and market power -- 3.4 Pricing by the dominant firm -- 3.5 Pricing structures and elasticities |
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3.5.1 Definition of price discrimination -- 3.5.2 Price discrimination family -- First-degree price discrimination -- Second-degree price discrimination -- Third-degree price discrimination -- 3.5.3 Conditions for price discrimination -- 3.5.4 Price discrimination and Big Data -- 3.6 Summary -- Notes -- Part II Stated preference models -- 4 Conjoint analysis -- 4.1 Pricing Scenario -- 4.1.1 The pricing problem -- 4.1.2 Terminology -- 4.2 Basic conjoint model -- 4.3 What the consumer sees -- 4.4 Specifying the Price attribute -- 4.5 Design matrix background -- 4.5.1 Orthogonal design -- 4.5.2 Hadamard design -- 4.5.3 Foldover designs -- 4.5.4 Balanced incomplete block designs -- 4.5.5 Full factorial design -- 4.5.6 Fractional factorial design -- 4.5.7 Why use a design? -- One-factor-at-a-time experiments -- 4.5.8 Generating a design -- 4.5.9 Creating the design -- 4.6 Estimation -- 4.6.1 Digression: Coding -- Dummy coding -- Technical details of dummy coding -- Effects coding -- Technical details on effects coding -- Creating an effects coded design -- 4.6.2 OLS estimation for the Pricing Scenario -- 4.6.3 Logit estimation for the Pricing Scenario -- 4.7 Utility calculations -- 4.8 Analysis -- 4.8.1 Part-worth summary plots -- 4.8.2 Attribute importances -- 4.8.3 Elasticities -- Discrete price case -- Continuous price case -- 4.9 Other conjoint approaches -- 4.10 Software -- 4.11 Summary -- Notes -- 5 Discrete choice models -- 5.1 Pricing Scenario -- 5.1.1 The pricing problem -- 5.2 Types of choice models -- 5.3 The choice model -- Utility maximization -- 5.3.1 Consumer homogeneity -- 5.3.2 Utility specification -- 5.3.3 Utility maximization -- 5.4 Choice probabilities -- 5.4.1 Digression: The EVI distribution -- 5.4.2 The conditional logit choice probability -- 5.4.3 Choice probability properties -- Equivalent differences property |
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The Independence of Irrelevant Alternatives property -- 5.4.4 The none option -- 5.5 Estimation -- Introduction -- 5.6 Treatment design: A Bayesian perspective -- 5.7 Estimation -- Continued -- 5.8 Analysis -- 5.8.1 Attribute importances -- 5.8.2 Demand and choice elasticities -- 5.8.3 Elasticity analysis -- Continuous price variable -- Discrete price variable -- Aggregation -- 5.8.4 Willingness-to-pay analysis -- 5.8.5 Profilers and simulators -- 5.9 Software -- 5.10 Summary -- Notes -- 6 MaxDiff models -- 6.1 A Pricing Scenario: Casino pricing strategies -- 6.1.1 The Pricing Scenario -- 6.1.2 The MaxDiff procedure -- 6.1.3 MaxDiff vs. discrete choice -- 6.2 Design matrix development -- 6.2.1 Conventional BIBD notation -- 6.2.2 Generating a BIBD -- 6.2.3 Pricing Scenario BIBD -- 6.2.4 An alternative design approach: Split-plots -- 6.2.5 Pricing Scenario final design -- 6.3 Estimation -- 6.3.1 Data arrangement -- 6.3.2 Aggregate-level estimation -- 6.3.3 Disaggregate-level estimation -- Digression: HB -- 6.4 Analysis -- 6.4.1 Counting analysis -- 6.4.2 Aggregate-level analysis -- Pricing Scenario: Aggregate estimation -- 6.4.3 Disaggregate-level analysis -- Pricing Scenario: Disaggregate estimation -- A brief introduction to TURF analysis following MaxDiff -- Pricing Scenario: TURF analysis -- 6.5 Pricing product add-on options -- 6.5.1 Pricing Scenario: Wedding caterer options -- 6.5.2 Treatment design -- 6.5.3 Estimation -- 6.6 Software -- 6.7 Summary -- Notes -- 7 Other stated preference methods -- 7.1 van Westendorp Price Sensitivity Meter -- 7.1.1 Pricing Scenario -- 7.1.2 The four questions -- 7.1.3 Analysis -- 7.1.4 The Pricing Scenario -- Analysis -- 7.2 Gabor-Granger -- 7.2.1 Methodology -- 7.2.2 Analysis -- 7.2.3 Pricing Scenario -- 7.2.4 Problems with the methodology -- 7.3 A/B price testing -- 7.3.1 Pricing scenario -- 7.3.2 Methodology |
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Digression on odds calculation -- 7.4 Software -- 7.5 Summary -- Notes -- Part III Price segmentation -- 8 Price segmentation: Basic models -- 8.1 What is price segmentation? -- 8.2 Why price segment the market? -- 8.3 Segmentation and heterogeneity -- 8.4 Developing pricing segments -- 8.4.1 Modeling engine: A priori methods -- 8.4.2 Modeling engine: Post hoc methods -- 8.5 Pricing Scenario -- 8.5.1 The business problem -- 8.5.2 Likely elasticities -- 8.5.3 Company data -- 8.5.4 Simulated data -- 8.5.5 Digression: Multilevel effects -- 8.6 A priori modeling -- 8.6.1 Model specification -- 8.6.2 Model estimation -- 8.7 Post hoc modeling -- 8.7.1 Unsupervised learning methods -- Pricing Scenario: Hierarchical clustering -- Pricing Scenario: K-means clustering -- Digression on coding and elasticities -- 8.7.2 Supervised learning -- Decision trees -- Pricing Scenario: Decision tree -- Ensembles of trees -- 8.8 Software -- 8.9 Summary -- Notes -- 9 Price segmentation: Advanced models -- 9.1 Latent variable modeling -- 9.1.1 Types of latent variable models -- 9.1.2 What is latent variable modeling? -- 9.1.3 Latent regression analysis -- Model components -- Digression on notation -- Latent regression model -- Estimation -- The expectation maximization algorithm -- Selecting the number of segments -- Pricing Scenario: Latent regression model -- 9.1.4 Choice latent class models -- Conjoint latent class models -- Discrete choice latent class models -- 9.2 Gaussian mixture modeling -- 9.3 Multilevel models -- 9.4 Software -- 9.5 Summary -- Notes -- Part IV Big Data and econometric models -- 10 Working with Big Data -- 10.1 A motivation for using Big Data -- 10.2 Big Data: Definition and issues -- Volume -- Velocity -- Variety -- Veracity -- 10.2.1 Aspects of Big Data -- 10.2.2 Pricing Scenario -- 10.2.3 Data warehouses and data marts |
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10.3 Big Data and pricing -- 10.3.1 NT≫Q: Significance testing issue -- 10.3.2 Q≫NT: Multiple comparisons issue -- 10.4 A role for sampling -- 10.5 Data visualization -- 10.5.1 Displaying leakages -- 10.5.2 Trends -- 10.5.3 Patterns -- 10.5.4 Anomalies -- 10.6 Software -- 10.7 Summary -- Notes -- 11 Big Data pricing models -- 11.1 Pricing Scenario -- 11.2 Modeling phases -- 11.2.1 Data Block -- Management input -- Data collection -- Data engine -- 11.2.2 Modeling Block -- Varying-intercept, constant-slope models -- Constant-intercept, varying-slope Models -- Varying-intercept, varying-slope Models -- 11.2.3 Analysis Block -- 11.3 Probability of a win model -- 11.4 Software -- 11.5 Summary -- Notes -- 12 Big Data and nonlinear prices -- 12.1 Linear and nonlinear pricing -- 12.1.1 A simple demand model -- 12.2 Forms of nonlinear pricing -- 12.2.1 Quantity discounts -- Devising a discount schedule -- Elasticity measurement -- Further complications -- 12.2.2 Bundling -- 12.2.3 Product line pricing -- 12.3 Software -- 12.4 Summary -- Notes -- Bibliography -- Index |
| Summary |
The theme of this book is simple. The price - the number someone puts on a product to help consumers decide to buy that product - comes from data. Specifically, it comes from statistically modeling the data. The economic theory component is sufficient to provide understanding of the basic principles for pricing, especially about elasticities, which measure the effects of pricing on key business metrics. Elasticity estimation is the goal of statistical modeling, so attention is paid to the concept and implications of elasticities. The statistical modeling component is advanced and detailed covering choice (conjoint, discrete choice, MaxDiff) and sales data modeling. Experimental design principles, model estimation approaches, and analysis methods are discussed and developed for choice models. Regression fundamentals have been developed for sales model specification and estimation and expanded for latent class analysis. This book gives the reader the statistical modeling tools needed to get the number to put on a product. But statistical modeling is not done in a vacuum. Economic and statistical principles and theory conjointly provide the background and framework for the models. Therefore, this book emphasizes two interlocking components of modeling: economic theory and statistical principles |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Description based on print version record |
| Subject |
Pricing.
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Quantitative research -- Computer programs
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BUSINESS & ECONOMICS -- Industrial Management.
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BUSINESS & ECONOMICS -- Management.
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BUSINESS & ECONOMICS -- Management Science.
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BUSINESS & ECONOMICS -- Organizational Behavior.
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Pricing
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| Form |
Electronic book
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| LC no. |
2020691553 |
| ISBN |
9781351713085 |
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1351713086 |
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9781315178349 |
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1315178346 |
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9781351713092 |
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1351713094 |
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9781351713078 |
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1351713078 |
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