Year 2022, Issue 145

Date published

20.5.2022

Table of content

  • Todor Krystevich
    PRICING ANALYTICS WITH R
    JEL: M31, C2, C8, C9
    Keywords: pricing, willingness to pay, pricing models, empirical methods, optimization
    Summary: Price optimization through quantitative methods of analysis allows companies to make informed decisions about effective pricing policy. By using data instead of assumptions, decision makers can determine the "right" price for their product or service in order to attract customers thus increasing sales revenue, profit or profit ... ... ABSTRACT Price optimization through quantitative methods of analysis allows companies to make informed decisions about effective pricing policy. By using data instead of assumptions, decision makers can determine the "right" price for their product or service in order to attract customers thus increasing sales revenue, profit or profitability. This monograph focuses on certain practical problems related to pricing research and price optimization solutions. Beyond the theoretical perspectives that are offered by a great number of scientific publications in the field of marketing pricing, this study systematizes concepts, methods and tools for practical implementation in an open source software environment for data analysis, statistical calculations and graphs. The study covers issues related to the fundamental theory of pricing that can be empirically evaluated, such as price awareness, price elasticity, willingness to pay and price response function, price optimization as well as certain issues of price differentiation and customized pricing. Hopefully, the study will be of particular importance to practitioners searching for affordable methods and tools to support optimal pricing decisions. CONTENTS Introduction 9 1. On the meaning of pricing analytics 12 1.1. Differentiation of concepts 12 1.2. Why is an analytical approach to pricing important? 15 2. The impact of prices on demand 17 2.1. Price salience and price awareness 18 2.1.1. Evaluation of price salience 18 2.1.2. Evaluation of price awareness 21 2.2. Price elasticity of demand 22 2.2.1. Approaches to estimating price elasticity 26 2.2.2. Data sources for estimating price elasticity 28 2.2.3. Models for estimating price elasticity 38 2.2.4. Choosing an adequate model for estimating price elasticity 41 2.3. Customers’ willingness to pay 48 2.3.1. Assumption for normal distribution of willingness to pay 50 2.3.2. Assumption for uniform distribution of willingness to pay 55 2.3.3. Assumption for logistic distribution of willingness to pay 56 2.4. Estimation of stated willingness to pay 60 2.4.1. The contingent valuation method 62 2.4.2. The Gabor-Granger method 85 2.4.3. The Van Westendorp method (PSM) 95 2.4.4. The Choice-based conjoint analysis (CBC) 107 2.5. Price response function 124 2.5.1. A deterministic approach to evaluating the price response function 127 2.5.2. Estimating price response function by Bootstrapping 134 3. Price optimization 136 3.1. A generic price optimization model 145 3.2. Optimal price with the linear price-response function 146 3.3. Optimal price with the isoelastic price-response function 147 3.4. Optimal price with the logit price-response function 149 4. Customized pricing 153 4.1. Price differentiation 156 4.2. Pricing structures 160 4.3. Optimal customized pricing 164 4.3.1. B2B purchasing decision making process 165 4.3.2. Required data 166 4.3.3. A B2B custom pricing model 166 4.3.4. The prospect theory and framing effects in purchase decision making 169 4.3.5. Prototyping the custom pricing model 173 4.4. Nonlinear pricing 179 4.4.1. Principles of bunde pricing 180 4.4.2. Optimal bundle pricing 183 Conclusion 191 References 192