Pricing Analytics Models and Advanced Quantitative Techniques for Product Pricing
Pricing Analytics Models and Advanced Quantitative Techniques for Product Pricing by Walter R. Paczkowski, published by Routledge in 2019, offers a comprehensive exploration of the intersection between economic theory and statistical modeling in the context of product pricing. This 317-page book presents the essential statistical tools necessary for determining effective pricing strategies, emphasizing the importance of data-driven decision-making in business and economics.
Readers will find a detailed examination of economic principles, particularly focusing on elasticities and their impact on business metrics. The book delves into advanced statistical modeling techniques, including choice models and sales data modeling, while also covering experimental design principles and regression fundamentals. By integrating these components, the text provides a robust framework for understanding and applying quantitative techniques in pricing decisions, making it a valuable resource for those involved in finance, marketing, and operations research.
Official synopsis Publisher
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, itcomes from statistically modeling the data.
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.
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.
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