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Pricing analytics = models and advanced quantitative techniques for product pricing /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Pricing analytics/ by Walter R. Paczkowski.
Reminder of title:	models and advanced quantitative techniques for product pricing /
Author:	Paczkowski, Walter R.
Published:	Boca Raton, FL :Routledge, : 2019.,
Description:	1 online resource (xix, 317.)
Subject:	Pricing. -
Online resource:	https://www.taylorfrancis.com/books/9781315178349
ISBN:	9781315178349 (ebk.)

Pricing analytics = models and advanced quantitative techniques for product pricing /
Paczkowski, Walter R.

Pricing analyticsmodels and advanced quantitative techniques for product pricing /[electronic resource] :by Walter R. Paczkowski. - Boca Raton, FL :Routledge,2019. - 1 online resource (xix, 317.)

part Part I Background -- chapter 1 Introduction -- chapter 2 Elasticities – Background and concept -- chapter 3 Elasticities – Their use in pricing -- part Part II Stated preference models -- chapter 4 Conjoint analysis -- chapter 5 Discrete choice models -- chapter 6 MaxDiff models -- chapter 7 Other stated preference methods -- part Part III Price segmentation -- chapter 8 Price segmentation: Basic models -- chapter 9 Price segmentation: Advanced models -- part Part IV Big Data and econometric models -- chapter 10 Working with Big Data -- chapter 11 Big Data pricing models -- chapter 12 Big Data and nonlinear prices.

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.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.

ISBN: 9781315178349 (ebk.)Subjects--Topical Terms:

565614
Pricing.

LC Class. No.: HF5416.5 / .P33 2019

Dewey Class. No.: 658.8/16

Pricing analytics = models and advanced quantitative techniques for product pricing /
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505 0 $a part Part I Background -- chapter 1 Introduction -- chapter 2 Elasticities – Background and concept -- chapter 3 Elasticities – Their use in pricing -- part Part II Stated preference models -- chapter 4 Conjoint analysis -- chapter 5 Discrete choice models -- chapter 6 MaxDiff models -- chapter 7 Other stated preference methods -- part Part III Price segmentation -- chapter 8 Price segmentation: Basic models -- chapter 9 Price segmentation: Advanced models -- part Part IV Big Data and econometric models -- chapter 10 Working with Big Data -- chapter 11 Big Data pricing models -- chapter 12 Big Data and nonlinear prices.
520 3 $a 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.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.
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856 4 0 $u https://www.taylorfrancis.com/books/9781315178349