Segmentation Revenue Management and Pricing Analytics 1st edition by Tudor Bodea, Mark Ferguson ISBN 0415898331 978-0415898331

Chapter 1 The Ideas Behind Customer Segmentation

Customer Segmentation Versus Product Segmentation

Examples of Product-Based Segmentation

Major Challenges of Product-Based Segmentation

The Dawn of Big Data and Business Analytics

Chapter 2 Forecasting

Introduction

Theory of Forecasting

Forecasting Accuracy Measures

Scale-Dependent Forecasting Accuracy Measures

Scale-Independent Forecasting Accuracy Measures

The Bias and the Tracking Signal

Illustration of the Use of Forecasting Accuracy Measures

The Importance of the Holdout Sample

Simple and Weighted Moving Average

Simple Exponential Smoothing

Double Exponential Smoothing

Triple Exponential Smoothing

Case Study: Forecasting SPSS Manual Sales

Summary

References

Chapter 3 Promotion Forecasting

Introduction

Introduction to Regression Analysis

Linear Regression and the Least Squares Estimation Technique

Estimating the Linear Regression Equation in Microsoft Excel

Common Mistakes When Using Regression Models to Forecast

Estimation Using the Regression Feature in Excel

Linear Regression and the Maximum Likelihood Estimation Technique

Multiple Linear Regression

Estimating Promotion Effects

Case Study: Promo Forecasting at Dominick’s Finer Foods

Summary

References

Chapter 4 Capacity-Based Revenue Management

The Single Order Opportunity Inventory Problem

The Newsvendor Model

Determining Booking Limits in Revenue Management Problems

Booking Limits with More Than Two Customer Segments

Solving the Nested Booking Limit Problem: The EMSR-b Solution

Expected Marginal Seat Revenue Model (EMSR)

Example of EMSR-b Method

Network Revenue Management

Bid Price Controls

Summary

References

Chapter 5 Unconstraining

Introduction

Unconstraining: Is it Really Needed?

Estimation of Demand Distributions of Censored Single-Period Items

Averaging Method (AM)

Double Exponential Smoothing (DES)

Expectation-Maximization (EM) Algorithm

Projection-Detruncation (PD)

Summary

References

Chapter 6 Pricing Analytics

Introduction

The Microeconomists’ View of Consumer-Purchasing Decisions

Willingness-to-Pay

Consumer Search Cost

The Pricing Analytics Process

Historical Price/Demand Data

The Price-Response Function

Measures of Price Sensitivity

Price Optimization

The Practice of Pricing Analytics

Estimating Price Elasticity

Summary

Note

References

Chapter 7 Dynamic and Markdown Pricing

Introduction

Dynamic Pricing

Markdown Optimization

Case Studies

Price Markdowns and Slow-Moving Items

Price Markdowns and Linear Programming

Summary

References

Chapter 8 Pricing in Business-to-Business Environments

Introduction

Relation to Traditional Price Optimization

Estimating the Probability Function

Price Optimization for Customized Pricing

Segmenting Customers Based on Historical Price Behavior

Measuring Performance

Implementing a Customized Pricing Optimization Package

Case Study: Interest Rate Optimization at a U.S. Online Auto Lender

Summary

Note

References

Chapter 9 Customer Behavior Aspects of Pricing

Introduction

Reference Pricing

Prospect Theory

Perceived Fairness of Pricing

Summary

References

Appendix A Dichotomous Logistic Regression

The Dichotomous Logistic Regression Model

Linear and Logistic Regression Models for Dichotomous Response Variables

The Estimation of Dichotomous Logistic Regression Models

The Logit Link Function and the Odds Ratio

The Quality of the Fit of a Dichotomous Logistic Regression Model

Note

Appendix B Advanced Analytics Using R

The R Environment

How to Install R and its Contributed Packages

Getting Started with R

Getting Help in R

Common Objects in R

Writing Functions in R

Handling External Files in R

Running R Scripts

Advanced Analytics Using R

Example 1: Sales Forecasting at Company X

Example 2: Retail Price Setting

Index