دانلود کتاب Essentials of Pricing Analytics: Tools and Implementation with Excel (Mastering Business Analytics)

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Cover Half Title Endorsement Title Page Copyright Page Dedication Table of contents About the Contributors Preface Chapter 1 Introduction 1.1 The purpose of the book 1.2 The impact of price management on profit 1.3 Pricing analytics Pricing analytics as a continuous process 1.4 Who can use pricing analytics? 1.5 Alternative approaches to pricing 1.6 Summary 1.7 Problems Notes Chapter 2 Fundamentals of price theory 2.1 Consumer preferences and price–response functions Indifference curves, budget lines, and the utility maximization problem The link between price changes, the budget line and the price–response function Consumer surplus 2.2 Costs that matter in pricing decisions Fixed costs (cf) Variable costs (cv) Fixed, variable, or sunk costs? Marginal costs 2.3 Deciding optimal price and quantity – an example Optimal price in the case of incremental fixed costs 2.4 The role of pricing under various market structures 2.5 Summary 2.6 Problems Notes Chapter 3 Segmentation and price differentiation 3.1 Price differentiation defined Degrees of price differentiation 3.2 The economics theory behind price differentiation 3.3 Ways to segment and price differentiate in practice Segmentation and price differentiation based on customer types Segmentation and price differentiation based on product versions Segmentation and price differentiation based on sales channel Segmentation and price differentiation using the two-part tariff Other approaches to segmentation and price differentiation 3.4 Challenges of segmentation and price differentiation 3.5 Summary 3.6 Problems Notes Chapter 4 Break-even analysis 4.1 Break-even analysis defined 4.2 Break-even analysis of price changes 4.3 Break-even with cost changes 4.4 Break-even sales curves 4.5 Summary 4.6 Problems Note Chapter 5 Price sensitivity and willingness to pay 5.1 Price sensitivity 5.2 Willingness to pay Uniform willingness-to-pay distribution Other willingness to pay distributions 5.3 Functional forms of price–response functions 5.4 Summary 5.5 Problems Chapter 6 Empirical estimations of price–response functions 6.1 Sources for price and demand data Historical market data Price experiments Direct surveys Indirect surveys Expert judgement 6.2 Preliminaries to data collection 6.3 Measuring price–demand relationships in a direct survey Example of a direct survey to obtain price/demand data Example of key questions 6.4 Addressing hypothetical bias using experiments 6.5 Empirical estimation of price–response functions Estimating a linear price–response function in Excel Estimating the constant elasticity price–response function Estimating the logit price–response function Estimating other non-linear price–response functions 6.6 Summary 6.7 Problems 6.8 Appendix Notes Chapter 7 Price optimization 7.1 Basic price optimization Solving the basic price optimization problem in Excel 7.2 Price optimization with capacity constraints Solving the constrained price optimization problem in Excel Opportunity cost, shadow price, and marginal contribution margin Calculating runout prices and opportunity costs in Excel 7.3 Optimal price differentiation with capacity constraints 7.4 Optimal time-based price differentiation Optimal variable prices with demand shifting Calculating optimal differentiated prices using Excel 7.5 Elasticity and optimization 7.6 Pricing with competition 7.7 Summary 7.8 Problems Notes Chapter 8 Case studyOptimal prices of movie theater tickets 8.1 Step 1: Develop a questionnaire 8.2 Step 2: Data collection and punching 8.3 Step 3: Data preparation 8.4 Step 4: Preliminary analysis Descriptive statistics Data visualization 8.5 Step 5: Estimation of price–response functions Estimating linear price–response functions Estimating logit price–response functions 8.6 Step 6: Optimize prices Optimization without capacity constraints and no demand shifting Optimization with capacity constraints and demand shifting Sub-step 6.1: Implement the functions for demand and demand shifting Sub-step 6.2: Define the objective functions and the constraints Sub-step 6.3: Optimize prices 8.7 Step 7: Implementation 8.8 Limitations and final notes 8.9 Problems Notes Chapter 9 Markdown optimization 9.1 What is markdown optimization? A two-period example 9.2 Formulating the markdown optimization problem 9.3 Implementation of markdown optimization in Excel Solving the deterministic markdown management model in Excel Step 1: Set starting values of the decision variables Step 2: Implement the function for demanded quantity per period Step 3: Implement the functions inventory levels and revenues for all the periods Step 4: Formulate the objective function Step 5: Define the problem in Solver Step 6: Solve the problem and analyze the results 9.4 Summary 9.5 Problems Note Chapter 10 The hedonic pricing model 10.1 What is price hedonism? 10.2 The model specification 10.3 Empirical applications of hedonic pricing 10.4 Implementation of hedonic pricing An empirical example Estimating the hedonic price model using Excel 10.5 Summary 10.6 Problems AcknowledgmentAndrew Musau has contributed to the content of this chapter. Chapter 11 Revenue management 11.1 History and applicability of revenue management A brief history of revenue management Applicability of revenue management 11.2 Implementing revenue management Revenue management strategy Booking control Protection levels 11.3 Optimal protection levels in a single period model The two-class model Finding optimal booking limits and protection levels Example 1 Solution Example 2 Solution The N-class model Example 3 Solution 11.4 Summary 11.5 Problems Acknowledgement Chapter 12 Big Data and pricing analytics 12.1 What is Big Data? 12.2 The business value of Big Data in pricing analytics 12.3 Big Data analytics techniques for pricing decisions Supervised versus unsupervised methods Basics of classification using k-nearest neighbor algorithm Basics of cluster analysis using k-nearest neighbor algorithm Basics of co-occurrence grouping 12.4 Implementation in Excel Business problem #1: Classify customers into high and low reservation prices Step 1: Create numerical values and normalize the data Step 2: Calculate the Euclidean distances between existing and new customers Step 3: Rank observations according to similarity Step 4: Choose value of k and predict class of new customers Step 5: Evaluate performance of classification models Business problem #2: Segment the market based on certain criteria Business problem #3: Create a special offer menu in a movie theater kiosk 12.5 Excel’s role in big data pricing analytics 12.6 Summary 12.7 Problems Notes Chapter 13 Monte Carlo simulation for pricing decisions 13.1 What is Monte Carlo simulation? 13.2 How can simulation be used for pricing decisions? 13.3 The basics of Monte Carlo simulation in Excel Drawing random numbers and sample from a discrete probability distribution Sample from the normal distribution using the RAND function Efficiently collecting and summarizing the results of a simulation study with Excel Summarizing and visualizing simulation results using frequency tables and histograms Summarizing results using COUNT, COUNTIF, COUNTIFS, and summary statistics Simulation using other distributions and Excel’s random number generator 13.4 Example of simulation models for pricing problems Simulating changes in profit from a price change Simulating willingness to pay and corresponding price–response functions Simulation-based optimization 13.5 Summary 13.6 Problems Note Chapter 14 Conjoint analysis for pricing decisions 14.1 Conjoint analysis 14.2 Estimate price–response functions with conjoint data Step 1: Estimate individual preference functions Step 2: Set a “status quo” profile alternative and calculate u^*_i Step 3: Calculate reservation prices for all consumers Step 4: Estimate mean and variance of the normal density of reservation prices Step 5: Create the price–response function and optimize prices 14.3 Implementation in Excel Step 1: Estimate individual preference functions Step 2: Set a “status quo” profile alternative and calculate u^*_i Step 3: Calculate reservation prices for all consumers Step 4: Estimate mean and variance of the normal density of reservation prices Sub-step 4.1: Calculate p-t and; the average and the standard deviation of the reservation price observations between pmin and Sub-step 4.2: Calculate q_1t and q_2t; the fraction of consumers with reservation prices below p_min and above p_max respectively Sub-step 4.3: Calculate Z_t,min and Z_t,max; the standard normal value at q_1t and 1 – q₂, respectively. Sub-step 4.4: Calculate Sub-step 4.5: Calculate Step 5: Create the price–response functions and optimize prices 14.4 Summary 14.5 Problems Acknowledgment Notes Chapter 15 Acceptance, ethics, and the law 15.1 Customer acceptance Praktiker AG discounts itself out of business J.C. Penney’s not so welcome pricing strategy Price presentation Fairness Dual entitlement Interpersonal fairness 15.2 Ethical constraints Price fixing Bid rigging Price differentiation Price skimming Price gouging 15.3 Legal issues Pricing laws Price misrepresentation Price marking of goods Predatory pricing Unit pricing code Payment surcharges 15.4 Summary 15.5 Problems Acknowledgment Bibliography Index