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

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 q1t and q2t; the fraction of consumers with reservation  prices below pmin and above pmax respectively
			Sub-step 4.3: Calculate Zt,min and Zt,max; the standard normal value at q1t  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