Robert L. Phillips

Pricing and Revenue Optimization

Second Edition

• Gebundenes Buch

Produktbeschreibung

"Employing analytical techniques derived from management science, and the author's extensive corporate experience, this is the definitive resource for what has emerged as critical and rapidly changing field in business strategy. MBA and executive courses will be drawn to the updates throughout the book, focusing on AI impact on revenue management, as well as compelling new cases on e-marketplace pricing at Amazon, Uber, and other leading companies"--

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Produktdetails

• Verlag: Stanford University Press
• 2 ed
• Seitenzahl: 472
• Erscheinungstermin: 18. Mai 2021
• Englisch
• Abmessung: 264mm x 190mm x 35mm
• Gewicht: 1086g
• ISBN-13: 9781503610002
• ISBN-10: 1503610004
• Artikelnr.: 59412333

Autorenporträt

Robert L. Phillips is Director of Pricing Science at Amazon. He was previously Director of Marketplace Optimization Data Science at Uber Technologies, Professor of Professional Practice at Columbia Business School, Founder and Chief Science Officer at Nomis Solutions, and CEO of Talus Solutions. He is the author of Pricing Credit Products (Stanford, 2018) and the co-editor of The Oxford Handbook of Pricing Management (2014).

Inhaltsangabe

Contents and Abstracts
1Background
chapter abstract
This chapter describes the historical background of pricing and revenue
optimization including the factors that have driven the growth of
analytical approaches to pricing, such as the success of revenue management
in the airlines, advances in information technology, the rise of
e-commerce, and the success of machine learning. Pricing decisions have
become exponentially more complex and dynamic to the extent that it is no
longer possible to manage prices effectively using spreadsheets. The use of
automated techniques to set and update prices dynamically has led to
profitability improvements of 10% or more in many different industries.
2Introduction to Pricing and Revenue Optimization
chapter abstract
This chapter introduces the basic concepts behind pricing and revenue
optimization. It discusses common pricing challenges such as lack of
consistent management, discipline, and analysis. I describe three purist
approaches to pricing-cost-plus, market-based and value-based and explain
the shortcomings of each. I define pricing and revenue optimization as a
process for managing and updating pricing decisions across an organization
in a way that most effectively meets corporate goals using mathematical
analysis. I introduce the pricing and revenue optimization cube as a
convenient way to think about pricing decisions across the organization and
describe the steps in an effective pricing and revenue optimization
process. I describe a closed-loop process for setting, evaluating and
updating prices. Finally, I discuss the role of mathematical analysis and
optimization in the pricing process and contrast explicit optimization with
data-driven approaches.
3Models of Demand
chapter abstract
This chapter introduces the price-response function, which describes how
demand for a product changes as a function of price. The price-response
function is a key component in pricing and revenue optimization. I show how
the price-response function can be derived from the distribution of
willingness to pay among potential customers and describe the properties
that a proper price-response function should possess. I describe the most
common measures of price sensitivity such as slope, elasticity, and hazard
rate and extend these measures to the case in which a seller is offering
multiple products that may compete with each other. I introduce the most
common price-response functions including the linear, constant-elasticity,
logit, and probit functions and describe their properties, as well as when
they can be best used.
4Estimating Price Response
chapter abstract
I show how a price-response function can be estimated from historical data
about prices and demand. Ways in which historical data can be obtained
include price tests, A/B tests, natural experiments, and regression
discontinuity design. I show how regression can be used to estimate the
parameters of different price-response functions including linear,
exponential, and constant elasticity. I introduce measures of fit including
root-mean-square error, mean absolute percentage error, and weighted mean
absolute percentage error. I show how the availability of potential demand
data can significantly improve estimation, and I introduce methods for
estimating a price-response function when potential demand data are
available. The estimation process is discussed, as well as challenges to
estimation including collinearity and endogeneity and how they can be
addressed. .
5Optimization
chapter abstract
In this chapter I show how to calculate an optimal price. The first step is
to determine an objective function to be maximized. Typically we assume
that contribution is to be maximized, but in some cases, revenue may be an
element in the objective function. I discuss how to calculate contribution.
Given contribution and a price-response function, the optimal price must
satisfy a set of conditions, such as marginal cost equaling marginal
revenue and elasticity equaling inverse unit margin. I discuss the
implications of these conditions for real-world prices and show how
explicit optimization can be used to calculate an optimal price. I discuss
how to address the case of multiple objective functions, when a seller
might be interested in maximizing both profit and market share. Finally, I
introduce a data-driven approach to finding an optimal price that does not
require a price-response function.
6Price Differentiation
chapter abstract
Price differentiation is the practice of charging different prices to
different customers for the same good or slightly different versions of the
same good. In this chapter, I describe various techniques for using price
differentiation to improve profitability including group pricing, channel
pricing, regional pricing, and couponing. Some of the most effective
tactics for price differentiation are inferior goods, superior goods,
product lines, product versioning, and time-based differentiation. I show
how to calculate optimal differentiated prices in the presence of arbitrage
and cannibalization and discuss the implications of price differentiation
for consumer welfare. One common approach to price differentiation is
nonlinear pricing, in which purchasing multiple products together can be
cheaper than purchasing them independently. I present models for two of the
most common nonlinear pricing approaches: volume discounts and bundling.
7Pricing with Constrained Supply
chapter abstract
Supply and capacity constraints are commonplace across many industries and
can have a strong effect on optimal prices. I start by discussing the
nature of supply constraints and the situations in which they occur. I then
discuss how a seller can determine the optimal price to charge when faced
with a supply constraint, and I introduce the important concept of
opportunity cost. I extend the calculation of optimal prices to the case
when a supplier has a segmented market and faces supply constraints. This
leads to the tactic of variable pricing, which is used when a supplier has
multiple units of constrained capacity and can change prices in order to
balance supply and demand. I discuss examples of variable pricing from
industries including ride-sharing, concerts, and sporting events.
8Revenue Management
chapter abstract
Revenue management is a profit-maximizing tactic used by sellers with a
fixed stock of perishable capacity. This chapter relates the history of
revenue management and shows how revenue management is an application of
price differentiation. I introduce revenue management tactics-the ways in
which companies can manage their capacity to maximize return. The airlines
and other travel-related industries were pioneers in revenue management. I
show how computerized reservation systems and global distribution systems
play major roles in the way that revenue management has been implemented in
these industries. I discuss the role of ancillary revenue and incremental
costs and how they influence revenue management in different industries. I
describe how the effectiveness of a revenue management program can be
measured and discuss the current status and future prospects of revenue
management in different industries.
9Capacity Allocation
chapter abstract
Capacity allocation is the problem of determining how many seats (or hotel
rooms or rental cars) to allow low-fare customers to book when there is the
possibility of future high-fare demand. I first analyze the two-class case
in some detail. I then consider the multiclass problem, with an emphasis on
the widely used expected marginal seat revenue (EMSR) heuristics. Next I
discuss the dynamic multiclass problem. I then relax the independence
assumption and discuss how booking limits can be set when demand in a fare
class can depend on which other classes are currently open. I introduce a
data-driven approach that does not depend on estimating parameters of a
demand distribution. Finally, I discuss how the performance of a capacity
allocation program can be measured and evaluated.
10Network Management
chapter abstract
Network management is the problem of determining how to manage resource
availability in the case when products can require multiple resources.
Examples include hub-and-spoke airline networks as well as multiday hotel
and rental car products. I introduce the network management problem,
describe different types of networks, and discuss the industries in which
network management is important. I show why the network management problem
is complex and why a simple and intuitive greedy heuristic does not work
well. I then present various approaches to the network management problem
including linear programming, virtual nesting, and bid pricing. Finally, I
discuss the issues involved in implementing network management.
11Overbooking
chapter abstract
Overbooking occurs whenever a seller with constrained supply sells more
units than he has available to hedge against the possibility of no-shows or
cancellations. The chapter begins with a short history of overbooking. I
then characterize industries in which overbooking is used and introduce
four different approaches to overbooking: a deterministic heuristic,
risk-based policies, service-level policies, and hybrid policies. I show
how booking limits can be determined under each policy in the case of a
single price and an uncertain level of no-shows. I then present a
data-driven approach to overbooking and discuss dynamic overbooking in the
face of cancellations and multiple fare classes. Finally, I present
extensions to the basic problem and discuss some alternatives to
overbooking for managing cancellations and no-shows.
12Markdown Management
chapter abstract
The goal of markdown management is to determine the timing and magnitude of
price reductions that maximize revenue from a fixed stock of inventory. In
this chapter I start by giving some background and show how, for certain
goods and services, markdowns segment the market and provide a simple
method by which retailers can profit from intertemporal price
differentiation. I outline the markdown management process that retailers
follow and some of the business issues that can constrain them. I formulate
the markdown problem as a constrained optimization problem and outline the
most common approaches to finding the optimal prices including explicit
optimization and exhaustive search, or enumeration. I discuss the
implications of strategic customers-those who anticipate future prices-on
markdown policies. Finally, I discuss the use of markdown management
systems and the experiences of companies using markdown optimization.
13Customized Pricing
chapter abstract
Customized pricing refers to the situation in which customers approach
sellers with their desired product and the seller responds with a bid. In
customized pricing, the seller can usually determine an individualized
price for each customer and has access to information about failed bids as
well as successful ones. I formulate the customized-pricing decision as an
optimization problem and show how that problem can be solved to maximize
the expected contribution margin from each bid. A key element in the
customized-pricing problem is the bid-response function, which specifies
the seller's expectation on how each customer will respond to his bid
price. I show how bid-response functions can be estimated for different
customers seeking to purchase different products. I then show how the
customized-pricing model can deal with objectives other than maximizing
expected contribution.
14Behavioral Economics and Pricing
chapter abstract
Classical approaches to pricing optimization assume that consumers are
rational in that they choose among available alternatives in a way that
maximizes the utility of the product purchased minus the price. However,
both common experience and research show that this is not the
case-customers are influenced by seemingly irrelevant factors such as how
the price is presented and what prices are offered to other customers. In
this chapter I describe the implications of customer irrationality for
pricing in three categories: violations of the law of demand, price
presentation and framing, and fairness. I discuss how these categories
should be taken into consideration in pricing. Examples are drawn from
Amazon, Coca-Cola, First National Bank of Chicago, and cruise lines, among
other industries.