How to Reap Higher Profits With Dynamic Pricing Arvind Sahay

V O L . 4 8 N O. 4
Arvind Sahay
How to Reap
Higher Profits With
Dynamic Pricing
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How to Reap Higher Profits With
Dynamic Pricing
un Microsystems Inc. chairman Scott McNealy forecast that
“With recent advances in wireless and information technology,
even our cars could … call for bids whenever the fuel tank runs
low, displaying a list of results from nearby gas stations right on
the dashboard.”1
It sounds far-fetched. But dynamic pricing — where prices respond to supply and demand pressures in real time or near-real time — is making inroads
in many different sectors, including apparel, automobiles, consumer electronics, personal services (such as haircuts), telecommunications and second-hand
goods. The advent of the Internet led to cost transparency, decreased search
costs and ease of price comparison. Some observers concluded that as a result, prices would decrease and equalize across different channels, and that
fixed prices would continue to be the norm.2 However, price dispersion continues to be widespread and dynamic pricing is entering new sectors. EBay
Inc. used auctions to sell more than $20 billion worth of goods in 2005. Ford
Motor Co. sold more than $50 billion worth of automobiles in North America
with demand-based DP in 2003, exceeding profit targets by $1 billion.
Fixed prices are, after all, a relatively recent phenomenon — a product of
mass manufacturing that came about after the Industrial Revolution. Before
that event, fixed prices were the exception; DP was the norm, with both
buyer and seller able to benefit in many DP transactions.
However, apart from airlines and hotels, which employ DP routinely,
most companies still use relatively simple strategies for determining prices:
competitive pricing (pegging prices to competitors’ prices) or cost-plus pricing (calculating the cost of a good or service and adding profit). Now
dynamic pricing optimization offers companies in many other sectors the
alternative of raising average realized prices in the face of increased pricing
Four principal reasons are driving the increasing use of DP today:
■ More companies can now access and deploy the technology for DP at affordable prices in new product and service categories.
■ Recent research shows that with the right approach, consumers will accept
DP even if they are currently buying using fixed prices.
■ Increased pricing pressures and supply constraints in different industries
are driving companies to look at new ways of extracting value and reallocating demand.
Arvind Sahay is a professor of marketing at the Indian Institute of Management Ahmedabad. Comment on this article or contact the author through [email protected]
More and more
companies are now
able to change their
prices in real time
to capture the full
possible value of
goods and services.
Here’s how to do
dynamic pricing right.
Arvind Sahay
In their continuing bid to increase efficiency, many companies
(having already integrated upstream supply chains with their
operations) are now turning to the downstream aspect, where DP
is a natural consequence.
Dynamic pricing, managed well, offers a feasible and attractive path to increase revenues and profits. (See “Why Dynamic
Pricing,” p. 56.) Implementing DP can improve revenues and
profits by up to 8% and 25%, respectively.3 But it’s not just that
DP offers greater profits. For example, for personal services such
as haircuts and public services such as road space in metropolitan
areas, DP can be used to reallocate demand to more suitable
times and manage a limited supply base. And where products
have a limited shelf life with a salvage value (for example, apparel), DP can be used to improve realizations from fast-moving
lines of goods by raising prices in real time or near-real time, and
similarly to push slow-moving goods by lowering prices. Also, in
formats such as Internet auctions (for example, eBay) and group
buying, DP can aggregate bigger audiences than is possible in a
physical setting. New, excess and reassigned inventory can be sold
for higher realizations using an auction format such as eBay.
F orms of Dynamic Pricing: Posted Price Mechanisms and Price Discovery
Dynamic pricing differs fundamentally from fixed pricing because it allows prices for the same good or service to change by
customer, time, aggregate demand and other situation-specific
parameters. There are two broad categories of DP: (1) posted
prices that customer can see; and (2) price discovery mechanisms,
in which customers determine prices through their own actions
during the transaction.
Fixed prices are a form of posted prices, of course. When companies fix the posted price of a product or service for a relatively
long period, this is mainly due to lack of accurate demand information, high transaction costs associated with changing prices
continuously, and the huge investments required for the software
and hardware necessary for implementing DP. But the increasing
power of information technology now enables access to demand
information in real time, permitting a matching of demand and
supply and, therefore, intertemporal DP. Technology enables a
typical retailer to make pricing and inventory decisions for hundreds of thousands of products. For example, there can be as
many as 50,000 stock-keeping units in a grocery store or drugstore, with items possibly priced differently across stores.
Dynamic posted pricing includes systems such as revenue
management for airlines — also called yield management (based
on demand with a supply constraint) — and demand-based variable pricing (prices change according to the demand for a
product), or a combination of these approaches. Revenue management and demand-based pricing use historical data and
mathematical models to predict demand at future points in time.
Different prices are then set for these different time points according to predicted demand, as well as adjusting prices for
actual demand.
When the price discovery approach is used, prices are determined by the active participation of the customer in the
transaction. In other words, the price changes during the transaction, whereas with dynamic posted pricing the price changes
across transactions. Price discovery-based mechanisms include
different forms of auctions, group buying and negotiations.
In auctions, customers bid (up) to buy the product of choice.
EBay is the most famous example of the straight auction where
the highest bidder wins. In a reverse auction, suppliers bid
(down) to sell goods to a buyer. Many large buyers employ this
technique, sometimes to squeeze upstream costs. Vendors such as
FreeMarkets Inc. have provided the platform to implement reverse auctions for clients including United Technologies Corp.
When Google Inc. went public in 2004, it employed a modified Dutch auction to price its shares. Would-be investors bid a
price at which they were willing to buy a minimum of five
shares. Google revealed the maximum number of shares being
sold and a potential price range that was adjusted over time according to market conditions. Investors stated the number of
shares they wanted and at what price. Once the minimum clearing price was determined, investors who bid at least that price
were awarded shares. When there were more bids than shares
available for a particular size of share lot, allotment was on a
pro-rata basis — awarding a percentage of actual shares available based on the percentage bid for. (A similar type of auction,
About the Research
The insights offered in this article draw on my research on
dynamic pricing and on a synthesis of DP activities in many
industries and regions. Using data collected on purchases of
DVD players (352 responses), prepaid mobile telephones (334
responses) and apparel retail (843 responses) in the United
Kingdom, I examined the impact of (1) the relative magnitude
of latitude of price acceptance, (2) the presence or absence of
communication with consumers about the reason for a type
of pricing, (3) the customer’s perceived fairness of DP and (4)
the customer’s search and purchase intention related to the
product. I controlled for the customer’s price sensitivity, product involvement, product knowledge, brand knowledge and
value consciousness to arrive at the result. For each product,
the population sample was relatively homogeneous in terms
of demographics and had relatively low levels of product
knowledge but had expressed the desire to buy that category
of product. The approach used a field experiment with a
nested factorial design.
Instead of setting a fixed price and then holding “fire sales,” and instead of depending on ad hoc data,
companies can use dynamic pricing approaches to both raise and lower prices right from the beginning.
the Yankee auction, differs in that winning bidders pay exactly
the prices they have bid.)
In group buying situations, customers, who may be known to
one another or be complete strangers, aggregate to bid (down) the
price — a form of volume discount obtained dynamically through
the actions of the customers. For example, sells
excess inventory for companies using the group buying process.
Finally, negotiations are a well-known way of arriving at a mutually agreeable price. Software can now be used to automate
negotiations in some contexts. (See “The Seven Types of Dynamic
Pricing,” p. 58.)
The conventional view is that DP is limited to perishable
products, such as hotel rooms and airline seats, whose usefulness
and functionality last a finite length of time; in the event that they
are not used prior to expiration, their value diminishes to zero.
There is, however, another category of products and services
whose value depreciates with time but has a salvage value, including apparel, automobiles, cell phones, computers, consumer
electronics items and some packaged goods. As new models are
introduced in the market, the older models become less desirable
and potentially obsolete. After a limited period — between one
and six months, depending on the product category — unsold
products have a residual value that may be 20% to 75% less than
the initial asking price.
Consider clothing lines that arrive on store shelves in autumn
and have a shelf life until the end of the season. In a fixed-price
regime the apparel lines will be marked at a constant price for three
months, regardless of whether a line is moving fast or slow, and
then will be marked down in end-of-season sales by up to 75%. In
recent years, consumer electronics and computers have begun to
behave like apparel: fixed prices for a season and then large markdowns. New cell phone models get marked down by as much as
30% after three months and by another 30% after six months.
They then move to other channels and get marked down further.
New automobiles have a shelf life of only a few weeks; if they have
not been sold from showrooms by then, they get marked down.
There is an alternative for these products. Instead of setting a
fixed price for two to four months and then holding “fire sales,”
and instead of depending on ad hoc and fragmented data to decide
on rebates, companies can use DP approaches to both raise and
lower prices right from the beginning. Managers can calculate these
price changes by examining the latitude of price acceptance.
How to Price Dynamically: Latitude of Price Acceptance
For the different categories of products that customers buy, they
have a latitude of price acceptance, which is a range of possible
prices within which price changes have little or no impact on their
purchase decisions. A McKinsey & Co. study shows that LPAs can
range widely: from 17% for branded consumer health and beauty
products to 10% for engineered industrial components and apparel to only 2% for some financial products.4
LPA can be discovered through three approaches. The first is
to observe the range of prices for a particular product that customers find in different channels. For instance, a Sony DVD
player will be found at one price in a Best Buy, at another price in
a Sony shop and at a third price at a supermarket such as Kroger.
Customers in effect learn an LPA from this observed price range.
The second approach to discovering LPA is based on surveys that
test consumers’ willingness to pay. The third approach uses
analysis of actual demand elasticities in geographies, products,
sales channels and customer segments.
Primary data that I have collected with consumers of DVD players, mobile phones and apparel (See “About the Research”) shows
that when a company uses DP, there is no difference between larger
and smaller consumer LPAs in either the relative impact on purchase intentions or their perceptions of price fairness compared to
a fixed-pricing regime.5 Companies can create LPA ranges in consumers’ minds through the variation of product prices across
different channels and geographies and through appropriate messages. For instance, Northwest Airlines Corp. sells tickets on the
Internet, by telephone and at airline counters. Like other airlines,
Northwest sets different prices for the three channels and changes
prices more frequently on its Web site than on the other channels.6
Corporations that move to the higher end of an LPA band can
substantially increase profits. The McKinsey study suggests, for
example, that a financial services company moving from the
middle to the top of a 2% LPA band for personal loan products
would generate an 11% increase in operating profits for those
products. By implementing the appropriate version of DP and by
remaining cognizant of the limits prescribed by LPA, companies
may very well increase their overall revenue realization — instead
of the guaranteed decrease in sales inherent in fixed prices.
So when should companies employ dynamic pricing? In the
following sections, I outline rules for when to use DP, what form
of DP to use in which situation and issues in implementing DP.
Eight Situations For Using Dynamic Pricing
1. The bigger the market, the larger the number of customers
and the greater the number of transactions, the greater the opportunity for DP.
The best example of this DP situation is a stock exchange like the
New York Stock Exchange Inc., where prices vary in real time continuously. The large numbers of transactions and lots (products)
enable numerous buyers and sellers to bid for the products (company stocks), and the price changes according to the demand.
Another example comes from the highly competitive American
automobile market, which is the world’s largest, with more than 15
million new vehicles sold. Over the past few years, the Big Three
(Ford, General Motors and DaimlerChrysler) have tried to maintain market share by resorting to aggressive price decreases through
incentives and rebates. Ford used DP in 2003 to preserve its margins, however. Instead of providing a single offer price with uniform
incentives and rebates for a car model across all its markets in the
United States, Ford used detailed market data about how a model
was selling in different regions and raised and lowered its final
prices to the customer (including rebates and incentives) according
to near-real-time demand. Using daily sales data from dealers, Ford
has changed rebate levels as frequently as once a week.7
Pricing based on evolving customer demand for different
product lines has allowed Ford to hold average prices across all
models steady in certain years; prices for some models such as
Explorer decreased, but those of other models such as Taurus
increased. While net vehicle prices at General Motors fell 2%
during the first quarter of 2003, a 0.2% average price gain across
all models allowed Ford to raise its revenue per vehicle during
the same period.
In this example, Ford employed a variant of demand-based
DP that depends on a near-real-time understanding of demand
for each model category by geography and price. The large number of transactions provides the base on which judicious use of
computing power, based on real-time sales information, leads to
profitable DP.
2. The more the customer is involved in the process, and the
greater the heterogeneity in valuation that customers put on
the same service, the greater the opportunity for DP to reallocate and manage demand.
Maria, a hairdresser in a London suburb, was turning away
customers on Saturdays because she did not have, or want to add,
the required capacity. During the week, however, her premises
were frequently half full. (She closed the salon on Sundays.) Maria
had four principal groups of customers: busy professionals living
in the area, pensioners, homemakers and mothers with children.
She observed that the professionals tended to come only on Saturdays. Maria considered raising her prices on Saturdays and
decreasing them on Tuesdays and Wednesdays as an incentive for
some of the mothers, pensioners and homemakers to switch to
midweek visits, so that she could cater to more of the professionals
who might be willing to pay higher prices on Saturdays.
The economics of the idea were sound given her customer
base, but Maria was not sure how customers would react. What
would they think about paying different prices for the same basic
service, a haircut? Maria informally floated the idea among her
customers. To her surprise, she found that many were not averse
to the idea; the pensioners, homemakers and mothers would benefit from lower prices and had schedules that were flexible enough
for them to come midweek. The busy professionals were happy to
pay a higher price and not have to wait the usual 30 to 45 minutes
(in spite of an appointment) on Saturdays. Customers placed different valuations and had different constraints on their time.
Getting the customers involved in
the pricing process (and pricing by the
relative demand for her perishable serWhy Dynamic Pricing?
vice) enabled Maria to implement a
form of DP, which at first blush did not
Beyond the point where costs have been covered, as the number of price points (relative
appear to be something that customers
to an existing fixed-price level) increases, so do the potential profits.
would find fair and equitable. Maria
also directed demand toward available
One Price
Two Prices
capacity and segregated her customers
of Buyers
of Buyers
by the ability to pay. Some form of cusPassed-up
tomer heterogeneity is present in every
market, and Maria was able to leverage
Money left
on the table
the heterogeneity in the valuation for
her services. DP increased Maria’s number of customers served, revenues (by
10%) and profits (by 25%). Customer
Cost Selling Selling
involvement in the DP process obviated
Price Price
possible negative backlash.
In the personal computer industry, Dell Inc. is able to predict its
near-term demand in the United States and Western Europe and
adjust prices to moderate demand. By lowering prices for those
product configurations that are in stock or already in the pipeline
and raising prices for configurations that may cause undue ripples
in its supply chain, Dell is able to optimize customer demand. Lowcost airline easyJet plc also follows a yield management form of
pricing that allows it to moderate and direct demand.
3. Products and services that have a well-defined shelf life (that
is, they eventually become obsolescent) are amenable to the use
of demand-based DP, even if they are not perishable in the conventional sense but nonetheless have a salvage value.
Clothing is one of the largest categories of perishable products.
Although people do not typically think of clothing as perishable,
fashion changes quickly, so retailers need to sell clothing before it
turns unfashionable — usually before the end of the season, at
which point an unsold line of apparel will have a salvage value that
is a fraction of its original price. (In comparison, the value of an
unsold airline seat becomes zero as soon as the plane takes off.)
Like airlines, apparel retailers can employ statistical models that
estimate demand and then track actual demand in different lines
and adjust prices. In addition, a clothing retailer can obtain more
supplies of a fast-selling line of clothing (unlike airlines, which
cannot change the number or type of seats on a scheduled flight)
— provided that information is available early in the season.
The difficulty is that a big retail store may stock tens of thousands of different clothing items. To perform DP, a retailer would
have to work up an elaborate mathematical model of how each
product performs in the market and then estimate the trade-off
between different pricing levels and the chances of purchase as the
product proceeds toward “perishability.”
As computing power and storage have become cheaper, however,
demand-based DP has become feasible for clothing retailers. Today,
a customer shopping at Macy’s, Gymboree, Ann Taylor or any of
several other retail chains may well be paying a price that changes on
a daily basis according to variations in aggregate demand for different lines of clothing. Macy’s does not reprice merchandise in its
stores more than once a day, but on its Web site every posted price
can be reset as frequently as desired. A statistical model can be used
to update prices continually to maximize gross margins.
CXB, a pseudonym for a department store that sells apparel to
mid- to upmarket clientele, gets new lines of menswear, women’s
wear and children’s wear every autumn that it sells for the marked
price until around mid-November. At that point, promotions
(10% off, 20% off and so on) start and continue until an end-ofseason clearance sale just after Christmas, when prices are usually
marked down between 50% and 75% from the original price.
My research suggested that if CXB were able to implement
demand-based DP successfully, it could raise profitability by up
4. The more that a company needs to sell excess or reassigned
inventory, the greater the potential role for DP.
“Investment recovery” from sales of assets that are no longer
needed (such as surplus inventory), at significant discounts, is a
standard practice at almost all companies. The use of DP can
increase the recovery price and therefore decrease the loss due to
write-downs. Online sites such as LetsBuyIt, eBay and,
among others, have persuaded different customers to pay different prices using various DP mechanisms for new, excess, mature
or reassigned inventory. EBay in effect acts as an inventory clearinghouse for companies such as Hewlett-Packard Co. and Sun
Microsystems. Use of these channels has increased both the pool
of potential buyers and the price realization for companies.
LetsBuyIt, an online B2C Web site based in Gateshead, United
Kingdom, uses a price-discovery type of DP to sell a limited assortment of mainly white goods. Prices depend on the number of
people who have signed up to buy a product. If, for example,
between one and five people sign up to buy a Palm V PDA, the
price decreases from £300 (the original retail price) to £259. If six
to nine people register, the price goes down to £245. If 10 people
or more sign up, then the price
falls to £225, the lowest price ofThe Seven Types of Dynamic Pricing
fered. The consumer can sign up
to buy at only the lowest price
(that is, only if 10 or more people
Dynamic Pricing Mechanism
Dynamic Pricing Type
sign up) or can opt to buy at the
1. Yield Management: Pricing by different categoprevailing price when the “coPosted Prices
ries of customer and on available inventory, based
buy” closes. The potential number
on systems and procedures to maximize results
of people from whom the “group”
from the sale of a product or service that has a relatively fixed supply and a revenue-producing ability
can emerge to accomplish the cothat decreases with time and diminishes to zero (for
buy is orders of magnitude higher
example, airline tickets).
than the number in a brick-and2. Demand-Based Pricing: Prices vary according to
mortar situation. The Internet
aggregate demand for a product category in which
the revenue-producing ability decreases with time
provides an aggregation and acbut has a residual value (for example, automobiles).
cess level that otherwise would
1. Auction: A seller engages several buyers to outPrice Discovery
not be possible.
to 15%. As a result, management launched a demand-based pricing system. Newly installed inventory and transaction recording
systems enabled near-real-time tracking of sales momentum by
different lines of apparel in each store. When demand for a line
increased, the price would also increase. Prices of slow-moving
items would be cut. Thus the price of the Roca line of menswear,
which came in on September 1, rose by 2% overall on September
8 and by another 3% on September 15, since the Roca line was
moving very quickly. On the other hand, the price of the Canyon
line of menswear was lowered by 7% on September 8 since it was
not selling at all. In the first season that DP was implemented,
prices were changed on a weekly basis in response to the variations in demand. At the end of that season, the clearance sale
covered only 30% of the usual volume of apparel that went on
sale. In the following season, prices were changed twice weekly,
and the end-of-season clearance sale was left with only 20% of the
baseline volume. CXB changed its prices within a range of 10%,
which was determined as the latitude of price acceptance for its
clothing lines. In both seasons, profitability increased as a consequence of higher sales.
bid one another. Seller benefits by securing
highest bid (for example, eBay).
2. Reverse Auction: One buyer engages several qualified sellers to compete with downward pricing.
Sometimes the buyer proposes price (for example,
3. Dutch and Yankee Auctions: One seller engages
several buyers to bid for multiple identical items (for
example, Google’s IPO).
4. Group Buying: A form of demand aggregation in
which a fragmented community of small buyers combines to create a larger purchase with limited-time
buying opportunities (for example, LetsBuyIt).
5. Negotiations: Buyer and seller engage in either
direct or automated settlement.
5. The greater the possibility of
using one-off transactions to obtain inputs for production, the
greater the potential use of DP
(specifically, reverse auctions).
FreeMarkets Inc. (now part of
Ariba Inc., which is headquartered in Sunnyvale, California)
creates markets called competitive bidding events where
suppliers (sellers) bid for the
business of manufacturers (buyers). The FreeMarkets approach
Sun Jewelry lists an item on eBay, observes the bidding process and the final prices offered,
and then uses the information gathered to set prices for similar products in the store.
is to provide the total package of services necessary for a reverse
auction process. The staff works with the suppliers to prequalify
them for the event.
In one case, 35 vendors spread around the world bid to supply
a global manufacturer with printed circuit boards in 12 lots. The
manufacturer planned to award the business to the lowest bidder.
In this reverse auction, bidding started at a reserve price level (corresponding to the price at which the manufacturer had bought the
components the previous year) and prices then decreased. This
auction saved the buyer 43% from the previous year’s price.8 On
average, buyers using FreeMarkets have experienced savings of
between 2% and 25%.
Smaller organizations can experience similar benefits if they
band together to exercise buying power though FreeMarkets or
industry vertical portals. The volume of purchases the buyers
collectively offer can be sufficient to induce several suppliers to
enter into a reverse bidding war. In essence, by increasing the
number of possible sources of goods, the buyer is using a price
discovery mechanism to get a discount.
6. Where the final price has little relation to cost and the product can be viewed and evaluated at a distance, DP methods such
as auctions can be used to determine a price range.
Los Angeles-based Sun Jewelry has used eBay as a test bed for
the fixed prices it offers in its physical stores. Sun lists an item on
eBay, observes the bidding process and the final prices offered,
and then uses the information gathered to set prices for similar
products in the store. Sun saves itself the cost of conventional
market research to determine customer willingness to pay; packaged goods companies and automakers, for example, have
traditionally spent large sums to determine appropriate price
levels through extensive contacts with potential customers. Sun is
able to set prices that are representative of what customers are
willing to pay, potentially increasing the profits it can make from
its lines of jewelry through higher volumes.
7. Where there is a need to recover money quickly for improved cash flow, a DP method such as negotiations can be
very useful.
On many occasions, an insurer and a doctor will haggle about
payments over claims that the doctor has made. The insurer ordinarily has 45 days after processing the claim to send payment
to the doctor. The doctor would like to get the money sooner,
while the insurer has an incentive to pay out less money.
Automated software now exists that allows insurers and doctors to negotiate. Some insurers and doctors have agreed to
automate their negotiations because it is cheaper than having
outside negotiators (working for the insurance companies) call
doctors, who are often too busy to talk. Insurance payments, even
at the low end, tend to be high enough to make the negotiation
software worthwhile for both the doctor and the insurer.
As the negotiations proceed, the software adjusts itself. It has
been found that 98% of doctors reject the first offer and come
back with a counteroffer. However, 75% of the negotiations end
after the second offer. If a deal cannot be made, then the insurer
pays the full claim under the original 45-day time frame. In more
than 20,000 transactions executed during an 18-month period in
2002 and 2003, the average negotiated settlement using the software was $2,000, with insurers saving an average of $400 per
settlement.9 The insurer improves its bottom line and the doctor
is able to improve his or her cash flow.
The traditional Dutch auction for a sale lot begins with a high
asking price, which is lowered until some participant is willing to
accept the auctioneer’s price. That participant pays the last announced price. Google’s intention in using a modified form of
Dutch auction for its initial public offering was to obtain the
highest price for itself, to prevent or minimize the typical firstday jump in prices of its shares, and to make the IPO accessible
to a high number of potential buyers.
Google’s IPO sold 19.6 million shares at $85 each to raise
$1.67 billion. Because of the low market price for other Internet
companies at that time, Google shares were initially priced near
the low end of that range, but they closed the first day just above
the middle of that range. There were no huge swings in price, up
or down, in the immediate aftermarket. Even in a fairly adverse
market, Google’s Dutch auction IPO undercut investment bankers — their average commission on the Google deal was 2.8%
compared with 4% to 7% in conventional IPOs.
Four Keys to Implementing Dynamic Pricing
This article has so far viewed dynamic pricing from the perspective
of the company. In implementing DP, it is important that customers
accept the practice and not view it as iniquitous. Four factors must
be carefully considered to ensure a successful implementation.
First, DP cannot be perceived to be inequitable. That can be
deadly for a company when consumers can communicate and
compare experiences with other consumers — as was the case
with Inc.’s attempt to price DVDs differentially in
1999. Some Amazon customers discovered that they had been
charged different prices for the same DVDs. Inferring that the
price discrimination was based on their past purchasing behavior, customers complained vociferously in chat rooms and on
Internet bulletin boards. The snowballing controversy led to
Amazon’s retraction of the practice.10
As this experience demonstrates, consumers are resistant to
DP that is based on their past individual behavior. They also resist dynamic prices derived from their individual capacity to pay.
However, customers are much more accepting of DP mechanisms where they are involved in the pricing process. DP that uses
price discovery mechanisms such as auctions and group buys always has a high degree of acceptance from buyers; their
participation represents an acceptance of the practice. In posted
price mechanisms, conversely, extra care needs to be exercised.
Second, an astute use of the latitude of price acceptance
within a product category makes it more likely that consumers
will accept DP. As previously discussed, my primary research
shows that as long as posted prices stay within an LPA, consumers’ purchase intentions are not affected, nor do they consider DP
unfair compared to a fixed-pricing regime. Fear of customer
backlash has been an important factor holding back companies
from implementing DP. A corollary is that involving customers
in the pricing process makes them more accepting of a price format that clearly discriminates.
Third, despite increasing price transparency brought about by
the Internet and the rapid spread of search bots, customers remain willing to pay different prices for the same product for
several reasons: prior experience in the category, their personal
tastes, situational exigencies and different levels of price consciousness. Individual customers show different reactions to
prices of the same product in different situations, channels and
occasions of use. For example, research on bookstore prices suggests that price dispersion is greater among online stores than
among offline stores.11 Between 1997 and 1999, market leaders
Amazon and raised online book prices by
8% and 7%, respectively, while discount competitor lowered prices by 30%. Even with the proliferation of
shopping bots, Amazon’s market share increased from 64% to
72% and’s share from 12% to 15%. Such
“stickiness” augurs well for DP.12
Finally, for retailers implementing DP, there are logistical issues of changing prices on the price tags, apart from integrating
the supply and demand information. Retailers that have implemented DP usually have replaced paper tags with electronic tags.
Once the mechanisms are in place, large store chains can save up
to $100,000 per store per year that they previously spent on
physically implementing price change announcements.13
Price to the Beat of Your Customers’ Shopping Habits
Successful implementation of dynamic pricing requires that the
seller manage the context in which product demand varies. The
more the seller understands the buying cycles and habits of the
customer, the more he or she is able to manage price margins to
the rhythm of the customer’s shopping, to segment customers
and to develop price discrimination. Customer participation in
the pricing process decreases the chances of a consumer backlash. Customers also tend to embrace DP where the price
reflects intensity of demand for the product, there is communication between the seller and the consumer, and the price
difference is explained by a difference in perceived value across
channels through which the transaction occurred. A company
that masters dynamic pricing has a potential new source of
competitive advantage.
1. S. McNealy, “Welcome to the Bazaar,” Harvard Business Review 79
(March 2001): 18-19.
2. R. Kuttner, “The Net: A Market Too Perfect for Profits,” BusinessWeek,
May 11, 1998, 20.
3. M.J. Ashworth, “Revenue Management Builds Higher Profits,” Electric
Light & Power 75 (November 1997): 4; and W. Zhao and Y.S. Zheng,
“Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous
Demand,” Management Science 46, no. 3 (March 2000): 375-388.
4. W. Baker, M. Marn and C. Zawada, “Price Smarter on the Net,” Harvard Business Review 79 (February 2001): 122-127.
5. A. Sahay, “Consumer Reactions to Dynamic Pricing,” working paper,
Indian Institute of Management, Ahmedabad, India, 2005.
6. M. Maynard, “Will This Idea Fly? Charge Some Travelers $10 for
Showing Up,” New York Times, August 25, 2004.
7. D. Welch, “Ford Tames the Rebate Monster,” BusinessWeek, May 5,
2003, 5-6.
8. I. Merson, “Reverse Auctions: An Overview,” Acquisition Directions
Advisory, July 2000.
9. K. Belson, “Digital Dealmakers Meet in the Middle,” New York Times,
Sept. 11, 2003, sec. E, p 1.
10. “ Varies Prices of Identical Items For Test,” Wall Street
Journal, Sept. 7, 2000, sec. B, p. 19.
11. E. Brynjolfsson and M.D. Smith, “Frictionless Commerce? A Comparison of Internet and Conventional Retailers,” Management Science
46, no. 4 (April 2000): 563-585.
12. M. Marn, C. Zawada, D. Swinford and W. Baker, “Internet Pricing:
A Creator of Value — Not a Destroyer,” McKinsey Marketing Practice,
September 2000, 2.
13. D. Levy, M. Bergen, S. Dutta and R. Venable, “The Magnitude of
Menu Costs: Direct Evidence From Large U.S. Supermarket Chains,”
The Quarterly Journal of Economics 112, no. 3 (August 1997): 791-825.
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