Welcome
B2C eCommerce
Trends in Pricing
Jonathan Wareham
[email protected]
Price Levels
Assumption that electronic markets have less friction
than comparable markets.
• Search costs lower
• Competition increases
• Average prices should fall, converging on market
level
Study of prices of books and CDs and software sold on
internet:
Higher prices & greater variance in
electronic channel !!!!!
u
u
u
u
Possible Causes
1. Superior disc. pricing techniques: lower
registration and menu costs
2. Heterogeneity: wine in store or restaurant
• Versioning
3. Temporal preference: consumer behavior
and types
4. Imperfect information: bait and switch
5. Neural real estate: 5% sites/75% traffic
6. Market immaturity: eMarkets too young
Fixed Prices
P
$1.00
1 Coke
Q
Fixed Prices
P
P Consumers Surplus
Dead Weight Loss
Q
MC
Q
Get a little more revenue
P
P1
P2
P3
Q1
Q2
Q3
Q
2nd Degree Price Discrimination
 “product line pricing”, “market
segmentation”, “versioning”
 Gold Club, Platinum Club, Titanium Club,
Synthetic Polymer Club
 First Class, Business Class, World
Traveler Class
 Professional Version, Home Office
3rd Degree Price Discrimination
 The practice of charging
different groups of consumers
different prices for the same
product
 Examples include student
discounts, senior citizen’s
discounts, regional &
international pricing, coupons
Maximize the Revenue !
Perfect (1st degree) Price Disc.
P
Q
Perfect Price Discrimination
Price $
Profits:
.5(4-0)(10 - 2)
= $16
10
8
6
4
Total Cost
2
MC
D
1
2
3
4
5
Quantity
Prefect Price Discrimination
 Practice of charging each
consumer the maximum amount
he or she will pay for each
incremental unit
 Permits a firm to extract all
surplus from consumers
 Difficult: airlines, professionals
and car dealers come closest
Caveats:
 In practice, transactions costs and
information constraints make this is difficult
to implement perfectly (but car dealers and
some professionals come close).
 Price discrimination won’t work if you
cannot control three things:
 Preference profiles
 Personalized billing; (anonymous
transactions lesson seller’s discriminatory
power over consumers)
 Consumer arbitrage
What is different about this site?
Conclusions
1. Internet double edged sword:
•
Consumers enjoy lower search costs, but…
•
eMarketers have superior tools to register
your consumption patterns and price
sensitivity
2. The end of fixed pricing???
•
Fixed pricing as an institution only 100 years
old!!
•
Developed in response to large scale
economies/production models….with
standard products !!!!
Horizontal Differentiation
 The game of location (proximity to
customer’s tastes)
1/2
Bob
Bob
Alice
Alice
Vertical Differentiation
Price
High
Low
Quality
How???
1. Versions
2. Timing and delays
3. Ease of use
4. Pathways into site
5. Segregation of markets and users
6. Analysis of click stream and previous purchasing
history
Making Self-Selection Work
 May need to cut price of high end
 May need to cut quality at low end
 Value-subtracted versions
 May cost more to produce the lowquality version.
 In design, make sure you can turn
features off!
How Many Versions?
 One is too few
 Ten is (probably) too many
 Two things to do
 Analyze market
 Analyze product
Analyze Your Market
 Does it naturally subdivide into
different categories? AND
 Are their behaviors sufficiently
different?
 Example: Airlines
 Tourists v. Business travelers
 “This created visible differentiation in
customer service. It was essential for
our customers to see the perks that
the others were getting.”
Analyze Your Product
 Dimensions to version
 High and low end for each dimension
 Design for high end, reduce quality
for low end
 Low end advertises for high end in
service industries – Cheap rates
 High end – Flagship products advertises for low end in many
products.
Goldilocks Pricing
 Mass market software (word,
spreadsheets)


Network effects
User confusion
 Default choice: 3 versions
 Extremeness aversion
 Small/large v. small/large/jumbo
Extremes Aversion
 Bargain basement at $109,
midrange at $179
 Midrange chosen 45% of time
 High-end at $199 added
 Mid-range chosen 60% of time
 Wines
 Second-lowest price
 “Framing effects”-example
Cross-Subsidies
 Prices charged for one product are
subsidized by the sale of another product
 May be profitable when there are significant
demand complementarities effects
 Examples
 Browser and server software
 Drinks and meals at restaurants
 Long distance and local access
 Auto spare parts
 Razor & Blades
 Burger, fries, drinks
 Auto financing
Lessons
 Version your product
 Delay, interface, resolution, speed,
etc.
 Add value to online information
 Use natural segments
 Otherwise use 3
 Control the browser, access,
comparisons, etc.
 Bundling & cross subsidies may
reduce dispersion
Down & Dirty
 First degree (perfect) price
discrimination
 “market of one”
 Second degree price discrimination
 “product line pricing”, “market
segmentation”, “versioning”
 Third degree price discrimination
 “different prices to different groups”
 Other definitions in literature…
RM coming of age
1978:

Airline deregulation in the U.S.
1985:
1992:

People Express vs. American Airlines


Edelman Award: RM for AA $1.4 billion in 3 years
virtually every airline has implemented RM
National Car Rental (vs. GM)

Edelman Award: RM for SNCF

AA: $1 billion incremental revenues from RM
Marriott Int’l RM: 4.7% increase in room revenue

1997:

1999:
2000-01:
2003:

Deregulation Europe: telecom, media, energy …
e-distribution supports dynamic pricing & profiling

Dell, Amazon & Coca Cola experiment dynamic pricing

RM spans wide range of industries …

RM Evolution
HealthCare/
Hospitals
Telco/ISP
Insurance/
banking
Sports
Parks
Cruise lines
Entertainment
Car rental
Airlines
1980
Rail
Transp.
Hotels
1985
1990
Freight,
Cargo
Energy
Tour
Operators
Media
1995
Manufact.
2000
Retailers
Revenue Management
Strategies & tactics for
OPTIMIZING PROFITS
based on
DYNAMIC PRICE
SETTING
INVENTORY
CONTROL
under real-time, disaggregate updating of
DEMAND FORECASTS
The RM Challenge
Arrivals of
high paying
customers…
Closer to
departure!
Arrivals of
low paying
customers
…Earlier!
Overbooking metrics
 Service level based:
 P(denial) =0.05
 E[#denials]=2
 Etc.
 Cost based: assign a cost to each and
optimize
Overbooking cost (airlines):




Direct compensation cost
Provision cost of hotel/meal
Reaccom cost (another flight/airline)
Ill-will cost (~ “lifetime customer value”)
Industries
Overbooking
 Airlines
 Hotels
 Car rentals
 Education
 Manufacturing
 Media
No Overbooking
 Restos
 Movies, shows
 Events
 Resort hotels
 Cruise lines
...Decisions Are Not Always
“Rational”
Tickets; $7.95
Tickets; $6.95
$1.00 Discount
for Children &
Seniors
$1.00 Extra
for Middle Aged
People
Price Perception Issues are
Complex...
More Acceptable
Pricing
Product-Based
Open
Discretionary
Discounts and
Promotions
Rewards
Less Acceptable
Pricing
Customer-Based
Hidden
Imposed
Surcharges
Penalties
CRM
DPRM
 “Attract & retain
customers”
 maximize profit from
each customer
 Segment by customer
LTV
 Price/availability= fct. of
forecasted customer
LTV to the organization
 Ignores capacity issues
and opportunity costs
(displacement)
 Wealth of data
 “generate revenue”
 maximize profit from
available assets
 Segment by customer
WTP
 Price/availability = fct.
of forecasted demand &
available supply
 Ignores customer value
issues and long term
revenues
 Quantifiable value
Maximize long-term profits
CRM & RM
Variables to track







Actual win or loss
Number of days played
Credit history
Length of stay at hotel
Individual spending preferences
Demographics
Psychographic profiles
Theoretical Revenue
 Theoretical =
(total amount wagered) X
(house advantage)
100$ hand x 10 hours x 100 Hands/hour
x .01 (house adv. 49/51) = $1,000
Can you track every single person???
 Not always
 Difficult in table games
 Theoretical =
(total amount wagered) X
(house advantage)
Where..
Total amount wagered = estimated
average bet x estimated time played
Future estimates…
 ADT = Average Daily Theoretical
Revenue
 Assumes that this level is constant
 Multiply by estimated # of days of
future trip to gain value
 Combined with CRM data on
consumption of food and beverage,
entertainment, pshychographics, etc
Rooms, a scarce resource
 Heads in beds: make money on
gaming
 Comp. Rooms: traditionally a fixed
number of rooms given to big
gamblers
 Used averages to cost out, did not
dynamically look at “opportunity cost”
ReInvestment amount
% of the ADT
ADT $1,000
Reinvestment amount = 30%
= $300
Total value of the room, F&B,
Entertainment, etc. must be less than the
 Room 200, F&B 100, Ent. 80..more than
ADT x reinvest.
 Ergo…try and sell room..
 Sophisticated applications use dynamic
pricing to asses opportunity costs..





Requirements
 RM – Yield management like the
airlines..
 Player tracking systems..Use cards
like Harras, to register all activity and
psychographic profiles
 POS resturants, theaters, spas, retail
stores, entertainment, etc…
 CRM integrates all of the above!!
 Statistical analysis and optimization
applications.