Modelo/Ofício 12ª cruzada contra a fome (para os condomínios)
Pricing, Search, And Ot As Part 2
1. Revenue Management – Pricing,
Search and OTAs
Chris K Anderson
cka9@cornell.edu
Two Hotelies in trouble
Bill and Ted are suspected of a crime committed by two
persons.
persons They are being questioned by authorities in
two separate rooms.
Each is being encouraged to cooperate (confess). There
is very little evidence so if neither confess they will
get off w/ small fine.
1
2. Two Hotelies in trouble
Don’t
Confess T: S ll Fine
T Small Fi T: L
T Long Prison
Pi
B: Small Fine B: Free
Ted
T: Free T: Short Prison
Confess B: Long Prison B: Short Prison
Don’t Confess Confess
Bill
Likely outcome?
Don’t
Confess T: S ll Fine
T Small Fi T: L
T Long Prison
Pi
B: Small Fine B: Free
Ted
T: Free T: Short Prison
Confess B: Long Prison B: Short Prison
Don’t Confess Confess
Bill
2
3. Price Cut/War!
Price Cut/War!
Hold
Ted
Cut
Hold Cut
Bill
3
4. Price Cut/War!
Hold
T: M d t P fit
T Moderate Profit T: N M
T No Money
B: Moderate Profit B: Big Profit
Ted
T: Big Profit T: Tiny Profit
Cut B: No Money B: Tiny Profit
Hold Cut
Bill
What is the result?
HP vs D ll
Dell
Pampers vs Huggies
Marboro
Etc…
’92 fare wars
4
5. Fare Wars
’92 a lot of variance in fares, customer’s buying two
round trips to avoid S/SO
Airlines w/ lots of capacity LF ~60%
AA announces ‘value’ fares
Delta, UA follow
TWA undercuts
NWA 2 for 1
2-for-1
AA 50% off
Record load factors, -20% in $$
AA, drops value fares, chairman
“…we are more victims than villains – victims of our
“ i i h ill i i i f
dumbest competitor… the business is driven entirely
by the behavior of our competitors….each airline
doing what’s best for itself versus the industry”
5
6. Industry Characteristics & PWs
Supply Demand
Cost
C Price
P i sensitivity of
ii i f
Capacity Utilization demand
Product Perishability Efficient of shopping
Product Differentiation Brand loyalty
Growth rate
Price Customization
6
7. Price Customization
“If I have 2000 customers on a given route
and 400 different prices, I am obviously
short 1600 prices.”
-Robert L. Crandall
Former CEO of American
merican
Airlines
Number of rooms
Room Response Curve
Sales Response Curve
B
380
Pric below variable un cost
ce nit
A C
0.0
0.0 10 390
Variable Unit Cost
Sales Price
7
8. Room Response Curve
Sales Volume Sales Response Curve
B
380
Price below variable unit cost
190
D E
The Maximum
Profit Rectangle for
Single Price
(ADEF) C
0.0 A F
0.0 10 200 390
Passed Up Profit because reservation
Sales Volume
380 price under 200
B The Maximum Profit Rectangle for
Pric below variable un cost
Single Price
g
nit
X Money Left on the Table;
(25%) willing to pay more but priced
190 too cheap so people
paid the cheaper rate;
called consumer surplus.
50%
Y
ce
(25%)
0.0 A C
0.0 10 200 390
16
8
9. Sales Volume Room Response Curve
Sales Response Curve
380
B
Price below variable uni cost
X1
it
254
The Maximum Profit
127 Rectangle for
Y1
Price 1
The Maximum Profit
e
127 Rectangle for Y2
A Price 2
0.0 C
0.0 10 137 263 390
Differential Pricing
Tapping segments with different ‘willingness to pay’
Different ‘products’ offered to leisure versus business
Diff ‘ d ’ ff d l i b i
travelers
Prevent diversion by setting restricitions
9
10. Fences to Manage Segments
Differentiate Products
Purchase F
P h Fences
Value-added
Communicate Product Differentiation
Product-line Sort
As A Way to Build Fences
Develop a product line and have customers sort
themselves among the various offerings based on
their preference (e.g., room with view)
Can have vertical differentiation (good, better, best)
appliances
10
11. “Potential” Fences
Rule Type Advanced Refundability Changeability Must
Requirement Stay
Advance 3- Day Non refundable No Changes WE
Purchase
Advance 7-Day Partially refundable Change to dates of stay, WD
Reservation (% refund or fixed $) but not number of rooms
14- Day Fully refundable Changes, but pay fee,
must still meet rules
21-Day Full changes, non-
refundable
30-Day Full changes allowed
Biggest Mistakes in Price
Customization
Companies aim mostly for the low-price triangle
(discounting),
(discounting) but not for the high price triangle.
high-price triangle
Goal:Price customization should not bring the average
price down!
Fencing is not effective
Customer with high willingness to pay slip into low
price categories
LEAKAGE
11
12. Price cuts
Without perfect fences rate cuts ‘leak’ more demand
than they ‘tap’
Lessons from air travel
Post 2000
Growth of l
G th f low-fare airline, with unrestricted fares
f i li ith t i t df
Price matching by ‘legacy’ carriers
Increased consumer search
Movement to ‘simplified’ fares
12
14. Questions to ask?
How much must occupancy increase to profit from a
price decrease?
Unilateral action
Match
How much can occupancy decline before a price
increase becomes unprofitable?
i b fit bl ?
Unilateral action
Match or not match
Breakeven ANALYSIS
Calculate the minimum sales volume necessary
for the volume effect to balance the price effect.
Price Contribution margin (CM)
P1 CM = P – VC
ΔP A
P2
B A = CM lost B= CM gained
Variable Cost
Demand
Q1 Q2 Service/Rooms
ΔQ
14
15. BE ANALYSIS ΔP – assumed –ve here
i.e. price cut
(P-C)Q=Original Profit
(P+ΔP-C)(Q
(P+ΔP C)(Q +Δ Q)=New after decrease
(P-C)Q=(P+ΔP-C)(Q +Δ Q)
PQ-CQ=PQ+ΔPQ-CQ+PΔQ+ΔPΔQ-CΔQ
ΔQ (P-C+ΔP)=-QΔP
ΔQ/Q=-ΔP/(P-C+ΔP)
- ΔP
%BE = X 100
CM + ΔP
BE ANALYSIS
• Breakeven (BE) – Minimum change in sales volume
or occupancy to offset a price change
• Percent Breakeven (%BE) – Minimum percent
change in sales volume or occupancy to offset a
price change
%BE = ΔQ / Q X 100
- ΔP
%BE = X 100
CM + ΔP
15
16. BE Example
Suppose a hotel is considering a $25 per room night price increase
from its present price of $150 and its variable cost per room night is $15.
Room night decrease for the property to breakeven?
CM = P – VC = $150 - $15 = $135
- ΔP -$25
Percent Breakeven = x 100 = x 100
CM + ΔP $135 + $25
Percent Breakeven = -15.6%
P tB k 15 6%
Price increase must not cause more than a -15.6% loss
in volume for the hotel to break even!
MARKET – PRICE REACTION
Hotels are part of a competitive set
Constantly evaluating matching price actions by
competitors:
What is the minimum potential occupancy loss that justifies
matching a competitor’s price cut?
What is the minimum potential occupancy gain that
justifies not matching a competitor’s price increase?
16
17. PRICE REACTION
Competitor drops price ΔP
Assume we will loose some volume
How much? Are we better off losing volume or losing
margin?
If we follow - lost margin= ΔP/CM
If we don’t follow lost sales ΔQ
BE ΔQ/Q
BE= ΔQ/Q= ΔP/CM
Suppose a competitor lowers price by $10 and
current price is $100.
ΔP %Δ P
BE = or %BE =
CM %CM
Variable cost is $20.
CM = $100 – $20 = $80
%Δ P $10 / $100
%BE = = X 100 = 12 5%
12.5%
%CM $80 / $100
If the property loses more than 12.5% of room
nights sold, it will take a contribution loss!
17
18. Price Elasticity
P = Current price of a good
Q=Q Quantity d
i demanded at that price
d d h i
ΔP = Small change in the current price
ΔQ = Resulting change in quantity demanded
Percentage Change in Quantity
Elasticity =
Percentage Change in Price
ΔQ
Elasticity = Q
ΔP
P
Size of Price Elasticities
Unit elastic
Inelastic Elastic
0 1 2 3 4 5 6
Unit elastic: price elasticity equal to 1
• Inelastic: price elasticity less than 1
• Elastic: price elasticity greater than 1
18
19. SALES CURVES and PRICE ELASTICITY
Price Price
P2 P2
P1 Demand P1
Demand
Q2 Q1 Quantity Q2 Q1 Quantity
Elastic Inelastic
I l ti
E > 1 % Q > % P E< 1 % Q < % P
SALES CURVES and PRICE ELASTICITY
Price Price
P2 P2
P1
VC VC
P1
Q2 Q1 Quantity Q2Q1 Quantity
Elastic Inelastic
E > |1| P Contribution E<|1| P Contribution
19
20. SALES CURVES and PRICE ELASTICITY
If a market or market segment is price elastic (є > | 1 |),
then raising price will reduce contribution. So, lowering price
(or matching a competitor’s price reduction) is the only
contributory action!
If a market or market segment is price inelastic (є < | 1 |),
then lowering price will reduce contribution. So, raising price
(or matching a competitor’s price increase) is the only
l
contributory action!
Impact
Price cuts need to be segmented to be incremental
versus dilutive
Avoiding blanket discounts
Opaques (HW, PCLN, Top Secret)
Packages
Email offers Travelzoo
Search Engine Marketing/PPC
OTA promotion/positioning/flash offers
GDS positioning Amadeus Instant Preference, Sabre Spotlight
20
22. Median retail pricing is
provided to give
customers a realistic
benchmark for offers
Opaque Offer
p q
Guidance
22
23. • If the offer is unsuccessful, the
customer is given an invitation to “try
again” by changing one of their search
criteria
• Customers cannot resubmit their offer
• Only if the offer is accepted will the by only changing their offer price
customer receive specific hotel
information
Hotwire
23
25. Expedia
Extending reach
Inline banners on Results page to Opaque page
No access to results from home page
All inventory sou ced through Hotwire
ve o y sourced oug o w e
Co-branded as Hotwire
Pricing, sort, content from Hotwire
Launch integrates ‘basic’ opaque product
No reviews
No Bed Choice
Amenities limited
Filters limited
50
25
26. Expedia Opaque Performance
Performance metrics
Improved conversion by ~1%
Star rating distribution
Averages between HW Opaque
and Expedia Merchant
Booked ADRs boosted for hotels
Up 7.4% compared to Hotwire
2 2.5 3 3.5 4 4.5 5
Hotwire Expedia Opaque Expedia Merchant
51
The Six Points of Opacity
Less Opacity = More Dilution
Opaque Transparent
Priceline Hotwire Merchant
PRICES
26
27. How they work?
Travelocity
All opaque offerings li t d
ff i listed
Hotwire/Expedia Unpublished
One star per zone
Usually the lowest priced supplier
Priceline
Random allocation
PCLN - How A Hotel Is Chosen
Based on the customer’s search criteria, a list of eligible hotels is created
From this list begins the “First Look” process
One hotel is chosen at random without regard for rates or availability
random,
Then an availability search is done in Worldspan to see if the chosen hotel has
a qualifying priceline rate
If a qualifying rate is found, the reservation is made and the process is
complete
If the chosen hotel fails, begin the “Second Look” process
Remaining hotels are ranked in order of their recent 14 day performance with
priceline “First Looks” (hotel’s “Batting Average”)
Then one by one, priceline rates and inventory are searched in Worldspan for
one
each hotel
As soon as a hotel is found with a qualifying priceline rate, the reservation is
made and the process is complete
If no hotel has a qualifying priceline rate, the customer will be notified that
their offer could not be fulfilled
27
28. The Rate That Is Booked
The highest qualifying rate is usually booked giving hotels more revenue
Hotels are encouraged to load multiple rate tiers
Provides h t l ith
P id hotels with opportunity to accept more offers at various price points
t it t t ff t i i i t
45% of bookings are at rates above the minimum tier
For example: Guest offers: $100
Hotel available priceline rates: $100, $88, $78
Priceline will book: $88
If $78 and $88 rates are closed out, priceline may b k th $100 rate
d t l d t i li book the t
(making $0 margin) if no other partner has an available qualifying rate
DATA
28
29. Summary data of bids
Weekend
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
$125 $150 $175 $200 $225 $250 $275 $300 $325 $350 $375 $400 $425
Center for Hospitality Research
Setting Room Rates on Priceline: How to Optimize
Expected Hotel Revenue
http://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-
14705.html
http://www.hotelschool.cornell.edu/research/chr/pubs/tools/tooldetails-
14706.html
Making the Most of Priceline’s Name-Your-Own-
Price Channel
http://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-
15296.html
29
31. “Hotel Negotiator” initial release Fall 2009
Retail
Listings or Retail
radar – point to see
nearby hotels and
rates
Winning Bids
Shake or Select city
to see recent
Winning Bids
Re-designed Bid Now
Improved screen layout
makes it clear how to
change dates, adds a
“Help” option, and
supports user-entered
bid amounts.
Opaque Radar
See nearby areas and
winning bids. Plus,
both retail and opaque
radars gain new zoom
and filtering
capabilities.
31
32. Income Comparison: OTA Hotel Prospects
Income Comparison – OTA Hotel Prospects
(% breakdown of visitors to each OTA hotel section, Jan-Jun 2007)
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
<$30K $30-60K $60-100K $100K+
Expedia Prospects Orbitz Prospects T ravelocity Prospects PCLN NYOP Prospects PCLN Retail Prospects
32
34. BiddingForTravel – The Fanatics
http://biddingfortravel.yuku.com/topic/98782/t/The-Curtain-is-Parted-More-or-Less.html
34
35. Search – SEO/SEM
What influences online travel purchases?
Base: Total usual online shoppers
Note: What shopping for personal travel how influential are (insert) in deciding what to purchase?
travel,
Note: Reflects those respondents indicating these travel providers as being “strongly influential” or
“somewhat influential” on a 3-point scale
Source: The PhoCusWright Consumer Travel Trends Survey Ninth Edition
35
36. Goal 1: Rank High When Consumer
Searches on Internet
Goal 2: Click Through to Reservation
36
37. Search Engine Technology
Organic and Paid Searches
Paid Results
Organic Results
Local
Results
Organic Results
O i R lt
Organic
Results
37
38. Organic and Paid Searches
Organic and Paid Searches
Paid Results
38
39. How do SE determine page position?
Google s
Google’s Measure of Importance of Page
Download from www.google.com
Key to Success: The Right Keyword Phrases
Keyword Phrases
What are people looking for?
How are they finding you today?
How are they finding your
competition today?
Google’s Cache will show you what keywords it’s reading on the site.
39
40. Search: New York City Midtown Hotel
Search: New York City Midtown Hotel
40
41. The Long Tail of Search
The Head Branded
Head—Branded
The Tail—Unbranded
Uses Search Engines Pay to Search
Algorithmic Calculations Engines to Rank High
(Cost-per-Click)
41
42. PPC Performance
Google
2nd price sealed bid auction
Submit bid,
S b i bid pay 1 penny more than bidder cheaper
h bidd h
than you that gets accepted
42
43. Keyword types
Search – “red eye from LAX”
Negative keywords
Impressions (I)
Click–through rate (CTR)
Cli k h h
Cost per click (CPC)
Conversion rate (CR)
Average revenue (V)
43
47. Expected Return per booking – SELF
FUNDING KEYWORDS
+ve
O
-ve
BID
Quality issues
Both paid and natural search are quality adjusted lists
Content
C t t
CTR
Links
Google is maximizing its PROFITS!
47
48. What is Google Quality Score?
Quality Score for Google and the search network is a dynamic metric
assigned to each of your keywords. It's calculated using a variety of factors
and measures how relevant your keyword is to your ad group and to a user's
search query. The higher a keyword s Quality Score, the lower its minimum
keyword's
bid and the better its ad position.
The components of Quality Score vary depending on whether it's calculating
minimum bid or ad position:
Quality Score for minimum bid is determined by a keyword's clickthrough
rate (CTR) on Google, the relevance of the keyword to its ad group, your
landing page quality, your account's historical performance, and other
relevance factors.
Quality Score for ad position is determined by a keyword's clickthrough rate
(CTR) on Google, the relevance of the keyword and ad to the search term,
your account's historical performance, and other relevance factors.
Landing Pages
Landing Pages are also a factor in Quality Score
Load Time
Keyword Ri h Content
K d Rich C
Original Content
Sending the Right AdGroup to the Right Landing Page.
If you have “Wedding” related keywords, you should consider
sending them to a “Wedding” page on your site to improve
relevance and Quality Score
Q y
48
52. The Booking Experience on Your Website
4 Screens to Book 1 Reservation
The Booking Experience via OneScreen
52
53. Case Study – St. James Hotel
Best Practices in Search Engine Marketing and
Optimization: The Case of the St James Hotel
St.
http://www.hotelschool.cornell.edu/research/chr/pubs
/reports/abstract-15320.html
Search, OTAs and online booking: The Billboard
Effect
53
54. Do OTAs impact non-OTA reservation volume?
Experimental study with JHM Hotels facilitated by
p y y
Expedia
Four JHM properties
3 Branded
1 Independent
3 month period, cycled properties on and off Expedia
(7-11 days per cycle) For all arrival dates
40 days on Expedia
40 days off
Do OTAs impact non-OTA reservation volume?
“Data”
Reservations made during the experimental period
Stay dates both within and after the study period
Removed any reservations through Expedia
Compare (
p (non-Expedia) reservations during the on and
p ) g
off treatments
54
55. OTA Implications – Creating Visibility
OTA Impact on non-OTA reservations
Property Non-OTA
Volume Increase
Branded 1 7.5% 9 Brand family properties within
Branded 2 9.1% 15 miles
Branded 3 14.1% 3 Brand family properties ≈20 miles
Independent
I d d t 26%
OTA Implications – Creating Visibility
OTA Impact on non-OTA reservations/rate
Property Non-OTA ADR Increase
Volume Increase
Branded 1 7.5% 3.9%
Branded 2 9.1% 0.8%
Branded 3 14.1% 0.3%
Independent
I d d t 26% 0.8%
0 8%
ADR across several stay dates (in and beyond 3 month study period)
ADR increase controlling for DOW, DBA, LOS
55
56. Value Implications
OTA demand acquisition ‘costs’ spread over all
impacted demand
e.g. 10% reservations through OTA
Billboard Effect~20%
20% of the remaining originates/impacted by OTA
60% supplier direct - impacts 10% (50*1.2=60)
90% total - impacts 15% (
p (75*1.2=90))
OTA impacted volume = 10% + (10% to 15%)
Acquisition costs are less than ½ originally assumed
Lower the OTA share, further decrease costs
Billboard Effect I
Probably ~ 20% lift in non-OTA reservations created
through marketing effect of the OTA
depending on OTA volume results in reduction in
‘fees’ by factor of 2-4(or more)
Limitations
Li it ti
Only 4 (mid scale) properties
3 month sample window
56
57. Part II - Online consumer behavior
Online consumer panel ( million)
p (~2 )
All domain level internet traffic
2 months during each of 08,09 and 10
All upstream traffic of IHG.com bookings
Search @ Google, Bing, Yahoo
Travel site – OTA Meta Search ….
OTA,
60 days prior to booking
Online consumer behavior
74.7% of consumers visit OTA prior to booking at
supplier.com
82.5%
82 5% perform a search
f h
65% do both
31% OTA 1st, 29% same day, 40% search 1st
1/2 of searches are URL related
2/3rds are branded
only 10.3% direct to supplier.com (no search or OTA)
57
58. Travel Site/Search Distributions
0.35
0.3
requency
0.25
0.2
Relative fr
0.15
0.1
0.05
0
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Number of site visits
0.6
cy
Relative frequenc 0.5
0.4
0.3
0.2
0.1
0
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Number of searches
OTA site behavior – the first page or bust?
Average behavior per booking (supplier com)
(supplier.com)
Pages per Minutes per Number of
visit visit visits
OTAs 7.44 4.67 11.6
58
59. OTA site behavior – the first page or bust?
Average behavior per booking (supplier com)
(supplier.com)
Pages per Minutes per Number of
visit visit visits
All OTAs 7.44 4.67 11.6
Expedia
p 7.47 4.78 7.5
74.4% of OTA visits are to Expedia
OTA site behavior – by brand/scale
Average behavior per booking (supplier.com)
Pages Minutes Number
% Reservations
per visit per visit of visits
Candlewood Suites 9.1 5.5 6.2 5.9
Crowne Plaza Hotels 9.1 5.4 13.9 9.0
Holiday Inn 7.7 4.4 11.4 80.1
Staybridge Suites 8.1 4.7 9.9 3.9
Hotel Indigo 7.6
76 4.3
43 23.7
23 7 0.6
06
Inter-Continental Hotels 5.9 3.4 28.6 0.6
59
60. Channel Mix
Panel reservations at Expedia.com as well
IHG.com : Expedia.com reservations ~10:1
p
IHG.com Expedia.com
% Reservations % Reservations
Candlewood Suites 5.9 5.7
Crowne Plaza Hotels 9.0 13.8
Holiday Inn 80.1 73.2
Staybridge Suites
St b id S it 3.9
39 1.6
16
Hotel Indigo 0.6 0
Inter-Continental Hotels 0.6 5.7
Billboard Part II
% IHG.com Ratio IHG.com/Expedia
Reservations
Visit Expedia Expedia
All Impacted Expedia Only
Only OTA
61.8% 21.5% 8.7 3.0
60
61. Billboard Part II
Ratio IHG.com/Expedia Reservations
All Impacted Expedia Only
Candlewood Suites 7.4 2.6
Crowne Plaza Hotels 5.8 1.5
Holiday Inn 9.5 3.4
Staybridge Suites 20 9
Hotel Indigo ∞ ∞
Inter-Continental Hotels 1 0
Billboard Part II
% IHG.com Ratio IHG.com/Expedia
Reservations
Visit Expedia Expedia
All Impacted Expedia Only
Only OTA
61.8% 21.5% 8.7 3.0
~3+ reservations @ IHG.com (impacted by
visibility) for each @ Expedia
Similar to JHM commission reductions
Ignores non-IHG.com impact
61
62. Summary
View OTA as any other marketing expense
Part of the demand funnel
Visibility
Vi ibili at OTA i increases non-OTA reservation
OTA i
volume s.t. OTA margins are on order of ¼ (or less)
of actual transactional fees
The Billboard Effect: Online Travel Agent Impact
on Non-OTA Reservation Volume
http://www.hotelschool.cornell.edu/research/chr/pubs/re
ports/abstract-15139.html
Email and Flash Offers
Travelzoo
SniqueAway/Jetsetter/Expedia ASAP
S i A /J /E di
62
67. Travel Agent Targeted Advertising
Galileo Headlines
Generate Up to 3 Times More Sales
with Preferred Placement
Why Not Be Here
Tomorrow!
Your Hotel is
Here Today.
Preferred Placement Works
Research shows that agents are up to 3.5 times more likely to select hotels that
appear at or near the top of hotel displays.
2004 Travel Agent Media Study
67
