1. Two Hotelies in trouble
Don’t
T: Small Fine T: Long Prison
Revenue Management – Pricing, Confess
B: Small Fine B: Free
Search and OTAs Ted
T: Free T: Short Prison
Chris K Anderson Confess B: Long Prison B: Short Prison
cka9@cornell.edu
Don’t Confess Confess
Bill
Two Hotelies in trouble Likely outcome?
Bill and Ted are suspected of a crime committed by two
Don’t
persons. They are being questioned by authorities in T: Small Fine T: Long Prison
Confess
two separate rooms. B: Small Fine B: Free
Each is being encouraged to cooperate (confess). There
g g p ( ) Ted
is very little evidence so if neither confess they will T: Free T: Short Prison
get off w/ small fine. Confess B: Long Prison B: Short Prison
Don’t Confess Confess
Bill
1

2. Price Cut/War! Price Cut/War!
Hold
T: Moderate Profit 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
Price Cut/War! What is the result?
Hold
HP vs Dell
Pampers vs Huggies
Ted Marboro
Etc…
Cut
’92 fare wars
Hold Cut
Bill
2

3. Fare Wars Industry Characteristics & PWs
’92 a lot of variance in fares, customer’s buying two Supply Demand
round trips to avoid S/SO Cost Price sensitivity of
Airlines w/ lots of capacity LF ~60% demand
Capacity Utilization
AA announces ‘value’ fares Efficient of shopping
Product Perishability
Delta, UA follow
Product Differentiation Brand loyalty
TWA undercuts
Growth rate
NWA 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
dumbest competitor… the business is driven entirely
by the behavior of our competitors….each airline
y p
Price Customization
doing what’s best for itself versus the industry”
3

4. Room Response Curve
Price Customization Sales Volume Sales Response Curve
B
380
riable unit cost
“If I have 2000 customers on a given route
and 400 different prices, I am obviously
p 190
Price below var
short 1600 prices.” D E
-Robert L. Crandall The Maximum
Former CEO of American Profit Rectangle for
Airlines Single Price
(ADEF) C
0.0 A F
0.0 10 200 390
Number of rooms
Room Response Curve
Sales Response Curve
Passed Up Profit because reservation
Sales Volume
B 380 price under 200
380
Price below variable unit cost
B The Maximum Profit Rectangle for
Price below variable unit cost
Single Price
X Money Left on the Table;
(25%) willing to pay more but priced
190 too cheap so people
v
v
paid the cheaper rate;
called consumer surplus.
50%
Y
(25%)
A C 0.0 A C
0.0
0.0 10 0.0 10 200
Variable Unit Cost 390 390
Sales Price
16
4

5. Sales Volume Room Response Curve Fences to Manage Segments
Sales Response Curve
380
B
ariable unit cost
Differentiate Products
X1
Purchase Fences
254 Value-added
The Maximum Profit
Communicate Product Differentiation
Rectangle f
R t l for
Price below va
127 Y1
Price 1
The Maximum Profit
127 Rectangle for Y2
A Price 2
0.0 C
0.0 10 137 263 390
Differential Pricing Product-line Sort
As A Way to Build Fences
Tapping segments with different ‘willingness to pay’ Develop a product line and have customers sort
Different ‘products’ offered to leisure versus business themselves among the various offerings based on
travelers their preference (e.g., room with view)
Prevent diversion by setting restricitions Can have vertical differentiation (good, better, best)
(g )
appliances
5

6. “Potential” Fences Price cuts
Rule Type Advanced Refundability Changeability Must
Without perfect fences rate cuts ‘leak’ more demand
Requirement Stay than they ‘tap’
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 Lessons from air travel
Customization
Companies aim mostly for the low-price triangle Post 2000
(discounting), but not for the high-price triangle. Growth of low-fare airline, with unrestricted fares
Goal:Price customization should not bring the average Price matching by ‘legacy’ carriers
price down!
Increased consumer search
Fencing is not effective
Movement to ‘simplified’ fares
Customer with high willingness to pay slip into low
price categories
LEAKAGE
6

7. 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?
Unilateral action
Match or not match
Contemplating a price action? 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
7

8. BE ANALYSIS BE Example
ΔP – assumed –ve here Suppose a hotel is considering a $25 per room night price increase
i.e. price cut from its present price of $150 and its variable cost per room night is $15.
(P-C)Q=Original Profit
(P+ΔP-C)(Q +Δ Q)=New after decrease Room night decrease for the property to breakeven?
CM = P – VC = $150 - $15 = $135
(P-C)Q=(P+ΔP-C)(Q +Δ Q)
PQ CQ PQ ΔPQ CQ PΔQ ΔPΔQ CΔQ
PQ-CQ=PQ+ΔPQ-CQ+PΔQ+ΔPΔQ-CΔQ - ΔP -$25
$25
Percent Breakeven = x 100 = x 100
ΔQ (P-C+ΔP)=-QΔP $135 + $25
CM + ΔP
ΔQ/Q=-ΔP/(P-C+ΔP)
Percent Breakeven = -15.6%
- ΔP
%BE = X 100 Price increase must not cause more than a -15.6% loss
CM + ΔP in volume for the hotel to break even!
BE ANALYSIS MARKET – PRICE REACTION
• Breakeven (BE) – Minimum change in sales volume
or occupancy to offset a price change Hotels are part of a competitive set
• Percent Breakeven (%BE) – Minimum percent Constantly evaluating matching price actions by
change in sales volume or occupancy to offset a competitors:
What is the minimum potential occupancy loss that justifies
price change matching a competitor’s price cut?
%BE = ΔQ / Q X 100
- ΔP What is the minimum potential occupancy gain that
%BE = X 100 justifies not matching a competitor’s price increase?
CM + ΔP
8

9. PRICE REACTION Price Elasticity
Competitor drops price ΔP
P = Current price of a good
Assume we will loose some volume
How much? Are we better off losing volume or losing
Q = Quantity demanded at that price
margin? ΔP = Small change in the current price
If we follow - lost margin ΔP/CM
margin= ΔQ = Resulting change in quantity demanded
If we don’t follow lost sales ΔQ Percentage Change in Quantity
Elasticity =
BE= ΔQ/Q= ΔP/CM Percentage Change in Price
ΔQ
Elasticity = Q
ΔP
P
Suppose a competitor lowers price by $10 and Size of Price Elasticities
current price is $100.
ΔP %Δ P Unit elastic
BE = or %BE =
CM %CM Inelastic Elastic
Variable cost is $20. 0 1 2 3 4 5 6
CM = $100 – $20 = $80
%Δ P $10 / $100
%BE = = X 100 = 12.5% Unit elastic: price elasticity equal to 1
%CM $80 / $100 • Inelastic: price elasticity less than 1
If the property loses more than 12.5% of room
• Elastic: price elasticity greater than 1
nights sold, it will take a contribution loss!
9

10. SALES CURVES and PRICE ELASTICITY SALES CURVES and PRICE ELASTICITY
Price Price If a market or market segment is price elastic (є > | 1 |),
P2 P2 then raising price will reduce contribution. So, lowering price
(or matching a competitor’s price reduction) is the only
P1 Demand P1
contributory action!
Demand
If a market or market segment is price inelastic (є < | 1 |),
Q2 Q1 Quantity Q2 Q1 Quantity then lowering price will reduce contribution. So, raising price
Elastic Inelastic (or matching a competitor’s price increase) is the only
contributory action!
E > 1 % Q > % P E< 1 % Q < % P
SALES CURVES and PRICE ELASTICITY Impact
Price Price cuts need to be segmented to be incremental
Price
versus dilutive
P2 P2
VC
Avoiding blanket discounts
P1 P1
VC
Opaques (HW, PCLN, Top Secret)
Packages
Q2 Q1 Quantity Q2Q1 Quantity Email offers Travelzoo
Elastic Inelastic Search Engine Marketing/PPC
OTA promotion/positioning/flash offers
E > |1| P Contribution E<|1| P Contribution GDS positioning Amadeus Instant Preference, Sabre Spotlight
10

11. OPAQUE PRICING
Priceline Tutorial
Median retail pricing is
provided to give
customers a realistic
benchmark for offers
Opaque Offer
Guidance
11

12. Lastminute.com
• 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 Travelocity
12

13. Expedia 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
25 3 3.5
35 4 4.5
45 5
Hotwire Expedia Opaque Expedia Merchant
51
Extending reach The Six Points of Opacity
Less Opacity = More Dilution
Inline banners on Results page to Opaque page Opaque Transparent
No access to results from home page
All inventory sourced through Hotwire
Co-branded as Hotwire
Pricing, sort, content from Hotwire
Launch integrates ‘basic’ opaque product
basic
No reviews
No Bed Choice
Amenities limited
Filters limited
Priceline Hotwire Merchant
PRICES
50
13

14. The Rate That Is Booked
How they work?
The highest qualifying rate is usually booked giving hotels more revenue
Travelocity Hotels are encouraged to load multiple rate tiers
All opaque offerings listed Provides hotels with opportunity to accept more offers at various price points
45% of bookings are at rates above the minimum tier
Hotwire/Expedia Unpublished
One star per zone
For example: Guest offers: $100
Usually the lowest priced supplier
Hotel available priceline rates: $100, $88, $78
Priceline Priceline will book: $88
If $78 and $88 rates are closed out, priceline may book the $100 rate
Random allocation (making $0 margin) if no other partner has an available qualifying rate
DATA
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
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
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
14

15. Summary data of bids There’s an APP for that….
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
14706 ht l
Making the Most of Priceline’s Name-Your-Own-
Price Channel
http://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-
15296.html
15

16. “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
Income Comparison: OTA Hotel Prospects
Re-designed Bid Now Income Comparison – OTA Hotel Prospects
(% breakdown of visitors to each OTA hotel section, Jan-Jun 2007)
Improved screen layout
makes it clear how to 45%
change dates, adds a
40%
“Help” option, and
supports user-entered 35%
bid amounts. 30%
25%
20%
15%
10%
5%
Opaque Radar
0%
See nearby areas and
<$30K $30-60K $60-100K $100K+
winning bids. Plus,
both retail and opaque Expedia Prospects Orbitz Prospects T ravelocity Prospects PCLN NYOP Prospects PCLN Retail Prospects
radars gain new zoom
and filtering
capabilities.
16

17. HTTP://BiddingForTravel.com
BiddingForTravel – The Fanatics
http://biddingfortravel.yuku.com/topic/98782/t/The-Curtain-is-Parted-More-or-Less.html
17

18. Goal 1: Rank High When Consumer
Searches on Internet
Search – SEO/SEM
Goal 2: Click Through to Reservation
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?
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
18

19. Search Engine Technology Organic and Paid Searches
Organic and Paid Searches Organic and Paid Searches
Paid Results
Organic Results
Paid Results
Local
Results
Organic Results
Organic
Results
19

20. How do SE determine page position? Search: New York City Midtown Hotel
Google’s Measure of Importance of Page
Download from www.google.com
Search: New York City Midtown Hotel
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.
20

21. The Long Tail of Search PPC Performance
The Head—Branded
The Tail—Unbranded
Uses Search Engines Pay to Search
Engines to Rank High Google
Algorithmic Calculations
(Cost-per-Click)
2nd price sealed bid auction
Submit bid, pay 1 penny more than bidder cheaper
than you that gets accepted
21

22. Keyword types
Search – “red eye from LAX” CR
CTR
CPC
Negative keywords
BID
Impressions (I) Expected Daily spend
Click–through rate (CTR) CTR*CPC*I
Cost per click (CPC)
Conversion rate (CR)
Average revenue (V)
22

23. CR
CTR
Return/I
SPEND
CPC
BID BID
Expected Daily spend Expected Daily spend
CTR*CPC*I CTR*CPC*I
Expected Return per impression Expected Return per impression
CTR CR V CTR CPC
CTR*CR*V – CTR*CPC CTR CR V CTR CPC
CTR*CR*V – CTR*CPC
Expected Return per booking
(CTR*CR*V-CTR*CPC)/(CTR*CR)
23

24. Expected Return per booking – SELF What is Google Quality Score?
FUNDING KEYWORDS 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
bid and the better its ad position.
+ve 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
keyword's
O 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
-ve (CTR) on Google, the relevance of the keyword and ad to the search term,
your account's historical performance, and other relevance factors.
BID
Quality issues Landing Pages
Landing Pages are also a factor in Quality Score
Both paid and natural search are quality adjusted lists Load Time
Content Keyword Rich Content
CTR Original Content
Links 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
Google is maximizing its PROFITS! relevance and Quality Score
24

25. Strategic Link Building Check on Your Competitors
www.linkpopularity.com
www.compete.com
www.marketleap.com
Who’s Linking To You?
Why Link Building? Because it works…
25

26. The Booking Experience on Your Website
Different Search Engines View Links Differently
4 Screens to Book 1 Reservation
Facilitating The Reservation - Conversion The Booking Experience via OneScreen
26

27. Case Study – St. James Hotel Do OTAs impact non-OTA reservation volume?
Best Practices in Search Engine Marketing and
Experimental study with JHM Hotels facilitated by
Optimization: The Case of the St. James Hotel
Expedia
http://www.hotelschool.cornell.edu/research/chr/pubs Four JHM properties
3 Branded
/reports/abstract-15320.html
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
Search, OTAs and online booking: The Billboard Do OTAs impact non-OTA reservation volume?
Effect
“Data”
Reservations made during the experimental period
Stay d
dates b h within and after the study period
both i hi d f h d i d
Removed any reservations through Expedia
Compare (non-Expedia) reservations during the on and
off treatments
27

28. OTA Implications – Creating Visibility Value Implications
OTA Impact on non-OTA reservations OTA demand acquisition ‘costs’ spread over all
impacted demand
Property Non-OTA e.g. 10% reservations through OTA
Volume Increase
Branded 1 7.5%
7 5% Billboard Effect~20%
9 Brand family properties within
Branded 2 9.1% 15 miles 20% of the remaining originates/impacted by OTA
Branded 3 14.1% 3 Brand family properties ≈20 miles 60% supplier direct - impacts 10% (50*1.2=60)
90% total - impacts 15% (75*1.2=90)
Independent 26%
OTA impacted volume = 10% + (10% to 15%)
Acquisition costs are less than ½ originally assumed
Lower the OTA share, further decrease costs
OTA Implications – Creating Visibility Billboard Effect I
OTA Impact on non-OTA reservations/rate Probably ~ 20% lift in non-OTA reservations created
through marketing effect of the OTA
Property Non-OTA ADR Increase depending on OTA volume results in reduction in
Volume Increase
‘fees’ by factor of 2-4(or more)
Branded 1 7.5%
7 5% 3.9%
3 9%
Branded 2 9.1% 0.8%
Branded 3 14.1% 0.3%
Independent 26% 0.8% Limitations
ADR across several stay dates (in and beyond 3 month study period)
Only 4 (mid scale) properties
ADR increase controlling for DOW, DBA, LOS 3 month sample window
28

29. Part II - Online consumer behavior Travel Site/Search Distributions
0.35
0.3
Relative frequency
0.25
Online consumer panel (~2 million) 0.2
0.15
All domain level internet traffic 0.1
2 months during each of 08,09 and 10 0.05
0
All upstream traffic of IHG.com bookings 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150
Number of site visits
Search @ Google, Bing, Yahoo 0.6
Travel site – OTA, Meta Search …. 0.5
Relative frequency
0.4
60 days prior to booking 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
Online consumer behavior OTA site behavior – the first page or bust?
74.7% of consumers visit OTA prior to booking at
supplier.com
82.5% perform a search Average behavior per booking (supplier.com)
65% do both Pages per Minutes per Number of
31% OTA 1st, 29% same day, 40% search 1st
y visit visit visits
1/2 of searches are URL related OTAs 7.44 4.67 11.6
2/3rds are branded
only 10.3% direct to supplier.com (no search or OTA)
29

30. OTA site behavior – the first page or bust? Channel Mix
Panel reservations at Expedia.com as well
Average behavior per booking (supplier.com) IHG.com : Expedia.com reservations ~10:1
IHG.com Expedia.com
Pages per Minutes per Number of % Reservations % Reservations
visit visit visits Candlewood Suites 5.9
59 5.7
57
All OTAs 7.44 4.67 11.6 Crowne Plaza Hotels 9.0 13.8
Holiday Inn 80.1 73.2
Expedia 7.47 4.78 7.5 Staybridge Suites 3.9 1.6
Hotel Indigo 0.6 0
74.4% of OTA visits are to Expedia Inter-Continental Hotels 0.6 5.7
OTA site behavior – by brand/scale Billboard Part II
Average behavior per booking (supplier.com)
% IHG.com Ratio IHG.com/Expedia
Pages Minutes Number Reservations
% Reservations
per visit per visit of visits
Visit Expedia Expedia
Candlewood Suites 9.1 5.5 6.2 5.9 All Impacted Expedia Only
Only OTA
Crowne Plaza Hotels 9.1 5.4 13.9 9.0 61.8%
61 8% 21.5%
21 5% 8.7
87 3.0
30
Holiday Inn 7.7 4.4 11.4 80.1
Staybridge Suites 8.1 4.7 9.9 3.9
Hotel Indigo 7.6 4.3 23.7 0.6
Inter-Continental Hotels 5.9 3.4 28.6 0.6
30

31. Billboard Part II Summary
View OTA as any other marketing expense
Part of the demand funnel
Ratio IHG.com/Expedia Reservations
Visibility at OTA increases non-OTA reservation
All Impacted Expedia Only volume s.t. OTA margins are on order of ¼ (or less)
Candlewood Suites 7.4 2.6 of actual transactional fees
Crowne Plaza Hotels 5.8 1.5 The Billboard Effect: Online Travel Agent Impact
Holiday Inn 9.5 3.4 on Non-OTA Reservation Volume
Staybridge Suites 20 9
http://www.hotelschool.cornell.edu/research/chr/pubs/re
Hotel Indigo ∞ ∞ ports/abstract-15139.html
Inter-Continental Hotels 1 0
Billboard Part II Email and Flash Offers
Ratio IHG.com/Expedia
Travelzoo
% IHG.com
Reservations SniqueAway/Jetsetter/Expedia ASAP
Visit Expedia Expedia
All Impacted Expedia Only
Only OTA
61.8%
61 8% 21.5%
21 5% 8.7
87 3.0
30
~3+ reservations @ IHG.com (impacted by
visibility) for each @ Expedia
Similar to JHM commission reductions
Ignores non-IHG.com impact
31

32. Email Blasts
32

33. SniqueAway (Jetsetter)
33

34. 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
34