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Revenue Management – Pricing, Search and OTAs

Chris K Andersoncka9@cornell.edu

Two Hotelies in trouble

Bill and Ted are suspected of a crime committed by two persons They are being questioned by authorities inpersons. 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.

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Two Hotelies in trouble

T S ll Fi T L P iDon’t

T: Small FineB: Small Fine

T: Long PrisonB: Free

T: FreeB: Long Prison

T: Short PrisonB: Short Prison

Ted

Confess

Confess

Bill

Don’t Confess ConfessConfess

Likely outcome?

T S ll Fi T L P iDon’t

T: Small FineB: Small Fine

T: Long PrisonB: Free

T: FreeB: Long Prison

T: Short PrisonB: Short Prison

Ted

Confess

Confess

Bill

Don’t Confess ConfessConfess

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Price Cut/War!

Price Cut/War!

Hold

Ted

Cut

Bill

Hold Cut

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Price Cut/War!

T M d t P fit T N MHold

T: Moderate ProfitB: Moderate Profit

T: No MoneyB: Big Profit

T: Big ProfitB: No Money

T: Tiny ProfitB: Tiny Profit

Ted

Cut

Bill

Hold Cut

What is the result?

HP D llHP vs DellPampers vs HuggiesMarboroEtc…

’92 fare wars

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Fare Wars

’92 a lot of variance in fares, customer’s buying two round trips to avoid S/SOround trips to avoid S/SOAirlines w/ lots of capacity LF ~60%AA announces ‘value’ faresDelta, UA followTWA undercutsNWA 2 for 1NWA 2-for-1AA 50% offRecord load factors, -20% in $$

AA, drops value fares, chairman“ i i h ill i i i f“…we are more victims than villains – victims of our

dumbest competitor… the business is driven entirely by the behavior of our competitors….each airline doing what’s best for itself versus the industry”

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Industry Characteristics & PWs

SupplyC

DemandP i i i i fCost

Capacity UtilizationProduct PerishabilityProduct Differentiation

Price sensitivity of demandEfficient of shoppingBrand loyaltyGrowth rate

Price Customization

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Price Customization

“If I have 2000 customers on a given route and 400 different prices, I am obviously short 1600 prices.”

-Robert L. CrandallFormer CEO of American Former CEO of mericanAirlines

380

Room Response CurveNumber of rooms Sales Response Curve

B

nit c

ost

ce b

elow

var

iable

un

0.00.0

10 390Variable Unit Cost

A C

Pric

Sales Price

8

380

Room Response CurveSales Volume Sales Response Curve

cost

B

190

The Maximum Profit Rectangle forbe

low

var

iable

unit

D E

0.00.0 10 390

A200

Single Price (ADEF)Pr

ice

CF

Sales Volume

The Maximum Profit Rectangle forSingle Price

Passed Up Profit because reservationprice under 200

nit c

ost B

380

gX

Y

Money Left on the Table;willing to pay more but priced too cheap so peoplepaid the cheaper rate; called consumer surplus.

190

ce b

elow

var

iable

un

(25%)

50%

16

0.00.0 10 390200

Y(25%)Pr

ic

A C

9

380

Room Response CurveSales Volume Sales Response Curve

X1B

it co

st

Y1

X1

254The Maximum Profit Rectangle forPrice 1

The Maximum Profit

127

e be

low

var

iable

uni

0.00.0 10 390

Y2

263137C

Rectangle forPrice 2A

127

Price

Differential Pricing

Tapping segments with different ‘willingness to pay’Diff ‘ d ’ ff d l i b iDifferent ‘products’ offered to leisure versus business travelersPrevent diversion by setting restricitions

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Fences to Manage Segments

Differentiate ProductsP h FPurchase FencesValue-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 onthemselves among the various offerings based on their preference (e.g., room with view)Can have vertical differentiation (good, better, best)

appliances

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“Potential” Fences

Rule Type Advanced Requirement

Refundability Changeability Must Stay

Advance Purchase

3- Day Non refundable No Changes WE

Advance Reservation

7-Day Partially refundable (% refund or fixed $)

Change to dates of stay, but not number of rooms

WD

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) but not for the high-price triangle(discounting), but not for the high price triangle.

Goal:Price customization should not bring the average price down!

Fencing is not effectiveCustomer with high willingness to pay slip into low price categoriesprice categories

LEAKAGE

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Price cutsWithout perfect fences rate cuts ‘leak’ more demand than they ‘tap’

Lessons from air travel

Post 2000G th f l f i li ith t i t d fGrowth of low-fare airline, with unrestricted faresPrice matching by ‘legacy’ carriersIncreased consumer search

Movement to ‘simplified’ fares

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Contemplating a price action?

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Questions to ask?

How much must occupancy increase to profit from a price decrease?

Unilateral actionMatch

How much can occupancy decline before a price i b fit bl ?increase becomes unprofitable?

Unilateral actionMatch or not match

Calculate the minimum sales volume necessaryfor the volume effect to balance the price effect.

Price Contribution margin (CM)

Breakeven ANALYSIS

Demand

Variable Cost

P1

P2

ΔP AB

Contribution margin (CM)CM = P – VC

A = CM lost B= CM gained

Service/Rooms

Demand

Q2Q1

ΔQ

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(P-C)Q=Original Profit(P+ΔP C)(Q +Δ Q)=New after decrease

BE ANALYSIS ΔP – assumed –ve herei.e. price cut

(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-CΔQΔQ (P-C+ΔP)=-QΔPΔQ/Q=-ΔP/(P-C+ΔP)

- ΔP

CM + ΔP %BE = X 100

• Breakeven (BE) – Minimum change in sales volumeor occupancy to offset a price change

BE ANALYSIS

• Percent Breakeven (%BE) – Minimum percent change in sales volume or occupancy to offset aprice change

%BE = ΔQ / Q X 100%BE ΔQ / Q X 100- ΔP

CM + ΔP %BE = X 100

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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?

BE Example

Room night decrease for the property to breakeven?CM = P – VC = $150 - $15 = $135

-$25

$135 + $25=

P t B k 15 6%

Percent Breakeven =- ΔP

CM + ΔPx 100 x 100

Percent Breakeven = -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?

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PRICE REACTIONCompetitor drops price ΔPAssume we will loose some volume

How much? Are we better off losing volume or losing margin?

If we follow - lost margin= ΔP/CMIf we don’t follow lost sales ΔQBE= ΔQ/Q= ΔP/CMBE ΔQ/Q ΔP/CM

BE =Δ P

Suppose a competitor lowers price by $10 andcurrent price is $100.

or %BE =%Δ P

BE = CM

or %BE = %CM

CM = $100 – $20 = $80

%BE%Δ P

=$10 / $100 X 100 = 12 5%

Variable cost is $20.

%BE = %CM

= $80 / $100

X 100 = 12.5%

If the property loses more than 12.5% of room nights sold, it will take a contribution loss!

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Price Elasticity

P = Current price of a goodQ Q i d d d h iQ = Quantity demanded at that price

ΔP = Small change in the current priceΔQ = Resulting change in quantity demanded

PriceinChangePercentageQuantity in Change PercentageElasticity =

PriceinChangePercentage

Elasticity

QQ

PP

Δ

= Δ

Size of Price Elasticities

Unit elastic

Unit elastic: price elasticity equal to 1

0 1 2 3 4 5 6

Inelastic Elastic

Unit elastic: price elasticity equal to 1

• Elastic: price elasticity greater than 1

• Inelastic: price elasticity less than 1

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Price Price

SALES CURVES and PRICE ELASTICITY

Quantity

P2

P1

Q2 Q1

ElasticQuantity

P2

P1

Q2 Q1

I l ti

Demand

Demand

Elastic

E < 1 % Q % P<

Inelastic

E > 1 % Q % P>

Price

P2

Price

P2

SALES CURVES and PRICE ELASTICITY

Quantity

2

P1

Q2 Q1

ElasticQuantity

P2

P1

Q2Q1

Inelastic

VC VC

E > | 1 | P Contribution E < | 1 | P Contribution

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If a market or market segment is price elastic (є > | 1 |),

SALES CURVES and PRICE ELASTICITY

then raising price will reduce contribution. So, lowering price

(or matching a competitor’s price reduction) is the onlycontributory action!

If a market or market segment is price inelastic (є < | 1 |),then lowering price will reduce contribution. So, raising price

l(or matching a competitor’s price increase) is the onlycontributory action!

ImpactPrice cuts need to be segmented to be incremental versus dilutiveAvoiding blanket discounts

Opaques (HW, PCLN, Top Secret) PackagesEmail offers TravelzooSearch Engine Marketing/PPCOTA promotion/positioning/flash offersGDS positioning Amadeus Instant Preference, Sabre Spotlight

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OPAQUE PRICING

Priceline Tutorial

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Median retail pricing is provided to give customers a realistic

Opaque Offer

benchmark for offers

p qGuidance

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• If the offer is unsuccessful, the customer is given an invitation to “try again” by changing one of their search

• Only if the offer is accepted will the customer receive specific hotel information

criteria

• Customers cannot resubmit their offer by only changing their offer price

Hotwire

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Lastminute.com

Travelocity

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Expedia

Inline banners on Results page to Opaque pageNo access to results from home page

All inventory sourced through Hotwire

Extending reach

ve o y sou ced oug o w eCo-branded as Hotwire Pricing, sort, content from Hotwire

Launch integrates ‘basic’ opaque product No reviewsNo Bed ChoiceAmenities limitedFilters limited

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Performance metricsImproved conversion by ~1%Star rating distribution

Averages between HW Opaque

Expedia Opaque Performance

and Expedia Merchant Booked ADRs boosted for hotels

Up 7.4% compared to Hotwire2 2.5 3 3.5 4 4.5 5

Hotwire Expedia Opaque Expedia Merchant

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The Six Points of OpacityThe Six Points of OpacityLess Opacity = More DilutionLess Opacity = More Dilution

Opaque Transparent

Priceline Merchant HotwirePRICES

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How they work?

TravelocityAll ff i li t dAll opaque offerings listed

Hotwire/Expedia UnpublishedOne star per zone

Usually the lowest priced supplier

PricelineRandom allocation

Based on the customer’s search criteria, a list of eligible hotels is created

From this list begins the “First Look” processOne hotel is chosen at random without regard for rates or availability

PCLN - How A Hotel Is Chosen

One hotel is chosen at random, without regard for rates or availabilityThen an availability search is done in Worldspan to see if the chosen hotel has a qualifying priceline rateIf a qualifying rate is found, the reservation is made and the process is complete

If the chosen hotel fails, begin the “Second Look” processRemaining 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 forThen one by one, priceline rates and inventory are searched in Worldspan for each hotelAs soon as a hotel is found with a qualifying priceline rate, the reservation is made and the process is completeIf no hotel has a qualifying priceline rate, the customer will be notified that their offer could not be fulfilled

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The highest qualifying rate is usually booked giving hotels more revenue

Hotels are encouraged to load multiple rate tiersP id h t l ith t it t t ff t i i i t

The Rate That Is Booked

Provides hotels with opportunity to accept more offers at various price points45% of bookings are at rates above the minimum tier

For example: Guest offers: $100Hotel available priceline rates: $100, $88, $78Priceline will book: $88Priceline will book: $88

If $78 d $88 t l d t i li b k th $100 tIf $78 and $88 rates are closed out, priceline may book the $100 rate (making $0 margin) if no other partner has an available qualifying rate

DATA

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Summary data of bids

0.35

0.4

Weekend

0

0.05

0.1

0.15

0.2

0.25

0.3

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 RevenueExpected Hotel Revenuehttp://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-14705.htmlhttp://www.hotelschool.cornell.edu/research/chr/pubs/tools/tooldetails-14706.html

Making the Most of Priceline’s Name-Your-Own-Price Channelhttp://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-15296.html

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There’s an APP for that….

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“Hotel Negotiator” initial release Fall 2009

RetailListings or Retail

Winning BidsShake or Select city

radar – point to see nearby hotels and rates

Shake or Select city to see recent Winning Bids

Re-designed Bid NowImproved screen layout makes it clear how to

Opaque Radar

change dates, adds a “Help” option, and supports user-entered bid amounts.

See nearby areas and winning bids. Plus, both retail and opaque radars gain new zoom and filtering capabilities.

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Income Comparison: OTA Hotel Prospects

Income Comparison – OTA Hotel Prospects(% breakdown of visitors to each OTA hotel section, Jan-Jun 2007)

45%

0%5%

10%15%

20%25%

30%35%

40%45%

0%<$30K $30-60K $60-100K $100K+

Expedia Prospects Orbitz Prospects T ravelocity Prospects PCLN NYOP Prospects PCLN Retail Prospects

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HTTP://BiddingForTravel.com

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BiddingForTravel – The Fanatics

http://biddingfortravel.yuku.com/topic/98782/t/The-Curtain-is-Parted-More-or-Less.html

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Search – SEO/SEM

What influences online travel purchases?

Base: Total usual online shoppersNote: What shopping for personal travel how influential are (insert) in deciding what to purchase?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 scaleSource: The PhoCusWright Consumer Travel Trends Survey Ninth Edition

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Goal 1: Rank High When ConsumerSearches on Internet

Goal 2: Click Through to Reservation

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Search Engine Technology

Organic and Paid Searches

Paid Results

O i R lt

Organic Results

Local Results

Organic Results

Organic Results

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Organic and Paid Searches

Organic and Paid Searches

Paid Results

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How do SE determine page position?

Google’s Measure of Importance of Page

Download from www.google.com

Google s Measure of Importance of Page

Keyword Phrases

Key to Success: The Right Keyword Phrases

Keyword Phrases

What are people looking for?

How are they finding you today?

How are they finding yourHow are they finding your

competition today?

Google’s Cache will show you what keywords it’s reading on the site.

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Search: New York City Midtown Hotel

Search: New York City Midtown Hotel

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The Long Tail of Search

The Head—BrandedThe Head Branded

The Tail—Unbranded

Uses Search Engines Algorithmic Calculations

Pay to Search Engines to Rank High (Cost-per-Click)

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PPC Performance

Google

2nd price sealed bid auctionS b i bid 1 h bidd hSubmit bid, pay 1 penny more than bidder cheaper than you that gets accepted

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Keyword types

Search – “red eye from LAX”

Negative keywords

Impressions (I)Cli k h h (CTR)Click–through rate (CTR)Cost per click (CPC)Conversion rate (CR)Average revenue (V)

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CTRCR

CPC

BID

Expected Daily spendCTR*CPC*ICTR*CPC*I

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CTR

CPCSPEND

BID

Expected Daily spendCTR*CPC*ICTR*CPC*I

Expected Return per impressionCTR*CR*V – CTR*CPC

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CR

Return/I

BID

Expected Daily spendCTR*CPC*ICTR*CPC*I

Expected Return per impressionCTR*CR*V – CTR*CPC

Expected Return per booking(CTR*CR*V-CTR*CPC)/(CTR*CR)

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Expected Return per booking – SELF FUNDING KEYWORDS

O

+ve

BID

-ve

Quality issues

Both paid and natural search are quality adjusted listsC t tContentCTRLinks

Google is maximizing its PROFITS!

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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 minimumsearch query. The higher a keyword s Quality Score, the lower its minimum 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 rateQuality 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 PagesLanding Pages are also a factor in Quality Score

Load TimeK d Ri h CKeyword Rich ContentOriginal ContentSending 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 ScoreQ y

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Strategic Link Building

Why Link Building? Because it works…

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Check on Your Competitors

www.linkpopularity.com www.compete.comwww.marketleap.com

Who’s Linking To You?

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Different Search Engines View Links Differently

Facilitating The Reservation - Conversion

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The Booking Experience on Your Website

4 Screens to Book 1 Reservation

The Booking Experience via OneScreen

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Case Study – St. James Hotel

Best Practices in Search Engine Marketing and Optimization: The Case of the St James HotelOptimization: The Case of the St. James Hotel

http://www.hotelschool.cornell.edu/research/chr/pubs/reports/abstract-15320.html

Search, OTAs and online booking: The Billboard Effect

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Do OTAs impact non-OTA reservation volume?

Experimental study with JHM Hotels facilitated by p y yExpedia

Four JHM properties3 Branded1 Independent

3 month period, cycled properties on and off Expedia (7-11 days per cycle)

40 days on Expedia40 days off

For all arrival dates

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 ExpediaCompare (non-Expedia) reservations during the on and p ( p ) goff treatments

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OTA Implications – Creating Visibility

OTA Impact on non-OTA reservations

Property Non-OTA Volume Increase

Branded 1 7.5%Branded 2 9.1%Branded 3 14.1%I d d t 26%

3 Brand family properties 20 miles

9 Brand family properties within 15 miles

≈Independent 26%

OTA Implications – Creating Visibility

OTA Impact on non-OTA reservations/rate

Property Non-OTA Volume Increase

ADR Increase

Branded 1 7.5% 3.9%Branded 2 9.1% 0.8%Branded 3 14.1% 0.3%I d d t 26% 0 8%Independent 26% 0.8%

ADR across several stay dates (in and beyond 3 month study period)

ADR increase controlling for DOW, DBA, LOS

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Value Implications

OTA demand acquisition ‘costs’ spread over all impacted demandimpacted demand

e.g. 10% reservations through OTABillboard Effect~20%

20% of the remaining originates/impacted by OTA60% supplier direct - impacts 10% (50*1.2=60) 90% total - impacts 15% (75*1.2=90)p ( )

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 OTAthrough marketing effect of the OTA

depending on OTA volume results in reduction in ‘fees’ by factor of 2-4(or more)

Li it tiLimitations Only 4 (mid scale) properties3 month sample window

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Part II - Online consumer behavior

Online consumer panel (~2 million)p ( )All domain level internet traffic 2 months during each of 08,09 and 10

All upstream traffic of IHG.com bookingsSearch @ Google, Bing, YahooTravel site – OTA Meta SearchTravel site OTA, Meta Search ….60 days prior to booking

Online consumer behavior74.7% of consumers visit OTA prior to booking at supplier.com82 5% f h82.5% perform a search

65% do both31% OTA 1st, 29% same day, 40% search 1st

1/2 of searches are URL related2/3rds are branded

only 10.3% direct to supplier.com (no search or OTA)

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Travel Site/Search Distributions

0.2

0.25

0.3

0.35

requ

ency

0

0.05

0.1

0.15

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Number of site visits

Rel

ativ

e fr

0.5

0.6

cy

0

0.1

0.2

0.3

0.4

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

Number of searches

Rel

ativ

e fr

eque

nc

OTA site behavior – the first page or bust?

Average behavior per booking (supplier com)

Pages per visit

Minutes per visit

Number of visits

OTAs 7.44 4.67 11.6

Average behavior per booking (supplier.com)

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OTA site behavior – the first page or bust?

Average behavior per booking (supplier com)

Pages per visit

Minutes per visit

Number of visits

All OTAs 7.44 4.67 11.6Expedia 7.47 4.78 7.5

Average behavior per booking (supplier.com)

p

74.4% of OTA visits are to Expedia

OTA site behavior – by brand/scale

Pages Minutes Number

Average behavior per booking (supplier.com)

Pages per visit

Minutes per visit

Number of visits % Reservations

Candlewood Suites 9.1 5.5 6.2 5.9Crowne Plaza Hotels 9.1 5.4 13.9 9.0Holiday Inn 7.7 4.4 11.4 80.1Staybridge Suites 8.1 4.7 9.9 3.9Hotel Indigo 7 6 4 3 23 7 0 6Hotel Indigo 7.6 4.3 23.7 0.6Inter-Continental Hotels 5.9 3.4 28.6 0.6

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Channel Mix

Panel reservations at Expedia.com as wellIHG.com : Expedia.com reservations ~10:1p

IHG.com% Reservations

Expedia.com% Reservations

Candlewood Suites 5.9 5.7Crowne Plaza Hotels 9.0 13.8Holiday Inn 80.1 73.2St b id S it 3 9 1 6Staybridge Suites 3.9 1.6Hotel Indigo 0.6 0Inter-Continental Hotels 0.6 5.7

Billboard Part II

% IHG.com Ratio IHG.com/Expedia Reservations

Visit Expedia Expedia Only OTA All Impacted Expedia Only

61.8% 21.5% 8.7 3.0

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Billboard Part II

Ratio IHG.com/Expedia ReservationsRatio IHG.com/Expedia Reservations

All Impacted Expedia Only

Candlewood Suites 7.4 2.6Crowne Plaza Hotels 5.8 1.5Holiday Inn 9.5 3.4Staybridge Suites 20 9Staybridge Suites 20 9Hotel IndigoInter-Continental Hotels 1 0

∞ ∞

Billboard Part II

% IHG.com Ratio IHG.com/Expedia

~3+ reservations @ IHG.com (impacted by visibility) for each @ Expedia

ReservationsVisit Expedia Expedia

Only OTA All Impacted Expedia Only

61.8% 21.5% 8.7 3.0

visibility) for each @ ExpediaSimilar to JHM commission reductionsIgnores non-IHG.com impact

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SummaryView OTA as any other marketing expense

Part of the demand funnelVi ibili OTA i OTA iVisibility at OTA increases non-OTA reservation 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/reports/abstract-15139.html

Email and Flash Offers

TravelzooS i A /J /E di ASAPSniqueAway/Jetsetter/Expedia ASAP

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Email Blasts

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65

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SniqueAway (Jetsetter)

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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 WorksResearch 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