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Pricing, Search, And Ot As Part 2

The document discusses opaque hotel booking strategies and pricing. It provides examples of how opaque booking sites like Priceline, Hotwire and Expedia work, explaining that they show limited or no information about the specific hotel until after a booking is accepted. It also summarizes how hotels are selected on these sites, with Priceline choosing randomly from eligible hotels matching a customer's search criteria. The document advises that opaque bookings can boost average daily rates for hotels compared to regular online travel agency bookings.

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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
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
Price Cut/War! Price Cut/War! Hold Ted Cut Hold Cut Bill 3
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
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
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
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
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
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
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
“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
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
Contemplating a price action? 13
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
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
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
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
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
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
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
OPAQUE PRICING Priceline Tutorial 21
Median retail pricing is provided to give customers a realistic benchmark for offers Opaque Offer p q Guidance 22
• 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
Lastminute.com Travelocity 24
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
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
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
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
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
There’s an APP for that…. 30
“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
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
HTTP://BiddingForTravel.com 33
BiddingForTravel – The Fanatics http://biddingfortravel.yuku.com/topic/98782/t/The-Curtain-is-Parted-More-or-Less.html 34
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
Goal 1: Rank High When Consumer Searches on Internet Goal 2: Click Through to Reservation 36
Search Engine Technology Organic and Paid Searches Paid Results Organic Results Local Results Organic Results O i R lt Organic Results 37
Organic and Paid Searches Organic and Paid Searches Paid Results 38
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
Search: New York City Midtown Hotel Search: New York City Midtown Hotel 40
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
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
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
CR CTR CPC BID Expected Daily spend CTR*CPC*I 44
CTR SPEND CPC BID Expected Daily spend CTR*CPC*I Expected Return per impression CTR*CR*V – CTR*CPC 45
CR Return/I BID Expected Daily spend CTR*CPC*I Expected Return per impression CTR*CR*V – CTR*CPC Expected Return per booking (CTR*CR*V-CTR*CPC)/(CTR*CR) 46
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
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
Strategic Link Building Why Link Building? Because it works… 49
Check on Your Competitors www.linkpopularity.com www.compete.com www.marketleap.com Who’s Linking To You? 50
Different Search Engines View Links Differently Facilitating The Reservation - Conversion 51
The Booking Experience on Your Website 4 Screens to Book 1 Reservation The Booking Experience via OneScreen 52
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
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
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
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
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
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
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
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
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
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
Email Blasts 63
64
65
SniqueAway (Jetsetter) 66
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

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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 introuble 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 Withoutperfect 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
  • 13.
  • 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 ahotel 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 dropsprice 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 andPRICE 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 andPRICE 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
  • 21.
  • 22.
    Median retail pricingis 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
  • 24.
  • 25.
    Expedia Extending reach Inlinebanners 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 Performancemetrics 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 ThatIs 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 ofbids 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
  • 30.
    There’s an APPfor that…. 30
  • 31.
    “Hotel Negotiator” initialrelease 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: OTAHotel 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
  • 33.
  • 34.
    BiddingForTravel – TheFanatics http://biddingfortravel.yuku.com/topic/98782/t/The-Curtain-is-Parted-More-or-Less.html 34
  • 35.
    Search – SEO/SEM Whatinfluences 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: RankHigh When Consumer Searches on Internet Goal 2: Click Through to Reservation 36
  • 37.
    Search Engine Technology Organicand Paid Searches Paid Results Organic Results Local Results Organic Results O i R lt Organic Results 37
  • 38.
    Organic and PaidSearches Organic and Paid Searches Paid Results 38
  • 39.
    How do SEdetermine 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 YorkCity Midtown Hotel Search: New York City Midtown Hotel 40
  • 41.
    The Long Tailof 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 2ndprice 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
  • 44.
    CR CTR CPC BID Expected Daily spend CTR*CPC*I 44
  • 45.
    CTR SPEND CPC BID Expected Daily spend CTR*CPC*I Expected Return per impression CTR*CR*V – CTR*CPC 45
  • 46.
    CR Return/I BID Expected Daily spend CTR*CPC*I Expected Return per impression CTR*CR*V – CTR*CPC Expected Return per booking (CTR*CR*V-CTR*CPC)/(CTR*CR) 46
  • 47.
    Expected Return perbooking – 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 GoogleQuality 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
  • 49.
    Strategic Link Building WhyLink Building? Because it works… 49
  • 50.
    Check on YourCompetitors www.linkpopularity.com www.compete.com www.marketleap.com Who’s Linking To You? 50
  • 51.
    Different Search EnginesView Links Differently Facilitating The Reservation - Conversion 51
  • 52.
    The Booking Experienceon 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 impactnon-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 Panelreservations 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 ViewOTA 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
  • 63.
  • 64.
  • 65.
  • 66.
  • 67.
    Travel Agent TargetedAdvertising 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