Pricing, Search, And Ot As
- 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