Approaches to Availability Processing - The Increasing Problem of Shopping -

1. Approaches to Availability Processing- The Increasing Problem of Shopping - Richard Ratliff AGIFORS R&YM Study Group Honolulu – June 2003

2. Outline • e-Commerce Impacts on Availability • Emergence of On-line Channels • Assessing Competitiveness • Methods • CRS Upgrades / Multihosting • AVS • Caching • Proxy-based Methods • Sabre’s experiences • Air • Hotel • Future Directions

3. e-Commerce Impacts on Availability

4. Emergence of On-line Channels • “Look-to-Book” ratio defined (a.k.a. LTB) Look-to-book = shopping requests / net (actual) bookings • On-line factors driving increased LTB ratios • Consumers are comparison shopping across websites • Robotics to “mine” websites for competitive information • Low fare search engines growing more complex and returning more options to customers (necessitating more availability checks) • Systems challenges at Sabre • Availability requests have grown over 50% over the past two years, due to rising LTB ratio and increased low fare search activity • Necessitated movement of low fare search functions off mainframe TPF and onto massively scalable (MPP) computer systems • Announced a $100M, 10-yr deal with HP

5. Agency LTB Website LTB Rise in On-line Shopping • Significant productivity differences exist between traditional vs. on-line channels • Travel agencies • More experienced users with a productivity-oriented focus • LTB is low; Sabre-connected agency is typically 12-to-1 • On-line channels • Less experienced users who view Internet channels as a “free” resource • 15% of Travelocity sessions involve repeat requests for the exact same market and date combinations (in the same session) • LTB varies widely by website • Ranges from 100-to-1 to as high as 2000-to-1 • Working assumption in this presentation is that 200-to-1 LTB is indicative of on-line channel productivity

6. Comparison Shopping via Robotics • Robotics are easy to build and increasingly utilized to obtain competitive information across websites • Predominant information source for rental car and hotel companies (since no comprehensive centralized information sharing exists) • Airlines - ATPCO data is useful but limited • Webfare specials (not necessary to file everything via ATPCO) • GDS and on-line sum-of-local fares; cited by Continental Airlines in previous AGIFORS * • Two webfare vendor examples (not an exhaustive list) • SideStep • FareChase • Some suppliers have taken legal action against webfare vendors to block them from mining website information • Indicative of worsening channel conflict and/or increased system processing costs • Sabre’s experience • Suppliers are a major source of travel-related robotics • Need to distinguish between “friendly” and “unfriendly” * References: see Kinloch – 2001 & Brunger - 2002

7. Methods of Checking Availability

8. “Single Image” is having a single, accurate picture of inventory in one place Airlines = widely, but not always, utilized (e.g. block space agreements with tour operators or for alliance code-shares) Hotels = more a goal than a reality Seamless availability is accessing the single image inventory in real-time (for greatest accuracy) Travel Agents Seamless Availability GDSs Call Center Own website Travelocity, Priceline, Hotwire, Travelweb Have everyone use seamless availability! Mark Travel Liberty 6 7 Expedia Hotels Jetset 11 9 The Reality The Goal Trip.com Quikbook 2 5 100 55 CCV Orbitz Hotels 3 2 CRS CRS Single Image Inventory & Seamless Availability

9. Inventory CRS Upgrades • Impacts of increasing “look-to-book” ratios • Seamless availability necessitates responses from supplier CRS within only 1-3 seconds • Airline (and hotel) CRS’s are finding it difficult to handle the increased volumes • Results in timeouts • Fallback is to use leg availability status information (AVS) • Results in less accurate responses (and increased UCs) • Major threat to the travel distribution ecosystem • Managing increased demands via CRS upgrades and additional capacity • Expensive, real-time CPU resources are involved • Scalability constraints may prevent adding new capacity, necessitating redesign of core processes • Escalating costs for carriers Supplier Availability Request Availability Request Seamless Availability Process Flows (Traditional) Travel agencies / consumers Availability Response Availability Response CRS

10. Use of Multihosting – Top 15 Airlines’ CRS* Airline2001 Pax (millions)CRS (circa 2001) Delta 104.9 Deltamatic — Managed by Worldspan American 80.7 Sabre United 75.4 Apollo (Galileo) Southwest 64.6 SAAS (Sabre) US Airways 56.1 Sabre Northwest 54.1 Worldspan Continental 44.2 EDS SHARES All Nippon 43.2 In-House British Airways 40.0 Amadeus Lufthansa 39.7 Amadeus Air France 38.6 Amadeus Japan Airlines 32.2 In-House Iberia 27.3 Amadeus Alitalia 24.9 In-House Air Canada 23.1 In-House — managed by IBM * Reference: Giga Information Group and Airline Business magazine, September 2002 • Observations • Only 4 carriers listed maintain their own CRS • Multihosting is a common approach to managing the complex system challenges • Doesn’t negate effective management usage (due to increased transaction fees)

11. AVS (Availability Status Messages) • Leg and Segment AVS • Traditional method in widespread use today • Standards are established and universally adopted • Not timely; updates can sometimes lag by a full week or more • Could be improved via use of publish-subscribe technology (or SITA) • Can be inaccurate, especially close to departure • O&D AVS? • O&D AVS proposals • Worldspan • Lufthansa • No standards yet exist • Polynomial increase in size of controls being managed • Relies heavily on frequent exchange of status (e.g. via pub-sub) • YM control or sales changes on one ODF would create a flood of O&D AVS messsages • Statusing logic is complex; can‘t always identify other ODF impacts • If kept up-to-date, should provide greater accuracy that leg AVS

12. Caching - Defined • What is caching? • Rather than checking availability live (in real-time), use a previously stored (i.e. cached) availability result for the specific ODF and date in question • Actively used by Expedia and Orbitz • Worldspan uses this as their primary solution to rising LTB ratios • Types of caching • Passive – reuse results of any previous seamless availability checks that were made during sell process • Active – proactively poll the supplier CRS to obtain the current availability • Availability usage differences • Some on-line retailers and GDSs believe that small inaccuracies in availability are tolerable during the shopping process • e.g. a customer is shown a fare is available when in fact it’s not • When a fare is actually sold, almost everyone agrees that seamless availability is necessary • Creates risk, because errors result in agency debit memo exposure or risk of PNR cancellation by the carrier

13. Caching – Benefits • More accurate than leg AVS • Data are more current and specific than leg AVS (e.g. by ODF) • Can be used in conjunction with leg AVS (to highlight ODFs and dates that have changed) • Simpler integration • Very easy to develop using robotics • Pub-sub (event-triggered) updates are more difficult • More accurate than scheduled polling • Requires greater integration effort and partnering with supplier • Uni-lateral decision making • Retailer and/or GDS doesn’t need agreement from supplier to begin to cache results (i.e. “Just Do It”) • No need to agree on an industry standard

14. Caching - Problems • Inaccuracy • The continual challenge is data freshness (or lifespan of the cached result) • To improve accuracy requires more frequent polling, which (paradoxically) drives up the LTB ratio! • Not real-time and can be hard to troubleshoot, so it should be combined with other real-time diagnostics (such as # of DCS failures) • Combinatorial explosion • Maintaining cache at a low level of detail (i.e. by ODF) results in a larger data space than at a higher level (e.g. by leg class) • Careful analysis is needed to maximize cache accuracy while minimizing volume of cached results • Which ODF and date ranges work best with caching? • Best: Off-peak periods (where sales activity is low) • Maybe: Uni-directional sales (i.e. once a class is closed, it stays closed without reopening) • Poor: ODFs and date ranges with frequent re-booking and cancellation activity are more problematic (i.e. fractional closures)

15. Proxy-based Availability • Proxy-based methods offload the expensive, real-time CRS processing onto open systems devices (run locally at a remote location) • Keep the inventory business logic and raw information synchronized with airline host • As inventory changes in the airline host environment, proxies are modified and updated • Benefits • Accuracy comparable to seamless (and faster since run locally) • Should be less expensive than CRS upgrades; can use commodity processors rather than mainframes • Platforms can be made more scalable • Can utilize pub-sub technology with reliable messaging delivery for robust, fault-tolerant synchronization • One server farm could be the supplier “availability” hub for all distribution channels • Problems • Since inventory processing logic (or a facsimile) must be replicated, requires high integration effort compared to other methods • Partnership approach means decision to use must be bi-lateral

16. Inventory Proxy-based Availability Seamless Availability Process Flows (Proxy-based) Avail. Proxy Supplier Availability Request Availability Request Travel agencies / consumers Inventory Updates Availability Response Availability Response CRS • Why does this approach work? • Viewed against the actual CRS workload, the LTB ratio drops to 1-to-1 (due to functional offload of shopping – only sells remain) • Since shopping requests outnumber bookings (by a large integer number), the inventory update and synchronization volume is comparatively low Note: the process depicted above is currently patent pending by Sabre

17. Sabre’s Experiences

18. Air - Availability • AVS at Sabre • As of 5/28/03, Sabre manages more than 142 million separate, active AVS items • Across all carriers, markets, and future dates • These messages need to be handled consecutively, in the exact order received, to be properly applied (otherwise it’s based on the old status) • Re-application of AVS status is one of the major components involved in schedule change processing • Current AVS standard assumes that airline and GDS schedules are 100% in sync, which is problematic because of OAG delays • E.g. BA sends close “cc” on LHR-BOM but the flight is LHR-DXB-BOM, we have to figure it out and close all 3 segments • Can O&D AVS work? • O&D controls to manage connecting markets • Point-of-Sale controls to manage discount selling channels • O&D and POS controls will pose severe difficulties due to a large increase in the existing number of AVS items • Feasibility is still unclear • Sabre’s strategy • Have proposed to CRS Harmonization working group and CASMA the consideration of proxy-based availability processing to address escalating LTB ratios • Can effectively deal with low levels of control (e.g. by ODF and POS)

19. Hotel - Caching • A leading hotel chain cited to Sabre that their LTB ratio is approaching 500-to-1 • “…most of the lowest hotel rates are being provided through the unregulated medium of the Internet…” * • Shopping activity is expected to comprise 50% of their total CRS processing capacity by year-end 2003 • Hotel merchant inventory by major on-line retailers • Expedia, Orbitz, Travelocity, etc. are increasingly taking a merchant position • Growth in merchant inventory requires “free sell” and seamless availability (rather than block allocations) • In the absence of seamless (since only a few chains elect to use this functionality), caching is required • Each of these “N” retail entities requires similar volume and quality of information to enable reasonable heuristics • The cached data are independently replicated “N” separate times! • Drives huge increases in LTB ratios * References: “Booking Hotels Online: An In-Depth Examination of Leading Hotel Web Sites”, William J. McGee, Consumer WebWatch, Apr. 24, 2003

20. Future Directions

21. On-line vs. Total Market • US and Canada total travel spending analysis * • Assumes total travel growth is 3% from 2002-2006 • Assumes CAGR of +20% online and -2% offline • Factors driving increased on-line usage by consumers • 18% of US households have broadband (est. 5X increase in DSL by 2005) ** • Wireless Internet access growing at “hot spots” (e.g. Starbuck’s & airports) • Supplier’s pushing e-technology (e.g. e-ticketing, online check-in) • Reduction in agency locations (ARC decreases = 16% from 9/00 – 8/02) *** * References: Various incl. Forrester, Jupiter, and PhoCus Wright estimates ** References: Forrester – 3/03 *** References: ARC website

22. Trends in Availability Requests • Approximate impacts of on-line channel shift Typical Today (2002) (Agency Share * Agency productivity) + (on-line share * on-line productivity) = (82.4% * 12 LTB) + (17.6% * 200 LTB) = 45.1 LTB ratio (2002) Typical in Future (2006) (Agency Share * Agency productivity) + (on-line share * on-line productivity) = (68.0% * 12 LTB) + (32.0% * 200 LTB) = 72.2 LTB ratio (2006) Est. 60% increase in availability requests over next 4 years • Availability-related problems are going to grow worse over time • Above calculations don’t consider other impacts such as: • Widening use of robotics, increased dynamic packaging by on-line retailers (e.g. Expedia and Travelocity) & and new web service offerings by suppliers • CRM-related impacts (detailed on next page)

23. Increased Adoption of CRM • Customer Relationship Management • Customer-centric availability • Personalized pricing • “…industry consensus that the current US fare structure is dysfunctional” • From Joan Feldman, Air Transport World • Dynamic pricing & increased customer segmentation approaches are likely to emerge • From Brady and Cunningham - 2001

24. Customer-centric Availability Processing • Future integration of Customer Relationship and Yield Management (using bid price controls in this example) Real-time Rate ODF or LOS - S BP’s (bid prices across all legs or room nights) +/- POS and Distribution Channel Bias +/- Customer Marketing Value Adjustment +/- Specific Overbooking Risk Adjustment = Net Value ODF or LOS(considering multiple attributes) Today Future • Overbooking risk and customer value have clear business benefits • Will compound the limitations inherent in O&D AVS or caching approaches due to exponential explosion in controls to manage

25. References

26. Selected References • “Exploring Predatory Pricing in the Airline Industry”, Brady and Cunningham, Transportation Journal, pgs. 10-11, Fall 2001 • “RM from the eCommerce Point of View”, Bill Brunger - Continental Airlines, AGIFORS R&YM Study Group, Berlin – 2002 • “Managing Your Look to Book Ratios”, Madeleine Gray – Sabre, CASMA conference (Computerized Airline Sales and Marketing Association), Oct. 2002 • “Net Gains, Net Losses?”, Feldman, ATW, pg. 37, Feb. 2002 • “Why O&D Doesn't Work“, Leon Kinloch - Continental Airlines, AGIFORS R&YM Study Group, Bangkok - 2001 • “Booking Hotels Online: An In-Depth Examination of Leading Hotel Web Sites”, William J. McGee, Consumer WebWatch, Apr. 24, 2003

27. Questions?

28. Appendix

29. AVS – More Information • Controls: Carrier Flight Number, Date, Class/All Classes, Leg or Segment City Pair and Open, Numeric or Restrictive Status currently in effect • Enforces: Segment selling restrictions, Waitlist accumulation restrictions, Polling activation, and provides support for Leg Overrides to fully restrict ALL passenger flow over multi-leg flight routings • Effects: Manages “Sum-of-Locals” and “Through Passenger” revenues on a single flight-by-flight basis • Uses: Can be relatively accurate when used correctly. It’s a vital fail-over mechanism for sell and report processing when direct system access is off-line. It functions between automated and non-automated environments