Approaches to Availability Processing - The Increasing Problem of Shopping - Richard Ratliff AGIFORS...

Approaches to Availability Processing

- The Increasing Problem of Shopping -

Richard RatliffAGIFORS R&YM Study GroupHonolulu – June 2003

Confidential 2 AGIFORS R&YM Study Group – June 2003

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


e-Commerce Impacts on Availability


• “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

Emergence of On-line Channels


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

Agency LTB

WebsiteLTB


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


Methods of Checking Availability


Single Image Inventory & Seamless Availability

• “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

CRS

Have everyone use seamless availability!

100

The Goal

CRS

Seamless AvailabilityGDSsCall CenterOwn websiteTravelocity, Priceline, Hotwire, Travelweb

Liberty

Jetset Expedia Hotels

Trip.com Quikbook

CCV Orbitz Hotels55

119

76

5

3

2

2

Mark Travel

The Reality


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

Seamless AvailabilityProcess Flows

(Traditional)

Travel agencies / consumers

Availability Response

Availability Request



Inventory

CRS

Supplier


Use of Multihosting – Top 15 Airlines’ CRS*

Airline 2001 Pax (millions) CRS (circa 2001)

Delta 104.9 Deltamatic — Managed by WorldspanAmerican 80.7 SabreUnited 75.4 Apollo (Galileo)Southwest 64.6 SAAS (Sabre)US Airways 56.1 SabreNorthwest 54.1 WorldspanContinental 44.2 EDS SHARESAll Nippon 43.2 In-HouseBritish Airways 40.0 AmadeusLufthansa 39.7 AmadeusAir France 38.6 AmadeusJapan Airlines 32.2 In-HouseIberia 27.3 AmadeusAlitalia 24.9 In-HouseAir 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)


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


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


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


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)


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


Proxy-based AvailabilitySeamless Availability

Process Flows(Proxy-based)

Travel agencies / consumers





Inventory Updates

Inventory

CRS

Supplier

Avail.Proxy

• 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


Sabre’s Experiences


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)


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


Future Directions


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

* References: Various incl. Forrester, Jupiter, and PhoCus Wright estimates** References: Forrester – 3/03*** References: ARC website

( $US) 2002 2003 2004 2005 2006

Total Travel Spend $239b $244b $251b $260b $271b

On-line Travel Spend $42.2b $50.5b $60.5b $72.4b $86.7b

On-line Share 17.6% 20.7% 24.1% 27.9% 32.0%

• 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) ***


• 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)

Trends in Availability Requests


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


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

- 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


References


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


Questions?


Appendix


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

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