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Approaches to Availability Processing - The Increasing Problem of Shopping -. Richard Ratliff AGIFORS R&YM Study Group Honolulu – June 2003. Outline. e-Commerce Impacts on Availability Emergence of On-line Channels Assessing Competitiveness Methods CRS Upgrades / Multihosting AVS

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approaches to availability processing the increasing problem of shopping

Approaches to Availability Processing- The Increasing Problem of Shopping -

Richard Ratliff

AGIFORS R&YM Study Group

Honolulu – June 2003

outline
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
emergence of on line channels
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
rise in on line shopping

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
comparison shopping via robotics
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

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

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
crs upgrades

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

use of multihosting top 15 airlines crs
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)
avs availability status messages
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
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
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
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 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 availability1

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

air availability
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
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

on line vs total market
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

trends in availability requests
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)
increased adoption of crm
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
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
selected 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
avs more information
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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