Tom Gregorson

Vice President

Products & Solutions

Tom GregorsonVice President,

Products & SolutionsATPCO

2015: WE STARTED WITH A WHITEPAPER

Dynamic Offering of Fare Levels

Create Services and Brands

Personalization Dynamic Fare Adjustment Generations

DRIVING THE INDUSTRY FORWARD

INDUSTRY WORKING GROUPS FURTHER STAKEHOLDER INPUT

Research Paper with PODS, LLC

Research and Alternatives to Expand RBDs

Dynamic Adjustment Pilots

Most Recent Dynamic Price WG

73 organizations

257participants

2016 20182017

RESEARCH PAPER AGENDA

• Background on Revenue Management

• Definition Framework

• Pricing & Revenue Management in Other Industries

• Next Generation Pricing Mechanisms

• Implications of Next Generation Pricing Mechanisms

• Summary and Conclusions

PILOT APPROACH 1 – PROS-ATPCO

Pricing

(Published Fares)

Request Availability

Availability Price with “Trigger”

Price AdjustmentAssign Code

“Trigger”

Return Availability

+ Trigger

Data Collection

and Distribution

CONTENT PROS

Request Response

Price Adj +Trigger

Availability Schedules

Fares/Rules Ancillaries

ATPCO

AIRLINE

PRICING

SYSTEM

FBR with Code

“Trigger” at all

relevant price points

“Trigger” reflected on

the ticket. No change to

downline processing

PILOT APPROACH 2 - SABRE-FARELOGIX-ATPCO

Dynamic Price Adjustment

Shopping

Booking

Pricing

Reporting

Inventory

ET Server

Dynamic

Pricing

Engine

BSPRevenue

Accounting

ATPCO Message Hub and DPE

Private Fare Filling

External Data Inputs

(e.g. Lowfare Shopping Cache)

CRS/GDS AIRLINE SYSTEM(S)

shoppingID (for reference only)

pricingID

RET HOT LIFT

Ticketing

Manish NagpalVice President,

Program DevelopmentFarelogix

John McBrideHead of Global Travel Product Management

PROS

Richard RatcliffSenior Research

ScientistSabre

Peter BelobabaPrincipal Research Scientist

International Center for Air TransportationMassachusetts Institute of Technology

Peter BelobabaPrincipal Research Scientist

International Center for Air TransportationMassachusetts Institute of Technology

Advances in Airline Pricing, Revenue Management, & Distribution

Implications for the Airline Industry

Peter BelobabaBill Brunger

Michael D. Wittman

Prepared for ATPCO by PODS Research LLC

October 2017

Purpose of Research Study• ATPCO commissioned PODS Research LLC to review current and next-

generation pricing practices in the airline industry.

Review the historical development of pricing and RM in

the airline industry

Examine pricing and RM practices in other industries and

describe similarities/differences with airlines

Describe next-generation pricing practices that are

currently under development in the airline industry

Assess potential implications of next-generation pricing

on revenues, competition, customers,

and internal processes

Pricing and Revenue Management Are Small Parts of a Complex Airfare Shopping Process

PRICING

Q

Y

M

B

X

REVENUEMANAGEMENT

DISTRIBUTION

File a set of fare products

Y $400 No AP N/A

B $300 3D AP NONREF

M $200 7D AP NONREF

Q $100 14D AP NONCHG

Send farestructure

to RM

Sendbooking limits/bid prices

Poll INV for availability

Referencefare filingsto determinefeasible fareproducts

INVENTORY

Calculate fare quote

FARE QUOTE

The New Distribution Capability Could Start to Change Some of these Legacy Processes

• IATA’s New Distribution Capability (NDC) is an XML-based standard for distribution communication.

• NDC allows for more information to be exchanged in the ticket shopping process besides itineraries, availabilities, and fares.

• Including information regarding ancillary services, rich media, and optional personal information about the customer making the request

• Prices and product offerings could be customized to each request and generated in real time.

• NDC has started a discussion about “next-generation” approaches to airline pricing and revenue management.

Source: IATA

A Definitional Framework of Pricing Mechanisms

• Framework to describe selection of price for a given product.

• Traditional airline pricing and revenue management can be best classified as assortment optimization.

AssortmentOptimization

Dynamic PriceAdjustment

Start with assortment optimization, then adjust price points up or down in certain situations.

Continuous Pricing

Select prices freely from among a continuous range of possible values.

Create a finite set of possible price points, and then select prices from this set to offer to customers.Q

Y

M

B

X$249

$229

$499

$199

Price Selection Could Be Made Infrequently, or on a Transaction-by-Transaction Basis

• Pricing mechanisms vary on the number of allowable price points and the frequency with which prices are selected:

• With transactional continuous pricing, prices are computed individually for each transaction.

Frequency of Price Selection

Less Frequent(e.g., Weekly)

More Frequent(e.g., Transactional)

Number of Possible Price Points

Fewer Price Points(Assortment Opt.)

More Price Points(Continuous Pricing)

$$

Pricing Mechanisms in a Variety of Industries Were Reviewed

Industry Pricing MechanismPublicly-Available

Price Structure?Tra

vel-

Rela

ted

w/ A

sst. O

pt.

Airlines Assortment optimization Yes

Hotels Assortment optimization No

Passenger rail Assortment optimization Yes

Rental cars Assortment optimization No

Ind

ustr

ies

with

Advanced P

ricin

g

Online Retail Dynamic price adjustment No

Airbnb Continuous pricing No

On-demand transportDynamic price adjustment &

transactional continuous pricingYes/No

Airlines remain sophisticated compared to other travel-related industries, but other industries may not have the same technological constraints.

Six Next-Generation Pricing Mechanisms Are Currently Under Development in the Airline Industry

1. More Frequent Updating of Fare Structures

2. Dynamic Availability of Fare Products

4. Dynamic Pricing Engines

3. Advanced RBD Capabilities

6. Dynamic Offer Generation

5. Continuous Pricing

Most Complex

Least Complex

Mechanism 1: More Frequent Updating of Fare Structures• The frequency of fare filings has increased in recent years:

• More frequent filing of fares allows airlines to update the menu of possible price points more often.

• But the airline still practices assortment optimization by selecting prices among a pre-set menu of possible price points.

• At the limit, a unique fare structure could be filed for each departure day in each market.

2007 2017

Distribution FrequencyInternationalUS/CA Domestic

8 times/day3 times/day

Every hour4 times/day

Average Fare Changes per day 669k 3.9M

Source: ATPCO

Mechanism 2: Dynamic Availability of Fare Products

• Dynamic availability changes the RM availability of fare products based on characteristics of booking requests.

• Dynamic availability is an assortment optimization mechanism.

Y

B

M

QX

Class

If load factor < 50%and Corporate Code

= ABCDEFG…

Y

B

M

Q

Class

RM

Syste

m A

vailab

ilit

y Dyn

am

ic A

vaila

bility

XX

…open Class M for this transaction

Mechanism 3: Additional RBD Capabilities

• ATPCO’s Dynamic Pricing Working Group has investigated increasing the capabilities of existing RBDs.

• The current limit of 26 price points for each market may not be sufficient as airlines develop more complex products.

• With additional RBD capabilities, a single RBD could be divided into multiple price points that could be controlled independently.

• These changes would likely require more changes to existing technology than more frequent refiling of fares or dynamic availability.

Mechanism 4: Dynamic Pricing Engines• Dynamic Pricing Engines (DPEs) are a dynamic price adjustment mechanism

that adjusts (up or down) the prices of pre-filed fare products in certain situations.

• The DPE concept also emerged from the ATPCO Dynamic Pricing Working Group.

DynamicDiscounting

Dynamic Incrementing

In certain situations, apply a discount to the price of the lowest-available fare product for lower-WTP requests.

In certain situations, apply an increment to the price of the lowest-available fare product for higher-WTP requests.

Examples of Dynamic Pricing Mechanisms

Mechanism 5: Continuous Pricing

• Unlike Dynamic Pricing Engines, continuous pricing does not rely on a set of pre-determined price points.

• Mechanically, two possible options for implementing continuous pricing:• File a separate fare basis for each possible price point, then use continuous pricing to select which price to

display.

• Use the New Distribution Capability to distribute continuously chosen prices without reference to pre-determined price points.

• Either approach would require new WTP-based RM forecasting and optimization processes.• MIT PODS Consortium currently evaluating methods and outcomes of new approaches.

Mechanism 6: Dynamic Offer Generation• Dynamic offer generation combines the product creation and price

selection processes into a single mechanism:

Ancillaries Itineraries

• Preferences • WTP

Select and price a set of offer(s) that maximizes expected revenue from each booking request

Offer Set

Next-Gen. Pricing Mechanisms Could Have Wide-Reaching Effects on the Airline Industry• Next-generation mechanisms have the potential to impact:

Airline Competition

Legacy Processes

Consumers

Regulators

Airline Revenues

Simulations Have Suggested that Dynamic Pricing Engines Could Lead to Revenue Gains

0.4%

0.7%

1.3%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

Low Demand MediumDemand

High Demand% C

hange in A

L1 R

evenue fro

m R

ele

vant

Base

% Change in Revenue from Base when AL1 Uses

Dynamic Incrementing

3.7%

3.0%

2.1%

Low Demand MediumDemand

High Demand

% Change in Revenue from Base when AL1 Uses

Dynamic Discounting

Implications for Airline Competition

• Simulation studies do not capture the psychology of airline competition.

• Next-generation pricing technologies could lead to aggressive discounting to fill empty seats.

• There may be an incentive for each firm to undercut the other until one firm reaches its marginal cost.

• Could mitigated by the availability of improved pricing data.

AL1

$350$349$349$347

$99

…

AL2

$350$350$348$348

$100 (MC)…

Next-Generation Mechanisms Could Require Changes to Legacy Systems and Processes

Mechanism Most Likely to Affect…

More frequent updates to fare structuresPricingRevenue Accounting

Dynamic availability

PricingRevenue ManagementSales & Marketing

Additional RBD capabilities

Sales & MarketingDistributionPricing

Dynamic Pricing Engines

PricingRevenue ManagementRevenue IntegrityRevenue Accounting

Continuous PricingPricingRevenue Management

Dynamic Offer Generation

Merchandising and SalesDistributionRevenue ManagementPricing

Consumers May (or May Not) Notice Changes to Next-Generation Pricing Mechanisms

• Airline customers may be affected by changes to airline pricing mechanisms.

• The extent to which customers will notice and react to these changes will likely depend on the mechanism.

• Advanced RBD capabilities will remain out of sight for most customers, whereas dynamic offer generation and dynamic pricing engines could be more visible.

• Some customers believe that airlines are already using “dynamic” or even “customized” pricing—e.g., changing prices in response to a customer’s search or travel history.

• Customer perceptions of price “fairness” will likely depend on how next-generation mechanisms are framed.

Regulators Have Been Permissive of Next-Generation Pricing So Far: Will This Change?

• In the U.S., the DOT considered preliminary arguments about next-generation pricing in 2014.

• Price discrimination is not allowed on the basis of protected classes (i.e. race, gender, age), and anonymous shopping options will be required in the U.S.

• Transactional pricing methods may be more likely to draw regulatory review than other pricing mechanisms.

“We are tentatively not prepared to prohibit future [pricing] innovations that may better

match capacity with demand.”

Source: U.S. DOT Order to Show Cause for IATA Resolution 787, May 2014

Viewpoints on Next-Generation Pricing

• There is a debate in the industry regarding the feasibility and prudence of next-generation pricing mechanisms.

• Our report outlines two opposing viewpoints:

• These viewpoints do not necessarily reflect the views of the study’s authors, ATPCO or any particular airline.

Viewpoint 1: The Airline Industry Should Not Miss this Opportunity to Modernize its Pricing and RM Practices

Viewpoint 2: The Inherent Risks of Next-Generation Pricing Are Too High to Justify Further Development

Viewpoint 1: The Airline Industry Should Take Advantage of Next-Generation Pricing Mechanisms

The revenue potential of next-generation pricing and revenue management appears strong.

Next-generation pricing will finally allow airlines to move beyond constraints imposed by legacy systems.

New markets for information will emerge that are compatible with next-generation pricing mechanisms.

Some airlines will experiment with next-generation pricing, and a first-mover advantage appears to exist.

The greatest risk from next-generation pricing is likely from regulation and consumer reactions.

Viewpoint 2: Next-Generation Pricing is Too Risky to Justify its Implementation

Airlines are currently profitable and have well-understood pricing and revenue management processes.

Airlines are subject to inertia and resistance to change: why change what is not broken?

Next-generation pricing mechanisms risk destabilization of the underlying fare structure.

Next-generation pricing could reduce the ability of airlines to evaluate and respond to competitor price changes.

Next-generation pricing could have a disruptive influence on distribution relationships.

Conclusions – Preparing for a World with Next-Generation Airline Pricing

• Most stakeholders at airlines will have their own views that fall somewhere between these two extremes.

• The airline industry should proceed under the assumption that next-generation pricing will develop in some form.

• To prepare for next-generation pricing, airlines will need to develop new processes, techniques, and core competencies:

Automated processesfor next-gen pricing

New RM/forecasting for Conditional WTP

Interoperability with existing systems/practices

Corporate strategies fornew pricing mechanisms

HOW CAN YOU GET INVOLVED?

Use Research Attend Next Working GroupParticipate in Pilot*

Gianni Cataldogcataldo@atpco.net

Fred Footeffoote@atpco.net

Melanie Dezelakmdezelak@atpco.net

* C O N T A C T S

Manish NagpalVice President,

Program DevelopmentFarelogix

Manish Nagpal

A Complex Problem To Solve

FAR

ORD

DEL

FAR - DEL

LHR

FAR

ORD

LHR

DEL

600,000Fares

FAR - DELFAR – ORD, ORD – LHR, LHR- DEL

DEL – LHR, LHR- ORD, ORD- FAR

PRICING

FAR

ORD

LON

DEL

1.5Billion Fares

FAR - DELFAR – ORD, ORD – LHR, LHR- DEL

DEL – LHR, LHR- ORD, ORD- FAR

SHOPPING 50 inbound flights, 50 outbound flights andfind top 100 cheapest shopping solutions.

2500 flight combination * 600,000=

Oh, and it changes every hour

“Cat 25 fare by”

multiplier effect

Hundreds of rules per fare

20M filed fares

1 Airline

The Airline’s Challenge

What’s Driving Demand for Next Gen Shopping/Pricing?

Rise of Meta Search10,000 look to book is the norm

Dynamic Pricing Optimize offers (adjust ATP) using rules (today) or science (tomorrow)

NDC – Delivery vehicleAirline’s own shopping engine; airline as single source of truth

Pace of InnovationIntroduce products/differentiate on the airline’s timeline

NDC

Manish Nagpal

“ATPCO is essential.

FULL ATPCO PRICING CAPABILITY

• Proven system used by the world’s airlines

• Essential common model of distributed pricing

• Community model “by design”

New Generation Shopping and Pricing

– what’s it take?

New Gen Offer Optimization

OFFER

ATPCO

fare

New Gen Offer Optimization

OFFER

EXTERNAL DATA:

Social, Browser, Events, Trending, Buyer

Propensity, Weather, Trip purpose, Culture, Brand

Preferences

New Gen Offer Optimization

OFFER

AIRLINE DATA:

Purchase History, Load Factor, CRM, Search Data, RM, Loyalty, Preferences,

Corporate ID

EXTERNAL DATA:

Social, Browser, Events, Trending, Buyer

Propensity, Weather, Trip purpose, Culture, Brand

Preferences

New Gen Offer Optimization

OFFER

EXTERNAL DATA:

Social, Browser, Events, Trending, Buyer

Propensity, Weather, Trip purpose, Culture, Brand

Preferences

AIRLINE DATA:

Purchase History, Load Factor, CRM, Search Data, RM, Loyalty, Preferences,

Corporate ID

DATA SCIENCE

Machine Learning, AI, Predictive Analytics

New Gen Offer Optimization

BEST

OFFER

EXTERNAL DATA:

Social, Browser, Events, Trending, Buyer

Propensity, Weather, Trip purpose, Culture, Brand

Preferences

INFLUENCING RULES:

Channel, Corp. ID, Market, Season,

Preferred Connection, and more

DATA SCIENCE

Machine Learning, AI, Predictive Analytics

AIRLINE DATA:

Purchase History, Load Factor, CRM, Search Data, RM, Loyalty, Preferences,

Corporate ID

“Superior performance

PERFORMANT AND BUILT FOR PURPOSE

• Support Massive Volumes

• No look to book limitations/fees

• Subsecond response time

• No cache-based responses

• Adjust offers in real time

• Rules-based and easily updated by humans (today) or science (tomorrow)

New Generation Shopping and Pricing

– what’s it take?

“Maximum control for lowest cost of ownership

DESIGNED FOR AIRLINE CONTROLAND LOW COST OF

OWNERSHIP

• Linearly scalable on commodity hardware

• Limit PSS dependency

• Off-host availability and connection building

• Portable solution with airline hosting option

New Generation Shopping and Pricing

– what’s it take?

FLX Platform acting as DPE where offer is consumed by Sabre GDS

“New Gen Shopping and Pricing in Action

ATPCO Innovation Forum

Thursday, 11 October

FLXShop &Price

Thank you

John McBrideHead of Global Travel

Product ManagementPROS

John J. McBride, PhD

PROS

30 + years in revenue management

30300

Number of scientistsNumber of engineers 25% revenue into

R&D

About Vayant Travel Technologies

PROS + Vayant aligns revenue management with shopping and

merchandising, accelerating the path to full offer optimization

Enabling Airline Retailing &

Optimizing Distribution

Created in 2007

Global B2B IT service provider to airlines, agencies, and

PSSs

Supports Distribution and E-Commerce

GDS and PSS Agnostic

IATA NDC certified

Core values:

o Agility

o Control to Customer: Satisfaction & Value

o Advanced Technology

o Future-proof Solutions

What is Dynamic Pricing?

Decouples price from availability• Price is the determining factor – not class availability

• Fare families provide segmentation, but no classes within a family technically required

Generates an optimal price for an offer in real time• Price is based on expected demand and revenue to come

• Price depends on dimensions other than passenger type, itinerary, capacity, etc.

Dynamic Pricing #’s

Production Since 2013

10+ Airlines Using DP

5+ Airlines Migrating 2018

Shopping

Selling

IBE/GDSPROS

OneSearch

IBE/GDSPROS

OneSearch

Shopping Request

Offer Response

Request Info

Price and

Availability

Selling RequestCheck Availability

(Price)

Confirm Availability

(Price)

Issue Ticket

ATPCO

Check prices to issue ticket

Persisted ID needed

Airline pre-files set of discount codes

Increase

Revenue

+ (3 – 10)%

Increase

Conversion

+50%

Increase

Brand Loyalty

Why Dynamic Pricing?

Richard RatcliffSenior Research

ScientistSabre

Industry-standard Specifications for Air Dynamic Pricing Engines:

Progress Update

Richard Ratliff, Sr. Research Scientist, Sabre

ATPCO Global Conference

Washington, DC

Oct. 10, 2017

• Airline dynamic pricing has been discussed in various forums for more than a decade, but little progress was made due to both the disruptive nature of the technology and a lack of standardization

• Carriers want to be right-priced in all channels• Implication is that most carriers cannot unilaterally implement dynamic pricing in their CRS or with a

single GDS without causing issues with other GDS’s or agencies

• Beneficial for both airlines and GDS’s to adopt a common specification for dynamic pricing engines

• Less overall effort• Faster time to market

• Sabre and other parties recognized the need for an industry-standard specification in order to simplify and promote adoption

• Conduct underlying research work to demonstrate the benefits

• Good progress on DP research over the past few years• At AGIFORS RM in 2015, Amadeus reported simulation analysis on DPE

• Demonstrated net revenue improvements ranging from 5% to 7%

• Also supported by internal Sabre studies (unpublished) showing positive benefits

• MIT research showing benefits from DPE also been reported at AGIFORS and PODS meetings

• In 2017, Prof. Guillermo Gallego reported on benefits of dynamic pricing for both suppliers and consumers

Background

Basic Concepts of Dynamic Pricing

• For several years, Sabre has been working on the design and development of new shopping data sources, customer choice models and fare optimization algorithms to help airlines continually adapt their selling price positioning to maximize profits

• These market-adaptive dynamic pricing models are emerging as the next wave of advanced pricing and revenue management (RM) science

• Dynamic pricing team has been working on approaches on how these new tools could be employed by airlines for improved pricing and RM performance in their own and global distribution system channels

• Sabre’s vision of a comprehensive, real-time dynamic fares engine is based on three main components (documented in a white paper written in Feb. 2016)

• Customer Retailing• Accurate inventory controls• Market Adaptive Pricing (customer choice and

optimization models considering relative price and schedule quality)

Background

Elements of Market Adaptive Pricing

Optimize host itinerary prices trade off

selection prob vs. yield

Lowfare Search results by mkt, POS,

date

Assess attractiveness

Estimate itinerary probability of selection

Choice models provide ability to estimate selection probabilities considering competition

Price optimization model

Gather current competitive conditions

Customer-choice models

• What re-pricing opportunities do you observe in this JFK-ATH example?

• SU?• FI?• QR?

Market Adaptive Pricing Example: Discussion

Progress on the Pilot

InventoryInventoryDynamic Pricing Engine

API Validation Pilot: Progress-to-date (ATPCO, Farelogix, Sabre)

Shopping

ET server*

BSP*Revenue

Accounting*

Dynamic Pricing Engines

(Farelogixand

Sabre)

Booking*

Pricing

Ticketing*

Reporting

Sabre Vendor Systems

RET HOT

In-scope items shown in GREEN* Items not included in pilot testing

New items in RED

Inventory

LIFT

shoppingID (for reference only)

pricingID*

ATPCO Message Hub and DPE Private Fare Filing

External Data Inputs(e.g. Lowfare Shopping Cache)

new