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Amadeus Artificial Intelligence forCorporate travelOverview Marion MESNAGE Head of Amadeus Research, Innovation & Ventures
November 2018
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Q3 2018
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Mobility in travel
Book
Transfer
Check-in
Bag Drop
Security
In-flight
Rent a car
Corporate Overview 630+ million total bookings processed in 2017 the Amadeus distribution platform Q3using2018
20 75,000+
1,657 millions passengers boarded in 2017 with Amadeus and Navitaire solutions
Destination
Hotel
Average net bookings per second
Transactions per second (at peak)
2036 forecast reveals air passengers will nearly double to 7.8 Billion* *IATA – October 2017
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Search
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Tomorrow
people will spend much less time arranging travel and much more time enjoying the travel experience 3
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Today Too many irrelevant ads
Travel search is painful
Price does not make sense Too expensive (to me)
Information is fragmented
Too many offers Too many combinations
Which accommodation to book? Book now or later? Will the price change?
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Pre-Travel
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Today Will my flight be delayed / canceled?
What is the weather like?
Safety? Activities?
Where is my luggage?
Seated together with my friends?
Too many security checks Will I have time for the transfer? Which gate should I go to?
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During Travel
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The traveler journey Customer Support
Reinforcement Learning for Revenue Management
Dynamic Pricing
Baggage Weight prediction Transfers Recommender
Pre-Travel
Choice Modeling
Recommender Systems: Hotels and Ancillaries Fare Family Design
Travel
Price Prediction
Digital Advertising
Disruption Management Passenger Flow
Gate assignment and aircraft take off sequencing
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How A.I can help
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1. AI for Personalization © Amadeus IT Group and its affiliates and subsidiaries
(Choice modelling )
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The problem I want to travel Santiago
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Nice (France) to Santiago (Chile): December 3 - 5
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The problem The itinerary choice
I am not sure, so how Airlines can be?
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Which one? Cheapest? Fastest?
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Ingredients Geographic information
Itinerary Quality
Date / Time Preference
Models Price elements Discrete choice models RNN / Pointer Networks Random Forest
Airline/Aircraft Preference
Fare Families
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The results
of exact choice prediction (among 50 alternatives)
vs 2% without CCM
of predictions
on Top 5 alternatives (among 50 alternatives)
vs 10% without CCM
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20%
60%
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Our current models assume this behavior
Econs vs Humans "The agent of economic theory is rational, selfish, and his tastes do not change" 13
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The results
The Decoy!
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Decoy effect?
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2. AI for Customer service © Amadeus IT Group and its affiliates and subsidiaries
(Cognitive applications)
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Messaging platforms surpassed social networks Viber
Messaging platforms surpassed SMS and emails KakaoTalk
LINE Facebook
SMS
LINE
QQ Email (excl.spam )
8.6B MAU 2017
Messages/day 2016
Qzone
Twitter Weibo Messenger
A business should respond to me 24 hours a day, 7 days a week
Source: The 2016 Mobile Messaging Report
Whatsapp + Messenger
Assuming both are free and possible, how would you rather talk to a business?
5.5 billion Messaging Platform users worldwide
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AI for customer service
2. Can we reduce customer pain by providing 24x7 omni-channel support?
By 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human. Gartner
Businesses spend $1.3 trillion on 265 billion customer service calls each year. Chatbots can help businesses save on customer service costs by speeding up response times, freeing up agents for more challenging work, and answering up to 80% of routine questions. IBM
“For the travel industry, we expect 40% of cost reduction in customer service and 10% direct digital sales share sales through conversational platform.” Accenture
© 2015 Amadeus IT Group SA
1. Can we reduce customer service costs for our customers?
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It is easy to create a chatbot Messaging platforms
Services
NLU platforms
Machine learning
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It is easy to create a chatbot BUT It is difficult to create a good one!
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It is easy to create a chatbot BUT It is difficult to create a good one!
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Industrialize chatbot for travel
Industrial Natural language processing for travel Cognitive computing, NLP User context (travel) Dialog design and management Knowledge, reference data Services, Software framework
Š 2015 Amadeus IT Group SA
Chatbots Emails Voice Connected cars, homes
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3. AI for Revenue management © Amadeus IT Group and its affiliates and subsidiaries
(Reinforcement learning)
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Reinforcement learning The main elements
AGENT
Reward
Action ENVIRONMENT
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State
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Reinforcement learning at Amadeus Revenue management Reward = revenue received from the customer “Simple” scenario: One flight leg
•
200 seats
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One product
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10 fares
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Monopoly
•
WTP may change
AGENT
State
Reward
Action ENVIRONMENT
State = {number of booked customers and time}
Actions = {RL sets price}
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Reinforcement learning at Amadeus Revenue management
100%
102%
Airline using Amadeus Altea Revenue Management
Airline using Amadeus Altea Revenue Management powered by Reinforcement Learning
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Revenues $$
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3. AI for Continuous optimization © Amadeus IT Group and its affiliates and subsidiaries
(Reinforcement learning)
Beyond A/B testing
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Continuous experimentation
Start with some traffic Easily create variations Automate parallel deployments
Define use cases UI variations Advertisement
Reinforcement learning MS Azure custom decision support API Traffic simulator to validate AI behavior
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4. AI for Passenger flow © Amadeus IT Group and its affiliates and subsidiaries
(Computer vision)
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Leveraging real-time passenger flow data
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Crowdvision
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Real-time analytics, dashboards and alerts
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Crowdvision
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To drive real-time decision-making (365/24/7)
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Crowdvision
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Simulating passenger flow Amadeus
More than 100
000 passengers / day at Munich Airport
> What kind of passengers are expected?
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> How many passengers will pass in front of a particular shop?
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Simulating passenger flow Amadeus Bookings Data (very incomplete)
Machine Learning Methods
Flight Schedule and Status
Enriched Data Š Amadeus IT Group and its affiliates and subsidiaries
(e.g., estimated nationality)
Pax Flow Simulation Analytics
Choice Model
(eg., how many Japanese women at Munich tomorrow?)
Analytics (e.g., what is the probability they pass in front of my shop?)
3D Animation 36
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Simulating passenger flow to drive conversion
Attention Marketers! A flow of Japanese people are expected at 10:00 and 17:00 (current accuracy = 50%)
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Amadeus
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5. AI for Disruption Management © Amadeus IT Group and its affiliates and subsidiaries
(Predictive models)
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Flight delay
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The estimated cost of disruption to airlines is 8% of airline revenue, or $60 billion worldwide
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Flight delay
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The domino effect of disruption management
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Flight delay
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Voice of the passengers
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Flight delay prediction Lumo Weather and airport conditions Real-time flight data
Event intelligence Flight history and schedules
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Air traffic control constraints
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The traveler journey Customer Support
Reinforcement Learning for Revenue Management
Dynamic Pricing
Baggage Weight prediction Transfers Recommender
Pre-Travel
Choice Modeling
Recommender Systems: Hotels and Ancillaries Fare Family Design
Travel
Price Prediction
Digital Advertising
Disruption Management Passenger Flow
Gate assignment and aircraft take off sequencing
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How A.I can help
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@amadeusinnov innovation@amadeus.com linkedin.com/in/marionmesnage
amadeus.com Amadeus.com/blog
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