Marion MESNAGE — Amadeus — Artificial Intelligence for Travel

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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 )

WeChat

8.6B MAU 2017

Instagram

WeChat

Messages/day 2016

Qzone

WhatsApp

Twitter Weibo Messenger

linkedin

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

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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

•

One product

•

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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•

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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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