The Booking Assistant

Building the Booking.com Virtual Assistant.

Phil Parsons & Maarten Versteegh

Booking.com 101.

Founded in 1996Employ 18 500 204 office in 77

different countries

World leaders connecting travellers with the widest variety of great places to stay

Joined Priceline Group 2005

28,373,691 bookable listings

With 131,269 unique destinations

In over 227 countries and territories

Serving 43 languages

1.55 million Number or room nights reserved each night

851,000 Population of Amsterdam

851,000 + 600,000 = 1.41 million Koningsdag (Kings Day) in Amsterdam

Planning Search and discover

Decide Book

The booking experience.

Holiday confirmed

But what happens after booking?

People have questions and change their plans… a lot!

Typical questions that we get.

Cancellation

“I can’t get the time off of work as

promised, can I cancel my

reservation?”

Pets

“My mother-in-law has invited

herself along for our holiday, is it

possible to get an extra bed?”

Parking

“Can I book a parking space for

the duration of our stay?”

Room related

“I have booked a twin room for

two, can it fit six men?”

How can we automate workload for our customer service and accommodation partners whilst providing a seamless experience for our

guests?

Meet the Booking Assistant, a

virtual customer service agent

in training.

End-to-end guest timeline.

Planning Search and book Pre-stay build up In-stay experience Post-stay

This stage is where we chose to focus to provide clear customer value

What can it do?

Automate answers

Answer questions automatically

using ever-evolving Artificial

‘Narrow’ Intelligence

Offer direct self-service

Empower customers through a

variety of contextually served

self-service options

Offer proactive assistance

Deliver relevant info proactively.

Push help with checking in,

finding things to do

Connect humans

Talk to Booking.com CS or

Partner for help when the

Assistant can’t answer

What type of queries can it deal with automatically?

● Arrival and departure time● Parking queries● Transport questions● Payments information● Add extra beds

● Confirm booking details● Directions to and from properties● Manage room cancellations● Manage date changes

You may have realised we haven’t called it a chatbot?

Human Robot

‘Cyborg assistant’

Why the Assistant needs humans.

True AI is still a long way away

Human-in-the-loop training is

constantly required to train the

assistant

The assistant is hungry for data

Even simple prediction models

need tens of thousands relevant,

clean and annotated data points

The assistant can’t think

It isn’t able to understand context

or accurately define intent

The assistant can be unpredictable

Even the most precise prediction

models make decisions that cannot

be understood fully

What does the guest need?

What can the bot give?

Agile Bot Development

Check-in/Check-outPaymentsDirections/public transportShuttle pickupsCancellationDate changeLuggage dropHotel facilitiesRoom facilitiesCar parkingBreakfast dealsRoom type requestsBed type requestsPetsWi-fiThank youGreetingsSmoking preferenceLanguage detection...

Start from guest intent

Aim for guest satisfaction

Prefer simple systems

DesignLean machine learning

Ride on winners

Find value through experimentationDouble down on areas of impact

Fast Iteration

Pareto Principle80% of effects from 20% of causes

Impact over accuracy

Pragmatism beats smartsFix it in the copy

Customer facing success metrics

Find value fast Abandon line on diminishing returns

Customer message

CHANGE DATESPARKING

PAYMENTS

Modular Architecture

More automated replies

Deliver more answers automatically,

without need for human support,

allowing for scale

More users in the assistant

Grow the audience of guests with

access to the Assistant, to generate

more messages & learn faster on topics

most relevant to guests

More training data

Messages serve as training data. CS agents annotate topics

to improve the models for topic detection and routing,

allowing for better automation

Booking

Assistant

strategy

Listen to Spanish conversation

Ask for meaning of unknown words

Get explanation of meaning

Learn from explanation and expand vocabulary

Active Learning

Monitor incoming messages

Select unknown messages for annotation

Receive annotations

Learn from annotations

Active Learning

Efficiently put humans in the loop

in model improvement

process

The Booking Assistant

● Scalable, personalized help on the go● Human-bot hybrid for best guest experience● Design-driven development● Leverage machine learning and human expertise

for continuous improvement

Thank you!