bto 2014 - jose luis cordoba - andalucia lab

@jlcordoba

Taking Better Decisions in the Travel

Industry Through Big Data

December 2014

@jlcordoba

Big Data: sorry to simplify it !1

Big Data: a great opportunity for the travel industry2

Some existing examples3

Taking better decisions in the Travel Industry through Big Data

@jlcordoba

Big Data: sorry to simplify it !1

Taking better decisions in the Travel Industry Through Big Data

Big Data: Sorry to simplify it1

@jlcordoba

Behavioural patterns to take better decisions

Big Data: sorry to simplify it1

@jlcordoba

Big Data is data analysis taking into account 4 things:

●The amount, diversity, and speed of information have

all increased incredibly: the Internet, introduction of

sensors, new data bases, etc.

●The data storage and processing capacity has rapidly

increased.

● There are more powerful ways to visualize and

represent the information: Geocommons, Manyeyes or

CartoDB.

●Availability of data in real time.

@jlcordoba

Big Data: a great opportunity for the travel

industry2

Taking better decisions in the Travel Industry through Big Data

Big Data: a great opportunity for the travel industry2

@jlcordoba

Huge amounts of diverse and real time information, constantly

growing.

●More than 115 new contributions per

minute.

●More than 70 million members

worldwide.

●On average almost 2,600 new subjects

are posted every day in the forums.

● More than 85% of the questions are

answered in less than 24 hours.

● 70 millon unique visitors per month

Big Data: a great opportunity for the travel industry2

@jlcordoba

Huge amounts of diverse and real time information, constantly

growing.

Big Data: a great opportunity for the travel industry2

@jlcordoba

Huge amounts of diverse and real time information, constantly

growing.

●20 rates per room x 20 variations

depending on demand = 400 rates

for a certain day.

●400 rates x 365 days = 146,000

rates per year.

●146,000 rates x 1,000 hotels= 146

million rates just in Andalucía.

●etc

Big Data: a great opportunity for the travel industry2

@jlcordoba

Huge amounts of diverse and real time information, constantly

growing.

●Events

●Security

●Communication

●Transport

●Health

Big Data: a great opportunity for the travel industry2

@jlcordoba

Main data sources in the travel industry

●Booking channels

●Loyalty programs

●Web search

●Open Sources:

●Reputation

●Prices

●Geotagged images

@jlcordoba

Some existing examples3

Big Data: a great opportunity for the travel industry

Some existing examples3

@jlcordoba

Hotels: Intercontinental Hotels Group

with 4,600 hotels worldwide and

675,000 rooms the group is

collecting huge amounts of

data through its different

brands.

Some existing examples3

@jlcordoba

Main sources:

Internal:

●150 million reservations per year:

○ Booking channel.○ Booking time.

○ Booking Location.

○ Customer preferences.

●71 million subscribers to the

loyalty program (Priority Club

Rewards) including data provided by

its alliance with 45 airline companies.

●Internal Satisfaction Surveys

Hotels: Intercontinental Hotels Group

Some existing examples3

@jlcordoba

Main Sources:

External (from their hotels and competitors):

●Prices.

●Facilities.

●Services.

●Staff experience

●Main generators of local demand.

●Density and proximity of

competitors.

Hotels: Intercontinental Hotels Group

Some existing examples3

@jlcordoba

Main targets:

●Personalize user web

experience.

●Increase conversion rates.

●Increase direct bookings.

Hotels: Intercontinental Hotels Group

Data are collected and analyzed in

real time, they are used to revaluate

the marketing plan in a permanent

basis.

Some existing examples3

@jlcordoba

Kayak: manages one billion of searches per year

Metasearchers / Content agregators

Analytics models: consistency between prices shown in their web and those

shown in the airline webs (there is a synchronization gap between webs).

Some existing examples3

@jlcordoba

Kayak: manages one billion of searches per year

Metasearchers/ Content agregators

Predictive models: calculates the probability that the price of a certain flight

could increase or decrease during the following 7 days.

Some existing examples3

@jlcordoba

Kayak: manages one billion of searches per year

Metasearchers/ content agregators

Predictive models: to find out the most appropiate search results.

Some Existing Examples3

@jlcordoba

Algorithms to decide hotel ranking at Booking.com: 170 Million

unique visitors per month

Metasearches/ Content agregators

Some existing examples3

@jlcordoba

Tripadvisor launches personalized recommendations for their users.

Metasearchers / Content agregators

•Search history

•Reviews.

Personalization

depends on customer

information availability:

“Just for you”.

Some Existing Examples3

@jlcordoba

B2B

● Startup

launched in

2005

● Investment:

45M$

Ad Tech Is

Driving One-to-

One Marketing

in Travel

Booking

Some Existing Examples3

@jlcordoba

B2B Partners

Your customer data is very valuable.

Unleash its potential.

When you partner with ADARA, we turn

your booking, search, and loyalty data

into additional revenue while providing

you with knowledge about your customers

to help you make better product and

marketing decisions. And, data privacy is

always paramount.

More than 80 top global travel brands

already trust us to do so.

Some Existing Examples3

@jlcordoba

B2BAdvertisers

The ADARA Magellan travel intelligence

platform knows what customers are

buying and want to buy. It leverages

more than 300 million monthly uniques,

5 billion annual searches and 250

million annual transactions to make that

happen.

This valuable, anonymous data comes

right from the source – our world-class

partners.

Some Existing Examples3

@jlcordoba

Destination Management Organizations

•793 hotels, medium – high category• 8 big cities and main coasts •1 month sample: May 2014

Some Existing Examples3

@jlcordoba

Destination Management Organizations

• 8 OTAs• 7 attributes

Some Existing Examples3

@jlcordoba

Destination Management Organizations

Some existing examples3

@jlcordoba

Destination Management Organizations

Radiography of the Destination online Reputation

Some Existing Examples3

@jlcordoba

Destination Management Organizations

Radiography of the Destination Reputation

Some Existing Examples3

@jlcordoba

Destination Management Organizations

Clusters of OTA´s

Some Existing Exapmples3

@jlcordoba

Destination Management Organizations

Cities are able to download their results

Some existing Examples3

@jlcordoba

Destination Management Organizations

www.bigdata.andalab.org

Some existing Examples3

@jlcordoba

Destination Management Organizations

@jlcordoba

¡Grazie!

December 2014