Download - BTO 2014 - Jose Luis Cordoba - Andalucia Lab
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@jlcordoba
Taking Better Decisions in the Travel
Industry Through Big Data
December 2014
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@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
![Page 3: BTO 2014 - Jose Luis Cordoba - Andalucia Lab](https://reader033.vdocuments.us/reader033/viewer/2022060206/55a231401a28ab7d428b489b/html5/thumbnails/3.jpg)
@jlcordoba
Big Data: sorry to simplify it !1
Taking better decisions in the Travel Industry Through Big Data
![Page 4: BTO 2014 - Jose Luis Cordoba - Andalucia Lab](https://reader033.vdocuments.us/reader033/viewer/2022060206/55a231401a28ab7d428b489b/html5/thumbnails/4.jpg)
Big Data: Sorry to simplify it1
@jlcordoba
Behavioural patterns to take better decisions
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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.
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@jlcordoba
Big Data: a great opportunity for the travel
industry2
Taking better decisions in the Travel Industry through Big Data
![Page 7: BTO 2014 - Jose Luis Cordoba - Andalucia Lab](https://reader033.vdocuments.us/reader033/viewer/2022060206/55a231401a28ab7d428b489b/html5/thumbnails/7.jpg)
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
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Big Data: a great opportunity for the travel industry2
@jlcordoba
Huge amounts of diverse and real time information, constantly
growing.
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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
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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
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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
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@jlcordoba
Some existing examples3
Big Data: a great opportunity for the travel industry
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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.
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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
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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
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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.
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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).
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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.
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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.
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Some Existing Examples3
@jlcordoba
Algorithms to decide hotel ranking at Booking.com: 170 Million
unique visitors per month
Metasearches/ Content agregators
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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”.
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Some Existing Examples3
@jlcordoba
B2B
● Startup
launched in
2005
● Investment:
45M$
Ad Tech Is
Driving One-to-
One Marketing
in Travel
Booking
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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.
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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.
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Some Existing Examples3
@jlcordoba
Destination Management Organizations
•793 hotels, medium – high category• 8 big cities and main coasts •1 month sample: May 2014
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Some Existing Examples3
@jlcordoba
Destination Management Organizations
• 8 OTAs• 7 attributes
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Some Existing Examples3
@jlcordoba
Destination Management Organizations
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Some existing examples3
@jlcordoba
Destination Management Organizations
Radiography of the Destination online Reputation
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Some Existing Examples3
@jlcordoba
Destination Management Organizations
Radiography of the Destination Reputation
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Some Existing Examples3
@jlcordoba
Destination Management Organizations
Clusters of OTA´s
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Some Existing Exapmples3
@jlcordoba
Destination Management Organizations
Cities are able to download their results
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Some existing Examples3
@jlcordoba
Destination Management Organizations
www.bigdata.andalab.org
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Some existing Examples3
@jlcordoba
Destination Management Organizations
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@jlcordoba
¡Grazie!
December 2014