alexander trieb, ceo at travel audience gmbh - an amadeus company - intelligent decisions based on...
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Intelligent decisions based on relevant
data
Alexander TriebManaging Director
Travel Tech Conference27.10.2016
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RelevantData
Foundation
Real-Time Product
Selection
Accurate Prediction
Models
Dynamic Bidding
AcquireEngageConvertRetain
Closed Feedback Loop
The winning formula in data driven advertising
++ ++++
Relevant traveler data
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Relevant traveler data
DESTINATION DATA
LOCATIONDATA
TRAVELINTENT
BEHAVIORDATA
PRODUCTPREFERENCES
MACHINEPREDICTIONS
CROSS-CHANNEL &
DEVICE
PASTTRAVELS SOCIO-
DEMO-GRAPHIC
3rd PARTYDATA
TRUSTED TA / 1ADATA
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Early (non-)machine learning prediction models
DESTINATION DATA
LOCATIONDATA
TRAVELINTENT
PRODUCTPREFERENCES
MACHINEPREDICTIONS
CROSS-CHANNEL &
DEVICE
PASTTRAVELS SOCIO-
DEMO-GRAPHIC
TRAVELINTENT
BEHAVIORDATA
YES
NO
Daue
r<2
8 Ta
ge
NO
YES
WEEKENDSTAY
STAY< 3 DAYS
Business
Traveler
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
7
Known travelers Mapped to online users
Analyze historical user journeys and/or online behaviors
Identify patterns that are unique to a specific traveler
characteristic
Unknown travelers
?
BUT: known user journey and online behavior from data
partners
Compute similarity of user journey with labeled patterns
Assign profile targeting characteristics based on
similarity to known users
e.g. female 89% similarityfrequent traveler 75% similarityluxury traveler 67% similarity
Make constant updates based on observed user behavior
Machine learning prediction models
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
8
Known travelers Mapped to online users
Analyze historical user journeys and/or online behaviors
Identify patterns that are unique to a specific traveler
characteristic
Unknown travelers
?
BUT: known user journey and online behavior from data
partners
Compute similarity of user journey with labeled patterns
Assign profile targeting characteristics based on
similarity to known users
e.g. female 89% similarityfrequent traveler 75% similarityluxury traveler 67% similarity
Make constant updates based on observed user behavior
Machine learning prediction models
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Predicting traveler‘s intent
4 hours priorto booking
Meta search
OTA
1 day prior to booking
2 days prior to booking
Hours prior to a flight booking
airline.com
Uniq
ue Tr
avel
ers
Bookingmoment
Source: travel audience, aggregated sample for a single booking date
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Understanding traveler‘s intent
Hotel
Meta
OTA
Vacation Rentals
Weather
Long-Haul Leisure Destinations
Short-Haul Leisure Destinations
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Accuracy and speed deliver greater results
Static Prices
ManualPrice Updates
Daily “Cached” Price Updates
InstantPrice Updates
795€
OTA
OTA.com
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Dynamic fare integration
+27%
-34% -20%
Click Through Rate Conversion Rate Cost Per Order
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
A traveler‘s journey
Meta flight search
Destination activity
research
Meta flightsearch
Destination weather research
Airline.com
OTAsearch
OTAsearch
OTAbooking
Meta flightsearch
Destination activity
research
Destination weather research
Bookingdate
Departuredate
Returndate
30 days 14 days
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Travel blogs
30 days
Dynamic Bidding across the entire traveler journey
Meta flight search
Meta flightsearch
Airline.com
OTAsearch
OTAsearch
OTAbooking
Meta flightsearch
Brand Ad Video AdDynamic
Display Ad
Social Ad Native Ad
Bookingdate
Departuredate
Returndate
14 days
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Travel blogs
Destination weather research
Destination activity
research
Destination activity
research
Destination weather research
Relevant
DataFoundat
ion
Real-Time
Product Selectio
n
Accurate
Prediction
Models
Dynamic
Bidding++ ++++
Stric
tly
CONFIDENTIAL
_Knowing your customers is cool, predicting what travelers need, want and will do before they do, is even cooler.
Key take aways
_ Data driven advertising is an extremely effective way to achieve your goals, if done intelligently.
_ The winning formula for success:+Relevant data -> Builds strong foundation+Prediction models -> Enables actionable insights+Near instant & personalized content -> Provides
greater relevance+Knowing when to bid through dynamic bidding ->
Increases profitability+Closed feedback loop -> Continuously improve
