Travel marketing, and the world in general, will be impacted dramatically by Big Data, the Deep Web and the Semantic Web. This keynote presentation by RockCheetah's Robert Cole at the Association of Travel Marketing Executives annual conference held in Miami on April 17, 2013.

1. How Big Data, Deep Web & Semantic
Technologies Change Travel Marketing
ATME Marketing Conference
Hyatt Regency – Miami, Florida
April 17, 2013
Image Credit: NASA Goddard Photo and Video (cc|flickr)

2. Session Overview
• Big Technology
– Big Data
– The Deep Web
– The Semantic Web
• Big Impact
– Curing What Ails You
– You Are Already Using Big Data
– Travel Examples
• What Could Possibly Go Wrong?
• The Future
– Big Travel
– The Killer App
2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 1
Image Credit: NASA Goddard Photo and Video (cc|flickr)

3. Big Data IS a Big Deal
• What is Big Data?
– Too Much, Too Fast, or Too Weird to Fit in a Database
• Why is There Big Data?
– Web 2.0 – Introduced User Generated & Shared Content
• How Big is Big?
– US Library of Congress = 235 Terabytes Data
– 30 Billion Pieces of Content Shared/Month on Facebook
– $600 Disk Drive Now Stores All The World’s Music
• You’ve Already Seen Big Data at Work
– Google Analytics – Page Traffic, Source & Navigation
– Netflix – Recommendations & Manages its Video Streams
– Jeopardy – IBM’s Watson Computer Beat Ken Jennings
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Statistics: McKinsey Global Institute 2011 | Image Credit: NASA Goddard Photo and Video (cc|flickr)

4. Big Data – Big Insights
• Definition
– “Big Data is High Volume, High Variety, High Velocity, High
Veracity Information Assets for Enhanced Insight and
Decision Making”
• High Volume
– 90% of World’s Data Created Over Last Two Years
• High Variety
– Structured | Unstructured | Video | Images | Signals
• High Velocity
– Real-time Response | Engage Customer | Avoid Fraud
• High Veracity
– 1/3 Business Leaders Don’t Trust Information for Decisions
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5. Keys to Big Data Success
• Robust Platform
– Good Technology (Hadoop / MapReduce)
• Accurate Algorithms
– Good Math
• Smart Insights
– Good Interpretation
• Effective Communication
– Correct Context & Relevance
• Solution: The Right People
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6. Major Big Data Challenges
• Human Capital Shortage
– US Needs 140,000 to 190,000 More People with
Deep Analytical Skills
– US Needs 1.5 Million Managers & Analysts to
Analyze Big Data to Make Better Decisions
• Adopting New Technological Infrastructure
– Navigating a Transitional Roadmap
– Purging Inaccurate Data from Existing Systems
• Privacy Safeguards
– Protecting Consumer & Corporate Data
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Statistics: McKinsey Global Institute 2011 | Image Credit: NASA's Marshall Space Flight Center (cc|flickr)

7. Google Versus Swine Flu
• Predictive Analytics
– Discovered Close Relationship Between Web
Searches for Flu-related Topics & People with Flu
Symptoms
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Image Credit: www.brettarthurphoto.com (cc|flickr)

8. Target Versus Pregnancy
• Opportunities to Change Shopping Patterns
– Graduation | Relocation | Job Change |Child Birth
– Traditional Method: Source Birth Records
– Shift Change: Target Identifies in 2nd Trimester
• Predictive Analytics
– Identify Conscious & Unconscious Patterns
– Establish Cue-Routine-Reward Loops
– Email Coupon – Weekend Shopping – Free Starbucks
• Every Purchase Linked to Guest ID
– Look at 25 Products Together – Estimate Delivery Date
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Image Credit: MOCO Big Ideas Blog

9. Jack Andraka Versus Cancer
• 15 Year Old Kid
– Limited Access to Unstructured Big Data
– Research Sources – Google & Wikipedia
– Early Detection Test for Pancreatic / Lung / Ovarian Cancer
• Diligent Effort + Innovative Idea = Massive Disruption
– 8,000 Possible Proteins – Identified Mesothelin Biomarker
– Idea: Carbon Nanotubes Bind Antibodies/Electrical Charge
– Sent 200 Lab Space Requests; Received 199 Rejections
• A Faster / Better / Cheaper Test
– Takes 5 Minutes versus 14 Hours
– 400x More Sensitive & 50% More Accurate
– $0.03 Per Paper Strip (10 Tests per Strip) versus $800/Test
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Image Credit: TED Conference (cc|flickr)

10. Keys to Big Data Success
• Base on Business Needs, Not Tech Dreams
– Executive Level Support
– Make Data Driven Decisions
• Align Projects with Organizational Goals
– Big Data Initiatives to Support Specific Objectives
• Start Small
– Use Early Successes to Demonstrate Benefits
– Gain Momentum
• Expand from Foundational Projects
– Extend Capabilities By Adding New Data Sources
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Image Credit: Antique Hardware (cc|flickr)

11. The Deep Web
• Definition:
– Underlying Data that Populates Dynamic Web
Pages and is Not Captured by Search Engines
• Airline Example:
– Airfares, Seat Inventory, Fare Rules, Bag Fees
• Hotel Example:
– Stay, Arrival, Extra Guest, Room Type, Bed Type
• Car Rental Example:
– Weekend, Time, One Way, Discount Eligibility
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Image Credit: g-na (cc|flickr)

12. The Growing Abyss Of Travel Data
Website Email
Partner
Offers
Upsell /
Supplier
Cross-Sell
Inter-
Search
mediary
GDS Structured Advertising
Direct Social
Connect Media
Tech
Advertising
Provider
Payment Multimedia Unstructured Location
Processor Services
Form of Ratings &
Payment Reviews
Virtual
Wallet
Reality
User CRM /
Platform Loyalty
Counter /
Kiosks
Call Center On-board
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Content: OpenTravel Alliance / RockCheetah

13. Why Google Bought ITA Software
• Access to Airline
Deep Web Data
• Search from Hell
– 5 Origin Airports
– 5 Destinations
– Different Dates
• Crazy Fast
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14. Semantic Web – Provides Context
• Before: The Internet of Pages
– Search Engines Return Pages with Lots of Content
– Mixed Page Content - Varying Degree of Relevance
• Now: The Internet of Data
– Profile & Navigation Determines Dynamic Content
– Travel Industry Largely Lacking Semantic Schemas
• Next: The Internet of Things
– 25 Billion Connected Devices | 2015
– 50 Billion Connected Devices | 2020
2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 13
Image Credit: ...-Wink-... (cc|flickr)

15. Why Context is Important:
Jaguars
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Image Credits: Tambako the Jaguar, RobDurfee &mutrock (cc|flickr)

16. The Semantic Web | 2 + 2 =5
• Definition:
– Association Indicating How One Entity Relates to One
or More Other Entities to Create Meaning that Goes
Beyond the Components Themselves
• Key Benefit
– Allows Systems to Understand Relationships
• RDF Triple - Resource Description Framework
– Simple Subject | Predicate | Object Structure
• Examples:
– I Know Henry | Eric Clapton Plays Guitar
– Structure Applies to EVERYTHING
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Image Credit: GustavoG (cc|flickr)

17. A Common Vocabulary is Essential
• GoodRelations Ontology
– Google
– Yahoo!
– Best Buy
– Bing
– Volkswagen
• Requires Cooperation
– Travel Suppliers
– Technology Providers
– Intermediaries
– Retailers
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Image Credits: Millzero Photography (cc|flickr)

18. GoodRelations Vehicle Rental
Vocabulary for Car Descriptions
• vso:Automobile (rdf:type owl:Class) - - - - - - - - - - - - - - - - - - - - Defines a Classification Named “Automobile”
• rdfs:subClassOf vso:MotorizedRoadVehicle - - - - - - - - - - - - - - - Automobiles are a type of Motorized Vehicle
• URI http://purl.org/vso/ns#Automobile - - - - - - - - - - - - - - - - - Resource For Car Rental Relationships
• vso:ACRISSCode • vso:engineType • vso:previousOwners
• vso:VIN • vso:feature • vso:productionDate
• vso:acceleration • vso:firstRegistration • vso:rentalUsage
• vso:axles • vso:fuelConsumption • vso:roofLoad
• vso:bodyStyle • vso:fuelEconomy • vso:seatingCapacity
• vso:cargoVolume • vso:fuelTankVolume • vso:speed
• vso:color • vso:fuelType • vso:steeringPosition
• vso:condition • vso:gearsTotal • vso:tongueWeight
• vso:damages • vso:height • vso:trailerWeight
• vso:doors • vso:length • vso:transmission
• vso:driveWheelConfiguration • vso:meetsEmissionStandard • vso:weight
• vso:engineDisplacement • vso:mileageFromOdometer • vso:weightTotal
• vso:engineName • vso:modelDate • vso:wheelbase
• vso:enginePower • vso:payload • vso:width
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Image Credit: antonychammond (cc|flickr)

19. The Semantic Stack - Technology
• A Lot More User Interface & Applications
Technologies
Trust
Involved
Proof
• Address Unifying Logic
– Vocabulary
Authentication
Ontology: Rules:
– Search Queries
Encryption
Query: OWL RIF
– Rules SPARQL
Taxonomies: RDFS
– Validation
Data Interchange: RDF / RDFa
– Security
Syntax: XML / XML Namespaces
• Some Don’t Exist
Identifiers: URI Character Set: Unicode
– Proof / Trust
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Graphic Source: Tim Berners Lee & World Wide Web Consortium (W3C) | Image Credit: MoHotta18 (cc|flickr)

20. Who’s Investing in Semantic Web?
• Schema.org – Semantic Search
– Bing, Google, Yahoo! & Yandex Cooperating
• Google Knowledge Graph - Immediate Answers
– Integration with Google Places | Local | Google+
– Mobile Integration in Android Google Now Cards
• Facebook Open Graph - Apps Tell Stories
– About People
– The Things They Did
– Who They Were With
– Places Where It Happened
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21. TripAdvisor & Airbnb v. Review Spam
• Both Utilize the Facebook Open Graph
– Simplified Registration Process
– Filter Results For Connected Listings (Airbnb)
• Benefits
– Social Validity – Opinions of Real People You Know
– Social Authority – Identify Thought Leadership
– Social Commonality – Identify Similar Preferences
• Results
– Average User Engagement Increased 20% (TA)
– Connected Facebook Users Contribute Content 2x More
– Gained Access to Primary Email Address (RKC)
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Image Credit: gato-gato-gato (cc|flickr)

22. Keys to Semantic Web Success
• Start with Consumer Marketing Needs
– What Type of Content is Desired?
– How is it Consumed?
– What Questions Must Be Answered?
• Identify Available Data Sets
– Start with the Linked Data Cloud
• Results from Using Sematic Structure
– Best Buy: 30% Increase in Store Page Traffic
– Yahoo: 15% Increase in Click Through Rates
– Volkswagen UK: Easily Integrates 3rd Party Content
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Image Credit: alvaro.stuardo (cc|flickr)

23. What Could
Possibly Go
Wrong?
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Image Credit: Public Domain

24. Bad Data - The Four Seasons Bathrobe
• Mid-1980’s – The Dawn of Word Processing
• Background – Hotel 60% Repeat Guest Ratio
• The Challenge – Induce 1st Timers to Return
• Poorly Executed Cue-Routine-Reward
– Send 1st Time Guests Complimentary Bathrobe
• Problems
1. Poor Matching – Sent to Frequent Guest
2. Sent to Guest Home Address – Surprise!
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25. Innovative Technology on Back Burner
• Sabre Labs – Application Prototypes (1998)
– Collaborative Filtering for Hotel Recommendations
– Drive-pathing for Auto Travel Itineraries
• SideStep.com – Meta-search App (2004)
– Acquired by Kayak | Site & App Shut Down
• UpTake.com – Semantic Reviews (2008)
– Acquired by Groupon | Site Closed
• Room 77 – Hotel Room-specific Reviews (2011)
– Hotel Systems Could Not Support Functionality
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26. New Distribution or Old Guard?
• ATPCO Owners • IATA Board of Governors
– OneWorld – OneWorld
• AA-BA-IB-JL • BA-CX-IB-JL-LA-QF
– SkyTeam – SkyTeam
• AF-DL-KL • SU-AM-AF-MU-CZ-DL-KQ-KL-KE-SV
– Star Alliance – Star Alliance
• AC-LH-SK-SR-UA-US • AC-MS-LH-SR-TK
– Others – Others
• AS-FX-HA • KM-AV-ET-EY-FX-GA-9W-B6-QR
• OpenAxis Founders • GDS Founders
– OneWorld – Amadeus (AF-LH-IB-SK)
• American Airlines (AA-US) – Sabre (AA)
– Sky Team – TravelPort
• Delta Airlines (DL) • Apollo (UA)
– Star Alliance • Galileo (AC-BA-KL-AL-AZ-SR-OS-OA-
• Air Canada (AC) SN-TP-EI)
• United Airlines (UA-CO) • Worldspan (DL)
– ATPCO (Allied)
– Farelogix (Allied) Schema
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Image Credit: Richard Cawood (cc|flickr)

27. Obstacles That Postpone Disruption
• Three Horseman of Stagnation
– Control – Dominant Partners in Business Model
– Capital – If Allocated to Maintaining Status Quo
– Captive Consumers – Significant Barriers to Entry
• Focus Becomes Company – Not Customer
– Defend Position
– Manage to Quarterly Results
– Core Business Orientation – Diversification Resistance
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Image Credit: Adam Foster | Codefor (cc|Flickr)

28. The Future – Major Tech Disruption
• New Interfaces
– Google Glass Visual Display
– Apple Siri Voice Input
• New Payments
– Square | Wallet Provides Seamless Payments
– Dwolla | $0.25 Fee for Any Purchase Amount
• Self-Driving Cars
– Google / Toyota / Texas Instruments | Target 5 Years
• DNA Storage
– 700 Terabytes of Data Stored in Single Gram of DNA
– Dense | Volumetric | Stable (Thousands of Years)
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Image Credit: kevin dooley (cc|flickr)

29. Marketing Challenge: Mouths to Feed
• Suppliers
– Must Manage Complex Operations
• Technology Providers
– Design & Support Innovative, Stable Platforms
• Wholesalers
– Aggregate Demand Better Than Single Suppliers
• Retailers
– Understand Merchandising & The Customer
• Consumers
– Want Value & Best Available Deal
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Image Credit: mclcbooks (cc|flickr)

30. Big Travel: Seven Step Travel Process
Inspiration
Sharing Research
Travel Planning
Booking Validation
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Image Credit: Andrew and Annemarie (cc|flickr)

31. Big Travel: Five Travel Transactions
• Stand-alone Component
– Independent Purchase
• Value Added Combination
– Hotel Room with A Meal, Massage or Round of Golf
• Bundled Purchase
– Multiple Interchangeable Travel Components
• Dynamic Packaging
– Dynamically Priced / Rules Based / Collaborative Process
• Sequential Packaging
– Purchase Enables Secondary Offer or Product Multiples
• Distributed Packaging
– You’ll Need to Ask Me About This & Sign an NDA
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32. Big Travel: End to End Experience
• Home to Airport (Taxi / Limo / Car & Parking)
• Airport Experience (Waiting & Boarding)
• In-Flight Services
• Ground Transfer to Hotel | Car Rental
• Hotel | Meals | Tours | Activities | Memories
• Hotel to Airport Transfer | Car Return
• Return Flight
• Airport to Home (Taxi / Limo / Car & Parking)
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33. Big Travel: Big Personalization
• Traveler Personas
– May Be Travel Agent or Supplier Brand Loyalty
– May Prefer Packages or Avoid Packages
– May Seek Same or Different Destinations
• The Multi-Persona Traveler
– Needs Change Based on Itinerary
– Business Trip | Family Vacation | Spouse Getaway
• Multiple Travelers Create Complex Itineraries
– Couples & Families with Divergent Interests
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34. The Killer Travel App - Product
• Provides the RIGHT Experience
• To the RIGHT People
• Doing the RIGHT Things
• In the RIGHT Places
• At the RIGHT Times
• Through the RIGHT Channels
• With the RIGHT Products
• At the RIGHT Price
• And the RIGHT Value
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Image Credit: Tom.Bricker (cc|flickr)

35. The Killer Travel App - Technology
• Accurate
– Based on the Latest Real-time Information
– Not a List of Options | A Suggested “BEST” Option
• Personal
– Relevant to Specific Needs of That Traveler
– Customized for That Particular Itinerary
• Fast
– 1-second Delay = -11% Pageviews, -7% conversions & -
16% Customer Satisfaction
• If Really Good, Gives Answer Before Being Asked
2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 34
Statistics: Aberdeen Group , 2011

36. The Killer Travel App - Efficiency
• Leverages Industry Standards
– Facilitate Communication
– Streamline Processes
• Technology Adds Functionality / Reduces Cost
– Big Data
– Deep Web
– Semantic Relationships
• Disruptive Innovation
– A Holy War to Democratize Information
– Some People Are Not In It for the Money
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37. Multi-Origin / Multi-Destination
Multi-Modal / Personalized Itinerary
5-Apr 6-Apr 7-Apr 8-Apr 9-Apr 10-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr
Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon
MKE-ORD
Avistar ORD
TP SVQ-LIS
LH ORD-MUC LH MUC-CDG
TP LIS-LHR
U2 VCE-CDG UA LHR-ORD
SNCF
Hotel Windsor-Opera (PAR)
Versailles
Notre Dame
Louvre
Eiffel Tower
d'Orsay
PAR-MAD
MAD-SVQ
Hotel Belquer (SVQ)
Giralda
Carriage
Alcazar
ORD-MKE
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38. Impossible? Remember Jack Andraka?
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39. Embrace Big Data, Deep Web &
Semantic Web
• Or Don’t…
• Millions of Kids, Like Jack, Will Be Disruptive
• Smartly Using Available Technology
– Better
– Faster
– Cheaper
• Maybe Without the Same Profit Motive
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40. How Big Data, Deep Web & Semantic
Technologies Change Travel Marketing
Questions?
Copies of the Presentation?
Comments?
Robert Cole
skype: robertkcole
twitter: @robertkcole
phone: +1.262.309.9560
web: rockcheetah.com
email: robert@rockcheetah.com
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