atme travel marketing conference - how big data, deep web & semantic technologies change travel...

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)

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)

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 2 Statistics: McKinsey Global Institute 2011 | Image Credit: NASA Goddard Photo and Video (cc|flickr)

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 3

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


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 2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 5 Statistics: McKinsey Global Institute 2011 | Image Credit: NASA's Marshall Space Flight Center (cc|flickr)

Google Versus Swine Flu

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 6 Image Credit: www.brettarthurphoto.com (cc|flickr)

• Predictive Analytics

– Discovered Close Relationship Between Web Searches for Flu-related Topics & People with Flu Symptoms

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 7 Image Credit: MOCO Big Ideas Blog

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 8 Image Credit: TED Conference (cc|flickr)

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 9 Image Credit: Antique Hardware (cc|flickr)

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 10 Image Credit: g-na (cc|flickr)

Website Email Offers

Upsell / Cross-Sell

Search

Advertising

Social Media

Advertising

Location Services

Ratings & Reviews

Virtual Reality

CRM / Loyalty

Counter / On-board Call Center

Kiosks

User Platform

Wallet

Form of Payment

Payment Processor

Tech Provider

Direct Connect

GDS

Inter-mediary

Supplier

Partner

Structured

Unstructured Multimedia

The Growing Abyss Of Travel Data

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 11 Content: OpenTravel Alliance / RockCheetah

Why Google Bought ITA Software

• Access to Airline Deep Web Data

• Search from Hell

– 5 Origin Airports

– 5 Destinations

– Different Dates

• Crazy Fast


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

Image Credit: ...-Wink-... (cc|flickr)

Why Context is Important:

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 14 Image Credits: Tambako the Jaguar, RobDurfee &mutrock (cc|flickr)

Jaguars

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

Image Credit: GustavoG (cc|flickr)

A Common Vocabulary is Essential

• GoodRelations Ontology

– Google

– Yahoo!

– Best Buy

– Bing

– Volkswagen

• Requires Cooperation

– Travel Suppliers

– Technology Providers

– Intermediaries

– Retailers

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 16 Image Credits: Millzero Photography (cc|flickr)

GoodRelations Vehicle Rental Vocabulary for Car Descriptions

• vso:ACRISSCode

• vso:VIN

• vso:acceleration

• vso:axles

• vso:bodyStyle

• vso:cargoVolume

• vso:color

• vso:condition

• vso:damages

• vso:doors

• vso:driveWheelConfiguration

• vso:engineDisplacement

• vso:engineName

• vso:enginePower

• vso:engineType

• vso:feature

• vso:firstRegistration

• vso:fuelConsumption

• vso:fuelEconomy

• vso:fuelTankVolume

• vso:fuelType

• vso:gearsTotal

• vso:height

• vso:length

• vso:meetsEmissionStandard

• vso:mileageFromOdometer

• vso:modelDate

• vso:payload


• 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:previousOwners

• vso:productionDate

• vso:rentalUsage

• vso:roofLoad

• vso:seatingCapacity

• vso:speed

• vso:steeringPosition

• vso:tongueWeight

• vso:trailerWeight

• vso:transmission

• vso:weight

• vso:weightTotal

• vso:wheelbase

• vso:width

Image Credit: antonychammond (cc|flickr)

Graphic Source: Tim Berners Lee & World Wide Web Consortium (W3C) | Image Credit: MoHotta18 (cc|flickr)

The Semantic Stack - Technology

• A Lot More Technologies Involved

• Address

– Vocabulary

– Search Queries

– Rules

– Validation

– Security

• Some Don’t Exist

– Proof / Trust


User Interface & Applications

Data Interchange: RDF / RDFa

Unifying Logic

Encryp

tion

Proof

Rules: RIF

Ontology: OWL

Taxonomies: RDFS

Query: SPARQL

Trust

Syntax: XML / XML Namespaces

Character Set: Unicode Identifiers: URI

Au

then

tication

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


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)


Image Credit: gato-gato-gato (cc|flickr)

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


Image Credit: alvaro.stuardo (cc|flickr)

What Could Possibly Go

Wrong?

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 22 Image Credit: Public Domain

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!


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


New Distribution or Old Guard? • ATPCO Owners

– OneWorld • AA-BA-IB-JL

– SkyTeam • AF-DL-KL

– Star Alliance • AC-LH-SK-SR-UA-US

– Others • AS-FX-HA

• OpenAxis Founders

– OneWorld • American Airlines (AA-US)

– Sky Team • Delta Airlines (DL)

– Star Alliance • Air Canada (AC) • United Airlines (UA-CO)

– ATPCO (Allied) – Farelogix (Allied) Schema

• IATA Board of Governors – OneWorld

• BA-CX-IB-JL-LA-QF

– SkyTeam • SU-AM-AF-MU-CZ-DL-KQ-KL-KE-SV

– Star Alliance • AC-MS-LH-SR-TK

– Others • KM-AV-ET-EY-FX-GA-9W-B6-QR

• GDS Founders

– Amadeus (AF-LH-IB-SK) – Sabre (AA) – TravelPort

• Apollo (UA) • Galileo (AC-BA-KL-AL-AZ-SR-OS-OA-

SN-TP-EI) • Worldspan (DL)

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 25 Image Credit: Richard Cawood (cc|flickr)

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 26 Image Credit: Adam Foster | Codefor (cc|Flickr)

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)

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 27 Image Credit: kevin dooley (cc|flickr)

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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 28 Image Credit: mclcbooks (cc|flickr)

Big Travel: Seven Step Travel Process


Inspiration

Research

Planning

Validation Booking

Travel

Sharing

Image Credit: Andrew and Annemarie (cc|flickr)

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


• 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)

Big Travel: End to End Experience


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


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

2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 33 Image Credit: Tom.Bricker (cc|flickr)

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

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


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

LH ORD-MUC LH MUC-CDG TP SVQ-LIS

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


Impossible? Remember Jack Andraka?


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

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: [email protected] 2013 ATME Marketing Conference Big Data, Deep Web & Semantic Technologies Robert Cole ● RockCheetah | 39

atme travel marketing conference - how big data, deep web & semantic technologies change travel...

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