Mena Machine-to-Machine Marketing (M3) via Anonymous Advertising Apps Anywhere Anytime (A5)

Erscheinungsjahr 2013
ISBN: 978-1-4398-8192-7
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 436 Seiten

In today’s wireless environment, marketing is more frequently occurring at the server-to-device level—with that device being anything from a laptop or phone to a TV or car. In this real-time digital marketplace, human attributes such as income, marital status, and age are not the most reliable attributes for modeling consumer behaviors. A more effective approach is to monitor and model the consumer’s device activities and behavioral patterns.

Machine-to-Machine Marketing (M3) via Anonymous Advertising Apps Anywhere Anytime (A5) examines the technologies, software, networks, mechanisms, techniques, and solution providers that are shaping the next generation of mobile advertising. Discussing the interactive environments that comprise the web, it explains how to deploy Machine-to-Machine Marketing (M3) and Anonymous Advertising Apps Anywhere Anytime (A5). The book is organized into four sections:

- Why – Discusses the interactive environments and explains how M3 can be deployed

- How – Describes which technologies and solution providers can be used for executing M3

- Checklists – Contains lists of techniques, strategies, technologies, and solution providers for M3

- Case Studies – Illustrates M3 and A5 implementations in companies across various industries

Providing wide-ranging coverage that touches on data mining, the web, social media, marketing, and mobile communications, the book’s case studies show how M3 and A5 are being implemented at JP Morgan Chase, Hyundai, Dunkin’ Donuts, New York Life, Twitter, Best Buy, JetBlue, IKEA, Urban Outfitters, JC Penney, Sony, eHarmony, and NASCAR just to name a few. These case studies provide you with the real-world insight needed to market effectively and profitably well into the future.

Each company, network, and resource mentioned in the book can be accessed through the hundreds of links included on the book’s companion site: www.jesusmena.com

Zielgruppe

Web, mobile, IT, and marketing professionals; as well as students in the field.

Autoren/Hrsg.

Mena, Jesus

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Social Media, Semantic Web, Web 2.0
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Marketing
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management E-Commerce, E-Business, E-Marketing

Inhaltsverzeichnis

Introduction

Why?
M3 and A5
What, Where, and How to Monetize Device Behaviors
Building A5s
Search Marketing versus Social Marketing via A5s
Google, Facebook, and Twitter Places
M3 via GPS and Wi-Fi Triangulation
You Are Where You Will Be
Data Mining Devices

How
M3 via Machine Learning
Clustering Autonomously Device Behaviors
Real-Time Demographic Networks
Geolocation Triangulation Networks
Deep Packet Inspection for M3
Mob M3
Data Aggregation and Sharing Networks
Twitter Is Organic TV for M3
Blogs Are Studios for M3
Dialing Up iPhone and Android A5 Numbers
Mobile Cookie A5s for M3
Mobile Advertising Networks for A5
M3 via Voice Recognition
Facial Recognition
Mobile Rich Media for M3 and A5
Mobile Ad Exchanges for A5
Anonymous Consumer Categories for M3
Digital Fingerprinting for A5 and M3

Checklists
Why M3 Checklists?
Checklist for Clustering Words and Consumer Behaviors
Checklist of Clustering Software
Checklist of Text Analytical Software
Checklist of Classification Software
Checklist of Streaming Analytical Software for M3
A5 Checklist
M3 Privacy Notification Checklist
Checklist of M3 Marketing Terminology, Techniques, and Technologies
Checklist of Web A5s Software and Services
Ad Network M3 Checklist
M3 Marketers Web Checklist
Checklist of Social Metric Consultancies for M3
Social Marketing Agencies’ Checklist for M3
Recommendation Engines’ Checklist for M3
Data Harvesters’ Checklist for A5
WOM Techniques and Companies’ Checklist for M3
Checklist of Mobile Website Developers for A5s
Checklist for Constructing A5s
Checklist of A5 Developers
Checklist of A5 Marketing Companies
M3 Marketer’s Checklist
Checklist of Digital M3 and A5 Agencies
Final M3 Marketer Checklist

Case Studies
Examples of M3 and A5 in Action
WizRule Case Study
Groupon Case Study
Living Social Case Study
Zynga Case Study
Tippr Case Study
BuyWithMe Case Study
Hyundai Case Study
Instapaper Case Study
Kony Solutions Case Study
Urban Airship Case Study
Foursquare Case Studies
Gowalla Case Studies
Hipstamatic Case Study
PointAbout Case Study
MLB Case Study
Dunkin’ Donuts Case Study
Skyhook Case Study
eBay Mobile Case Study
TheFind Case Study
Vivaki Case Studies
Razorfish
Digitas
360i Case Studies
Skype Case Studies
Clearwire Case Study
Greystripe Case Studies
Univision Case Study
LTech Case Studies
Advent International
New York Life
Challenges
PC Magazine
PayPal
Discovery Communications Case Study
Touch Press Case Studies
Major League Entertainment Experience
Executive-Class Travel Experience
Twitter Case Studies
Best Buy
Etsy
JetBlue
Moxsie
Salesforce Case Study
Shopkick Case Study
IKEA Case Study
Urban Outfitters Case Study
Tumblr Case Study
Crimson Hexagon Case Study
Usablenet Case Studies
ASOS
Fairmont Hotels
Garnet Hill
JC Penney
Marks & Spencer
PacSun
Bazaarvoice Case Studies
Benefit Cosmetics
Sears Canada
DRL
Evans Cycles
Epson
Quova Case Studies
BBC
24/7 Real Media
Procera Case Study
Clickstream Technologies Case Studies
RapLeaf Case Study
TARGUSinfo Case Study
Quantcast Case Studies
BrightCove Case Study
Rocket Fuel Case Studies
Belvedere Vodka
Brooks®
Ace Hardware
Lord & Taylor
Admeld Case Studies
Pandora
IDG’s TechNetwork
Forward Health
adBrite Case Study
Datran Media Case Studies
ChaCha
PGA
Sony
eHarmony
NASCAR
BabytoBee
NetMining Case Studies
interclick Case Studies
Audience Science Case Studies
Automotive
Consumer Products
Entertainment
Finance
Manufacturing
Pharmaceutical
Retail
PubMatic Case Studies
Turn Case Studies
Automotive
Retail
Telecommunications
Red Aril Case Study
DataXu Case Studies
Education
Travel
Financial
Triggit Case Study
BlueKai Case Studies
Automotive
Travel
Appliances
Xplusone Case Study
Placecast Case Studies
The North Face
White House Black Market
SONIC
O2
TellMe Case Studies
Financial
Banking
Shipping
Mobile Posse Case Study
Medialets Case Studies
HBO
JP Morgan Chase
MicroStrategy
AdMob Case Studies
Flixster
Volkswagen
Adidas
PhoneTag Case Study
Xtract Case Study
BayesiaLab Case Study
PolyAnalyst Case Study
Attensity Case Study
Clarabridge Case Study
dtSearch Case Studies
Simon Delivers
Cybergroup
Reditus
Lexalytics Case Studies
DataSift
Northern Light
Leximancer Case Study
Nstein Case Studies
ProQuest
evolve24
Gesca
Recommind Case Studies
Law
Energy
Search and Social
C5.0 Case Study
CART Case Study
XperRule Miner Case Studies
Financial
Energy
StreamBase Case Study
Google Analytics Case Study
SAS Case Study
Unica Case Studies
Citrix
Corel
Monster
WebTrends Case Studies
Virgin Mobile
Rosetta Stone
Gordmans
ClickTale Case Study
24/7 RealMedia Case Studies
Jamba Juice
Accor Group
Personal Creations
Forbes
AdPepper Case Studies
BBC
BDO Stoy Hayward
T-Mobile
Adtegrity Case Study
BURST! Media Case Studies
Take Care Health Systems
Fuse
Kaboose
Casale Media Case Studies
Industry: Publishing
Industry: Telecommunications
Industry: Automotive
Federated Media Case Studies
Client: Milk-Bone
Client: My Life Scoop (Intel)
Client: Hyundai Tucson Movie Awards Season
Gorilla Nation Media Case Study
InterClick Case Studies
Mobile
Automotive
Juice
Tribal Fusion Case Study
Value-Ad Case Study
DRIVEpm Case Studies
Linkshare Case Studies
Smartbargains.com
Toshiba
North Face
Epic Direct Case Study
ShareASale Case Study
AdKnowledge Case Study
Marchex Case Study
Vibrant Media Case Studies
Bing™
Toyota
Best Buy
Canon
BlogAds Case Studies
Norml
Gala Darling
Funky Downtown
Drudge Retort
Pheedo Case Study
Sedo Case Study
Cymfony Case Study
Jivox Case Study
ContextOptional Case Study
KickApps Case Study
ATG Case Study
Aggerateknowledge Case Studies
InfiniGraph Case Study
SocialFlow Case Study
Hyperdrive Interactive Case Studies
Dreamfields Pasta
LaRosa’s Pizzerias
Sharpie
Sensor Technology Systems
Brains on Fire Case Study
Likeable Media Case Study
360 Digital Influences Case Study
BzzAgent Case Studies
HTC
Thomas
Black Box Wine
Keller Fay Group Case Studies
Fanscape Case Study
BrickFish Case Studies (Figure 4.17)
TREMOR Case Study
Porter Novelli Case Study
Room 214 Case Studies
Qwest
Travel Channel
Strategic Media
SmartPig
Converseon Case Study
Oddcast Case Studies
McDonald’s
Kellogg
Ford
M&M
Nokia
Mr. Youth Case Study
Blue Corona Case Study
Mozeo Case Study
Mobile Web Up Case Study
Mobify Case Studies
The New Yorker
Threadless
Alibris
Usablenet Case Studies
ASOS
Fairmont Hotels
JC Penney
Digby Case Study
Bianor Case Study
xCubeLabs Case Studies
McIntosh Labs
Eat That Frog
Glympse Case Study
DataXu Case Studies
Social
Mobile
Auto
GeniousRocket
Amazon
Heinz
Aquafina
MediaMath Case Studies
Financial Advertiser
Travel Advertiser
Retail Advertiser
Profero Case Study
x + 1 Case Study
Victors & Spoils Case Studies
DISH Network
Virgin America
Harley–Davidson
DoubleClick Case Study
ClickTracks Case Study
SiteSpect Case Study
Jumptap Case Studies
Hardees
Swap
Valtira Case Study
ContextOptional Case Study
Satmetrix Case Study
Nsquared Case Study
FetchBack Case Studies
Cosmetics
Clothing
Electronics
Future
Mobility
Intelligibility
$
Index

Über Autor(innen)

Jesús Mena is a former Internal Revenue Service Artificial Intelligence specialist and the author of numerous data mining, web analytics, law enforcement, homeland security, forensic, and marketing books. Mena has also written dozens of articles and consulted with several businesses and governmental agencies. He has over 20 years’ experience in expert systems, rule induction, decision trees, neural networks, self-organizing maps, regression, visualization, and machine learning and has worked on data mining projects involving clustering, segmentation, classification, profiling and personalization with government, web, retail, insurance, credit card, financial and healthcare data sets. He has worked, written, and lectured on various behavioral analytics and social networking techniques, personalization mechanisms, web and mobile networks, real-time psychographics, tracking and profiling engines, log analyzing tools, packet sniffers, voice and text recognition software, geolocation and behavioral targeting systems, real-time streaming analytical software, ensemble techniques, and digital fingerprinting.