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

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

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.