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

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

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Features

  • Discusses the interactive environments and how to deploy M3 and A5
  • Highlights the technologies and solution providers readers can use for executing M3
  • Provides checklists of techniques, strategies, technologies, and solution providers for M3
  • Offers case studies that show current modes of M3 and A5

Summary

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:

  1. Why – Discusses the interactive environments and explains how M3 can be deployed
  2. How – Describes which technologies and solution providers can be used for executing M3
  3. Checklists – Contains lists of techniques, strategies, technologies, and solution providers for M3
  4. 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

Table of Contents

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

Author Bio(s)

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.