Machine-to-Machine Marketing (M3) via Anonymous Advertising Apps Anywhere Anytime (A5): 1st Edition (Paperback) book cover

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

1st Edition

By Jesus Mena

Auerbach Publications

436 pages | 92 B/W Illus.

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Description

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

About the Author

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.

Subject Categories

BISAC Subject Codes/Headings:
BUS058000
BUSINESS & ECONOMICS / Sales & Selling
COM021030
COMPUTERS / Database Management / Data Mining
COM060000
COMPUTERS / Internet / General