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

22 Pages·2012·1.38 MB·English

Machine-to-Machine Marketing
(M3) via Anonymous Advertising
Apps Anywhere Anytime (A5)
Machine-to-Machine Marketing (M3) via
Anonymous AdJveErtiSsinUg SAp pMs AEnyNwhAere Anytime (A5)
© 2012 by Taylor & Francis Group, LLC
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Anonymous Advertising Apps Anywhere Anytime (A5)
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Library of Congress Cataloging‑in‑Publication Data
Mena, Jesus.
Machine-to-machine marketing (M3) via anonymous advertising apps anywhere
anytime (A5) / Jesus Mena.
p. cm.
“An Auerbach Book.”
Includes bibliographical references and index.
ISBN 978-1-4398-8191-0
1. Internet advertising. 2. Internet marketing. 3. Application software--Development.
4. Machine learning. 5. Electronic commerce. I. Title.
HF6146.I58M46 2012
658.8’72--dc23 2011052595
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
http://www.crcpress.com
Chapter 1
Why?
M3 and A5
The consumer is his digital device. If you buy into this, then you get what machine-
to-macMhinae cmhariknetein-g t(oM-3M) aandc ahnoinnyem oMus aadrvkeretistining gap p(s Man3yw)h evrei aan y time
(A5) are about. Marketing to machines is easier than marketing to humans: devices
Anonymous Advertising Apps Anywhere Anytime (A5)
do not evolve or change, do not divorce, do not go on unemployment, or do not file
for ban©kru p2tc0y.1 M2a chbinye sT arae sytaltoionra r&y t aFrgreats ntoc Mis3 mGarkreoteursp an,d LA5L pCrograms.
Human attributes, such as income, marital status, and age, are really not very
reliable to use in the modeling of consumer propensities in today’s volatile economy
and real-time digital marketplace. A more direct and technically sound approach is to
monitor and model the consumer’s device activities and behavioral patterns.
From the infancy of e-commerce, log server files have been capturing such
nonhuman features as operating system, key words used, and referring sites from
online visitors. Internet mechanisms such as cookies and beacons have expanded
this to include other machine-to-machine (M2M) attributes such as time of day,
browser type, and purchasing history. Today, new technology such as digital fin-
gerprinting can expand these M2M attributes to include font, color configuration,
and literally hundreds of very detailed and precise information unique to all types
of digital devices to enhance M2M profiles enabling M3.
M2M started as a utility meter application. M2M uses a device, such as a sen-
sor or meter, to capture an event, such as a traffic pattern, which is then instantly
relayed through a network, which can be wireless, wired, or hybrid to an applica-
tion, such as a program, that instantly translates the captured event into some
type of action, such as rerouting traffic in a network. So far, M2M applications
1
2  ◾  Machine-to-Machine Marketing (M3)
have been limited to monitoring machinery that works on production lines, such
as the assembly of automobiles and letting the auto producers know when certain
products need to be taken in for maintenance and for what reason. Another appli-
cation is to use wireless networks to update digital billboards to display different
messages based on time-of-day or day-of-the-week, such as for pricing changes
for gasoline.
What M3 does is elevate the capabilities and functionality of M2M from that
of a meter to that of a marketer. The rules used to issue alerts to divert network
traffic for M2M applications can instead be rules about device behaviors, which
can lead to improved sales and revenue. Some human features, such as gender and
age, are useful for the triangulation marketing along with other M2M features.
However, M3 is more concerned about where and what devices are looking for and
maybe where they are located. As with M2M meter applications, it is important
for M3 to monitor continuously activities in order to enable rules to be developed
from historical data patterns to generate relevant and real-time offers in a seamless
manner. To accomplish this, A5 must be developed and deployed.
Why M3? Because M2M data about devices’ behaviors and their human
owners are everywhere, ubiquitous in real time and intimately detailed, which mar-
keters need to triangulate via behavioral analytics and A5s. The data about their
Machine-to-Machine Marketing (M3) via
devices’ desires, needs, and passions are flowing continuously via the web and wire-
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devices precise offers and relevant content.
© 2012 by Taylor & Francis Group, LLC
M3 is about the convergence of M2M data from websites, social networks, and
mobile devices and using artificial intelligence software to model and monetize
devices’ behaviors—for the targeted placement of products, content, or services
via multiple channels—in a personal manner the instant they take place. M3 is
made possible by M2M technology that deals with the ability of both wireless
and wired systems to communicate with other devices of the same ability. M2M
was originally developed by wireless carriers to monitor traffic activity in order to
reconfigure their network.
A5 enables the positioning of the right product or service in front of the right
consumer via precise messages on the web, e-mail, texts, mobile devices, etc. The
success of A5 involves strategic planning and measured improvement of predictive
evolving models. The modeling of wired and wireless devices’ behaviors can be
accomplished by the strategic use of inductive and deductive analytical software,
which is relatively cheap and free, such as WizRule, CART, or RapidMiner.
For the M3 marketers, the analysis of consumer activity starts with the use of
a combination of human and device data using machine-learning algorithms that
can generate the following:
1. Clusters of device behaviors
2. Extract key concepts from unstructured content
3. Develop predictive business rules to quantify and monetize device behaviors
Why?  ◾  3
A5 Biz rule
IF Browser Firefox
AND Age 45–49
Server AND Keyword “Refinance” Device
THEN Fidelity Ad
Figure 1.1  A5 “fires” a predictive rule for M3.
A5 is about an organic networked approach to advertising—framed as swarming
intelligence that is a branch of artificial intelligence (AI)—which attempts to repli-
cate via machines, networks, and software how biological species behave and react
in their search for survival as in flocks of birds, packs of wolves, schools of fish,
and swarms of insects. Swarm intelligence proposes that intelligence derives from
interactions in a social world that is decentralized and totally self-organizational.
This is at the essence of A5 and M3 that is to offer the content, products, and
services—most aligned to devices’ desires, values, wants, and needs—at the micro-
second they occur. Figure 1.1 shows the design and execution of an A5 predictive
Machine-to-Machine Marketing (M3) via
rule for M3; however, this simple architecture can be developed for hundreds of
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conditions, such as location and time-of-day, along with some anonymous human
© 2012 by Taylor & Francis Group, LLC
attributes.
Machine learning is at the core of M3, originating from the field of AI;
machine-learning algorithms can be used to analyze vast amounts of data to
discover propensity to purchase behaviors. For M3, machine-learning software
enables the marketer to interrogate vast and diverse datasets from the web, social,
and mobile networks. Behavioral analytics is at the core of modeling, profiling,
and prediction.
Machine learning is the process by which pattern recognition software can pre-
dict some event or outcome, which in the case of M3 is to discover and model
devices’ behaviors. The field of machine learning seeks to learn from historical
activities in order to predict future behaviors (this can be anything from propensity
to purchase certain products or services) to the desire to view certain specific type
of content. One of the advantages to machine learning for M3 is that the server
can calibrate these marketing objectives in an understandable format. The outputs
of most machine-learning software are conditional predictive IF/THEN rules that
are easy to understand:
IF second visit to site
AND font Verdana
AND gender female
THEN Offer Product Code 51
4  ◾  Machine-to-Machine Marketing (M3)
The success of machine learning for M3 involves the strategic planning and
measured improvement of predictive evolving models and clusters. For example,
the key objective for executing and leveraging behavioral analytics for M3 is to
plan and design a framework from which consumers’ behaviors can be captured
and modeled. Similarly, this M3 strategy should be to create a continuous and
systemic method of quantifying device behaviors and to continuously measure
everything.
A good example of M3 and A5 comes from Groupon, the digital couponing
startup that daily sends deal-of-the-day e-mails to more than 70 million subscrib-
ers around the world. But it is not the location-based coupon e-mail offers that are
illustrations of the execution of M3 and A5 by Groupon. Now, it is their new offer-
ing—what the company calls Groupon Now—which uses M3 and executes A5 to
advertise its discounted offerings with surgical accuracy.
Say, you sell tacos and tortas in the neighborhood around the University of
Texas at El Paso (UTEP); while offering coupons by e-mail might entice some new
clients, the fact is that you have long lines of clients waiting to buy your fat and
juicy tortas already. This is where Groupon Now and its strategic use of M3 via A5
kick in. Let us say Tortas-R-Us does experience some slow hours during midday
around midweek. Using M3 and A5 technology and techniques, Groupon Now
Machine-to-Machine Marketing (M3) via
offers are made only during the hours of 1 p.m. to 3 p.m. but only on Tuesday,
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following, Groupon Now knows when and where to market phone devices using
© 2012 by Taylor & Francis Group, LLC
their mobile app:
IF Zip Code 79902-2214
AND Time-of-day 1:37
AND Day Wednesday
THEN Coupon $10 for $6 at Tortas-R-Us
This is a perfect example of a server marketing to a mobile device based on its
proximity to a restaurant and based on the time-of-day and day-of-the-week. At no
time are human attributes, characteristics, or demographics used in the marketing
of these digital coupons. Groupon Now is based entirely on nonhuman factors
and it is strictly M2M. When users open up the phone app, they will be presented
with two buttons by Groupon Now: “I’m hungry” and “I’m bored.” Clicking
either button will open up a list of time-specific daily deals based on their loca-
tion. Businesses can now choose when they want these deals to be available. Local
eateries have never really had a simple way to manage their perishable inventory,
especially labor and food (Figure 1.2). So why waste those vital resources during
slow periods when they can M3 to savings-savvy consumers with a highly targeted
Groupon deal via A5?
Why?  ◾  5
Figure 1.2  M3 by Groupon starts by targeting a device’s city.
Groupon has already filed a patent around its method for serving up deals via
e-mail. This is the abstract of the Groupon patent (Figure 1.3):
A system and methods to mutually satisfy a consumer with a discount
and a vendor with a minimum number of sales by establishing a tipping
Machine-to-Machine Marketing (M3) via
point associated with an offer for a good or service. If the tipping point
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charged and receives an indication of the discounted sale, such as a cer-
© 2012 by Taylor & Francis Group, LLC
tificate. If the tipping point is not met, the discount offer is abandoned
and the consumer is not charged. Once the tipping point is established,
the vendor receives a payment, even before the consumer uses the certif-
icate. The system and methods also include a reward or loyalty program,
an exchange or secondary market for the purchased deals, and a match-
ing algorithm that matches customers to relevant goods or services.
The only human attributes used to triangulate its offers by Groupon are the devices’
location by ZIP code and the age of the consumer, aside from that the company
instead uses M2M data for M3 and A5 via its phone app; increasingly these are the
only human attributes that M3 marketers can rely on since they are not subject to
change (Figure 1.4).
Groupon has managed to build the fastest growing company of all times, and
their success is based on M3 via A5. Mobile apps are the new medium for market-
ing via location, temporal, and behavior coordinates for triangulation at the right
time to the right moving devices. A5 will increasingly become important as more
and more consumers used their phones for shopping, price comparison, directions,
and local discounts that are time and location sensitive. Mobile apps allow M3
marketers to target devices as they move via A5s.
6  ◾  Machine-to-Machine Marketing (M3)
Start
Select vendor
110
Identify good or service
120
Establish tipping point
130
Determine terms of offer
140
Exploit offer
150
Receive consumer
participation
160
Machine-to-MCaalcuclahte innumebe rM of arketing (M3) via
consumer participation
Anonymous Advertising 1A70pps Anywhere Anytime (A5)
© 2012 by Taylor & Francis Group, LLC
Consumer
No Yes
participation equal
tipping point?
180 Execute sale
185
Stop
Figure 1.3  Main drawing of the Groupon patent.
Groupon Now could scale up to become an M3 advertising network, by running
the deals in a lot of other third-party apps. Groupon could offer specific deals based
on their dynamic, just-in-time ads based on the devices’ location and time-of-day.
These ads could direct people to a mobile website or a third-party app, where users
could complete their purchase. Then Groupon could give some money to the app
publisher, as with any lead-generated affiliate relationship, either on a cost-per-click
(CPC) or cost-per-acquisition (CPA) basis. If the deals are well targeted, Groupon
Now could have very high click-through rates with equally high purchase rates, via
its “I am Hungry” or “I am Bored” button ad network.
Why?  ◾  7
Figure 1.4  The only human attributes used are ZIP and age, and they are optional.
Mobile application development, such as the Groupon app, is the process by
which Msoftawcarhe iisn deeve-ltoope-dM fora pchohneins, feor sMpecaifirck peurtpionsegs, s(uMch 3as )p lavyiinag m usic
(Pandora) or games (Zynga). These applications are usually downloaded by custom-
Anonymous Advertising Apps Anywhere Anytime (A5)
ers from app stores or from the sites of the creators of the apps. Most of the method-
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of the application development process:
1. The app itself and its structure, in the case of Groupon getting discount deals
2. The business logic, such as location, proximity, age, gender, and time of day
3. The graphical user interface of the mobile app and its application
One of the most popular operating systems for apps is Android and it is a platform
from the Open Handset Alliance, whose 34 members include Google, HTC,
Motorola, Qualcomm, and T-Mobile and supported by all of these major soft-
ware, hardware, and telecom companies. It uses the Linux kernel as a hardware
abstraction layer (HAL). Application programming is mostly done in Java. The
Android specific Java software development kit (SDK) is needed for development
although any Java IDE may be used. Performance critical code can be written in
C, C++, or other native code languages using the Android native development kit
(NDK).
It is expected that by 2012 there will be more than 130 million Android users
around the world. For the M3 marketer, it is very important to leverage the creation
and deployment of these A5s, specifically for the Android phones and all Apple
devices (iPhone and iPad), the two dominant players in the United States.
Machine-to-Machine Marketing (M3) via
Anonymous Advertising Apps Anywhere Anytime (A5)
© 2012 by Taylor & Francis Group, LLC