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Marketers are discovering that data mined information from social networks is yielding

valuable. Research reveals that behaviors within these multiple personal links can be

highlighted and analyzed for accurate correlations.

On June 28th, 2009, Adweek.com featured an article called Connect the Thoughts: The

analysis of social networking data promises to take behavioral targeting to a new level. But can

it deliver? The article covers topics relating to data mining based on empirical evidence from

research, the changing model of marketing, consumers’ behavioral habits becoming a product,

and the ever-changing legal aspect concerning boundaries on consumers’ rights to privacy.

The article begins by introducing the notion of the phone possibly being one of the most

successful and oldest social networking tools. AT&T Labs Research sponsored an evaluation of

data collected from several AT&T phone records, which revealed that consumers within the

same social networks had similar buying habits. “Consumers are five times more likely to

respond to marketing from a brand that his or her friend uses.” (Morrissey)

The contemplation is the effectiveness of marketing products within social networks

based on the connections analyzed from online datamining. Theoretically, the data collected

from these knitted groups would prove rather relevant, thus yielding more effective for

marketers (Morrissey). However, this would require changing the dynamics of the industry

from service- and product-oriented to consumer behavior-oriented. Fitting-the-product-to-

consumer-needs would be transformed to consumer-habits-fitting-the-product, as well as that

of their peers; consequently, this division of media would need to remodel itself.

Dramatic changes in society require creative, unconventional modes of thought in order

to hold pace. “In 2008, Microsoft ad executive Joe Duran s in an effort

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to launch a new marketing analytics platform.” (Morrissey) One of the most notable obstacles

he recognizes is effective marketing to the limited number of existing customers. A possible

solution is to market that same product to the respective consumer’s closest friends within his

or her social links (Morrissey). This reinventive model would require a more thorough

evaluation of the targeted audience, which is performed by analyzing activity and

communication within online cliques. As a result, consumer habits have become a supply-and-

demand product.

Companies such as Lotame and 33across gather information for advertisers by combing

through platforms such as discussion forums, blogs, message boards, social network sites, and

other user generated content. “Their aim is to mine social networking data for advertisers.”

(Morrissey) This information provides consumer-oriented topics and concerns; as well as

displays insight into the dynamics regarding the interaction within various social links.

Consequently, this process of collecting behavioral

, Lotame, and 33 across all comply with industry standards and do not

collect personally identifiable information. Yet, the advertising industry is bracing for tighter

regulation of its data practices.” (Morrissey) A major idea for required practice in consumer

privacy is opting-in for third-party cookies; however this could have the potential of destroying

a $1 billion industry. The debate continues: How invasive are third-parties?

Regardless of privacy interpretation, targeting social networks is another integrated

strategy to add to the marketing industry. Dave Morgan, founder of Tacoda and backer of

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33across, claims that “It’s another layer of valuable data beyond behavioral or contextual. The

real key is not just peeling off another layer of the onion.” (Morrissey)

Further research indicates that data mining through social networking does

render effective results. However, this method tends to be intrusive by default due to its very

nature of shifting through mass databases for personal records.

A Technology Review article features research conducted on iPhones in a similar manner

to the AT&T research done with their records. Telenor, a Scandinavian wireless company, used

a method called social network analysis, in which they examined call detail records (CDRs):

incoming and outgoing phone numbers, duration of calls, device identification numbers,

frequency of texts, etc. This information revealed the strength of relationships within social

networks; researchers used this information to create a map of these social links. These maps

showed that the iPhone sales spread faster amongst more socially connected owners.

Individual X with 1 iPhone-owning friend was 3 times more likely to buy an iPhone than

individual Y with zero iPhone-owning friends. Individual Z with 2 iPhone-owning friends was 5

times more likely to buy an iPhone (Simonite).

The process of evaluating and analyzing online data is just as revealing for researching

correlations and relationships. The marketing software company Wingify explains that upon

surfing through the web, one’s browser transfers information from the cookies back-and-fourth

to the server. This process occurs every time a web page is being requested. The server

thoroughly documents the history of this data: geographic location, local time, browser used,

screen resolution, time spent on page, content on page, previous page that was visited,

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internet speed, number of visits to that page, operating system, search engine used, key words

used, made purchases, number of made purchases, and the value of made puchases (Wingify).

Interactive media has completely revolutionized the advertising, marketing, and public

relations industries. The immensely increased “audience” participation is almost reciprocating

the general media platform; the reversed model is resulting in these fields reinventing

themselves.

The Adweek CEO Joe Duran exploring new marketing ventures

involving evaluating the closest links between individuals within a social network in order to

create a larger customer base through existing customers. The article further explains

companies, such as Lotame and 33across, serve the function of gathering information for

advertising through online interactive sites and user-generated content. An interview

conducted with Associate Professor Computer Information Systems Dr. William Mink of

Camden County College provides further insight into the effectiveness of marketing via social

networking. He explained that he is part of a motorcycle club that holds online discussions.

“The motorcycle forum is covered with banner ads,” he said. “I have made purchases due to

the ads being so targeted to my particular interests.” He explained that the dealers of the

forum are supporting it, thus there is a sense of loyalty (Mink).

The marketing software company Wingify describes two methods of behavioral

targeting: ad-network behavioral targeting and on-side behavioral targeting. Ad-network

behavioral targeting places consumers into a profile or “bucket” based on the sites they have

visited. The buckets include titles such as “tech geek”, “food junky”, or “auto enthusiast” as

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Mink described himself. On-site behavioral targeting requires interaction and activity spent on

a site in order to be effective, such as a discussion forum. This type of behavioral targeting

dubs visitors as “loyalists”, “heavy spenders”, “frequent visitors”, and various other labels.

Wingify explains that an “information gatherer” can be upgraded to “spender” by cross-selling

or promoting based on the respective consumer’s online activity (Wingify). “Even though I am

not in the market for a new car, I always enjoy looking,” said Mink (Mink).

The Adweek article covers topics pertaining to companies combing though databases

and releasing the evaluated information for advertising purposes. Traditionally, a product is

targeted towards a consumer based upon general consumer behaviors. In contrast, the trend is

changing to the consumers’ behaviors becoming the product to be gathered and analyzed for

marketers to utilize. A dissertation done by professor Joseph Thai of the University of

Oklahoma reveals the dynamics of companies collecting data that is released to third parties.

Data is not mined from a single database; profiles, niche markets, and social network

correlations are the result of numerous databases that have been shifted through for

information that becomes compiled and analyzed. There are mass amounts of information that

exist about individuals’ personal habits from purchases, health records, finances, online

movements, and various other activities. “Database services offer algorithms that find

correlations and relations with the data, such as people, organizations, and places.” (Thai)

Mink described business analytics and business intelligence as “emerging behavioral

sciences, statistical analysis, and relational databases.” (Mink) Companies such as LexisNexis

produce software for gathering database information utilized for advertising and business

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purposes. They describe themselves as “Database that is bought intelligence.” One such

software is ExecRelateTM, which provides data on over 200,000 companies. This enables

correlations to be found throughout these networks by examining on-hand information: ages,

employment histories, educations, association memberships, annual compensations, and

business-to-business relationships (LexisNexis).

InterAction® is another software offered by LexisNexis, it specifically specializes in

finding relationships within social networks. The program explains two of its functions,

“Building and segmenting marketing lists and finding interconnections between people,

companies, and client activities.” This information is described as “relationship intelligence”

(LexisNexis).

As the Adweek article claims, data mining raises a notable host of privacy concerns. The

Fourth Amendment protects the civil liberties concerning privacy rights against government

and private entities; however the boundaries within this scope are gray and subject to

interpretation. The Fourth Amendment states “The right of the people to be secure in their

persons, houses, papers, and effects, against unreasonable searches and seizures, shall not be

violated…but upon supported by oath or affirmation, and particularly describing the place to be

searched.” The question of applying the “supported by oath or affirmation” boundary remains

undecided. Generally, if one signs or confirms information to a company or government entity,

that information is no longer regarded as “private”, unless specifically signed as such. Once

information is released to a particular entity, it is available for third party inquisition; this is

within full compliance in regards to the Bill of Rights (Thai).

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A major U.S. Supreme Court Fourth Amendment case was The United States v. Katz,

1967. The Court ruled that “what he seeks to preserve as private even in an area accessible to

the public, may be constitutionally protected.” The case set the standard that a person must

know certain information is made public in order for that information to no longer be covered

by Fourth Amendment protection (Thai).

In an effort to alleviate misinterpretation, Justice John Marshall Harlan produced a two-

step test. The first step is that there has to be a reasonable and logical expectation of privacy.

The second step is that general society renders this expectation as acceptable and customary

(Thai).

This test is exhibited in the Fourth Amendment case, the United States v. Miller, 1976.

This case rendered dialed numbers as unprotected, consequently reducing the privacy of

general CDRs. This Court held that the expectation of phone records to be regarded as private

is unreasonable. “All telephone users must realize that they must convey phone numbers to

the telephone company.” (Thai)

After decades of further court cases, the general standing is that personal records

collected from third-party databases are not reasonably expected to be held private. The

reasons are considered logical for personal information to be conveyed to credit card

companies, internet servers, banks for transactions, phone companies, and gps service

providers. In accordance to the Justice Harlan two-step test, the expectation for privacy is

unreasonable since a two-way exchange of information must occur, as customary by societal

standards (Thai).

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Evaluations conducted on first-hand research reveal considerable background and

further understanding of analyzing links within social networks. Information based on online

PlayStation 3 gamers and online Xbox gamers demonstrate the vastly numerous correlations

that can be produced from analyzing social networks. The PlayStation 3 and Xbox systems are

not compatible, thus there remains a level of independent variables, which kept records

separated on two separate tables within the database. However, other tables and queries had

to be made in order to make any sense of the data. Upon being organized into queries, the

information began to reveal several links and similarities within the two networks. According to

broad analyzing, for example, PlayStation 3 gamers seem to have traditionally higher levels of

education and appear to be online friends with gamers who they know on a personal level.

Xbox gamers appeared to attract more of a younger age range, as well as individuals who are

more extroverted, as indicated by the [Most Common Benefits] field consistently being

answered with “online community”. That answer appeared less frequently in the PlayStation 3

community; their responses consisted of “exclusive games” or “free online access”. Xbox

players appeared to be more lenient towards being friends with someone they did not know

when compared to PlayStation 3 players. As previously stated, these evaluations are simple

surface scratches and there are countless combinations and details that could analyzed. Data

mining seems to give an immensely in-depth view of behaviors within social networks; the view

on such publics is revealing on multiple levels. It is quite obvious how marketers are so on-

point within platforms that feature user-generated content and activity. Marketing and public

relations is now being told the exact ways their publics behave and operate; the need to

persuade or find common goals in values is less of a need when research is done easy.

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Bibliography

1. “LexisNexis.” Business Solutions & Software for Legal, Education, and

Government. LexisNexis. Web. 25 Feb. 2011. http://www.lexisnexis.com/sales-

marketing-hub/

2. Mink, Bill. “Database Mining and Behavior Marketing Interview.” Personal Interview. 1

Mar. 2011.

3. Morrissey, Brian. “Connect the Thoughts| Adweek. “Adweek|The Voice of Media. 28

June 2009. Web. 27 Feb. 2011. http://www.adweek.com/news/techonology/connect-

thoughts-99712

4. Simonite, Tom. “Wireless Companies Could Use Your Friends.” Editorial. Technology

Review: The Authority on the Future of Technology. MIT, 22 July 2010. Web. 25 Feb.

2011.

http://www.technologyreview.com/communications/25840/page1/

5. Thai, Joseph T. “Is Data Mining Ever A Search Under Justice Stevens’s Fourth

Amendment?” Diss. University of Oklahoma, 2005. OU College of Law. University of

Oklahoma College of Law. Web. 27 Feb. 2011.

http://jay.law.ou.edu/faculty/thai/pub/Thai%20%20Is%20Data%20Mining%20Ever%20a

%20Search.pdf

6. Wingify. Types of Behavioral Targeting. Wingify. Visual Website Optimizer. Wingify.

Web. 27 Feb. 2011. http://www.wingify.com/behavioral-targeting-whitepapers/types-of-

behavioral-targeting.php

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