Social Media Marketing

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Chapter 4

Social Media Marketing Web X.0 of Opportunities Lemi Baruh Kadir Has University, Turkey

ABSTRACT In recent years social media applications, which enable consumers to contribute to the world of online content, have grown in popularity. However, this growth is yet to be transformed into a sustainable commercial model. Starting with a brief overview of existing online advertising models, this chapter discusses the opportunities available for advertisers trying to reach consumers through social media. The chapter focuses on viral marketing as a viable option for marketers, reviews recent viral marketing campaigns, and offers recommendations for a successful implementation of social media marketing. In conclusion, the author examines future trends regarding the utilization of the emerging Semantic Web in marketing online.

INTRODUCTION The brief history of the World Wide Web is filled with stories of unprecedented commercial success as well as shattered dreams of hopeful online entrepreneurs. It should not be surprising that, just as their predecessors, Web 2.0 and social media also bring about important questions regarding their sustainability. On the one hand, since 2006, social media sites have been growing in number and popularity (Boyd & Ellison, 2007). For example, according to DOI: 10.4018/978-1-60566-368-5.ch004

comScore, a leading Internet information provider, as of December 2007 Facebook had close to 98 million unique visitors, and Fox Interactive Media, including MySpace, had more than 150 million. Similarly, recent years have seen a phenomenal growth in the popularity of weblogs (blogs): in 2007 every day, 175,000 new blogs were added to an estimated 67 million blogs that were already up and running (as cited in Rappaport, 2007). On the other hand, skeptics voice their belief that social media, despite their current popularity, may not have the staying power (“MySpace, Facebook and Other Social Networking Sites,” 2006).

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Social Media Marketing

An important component of skeptics’ concerns about the sustainability of social media pertains to the fact that there are no agreed upon ways of monetizing the rising popularity of social media (Allison, 2007; Hall, 2007). Perhaps, the most telling example of this problem is Facebook. Despite having a market value of around $15 billion, Facebook’s 2007 revenue was $150 million (McCarthy, 2008) – a considerably small share of the $21 billion online advertising industry. Then, the question of whether social media will be more than just a fad boils down to advertisers’ ability to utilize the unique opportunities presented by social media. Although advertisers and social media entrepreneurs are yet to agree on a marketing model for social media, recent discussions point to several important requirements that a successful model should accommodate. Given the decentralized architecture of the Internet in general and social media in particular, a central tenet of these recent debates concerns the relative merits of more conventional advertising methods and word of mouth (or word of “mouse”) based marketing approaches that cede control to the consumers. In the light of these debates, this chapter will start by summarizing online advertising methods. After this brief summary, the chapter will focus on the opportunities and challenges for online marketers that are brought about by the development of social media. Finally, the chapter will discuss viral marketing and integrated marketing communication principles to provide a roadmap for realizing the financial and marketing potential of Web 2.0.

BACKGROUND Online Advertising In its most traditional sense, advertising is defined as a paid form of communication appearing in media, usually with the purpose of reaching a large number of potential customers. Since 1993, when

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CERN announced that the World Wide Web would be available to anyone free of charge, advertisers experimented with different methods of reaching consumers online. Unsurprisingly, the first reaction of advertisers was to treat the World Wide Web as a natural extension of traditional media, such as newspapers and television. And, just as in conventional mass media, early online advertising methods, such as banners, pop-ups and interstitials, were characterized by intrusiveness and adoption of a one-way stimulus-response model within which information flows from the advertiser to the customer (McCoy, Everard, Polak, & Galletta, 2007; Rappaport, 2007). However, even in the early years of online advertising, signs of what was to come in interactive marketing were revealed. Shortly after banners became a popular online advertising method in 1994, keyword-activated “smart banners” were introduced. What set smart banners apart from their predecessors was that the contents of the banners were personalized in response to the search words entered by the users. As such, smart banners were one of the first examples of how content variability in new media (Manovich, 2001) can be utilized to customize information to consumers’ needs (Faber, Lee & Nana 2007).

Customization and Message Congruence in Interactive Media As noted by several researchers, content variability and the consequent ability to customize content according to the needs of the consumer are made possible by the interactive capabilities of new media (Baruh, 2007; Faber et al., 2007). Two important characteristics of interactive media are the ability to facilitate a two-way flow of communication and the related ability to track and store every bit of information about how consumers use a system (McCallister & Turow, 2002). Real-time information about how consumers use a medium, especially when combined with other data through data mining, enables marketers to


extract profiles about individuals that can then be used to tailor messages and products. The ultimate aim of this process is to target different consumer groups with specific messages that can tie a product to their informational needs, lifestyles or predispositions. Extant literature on online targeting suggests that consumers will be more receptive to messages that are tailored as such (McCoy et al. 2007; Robinson, Wyscocka, & Hand, 2007). To a large extent, this higher receptivity is the result of being able to promote the “right” product, at the “right” time and place and the “right” tone. A case in point that supports these research findings is the success of Google’s AdWords, which accounts for 40% of online advertising spending. The premise of AdWords is that the marketers can reach motivated consumers by providing them with contextual advertising messages congruent with their online keyword searches (and presumably, their interests). Similarly, a widely known feature of online vendors such as Amazon.com is their customized product recommendation systems. The recommendation system these online vendors utilize is based on a data mining system called market-basket analysis (also called association discovery). The premise of this system is that the marketer can create cross-selling opportunities by identifying the product types that a customer would be interested in (e.g., microwave popcorn) on the basis of other products that he or she has already purchased or is purchasing (e.g., a DVD movie). As such, what the market-basket analysis algorithm does is to identify product clusters that are purchased together or sequentially using the product purchasing history of customers whose tastes are similar to a specific customer.

ONE STEP FURTHER: WEB 2.0 OF OPPORTUNITIES Customization and Data from Social Media As can be inferred from the discussion above, collecting information about consumers is an important prerequisite of customizing advertising messages in accordance with the informational needs and lifestyles of consumers. Certainly, data about individuals’ online media consumption and purchasing behavior, especially when combined with other sources of data such as credit history, provide marketers with an unprecedented capability to not only determine which customers to target (and avoid) but also when and how to target them. Within this context, social network sites, such as Facebook, MySpace or LinkedIn, have a potential to extend what is already a large pool of data about consumers. Such social network sites are designed to allow users to create personal profiles and connect with other users, friends or strangers. And through the creation and perennial updating of their profiles, users of social network sites actively participate in the dissemination of information about themselves (Andrejevic, 2007; Solove, 2007). The types of information users of social network sites disclose include: information about their hobbies, interests, likes and dislikes, whom they associate with, a dinner they had a couple of days ago and, sometimes, disturbingly private details about their social and sexual lives. Blogs, another highly popular form of social media, are no different from social network websites. As Solove (2007) points out, any topic, any issue and any personal experience are fair game for more than 60 million bloggers around the world. The massive quantities of data that social media users reveal online are not left untapped by media companies and marketers. For example, MySpace has recently begun an effort to mine data from its more than 100 million users in order to better

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target advertising messages. Named as MySpace HyperTargetting, the system initially began mining data about general interest categories, such as sports and gaming, and is now further dividing interests into thousands of subcategories (Morrisey, 2007).

The Community Touch An important point to note with respect to the types of data available in social media is that the digital goldmine of information is not simply a more detailed version of data collected about consumers’ interests and behaviors in other forms of interactive media. Rather, in social media, the available data contain unprecedented details about the network affinities of users. The data about the network affinities of users can be utilized at two levels. First, through the “tell me about your friends and I’ll tell you about yourself” principle, marketers can make further refinements to consumers’ profiles based on the interests shared by members of the communities they belong to. Secondly, information about the communities that an individual belongs to can be used to identify the paths through which they can be reached. Recent marketing techniques devised by online vendors and social media outlets illustrate how information about social affinities can be used to reach consumers. For example, Amazon.com’s iLike application, a music service that markets new music and concerts to interested listeners, works by scanning the music libraries of its subscribers. The service connects like-minded listeners and promotes new music to users through add-ons such as Facebook’s iLike widget. Similarly, Facebook’s own Beacon platform tracks purchases Facebook users make on partnering online vendors and then informs users’ networks about the recent purchase (Klaassen & Creamer, 2007; Thompson, 2007; Tsai, 2008). In addition to leveraging existing social networks to disseminate marketing messages, some software applications, for example, Stealth Friend Finder automatically generate

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massive and targeted Facebook Friend Requests to directly connect with the consumers.

Web 2.0 of Opportunities: Viral Marketing in Social Media These examples of social targeting pinpoint the direction that marketing in social media can take. Rather than being an advertising distribution system, Beacon is a viral marketing tool that lets community members know what their co-members have purchased. In other words, with the Beacon system, the consumer, through the publication of his/her purchasing decision, assumes the role of an influencer. Subramani and Rajagopalan (2003) suggest that consumers may assume such a role either passively or actively. In the passive form, the consumer spreads the word simply by using or purchasing a product (as is the case when an e-mail from a Blackberry user contains a message saying the e-mail was sent using a Blackberry account). On the other hand, active viral marketing requires that consumers participate in the message dissemination process by contacting other potential customers (Clow & Baack, 2007). An important criticism of passive viral marketing systems in social media is that they fail to utilize an important characteristic of Web 2.0 in general and social media in particular. Instead of being a passive consumer of readily available content, Web 2.0 users are participants in both the creation and dissemination of content. Accordingly, despite utilizing social graphs to target messages more effectively, the “your friend just bought this book from Amazon.com” message is nevertheless an advertising method that affords the consumer very little power as a potential source of influence (Anderson, 2006; Windley, 2007). Considered from this perspective, a more appropriate way of utilizing the viral potential of social media users is to invite them to actively participate in promoting the product. First, existing research shows that close to a quarter of users of online social networks, such as Facebook, use


these sites to influence other users (Webb, 2007). Second, as evidenced by Facebook users’ negative reaction to Beacon, social network sites are relatively intimate environments and advertising intrusion (especially given an overall mistrust for advertising messages) is not welcome (Clemons, Barnett, & Appadurai, 2007; Gillin, 2007; Hall, 2007). In contrast, 94% of online social network users find product recommendations from friends to be at least very worthwhile to listen to (MacKeltworth, 2007). This finding is not surprising since recommendations coming from friends, family members, or colleagues are more likely to be trustworthy and relevant to one’s needs (Clemons et al., 2007). In fact, according to a recent survey, along with the reputation of the manufacturer, recommendations from friends and family members are the biggest factor that influences purchasing decisions made by individuals (Klaassen & Creamer, 2007). Third, thanks to synchronous connections between multiple users, a computer-mediated word of mouth can reach a larger number of people than word of mouth in the brick and mortar world. As briefly mentioned before, in addition to these three important advantages of inviting social media users to actively disseminate marketing messages, product information, or recommendations, social media also provide marketers with an unprecedented capability to identify the individuals who would be the best candidates in a social network to act as viral marketers. Domingos (2005) suggests that in addition to actually liking a product, a suitable viral marketing candidate should have high connectivity and should be powerful as a source of influence. Using social network analyses (Hanneman & Riddle, 2005; Scott, 2000; Wasserman & Faust, 1995), data regarding personal affiliations and social network memberships can be utilized to identify opinion leaders (“hubs”) who are central to and powerful in a given network. Recently, there have been several attempts to apply social network analysis to social media to

identify social network influencers. For example, Spertus, Sahami, and Büyükkökten (2005) used network data from Orkut.com to identify members who could be used to recommend new communities to users. Similarly, in a study of Flickr and Yahoo360 networks, Kumar, Novak and Tomkins (2006) were able to distinguish between passive users and active inviters that contributed to the extension of the network. And recently, MySpace announced that it is constructing an “influencer” option for advertisers who could be interested in reaching users with active and large networks. To identify potential influencers, MySpace will use data regarding users’ group memberships and interests, their friends’ interests, level of network activity in a given network and other factors (Morrissey, 2007).

The Integrated Marketing Communications Perspective In 1976, Wayne DeLozier suggested that marketing communication was a process of creating an integrated group of stimuli with the purpose of evoking a set of desired responses. According to this integrated marketing communications perspective, which has been adopted by many companies since the 1980’s, rather than being considered in isolation from one another, each component of the marketing mix should be coordinated to present a unified image to consumers. Considered from this perspective, fulfilling the viral marketing promise of Web 2.0 and social media requires that the viral marketing effort be part of a greater scheme of corporate communications. In other words, rather than merely focusing on spreading the word, the viral marketing effort should fit the brand personality (Webb, 2007). A particular case illustrating this point is the “Top This TV Challenge” campaign of Heinz®. In this campaign, Heinz® invited consumers to produce 30-second TV commercials for Heinz® Ketchup and submit the commercials on YouTube. The winner of the contest, determined first by a panel

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of judges and then by the votes of consumers, was awarded $57,000 and a chance to get the commercial aired on national television. The premise of the campaign was not only that it fit the “fun” brand image of Heinz® Ketchup but also that the consumers would play a crucial role in disseminating Heinz Ketchup’s name. Just as intended, many of the 4,000 qualified contestants who posted their videos on YouTube (as required) also created MySpace and Facebook pages promoting their own videos and consequently Heinz Ketchup. Another example illustrating the connection between viral marketing and an integrated marketing communications approach that provides a fit between the marketing campaign and the organizational image is the “Download Day” organized by Mozilla Firefox in June 2008. Mozilla is a not for profit organization that is mostly known for its Firefox Web Browser (a challenger of the market leader, Internet Explorer). The organization is a self-proclaimed open source project that publicly shares the source codes of their own software for the development of new Internet applications. Unlike its major competitors, such as Internet Explorer and Safari, the Firefox Web Browser is positioned as an “organic browser” that has been developed through a collaborative process whereby thousands of software developers – the majority of which are not employed by Mozilla – contribute to the software. Likewise, the dissemination of Firefox largely relies on volunteers “spreading” the software. In June 2008, Mozilla created a Download Day event to promote the third version of its Firefox Web Browser. The purpose of the Download Day was to set a world record in the number of downloads in 24 hours. To inform would-be users about the event, Mozilla heavily utilized social media and viral marketing. Following the initial announcement, the word of mouth about the Download Day first quickly spread through social news aggregators such as DiggTM and Reddit.com. Then, the links in the social news aggregators forwarded interested users to the

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Download Day homepage. In addition to asking individuals to pledge to download Firefox on Download Day and providing an update on the number of individuals who pledged to download, the homepage also invited them to engage in viral marketing by inviting their social networks to the event via Facebook, Bebo and MySpace, promoting the event on microblogging Twitterlike websites or organizing “Download Fests” on university campuses. These two examples provide important insights regarding the criteria for a successful viral marketing campaign online (and in social media): 1.

2.

Campaign-Organizational Image Congruence: In the Download Day example, the event, the promoted goal (setting a world record) and the method of dissemination of the information of the event (through social media) were in line with Mozilla’s overall image as a non-corporate, decentralized and innovative organization that relies on volunteers and users for its promotion as well as software development. Similarly, the “Top This TV Challenge” campaign of Heinz® fits the “fun” brand image of Heinz® Ketchup. Inciting Virality and Buzz: This is the key for creating a pull rather than inducing a push in an organization’s marketing campaign. An attractive event (in this case a world record setting event) or a message is a crucial component in developing an organic viral marketing process. The ability to create buzz through the event will also increase the chances that the viral marketing campaign will supplement other marketing communication goals: such as, providing material for other promotional efforts or getting coverage in traditional media— the latter being especially important for Firefox given that Mozilla does not have a centrally controlled advertising budget to spend on conventional media. For example,


3.

4.

the overwhelming interest in the Top This TV Challenge (with 5.2 million views) also helped create publicity for the company in the mainstream media and prompted Heinz® to repeat the challenge in 2008. Getting Consumers to be Personally Invested: Mozilla’s Download Day emphasized not only the possibility of a world record but that the consumers would be an integral part of this unique success. In this case, the prospects of being a part of a possible Guinness World Record-setting activity may have increased the chances that consumers identify with (and are invested in) not only the product or the brand but also the success of the campaign. Perhaps, for the contestants in the Heinz® Top This TV Challenge, the personal investment was even higher because their own success (in terms of getting enough votes to win the contest) partly depended on the popular votes they would get from other consumers. Creating Levels of Viral Involvement: In terms of options available for viral marketing, social media not only expand the available options but also create the possibility of multiple levels of viral involvement. For example, in the Heinz® Top This TV Challenge, the level of viral activity of a contestant that promotes his/her video will naturally be higher than a regular YouTube user who happens to come across a challenger’s video that is worth sharing with friends. The Mozilla’s Download Day event, on the other hand, systematically utilized the social media (and other venues) to create tiers of consumer involvement. For example, an enthusiastic Firefox user could go as far as organizing a download festival whereas a regular user of Facebook or MySpace could invite friends to pledge for the download on the Mozilla’s Download Day homepage.

FUTURE TRENDS As discussed in the preceding sections, a central tenet of the debates regarding the marketing potential of social media pertains to the balance that needs to be struck between the efficiency of automatic recommendation systems and the organic involvement created by the real community touch of viral marketing campaigns that invite consumers to actively participate in the dissemination of the marketing messages. On the one hand, systems such as Facebook’s Beacon platform and MySpace’s “influencer” option promise to deliver large-scale, automated word of mouth that can expand the reach of viral marketing campaigns. However, the perceived intrusiveness of such systems, as well as their tendency to use consumers as passive hubs to automatically relay marketing messages, may call into question the true virality of such advertising efforts, consequently reducing their appeal for consumers. Recent discussions regarding “Web 3.0” and the future of the Internet may point to the direction that this uneasy relationship between virality and automatic customization may take. Despite frequent disagreements regarding the definition of Web 3.0, an increasing number of commentators have started to use the concept interchangeably with the Semantic Web – a set of technologies that enable software agents to understand, interpret and extract knowledge from information, making it possible for them to complete “sophisticated tasks for users” (Berners-Lee, Hendler & Lassila, 2001). Michael Bloch provides a simple example explaining how the Semantic Web would work: You want to go out to dinner…and your car is in the shop… You issue a command for the agent to search for a restaurant serving Indian food within a 10-mile radius…You want a restaurant that has a 4 star rating issued by a well-known restaurant critic. Furthermore, you want the table booked and a cab to pick you up from your place. Additionally you want a call to be made

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to your phone once that’s all done; but you don’t want to be disturbed by the call as you’ll be in a meeting - just for the reservation details added to your phone organizer. (Bloch, 2007)

that potential customers arrive at as a result of the viral marketing effort.

CONCLUSION As this example suggests, the Semantic Web is more than a compilation of web pages. Rather, it is a network of systems and databases that can communicate with each other to perform tasks on an individual’s behalf. Moreover, as recent developments suggest, the Semantic Web will have the potential for subtler customization of information in accordance with the cognitive (and perhaps emotional) styles/needs of consumers. For example, an article by Hauser, Urban, Liberali and Braun (forthcoming) from MIT’s Sloan School of Management announces an algorithm that uses clickstream data to morph the website content and format to the cognitive style of its users. As evidenced by recently developed semantic web advertising applications (such as SemanticMatchTM – a semantic advertising platform that utilizes a natural language processing algorithm to understand content and sentiments and target advertising accordingly), when applied to online advertising, semantic capabilities can enhance customization, decrease errors that are associated with keyword targeted advertising and provide a more conversational interaction between the advertiser and the consumer. With respect to viral marketing, such advancements in language processing and customization can address an important shortcoming of passive virality by making it more personal. Whereas social network analyses aid the identification of hubs that can act as active viral marketers, improvements in natural language processing can prove beneficial in terms of understanding the communicative processes and dynamics within a social network. This information can help the marketing organization create different strategies to reach various potential hubs, create levels of viral involvement depending on the depth and the context of the communicative processes between network members, and customize the webpage

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In recent years, Web 2.0 applications that enable web users to contribute to the world of online content have grown in popularity. In 2008, the Top 10 most frequently visited web site list of Alexa Internet – a web traffic information service – consistently included several social media sites: namely, YouTube, MySpace, Facebook, Hi5, Wikipedia and Orkut.com (2008). Despite their popular appeal, however, many of the Web 2.0 initiatives are still struggling to turn their popularity into financial success. What is important to note is that when it comes to monetizing social media, there are no magic formulas. However, as explained above, the interactive nature of social media, combined with consumers’ participation in the creation and dissemination of information, make viral marketing a viable candidate to fulfill the promise of a Web 2.0 of opportunities. In contrast to impersonal advertising methods that consumers do not trust and find intrusive, viral marketing through social media has the potential to be a personal, personable, participatory and trustworthy. source of information. Nonetheless, this should not be taken for granted that any and all viral marketing efforts in social media would be successful. Extant literature suggests that there are certain prerequisites to a successful implementation of a viral marketing campaign in social media. First, as Webb (2007) suggests, because the company is going to have to rely on consumers to push the message, the message (and the product) should be worth pushing. Second, as consumers grow more suspicious of traditional advertising methods, marketers engaging in viral marketing in social media should pay the utmost attention to keeping viral marketing free from centralized interference that can damage its credibility. For


example, Coplan (2007) notes that to remain credible, consumer marketers should be “honest about their opinions good and bad, open about their affiliation – and unpaid” (p. 26). This second prerequisite of success in social media marketing is closely related to the third one: In the world of consumer marketers, companies should learn to “cede control to customers” (cited in Poynter, 2008, p. 12). Partially, this means that viral marketing may be mixed with negative word of mouth and backlash (Gillin, 2007; Giuliana, 2005). At the same time, both positive and negative word of mouth should be considered as an opportunity to engage in a conversation with customers. For example, recently Cadbury PLC decided to relaunch Wispa (a chocolate bar discontinued in 2003) as a response to demands from 14,000 Facebook members (Poynter, 2008). Finally, as evidenced by the recent negative public reaction to the inadequate privacy protection on Facebook, marketers should be aware of the relatively intimate nature of social network sites.

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Gillin, P. (2007). The new influencers: A marketer’s guide to the new social media. Sanger, CA: Quill Driver Books. Giuliana, D. (2005). Alternative marketing techniques for entrepreneurs. Retrieved January 3, 2008, from http://www.scribd.com/doc/35013/ Alternative-Marketing-Techniques-for-Entrepreneurs Hall, E. (2007). Study: Popularity of social networks hampers ad growth. Advertising Age, 78(31), 18. Hanneman, R., & Riddle, M. (2005). Introduction to social network methods. Retrieved December 17, 2007, from http://www.faculty.ucr. edu/~hanneman/nettext/C10_Centrality.html Hauser, J. R., Urban, G. L., Liberali, G., & Braun, M. (forthcoming). Website morphing. Marketing Science. Retrieved July 4, 2008, from http://web. mit.edu/hauser/www/Papers/Hauser_Urban_Liberali_Braun_Website_Morphing_May_2008. pdf Klaassen, A., & Creamer, M. (2007). Facebook to add shopping service to its menu. Advertising Age, 78(44), 39–40. Kumar, R., Novak, J., & Tomkins, A. (2006). Structure and evolution of online social networks. Paper presented at the 12th International Conference on Knowledge Discovery in Data Mining, New York. MacKelworth, T. (2007). Social networks: Evolution of the marketing paradigm. Retrieved March 12, 2008, from http://www.amacltd.com/pdf/ SocialNetworksWhitePaper.pdf Manovich, L. (2001). The language of new media. Cambridge, MA: MIT Press.

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McAllister, M. P., & Turow, J. (2002). New media and the commercial sphere: Two intersecting trends, five categories of concern. Journal of Broadcasting & Electronic Media, 46(4), 505– 515. doi:10.1207/s15506878jobem4604_1 McCarthy, C. (2008, February 1). Report: Facebook raises ’08 revenue projection. Retrieved March 6, 2008, from http://www.news.com/830113577_3-9862792-36.html McCoy, S., Everard, A., Polak, P., & Galletta, D. F. (2007). The effects of online advertising. Communications of the ACM, 50(3), 84–88. doi:10.1145/1226736.1226740 Morrissey, B. (2007). Social network ads: Too close, too personal? Adweek, 48, 11–11. Poynter, R. (2008). Facebook: The future of networking with customers. International Journal of Market Research, 50(1), 11–12. Rappaport, S. D. (2007). Lessons from online practice: New advertising models. Journal of Advertising Research, 47(2), 135–141. doi:10.2501/ S0021849907070158 Robinson, H., Wyscocka, A., & Hand, C. (2007). Internet advertising effectiveness: The effect of design on click-through rates for banner ads. International Journal of Advertising, 26(4), 527–541. Scott, J. P. (2000). Social network analysis: A handbook. London: Sage Publications. Solove, D. J. (2007). The future of reputation: Gossip, rumor, and privacy on the Internet. New Haven, CT: Yale University Press. Spertus, E., Sahami, M., & Büyükkökten, O. (2005). Evaluating similarity measures: A largescale study in the Orkut social network. Paper presented at the 11th International Conference on Knowledge Discovery in Data Mining, Chicago, IL.


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KEY TERMS AND DEFINITIONS Content Variability: Content variability refers to the notion that new media objects can exist in an infinite number of variations. This characteristic of new media is the result of the digital coding of content and consequently the modular nature of information. Data Mining: Data mining is a technologically

driven process of using algorithms to analyze data from multiple perspectives and extract meaningful patterns that can be used to predict future users behavior The market basket analysis system that Amazon.com uses to recommend new products to its customers on the basis of their past purchases is a widely known example of how data mining can be utilized in marketing. Interactive Media: Interactive media is a catch-all term that is used to describe the twoway flow of information between the content user and the content producer. In addition to enabling consumers to actively participate in the production of content, interactive media also allow for the collection of real time data, which can later be used for content customization. Semantic Web: The Semantic Web refers to a set of design principles, specifications, and web technologies that enable networked software agents to understand, interpret and communicate with each other to perform sophisticated tasks on behalf of users. Social Network Analysis: Social network analysis is a research methodology utilized in research to investigate the structure and patterns of the relationship between social agents. Examples of sources of relational data include: contacts, connections, and group ties which can be studied using quantitative methodologies. Social Network Sites: Social network sites are web-based systems that enable end-users to create online profiles, form associations with other users, and view other individuals’ profiles. Examples of social network sites include: Match. com, MySpace, Facebook, Orkut, Hi5, Bebo and LinkedIn. Viral Marketing: Viral marketing refers to a form of word of mouth marketing that relies on consumers relaying product information, a marketing message or a personal endorsement to other potential buyers. Web 2.0: Introduced in 2004, during a conference brainstorming session between O’Reilly Media and MediaLive International, Web 2.0 refers

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to the second generation of web-based content. Rather than merely pointing to technological changes in the infrastructure of the Internet, the concept of Web 2.0 underlines the notion that end-users can do much more than consume readily available content: The user of Web 2.0 also plays

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a key role in the creation and the dissemination of content. Popular examples include: video-sharing and photo-sharing sites, such as YouTube and Flickr; social network sites, such as Orkut, MySpace and Facebook; and Weblogs (blogs).