Viral Marketing and Social Networks – Chapter two

The Social Network

Viral Marketing and Social Networks

The key characteristic at the base of viral marketing and its success is represented by the efficient use of social networks. Viral marketing is based on the expectation that consumers will like or find interesting the marketing message in a significant proportion as to entice them to forward it to members of their social network: family, peers, friends, co-workers, and online acquaintances. Its advantage stems from the high power and influence social networks have today as a result of the Internet and the widespread use of social media.

Social networks are especially efficient for viral marketing campaigns because they allow for the distribution of commercial messages with viral potential and characteristics; they are also useful for marketers in tar- geting specific influential members of the network. Because individuals have different roles and positions within social networks, identifying the key influencers and leaders of a social network can work to the benefit of marketers when they try to launch a message.

We discuss the key theoretical and practical aspects related to social networks, some of the best known laws that govern social networks, and their capacity to exponentially increase the diffusion of information. This is important especially in the exposure phase of consumers to the viral message, when social networks allow for increased access to potentially viral marketing content.

Social Network Theory

Social network theory underlines the fact that networks can be speci- fied in terms of patterns of behavior and implications of the relationships between their members (Wasserman and Faust 1994). Social network the- ory can be used in predicting network behavior, structure, and operations (Schultz-Jones 2009). It seeks to explain how networks work, analyzes the complex set of relationships within a network of individuals or organiza- tions, and views the attributes of individuals as less important than their relationships and ties with other actors within the network (Granovetter 1983; Scott 2000).

The key differences between a social network explanation and the other sociological explanations of a process are the concepts of and infor- mation about relationships among units in a study. A corresponding concept, social network analysis, is the methodology used to research net- work behavior. Social network analysis views social relationships in terms of nodes and ties: nodes are the individual actors within the networks, and ties are the relationships between the actors. It studies networks as a function of different indices, including direction, frequency of relations, size, centrality, and density.

Mark Granovetter popularized an information diffusion model focused on the strength of weak ties that serve as bridges between network segments (Granovetter 1983). Strong ties express the reciprocal social relationships, whereas weak ties refer to loosely connected individuals. Tie strength depends on different factors such as frequency of contact, reciprocity, and friendship, the importance that individuals attach to the ties, the intimacy of the communications, and the emotional intensity of the ties.

As mentioned by Granovetter, weak ties create more opportunities for individuals to share information, interact, and expand their network and lead to greater levels of social capital. At the same time, as ties strengthen, individuals tend to become members of more dependent and related social circles, with less contact with other groups. Individuals are more likely to avoid making new contacts and entering other nonrelated net- works. Weak ties, such as acquaintances and casual friends, are considered important in making connections between groups and sharing informa- tion within and between social groups.

Evidently, social networks existed way before the development of online communications and comprised members of the same social cir- cles such as family, peers, co-workers, and schoolmates; however, the launch of the online environment created new opportunities. Researchers have underlined the constantly developing extent of network interaction, especially in online settings and virtual communities. Ever since the cre- ation of computers and Intranet and Internet networks, individuals and

organizations have organized themselves in online social networks with new communication and interaction opportunities. This applies to viral networks because the spread of viral information in the community is achieved through these networks (Tuten 2008).

Social networks are considered important in the word-of-mouth literature. Researchers found that social networks and social ties are important in information dissemination and propagation, including both weak and strong ties (Brown and Reingen 1987; Goldenberg et al. 2001). Social networks promoted by social media, such as Facebook and LinkedIn, allow the establishment of links connecting family members, friends, and peers. Strong ties have higher influence within the group, are more likely to be used as sources of information, and perceived as more credible than weak ties. Weak ties allow and promote the dissem- ination of information between groups, whereas strong ties are much more influential in word-of-mouth, referral behavior and promote the flow of communication between the members of a group (Brown and Reingen 1987). In the case of word-of-mouth and viral communication between consumers, strong ties represent the key influential points in the group with constant social influence on consumer choices as stable and permanent socialization agents, whereas weak ties allow individuals to extend their information sources to outside groups at certain moments and under specific conditions.

Because Granovetter estimated that stronger ties lead to greater credi- bility of the sender, we can expect that consumers will put a greater value on word-of-mouth and referrals coming from persons with strong ties within their social networks. Moreover, the electronic word-of-mouth lit- erature also found that stronger and more credible sources lead to more e-mails from these sources being opened, read, and forwarded. Consumers trust their strong ties more than their weak ties; so the strong ties greatly influence consumers’ purchase decisions (Chiu et al. 2007). Persons with stronger ties tend to communicate more often, which makes them prone to sharing consumption information and marketing content. In addition, researchers found that most individuals do not forward information to persons they do not know, socially or virtually. Moderately and highly connected respondents are more likely to forward marketing messages (Huang et al. 2009; Smith et al. 2007).

The diffusion of information for viral content also depends on the type of network (such as social centric communities [Facebook], content sharing social networks [YouTube], or different types of communication networks [Twitter], and specialized networks, professional or topic based [LinkedIn, Pinterest]) and the degree of formalized links and connections among the network members. Moreover, specific characteristics of online social networks, such as synchrony, encourage members of a social net- work to share information. Synchrony depicts the fact that a large group of people, sometimes, tends to perform similar actions in response to a contextual trigger (Choudhury and Sundaram 2009). This might include commenting, sharing, and awarding “likes” to specific content and infor- mation as a result of the influence of other group members who exhibit the same behavior and attitudes.

Nevertheless, as a result of new technologies, the impact of social net- works for viral marketing is significant. A person who forwards an e-mail marketing message to a few friends can make a small impact in the mar- ketplace, especially if some friends decide to share it too. However, if the same marketing message is posted in a social network such as Facebook, which encourages information sharing and diffusion, the distribution of the message grows exponentially (Tuten 2008). Next, we present a short overview of well-known laws and theories related to the functioning and, especially, the effects of social networks.

Network Laws and Benefits

Viral marketing capitalizes on the advantages of social networks, includ- ing their high capacity for diffusion of information. In this sense, there are a couple of laws and theories relating to the utility of networks and other aspects such as the critical mass of connectivity required for a net- work to be valuable.

Metcalfe’s Law

Metcalfe’s Law, which states that the utility of a network is proportional to the square of numbers of users, underlines the viral marketing ben- efits from the network output. Metcalfe’s Law states that the value of

a communications network is proportional to the square of the num- ber of connected users of the system. Metcalfe’s Law is related to the fact that the number of unique connections in a network of a number of nodes (n) can be expressed mathematically as the triangular number “n(n−1)/2,” which is proportional to n2. In its initial form, for example, if we take five computers, the most number of connections that can be made is 10, as it can be seen in Figure 2.1. Metcalfe’s Law was publi- cized under this name by George Gilder in 1993. Gilder attributed the formula to Robert Metcalfe, who had discussed networks not in terms of users, but in terms of compatible communicating devices, specifically Ethernet purchases and connections (Gilder 1993; Metcalfe 1973). After the spread of the Internet across the world, the law was considered in the context of the Internet, of social networks and their users, and even to explain the World Wide Web.

Figure 2.1 Network connections under Metcalfe’s law

With regard to social networks, the law was adapted to reflect an “n*logn” proportionality. Metcalfe’s Law points to a critical mass of con- nectivity after which the benefits of a network grow larger than its costs. Metcalfe has noted that social networks form around what might be called affinities, and if the number of people sharing an affinity is above a critical mass, then their social network might, especially with the Inter- net, become much more accessible to individuals (Metcalfe 2006). This can be used in analyzing the viral diffusion potential of online social networks. We can consider that the 12 to 17 year-old Facebook users have an average of 506 friends, while the average number of friends for all Facebook users aged 12 and up is around 300 (Edison Research 2013). Moreover, the typical teen Twitter user has 79 followers. Girls tend to have substantially larger Facebook friend networks compared with boys and older teens more than younger teens (Madden et al. 2013). In this case, the greater the number of users with Facebook, the more valuable the service becomes to the marketers intending to launch viral materials. With each friend added to the social network of an indi- vidual, its value increases exponentially.

Moore’s Law

Metcalfe noted that there is a critical mass of connectivity after which the benefits of a network grow larger than its costs and recommended using a combination of Metcalfe’s and Moore’s Laws. This combination refers to the fact that the number of users at which a network’s value exceeds its cost halves every two years.

Moore’s Law refers to remarks made in 1965 by Gordon Moore, cofounder of Intel. Moore observed that the number of transistors per square inch on integrated circuits had doubled every year since the inte- grated circuit was invented and predicted that this trend would continue for the foreseeable future (Moore 1965). Outside of its strict techno- logical focus, Moore’s Law applies to the capabilities of many digital electronic devices, from computers to digital cameras. These improve exponentially and create significant technological innovations in the mar- ket. As Metcalfe wrote, the law can be used in analyzing and forecasting

the development and impact of social media because of its close relation with new technologies.

Reed’s Law

Reed’s Law states that the utility of large networks, specifically of social networks, can increase exponentially with the size of the network. Accord- ing to Reed, the number of possible subgroups of a social network’s par- ticipants is “2n−n−1,” where n represents the number of participants (Reed 2001). Its potential for diffusion of information grows much more rapidly than the number of network members or the number of possible connections between members, as stated by Metcalfe’s Law, because of the network effect created by potential group membership.

Moore noted the existence of three types of networks: the one- to-many (broadcast) network, the individual-to-individual (transac- tional network), and the many-to-many (group-forming) network. The group-forming network is considered the most valuable with the highest growth potential and impact because it allows network members to form and maintain communicating groups. An example in this case is repre- sented by online communities, which serve as communication platforms that can significantly contribute to diffusing the viral message even more than broadcasts on transactional networks.

Beckstrom’s Law

Beckstrom’s Law is an economics model stating that the value of a net- work for its users equals the sum of the net value added to each user’s transactions conducted through the respective network, summed over all users, less the net present value of the costs of all transactions on the network over any given period of time (Beckstrom 2009). The users are defined as all parties doing transactions on that network.

This law can be applied to the valuation of any type of network, including social and electronic networks. From this point of view, one way of evaluating the value added by the network to each transaction is to assume that the network activities stop and, in this case, assess what

the additional transactions costs or loss would be. Beckstrom’s Law differs from other related network laws in that it does not base its evaluation only on the size of the network.

Dunbar’s Number

Research has noted that Reed’s Law and Metcalfe’s Law overstate network value because they do not take into account the impact of human cogni- tive limits on network formation and value. Dunbar’s Number compen- sates for cognitive limits and underlines the fact that there is a maximum number of social connections that an individual can manage (Dunbar 1992). Dunbar’s Number focuses on stable social relationships, where individuals know who each person is and how each member relates to every other person in the network. This does not include individuals that are no longer in touch nor acquaintances that are not in frequent social contact with the subject. Researchers noted that the number of stable social connections individuals can manage is between 100 and 230 with an average of 150.

Six (or Less) Degrees of Separation

Six degrees of separation is a theory stating that every person is six or fewer steps away, considering their social contacts, from any other person in the world. Originally discussed by Hungarian author Frigyes Karinthy, the theory was tested and analyzed by other authors, including Gurevich and Milgram (Gurevich 1961; Milgram 1967). In 2003, Columbia University conducted the first large-scale replication of Milgram’s experi- ment, in a web-based environment. Their effort was named the Columbia Small World Project and included 24,163 e-mail chains. Results about the mean chain length confirmed Milgram’s finding of six degrees of sep- aration (Dodds 2003). Of course, the theory was made very popular by the game “Six Degrees of Kevin Bacon,” invented with the goal to link any actor to Kevin Bacon through no more than six connections, where two actors are connected if they have appeared in a movie or commercial together.

More recently, studies have analyzed the theory in connection with social networks. In the context of Facebook, research found that six degrees overstates the number of links between typical pairs of users and found an average distance of 4.74 degrees (Backstrom 2011). Other researchers analyzed the theory and its applicability on Twitter and concluded that the average distance of 1,500 random users in Twitter is 3.435, calculated by using all the active users on Twitter (Bakhshandeh et al. 2011).

Whereas the valuation of a social network differs as a function of the formula used, as we have seen in the previously discussed laws and theories, the agreement among researchers and practitioners is that the potential for viral diffusion of information is significantly increased in networks than in person-to-person communication. One of the key plat- forms in this sense is represented by online social media, a topic that will be further discussed.

Related to Viral Marketing and Social Networks, there are a few key points that should be considered related to social networks and their benefits:

  • Tie strength depends on different factors such as frequency of contact, reciprocity, and friendship.
  • Weak ties create more opportunities for individuals to share information between groups.
  • Strong ties allow members to interact within more dependent and related social circles.
  • The diffusion of information in social networks happens exponentially and has significantly higher potential than traditional communication.

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