Effect of Social Networks in Marketing

MARKETING IN A CONNECTED WORLD
IMPACT OF SOCIAL NETWORKS ON PROMOTION AND ADVERTISING STRATEGY
Existing literature on impact of social networks, their underlying structure, characteristics of consumer in context of these network and process of contagion is reviewed in this paper and analysis on the impact of various concepts discussed above on advertising and promotion strategy of product and services is performed. BHARAT SINGHAL 27-Feb-14

ACKNOWLEDGEMENT
The present work is an effort to throw some light on the topic of “Marketing in a Connected World”. The work would possibly be not in its present shape without the able guidance, supervision and help of my teacher and mentor. With deep sense of gratitude I acknowledge the encouragement and guidance received from my project guide Shalini Aggarwal and other staff members of Shri Ram College Of Commerce, library for providing valuable data, the computer laboratories for their cooperation and support. I convey my heartfelt affection to all those people who have helped and supported me during the course, for completion of my Project Report.

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TABLE OF CONTENTS
1. INTRODUCTION…………………………………………………………………………………………………….3

2.

CONCEPTUAL FRAMEWORK……………………………………………………………………………………6

3.

PEOPLE IN THE CONTEXT OF NETWORKS…………………………………………………………………8

4.

INTERNET AND NETWORKS…………………………………………………………………………………….11

5.

TIES, INFLUENCE AND CONTAGION…………………………………………………………………………16

6.

CONCLUSION…………………………………………………………………………………………………………24

7.

BIBLIOGRAPHY…………………………………………………………………………………………………………25

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INTRODUCTION
Our world has transformed immensely in the past two decades. Moreover, never before has the pace of change been so tremendous, intense and fast. The biggest driver of this change has been the internet and information technology. In this scenario, it is inevitable that they will also affect business and trade. Internet has affected business in more than one ways. In has often brought a tectonic shift in the strategy and value proposition that companies offer. New industries like search engine, file sharing etc. have sprung from nowhere and now some of the most valuable companies in the world are in these sectors. It is worth noting that internet has more often than not led to transfer of greater economic value to consumers making it difficult for companies to maintain profitability and differentiation.(Porter, 2001) In such a scenario strategy and marketing become all the more important. In this regard, internet has been a mixed blessing. It has led to increased commodification of products and at the same time allowed companies to build a better understanding of customers and consumer behaviour, which allow them to come up with better positioning and differentiation strategy. Even in marketing, internet has impacted all the four P’s of marketing. It has changed how product is designed and how much customization is possible (example of Dell computers case in point). It has brought down price because of better access to information. And, it has most visibly changed the distribution channels. The focus of my review is the last P of marketing, namely Promotion. Internet has allowed researchers to collect data about consumer behaviour and preferences with enormous ease and accuracy. It has enabled social scientists to study consumers in various contexts and analyse new interdependencies and interrelationships. Some of the most influential work has been in the field of social network and it implications for marketing.

Marketing In A Connected World Social network are ubiquitous. We were born in one and we shall inevitably be a part of one for the rest of our lives. They have been known to influence everything from the amount of money we make to the level of happiness we experience in life to how we marry and divorce, whether or not we commit suicide (Christakis & Fowler, 2009) and also how much we weigh (Christakis & Fowler, The Spread of Obesity in a Large Social Network over 32 Years, 2007) The following excerpt from Nicholas Christakis and James Fowler book ‘Connected’ gives an insight into the kind of influence social networks can exercise on how we live and behave in the society.

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“A strange thing happened in Tanzania in 1962. At a mission boarding school for girls near Lake Victoria in the Bukoba District, there was an epidemic of laughter. And this was not just a few schoolgirls sharing a joke. An irresistible desire to laugh broke out and spread from person to person until more than one thousand people were affected.. . . The epidemic began on January 30, 1962, when three girls aged twelve to eighteen started laughing uncontrollably. It spread rapidly, and soon most people at the school had a serious case of the giggles. By March 18, ninety-five of the 159 pupils were affected, and the school was forced to close. The pupils went home to their villages and towns. Ten days later, the uncontrollable laughter broke out in the village of Nshamba, fifty-five miles away, where some of the students had gone. A total of 217 people were affected. Other girls returned to their village near the Ramanshenye Girls’ Middle School, and the epidemic spread to this school in mid-June. It too was forced to close when forty-eight of 154 students were stricken with uncontrollable laughter. Another outbreak occurred in the village of Kanyangereka on June 18, again when a girl went home. The outbreak started with her immediate family and spread to two nearby boys’ schools, and those schools were also forced to close. After a few months, the epidemic petered out.. . . . . As the villagers and the scientists investigating this outbreak realized, the epidemic was no laughing matter. It did not involve the spread of real happiness and joy—though this can happen too, albeit not in quite the same way. Rather, the outbreak was a case of epidemic hysteria, a condition that takes advantage of a deep-rooted tendency of human beings to exhibit emotional contagion. Emotions of all sorts, joyful or otherwise, can spread between pairs of people and among larger groups. Consequently, emotions have a collective and not just an individual origin. How you feel depends on how those to whom you are closely and distantly connected feel.” (Christakis & Fowler, Connected, 2009) Given their ubiquity and the impact they can have on us, it is natural that they also influence how we buy, when we buy and what we buy. All this has huge implications on what products marketers develop and how they reach out to consumers. It is also important to note that in the postFacebook world, we have strong presence in both online and offline communities and both of them affect and shape our overall behaviour including our buying decisions (Watts & Reiley, 2011).

Is Using Marketing Using Networks A New Concept? Marketing using networks is defined as the use of knowledge of social networks for promoting a product. Although it might seem that marketing using networks has been outcome of hyper competitive markets that have forced managers to find new and ingenious ways to make people ‘buy more’, it is not so. In fact, marketing using networks concept is older than advertising itself. Traders and businessmen have relied on word of mouth for eons to help them improve their sales and it is nothing other than marketing using networks.

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Marketing using networks is also known by various other names (as enumerated below), but is essentially the same concept: 1. 2. 3. 4. 5. Viral marketing Word of mouth marketing Referral Programmes Community marketing Influence(r) marketing

Objective of the study In the paper I shall cover some of the research done by social scientists into the nature of network, principles governing their formation and how all this affects spread of information and formation of opinions amongst the consumers. I have divided the literature review and analysis into three broad categories. The categories allow us to understand the research in the said perspective and help draw link between various concepts. The three categories are: 1. People in the context of networks 2. Ties, Influence and Contagion 3. Internet and networks In each chapter, I have introduce the research that has been undertaken, its insights and then its implications on how marketing is undertaken and shall be undertaken in the near future.

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CONCEPTUAL FRAMEWORK
Before introducing the chapters it is necessary to explain certain concepts that shall be repeatedly mentioned in the chapters. Any social network has two basic components. The people (known as nodes) and the connections (called edges) between them. Thus, a network can be defined as a set of nodes connected by edges. The attributes and characteristics of the nodes and edges will define how nature and characteristics of the network and determine the nature of interactions that happen. Node Node refers to the actors in a network. (as mentioned in fig. 1) In different networks nodes represent different actors or participants. e.g. In case of trade between nations nodes will represent countries. Similarly, nodes may depict ingredients in recipe. Nodes may be weighted or non-weighted. The weight may be depicted through the size of the node or by its colour. Often in network, nodes are weighted by their degree, Betweeness and even according to the community to which they belong.

Edges Edges are the lines depicting connections or interaction between various nodes (as mentioned in fig. 1) Edges may be weighted or non-weighted. So, if width of the edge depicts the level of interaction between the two nodes then it is called a weighted edge and if this is not the case then it is called a non-weighted edge.

ATTRIBUTES OF NODES
The importance of a node can be ascertained on the basis of its centrality. Centrality refers to how deeply embedded a node is in the network. Here are the three measures of centrality: Degree It refers to the number of edges a node has. For e.g. in fig. 1 node 1 has degree 1, node 2 has degree 2, node 3 has degree 3, node 4 has degree 2 and node 5 has degree 1.

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Betweeness It measures the number of shortest paths that pass through a node. Here shortest path refers to the path from one node to other in which smallest number of hops are taken. For e.g. shortest path from node 1 to any other node passes through node 3. Thus , node 3 has high Betweeness. Similarly, because no shortest path of any two nodes passes through 1, it has low Betweeness. Closeness It measures the number of steps it takes for a node to reach any other node in the network. So even if a node does not have high Betweeness or Degree, it may have high closeness as it may be connected to a node that has high Betweeness or high degree. This can be explained in the sense that even if you do not have large number of friends, you can get information about everyone else if even one of your friends is connected to a large number of people. Thus, through him/her you can obtain information about everyone else in the network and are thus connected to the rest of the network. E.g. In fig. 1 node 1 is connected to rest of the network through node3. We can now look at various insights that contemporary research provides into social network.

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PEOPLE IN THE CONTEXT OF NETWORKS
Just as our economic, cultural and social environment influences the trajectory of our live significantly, so does our position in the social network. In this chapter, we analyze what the latest research on social network and its impact on people says. We also analyze how various attributes regarding people define their position in the network and how information about these attributes can be used for promotion strategies. We shape our network Homophily is the tendency to associate with people that are like us. We seek out the people that share our interests, histories, and dreams. We choose the structure of the network in three important ways: 1. We decide how many people we are connected to 2. We influence how densely interconnected our friends and family are 3. We control how central we are to the social network (Christakis & Fowler, Connected, 2009) It has been long known that word of mouth has a crucial role to play in the success of a product and enterprise. However, in case of homophily there is a certain debate regarding causation and correlation. That is, whether people tend to become like each other or people who are similar tend to congregate and make a strongly knit group. The research on the topic is ongoing. However, research in clinical field has shown that there is influence excercised by people on their contacts. (Christakis & Fowler, The Spread of Obesity in a Large Social Network over 32 Years, 2007) Thus, there is significant chance that people have high influence on the overall lifestyle of their network. This insight gives marketer all the more reason to rely on word of mouth marketing and ensure positive impressions on the consumers. Different people play different roles We shape our network in more than one ways. Our nature and tendencies also determine the kind of role we come to play in the network. Here are three types of influence wielders and a synopsis about them: 1. Connectors are the people in a community who know large numbers of people and who are in the habit of making introductions. A connector is essentially the social equivalent of a computer network hub. They usually know people across an array of social, cultural, professional, and economic circles, and make a habit of introducing people who work or live in different circles. They are people who "link us up with the world...people with a special gift for bringing the world together". They are "a handful of people with a truly extraordinary knack [... for] making friends and acquaintances". Gladwell attributes the social success of Connectors to the fact that "their ability to span many different worlds is a function of something intrinsic to their personality, some combination of curiosity, selfconfidence, sociability, and energy".

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2. Mavens are "information specialists", or "people we rely upon to connect us with new information."[4]They accumulate knowledge, especially about the marketplace, a nd know how to share it with others. According to Gladwell, Mavens start "word-of-mouth epidemics" due to their knowledge, social skills, and ability to communicate. As Malcolm Gladwell states, "Mavens are really information brokers, sharing and trading what they know". 3. Salesmen are "persuaders", charismatic people with powerful negotiation skills. They tend to have an indefinable trait that goes beyond what they say, which makes others want to agree with them. (Gladwell, 2000) Identification of these actors in word of mouth marketing helps identify the flow of information and how a marketer can engineer his message and choose his initial target audience to ensure that he is able to spread virally. This phenomenon has been reported in how videos become viral on Youtube(Allocca, 2011). Our network shapes us Having an extra friend may create all kinds of benefits for your health, even if the other person doesn’t actually do anything for you. Being more central makes you more susceptible to whatever is flowing within the network. (Christakis & Fowler, Connected, 2009) Our friends affect us. What actually flows across the connections is also crucial. Social networks transport all kinds of things from one person to another. One fundamental determinant of flow is the tendency of human beings to influence and copy one another. Each tie to different people offers opportunities to influence and to be influenced. (Christakis & Fowler, Connected, 2009) Our friend’s friend’s friends affect us. Hypodyadic (connection between two nodes) spread is the tendency of effects to spread from person to person to person, beyond an individual’s direct social ties. The usual way we think about contagion is that if one person has something and comes into contact with another person, that contact is enough for the second person to get it. Like getting infected with a germ. But some things – like norms and behaviors – might not spread this way. They might require a more complex process that involves reinforcement by multiple social contacts. Ex: If we wanted to get people to quit smoking, we would not arrange them in a line and get the first one to quit and tell him to pass it on. Rather, we would surround a smoker with multiple nonsmokers. (Christakis & Fowler, Connected, 2009) In such situations there is a significant role played by the strong ties, which are explained the chapter 3. This als o plays a significant role in adoption of luxury and status symbol goods. These goods are usually of high cost and thus, it requires constant reinforcement to push us to buy these goods.

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Relative versus absolute standing People often care more about their relative standing in the world than their absolute standing. People are envious. Many consumer demands arise not from innate needs but from social pressure. People assess how well they are doing not so much by how much money they make or how much stuff they consume, but rather by how much they make and consume compared to other people they know. You don’t need to be the most beautiful or wealthiest person to get the most desirable partner; you just need to be more attractive than all the other women in your network. Many people may be more attractive than we are, but our only real competitors are the people in our intended social network. Gender and Influence Apart from homophily, even gender has been found to impact the capacity of a person to influence others in the network. Research in gender and its impact on influence by Sinan Aral (the data was sourced from Facebook.com) shows that: 1. Individuals that report their gender as male are significantly less influential than individuals that do not report their gender. 2. An individual’s reported relationship status has little to no effect on how influential they are, with the exception of individuals who report their relationship status as it’s complicated, who seem to exert negative influence on the adoption hazard of peers in their local network. 3. Individuals that drive change are not the influencers but the susceptibles 4. Individuals that report their gender as male are significantly less susceptible to influence than individuals that do not report their gender. 5. Susceptibility to influence increases non-linearly with the number of notifications received. 6. An individual’s susceptibility to influence increases with the commitment level of the reported relationship status until they are married (relative to individuals that do not report their relationship status), with susceptibility increasing from single to in a relationship to engaged. 7. Individuals that report their relationship status as married do not seem to be significantly susceptible to influence. 8. Finally, individual’s that report their relationship status as it’s complicated are the most susceptible to influence from their peers. (Aral & Walker, 2012) All these insights have implication on how marketers decide to market their product on Facebook and other social media sites. Also, it helps marketers optimize the virality of their message to ensure maximum impact and reach.

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INTERNET AND NETWORKS
It is often said that whole is greater than sum of its parts. It seems that this applies to networks too. Networks seem to have a life of their own. In Connected, authors say that social networks can have properties and functions that are neither controlled nor perceived by the people within them. These properties can be understood only by studying the whole group and its structure, not by studying isolated individuals (Christakis & Fowler, Connected, 2009). In this chapter, we analyse what these properties might be and how marketer can utilize their understanding to cater to its customer better. We later built on this knowledge to see how viral marketing is affected by the structure of the network. We also analyse how internet has affected the structure of our networks and how internet is helping marketers in better understanding networks. Random Networks. (Stewart, Ewing, & Mather., 2004) introduced a random viral marketing model (RVM) based on the random network model developed by (Erdos & Renyi, 1959) and described by (Albert & Barabasi., 2002). A random network can be generated by starting with a set of isolated nodes and allowing each of the N nodes to have a probability  of being connected by an edge to each other node. As noted by Albert and Barabasi (2002), in a random network the degree of its nodes follows a binomial distribution with parameters N — 1 and . As each node has the potential to connect up to N — 1 other nodes, on average we expect each node to be connected to λ=  (N — 1) other nodes, resulting in an expected total of 1/2λN links. In the context of viral marketing, a typical network has large N and small  resulting in the average degree λ remaining moderate. The degree of a node therefore has an approximate Poisson distribution with mean network connected ness A. Because the standard deviation is √λ it is very unlikely for a node to have degree of size comparable with N. In other words, it is unlikely that any node is directly linked to a significant proportion of the nodes in the network.

Scale-Free Networks Research into scale free networks has proliferated since their introduction by (Barabasi, 1999) and Albert and Barabasi (2002). These networks provide useful representations of many different selforganizing systems, ranging from the World Wide Web to citation patterns in scientific publications to the electrical power grid of western United States. The defining characteristic of a scale free network is in the shape of the probability distribution for the degree of each node, which determines the number of communication links or edges emanating from each member. The degree is assumed to follow a Power-law distribution, defined by P(k) . with y > 0, where P(k)

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denotes the probability that a node is connected to k other nodes. This is a "fat-tailed" distribution where, with increasing k, the probabilities decline at a much slower rate than those of the Poisson distribution which essentially underlies the RVM model. The Power-law distribution allows for a small number of nodes to be directly linked to a significant proportion of the nodes in the network while most nodes have few connections, thus keeping the mean number of connections comparatively low. These high degree nodes, often called hubs, ensure that the average distance L between any two nodes in the network is small (independent of the size of the network). The scalefree network structure emerges naturally as a consequence of two phenomena: dynamic growth and preferential attachment (Barabasi 1999, Albert and Barabasi 2002), both important features of social networks. Where a network is created by adding new members over time and these are connected to other members with a probability that is proportional to their connectivity, the resulting distribution for the degree or number of connections per node will exhibit a Power-law distribution. These structures are called scale-free networks because despite their growth, they preserve statistical properties such as the average distance L and the degree distribution. (Bampo, Ewing, Mather, Stewart, & Wallace, 2008)

Small World Networks Small world graphs were first introduced by (Watts & Strogatz, 1998) to model a class of social networks characterized by high clustering and short average distance between nodes. Clustering is a local property of the network and is a measure of the connectivity of a neighbourhood. The clustering coefficient C of a node is defined as the fraction of the node 's neighbours that are linked to each other. High clustering and long average distances are typical features of l attice networks (Dorogovtsev & Mendes, 2003), where nodes can be thought of as points in a multidimensional space and nearby points are linked by edges. In contrast, small world networks have short average distances between nodes. Small world networks can be constructed from lattice networks by applying a rewiring procedure: arcs connecting neighbors (within the clusters) are removed from the graph with probability rewiring probability r and substituted by random links (making connections outside of the cluster). As r increases, the average distance L decreases very quickly (Watts & Strogatz, 1998), producing a graph structure characterized by low node separation typical of random networks and strongly connected neighborhoods of regular networks. With increasing r, the graph starts to become more like a random network. Small world networks are also potentially applicable to viral marketing because they capture the connections generated through physical proximity. Tightly linked neighbourhoods reflect social structures based on friendship or professional relationships which are likely to form among people who interact within a confined physical environment. For example, Albert and Barabasi (2002) refer to a social system where people are well-connected with their neighbours and work colleagues but also have a much smaller number of connections with people who live far away, in another state or country. Random links represent the distant acquaintances and are useful in representing connections between local networks. A higher level of rewiring makes the viral message spread

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faster and thus saturates the network sooner. It means that a very small number of people are linked to everyone else in a few steps, and the rest of us are linked to the world through those special few.(Gladwell, 2000) However, small world has it shortcomings too. For eg. A small-world internet is efficient but also vulnerable to malicious hackers. A small-world electricity network delivers power well, but also enables minor faults to "cascade" into catastrophic blackouts, as happened in August across the north-eastern US. Small-world networks, in other words, combine robustness with sometimes surprising fragility.

Characteristics of Networks Various characteristics like virality, resilience of network emerges from the underlying structure of the network. Network that are resilient tend to become superorganism in the sense that they outlive their components (here being human), tend to be adaptive and yet can retain various properties even in midst of such change. 1. Collective intelligence Social networks can manifest a kind of intelligence that augments or complements individual intelligence, the way an ant colony is intelligent even if individual ants are not. Networks can have this effect regardless of the intelligence of the individual members. (Iain, Jens, Nigel, & Simon, 2005) (Christakis & Fowler, Connected, 2009) This explains occurrence of complex phenomenon even when the actors are following simple rules. 2. Trust in networks In many cases, it is not just that the people in your network are more trusting, or even that their trusting behavior engenders trust in you; rather, the network facilitates this trust and changes the way individuals behave. Social networks are often self-annealing. (Christakis & Fowler, Connected, 2009) 3. Networks are dynamic and affected by interactions In fact, when things such as sexually transmitted diseases or dollar bills flow through a network, this flow itself can define the ties and hence the structure of a particular set of network connections. (Christakis & Fowler, Connected, 2009)

These attributes explain how cultures, values and beliefs form, transform and how they persist even when the original members of the society no longer exist. This has huge implication for marketing as it shows how message can stick in a network well beyond the duration of advertising campaign. This stickiness of information and message can work both for and both against the interests of a company. This concept is elaborated further in chapter 3.

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Viral marketing and networks Duncan Watts writes in his book Six Degrees that viral adoption of product such as the massive sales of the Harry Potter books, may depend not at all on the intrinsic quality of the product but on its luck in dropping into a particularly "vulnerable" area of the network. If you manage to seed only a tiny part of the network, but that part has the right structure, the network will do the rest of the job for you. (Watts D. J., 2003) Also, simulations in how some music tracks become more popular than others show that popularity of a song or a video is influenced adoption by Influencers. (Allocca, 2011) It is also important to note that small world networks are seen to exist in case of human social networks. It has been found that these networks are less than optimal networks can for spread of information and in fact depend on weak ties to enable spread of message from one part of the network to another part. Also, such networks tend to be susceptible to attacks. Therefore, the marketing strategy should account for these factors and ensure that there is spread of information across the network Network and Word of Mouth Communication in a network is affected by the structure of the network. It determines the intensity of communication and also how much influence is excercised by different nodes in the structure. It has been found that the structure of communication is important in determining whether the population as a whole is likely to learn to use the superior product or solution or whether the population will settle for less than optimal solution. The most basic conclusion has been that the structure of the word-of-mouth process affects the tendency of a population to display conformity or diversity, with less communication making conformity more likely. (Ellison & Fudenberg, 1995)

Network and Social Learning Another research reveals that the larger the group the smaller the proportion of informed individuals needed to guide the group, and that only a very small proportion of informed individuals is required to achieve great accuracy. It has also been demonstrated that groups can make consensus decisions, even though informed individuals do not know whether they are in a majority or minority, how the quality of their information compares with that of others, or even whether there are any other informed individuals. (Iain, Jens, Nigel, & Simon, 2005) These two examples show how the characteristic of collective intelligence can determine how a product is adopted and rejected in a community. Even when the Influencers, Mavens and Persuaders are known, it becomes very important to understand the underlying structure of the network to be able to implement an effective marketing strategy.

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Internet and Social network sites Internet is the most expansive, influential and extensive network in the world and advent of social networking sites has led people to connect with each other in significant numbers and a ingenious manner. This has led to transformation in the way we now connect with people.

Social networking sites The distinctive features of social network sites are that it makes our web of connections visible to the user and to others. Moreover, unlike other sorts of online groups or communities like wikis and listservs, social network sites are organized around people, not topics. At their core, social network sites primarily reflect offline interactions. Although they allow us to maintain contact with people whom we would otherwise be tied only weakly, they are not organized around the introduction of strangers. The internet makes possible new social forms that are radical modifications of existing types of social network interactions in four ways: 1. Enormity: A vast increase in the scale of our networks and the numbers of people who might be reached to join them. 2. Communality: A broadening of the scale by which we can share information and contribute to collective efforts. 3. Specificity: An impressive increase in the particularity of the ties we can form. 4. Virtuality: The ability to assume virtual identities. Online networks do not appear to expand the number of people with whom we feel truly close, nor do they necessarily enhance our relationships within our core groups. However, they allows us to create and maintain more weak ties (elaborated in chapter 3) than is usually possible. It is also important to note that most of the research in the field of networks has been enabled by the Web. It is only after large social networking sites like Facebook and LinkedIn came into being that social network analysis has gained momentum. These sites act as repository of data and allow researchers to undertake various experiments on the same.

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TIES, INFLUENCE AND CONTAGION
Why some products become as famous as they do and why similar products never sell more than a few thousand units? What accounts for a sudden popularity of some idea, products and services? Such questions have intrigued marketers and social scientists for a long time. Consequently, a lot of research has been done on the topic of viral marketing, contagion and word of mouth marketing. Here I review some of the most important concepts and insights that the researches have revealed.

DIFFUSION OF INNOVATIONS
Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread through cultures. Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines. The theory states that there are four main elements that influence the spread of a new idea: 1. Innovation 2. Communication Channels 3. Time 4. Social System This process relies heavily on human capital. The innovation must be widely adopted in order to self-sustain. Within the rate of adoption, there is a point at which an innovation reaches critical mass. The categories of adopters are: 1. Innovators 2. Early Adopters 3. Early Majority 4. Late Majority 5. Laggards Diffusion of Innovations manifests itself in different ways in various cultures and fields and is highly subject to the type of adopters and innovation-decision process Diffusion of innovation is a celebrated concept and has informed the marketing strategy for years now. It has allowed marketer to formulate their advertising strategy at various stages of product life cycle bases on whether they are targeting innovators, early adopters, early majority, late majority, laggards.

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Recognition of Critical Mass has implications because it has been observed that after a product has achieved a market share of 15-18% the system tips and then the early majority and late majority accept instantaneously. Moreover, as findings from the diffusion of innovations literature suggest, w-o-m and advertising effects may differ among different market segments (Rogers, 1995). Marketers are advised to develop market research, which would provide estimates of these factors for different segments and products.

WEAK TIES, STRONG TIES
Granovetter is credited for the concept of weak ties and the role the play in diffusion of information and opinion formation. (Granovetter, 1983) Weak ties are defined as connections with those people in the network with whom interaction is comparatively less. Strong ties refer to ties with an individual’s family members, workplace and other groups where the level of interaction is high. Weak ties have been seen to play a crucial role in dissemination of information. Research in this field has shown that: 1. The influence of weak ties is at least as strong as the influence of strong ties. Their effect approximates or exceeds that of strong ties, in all stages of the product life cycle. (Rogers, 1995) 2. Also, external marketing efforts (e.g., advertising) are effective. However, beyond a relatively early stage of the growth cycle of the new product, their efficacy quickly diminishes and strong and weak ties become the main forces propelling growth. The results clearly indicate that information dissemination is dominated by both weak and strong w-om, rather than by advertising. 3. It has been observed that effect of strong ties diminishes as personal network size decreases. Market attributes were also found to mediate the effects of weak and strong ties. When personal networks are small, weak ties were found to have a stronger impact on information dissemination than strong ties. Also, The effect of strong ties on the speed of information dissemination diminishes as personal network size decreases 4. As the number of weak ties contacts increases, the effect of strong ties decreases while the effect of weak ties increases. 5. As the level of advertising increases, the effects of both strong and weak ties are marginally impacted, in inverse directions: the effect of strong ties increases while the effect of weak ties decreases

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6. Second, both types of social effects have a stronger influence on information dissemination than the effect of advertising 7. It was also found that although strong ties were more likely to be activated and perceived as influential in consumers ' decisions, weak ties were more likely than strong ties to facilitate w-o-m referral flows (Duhan, Johnson, Wilcox, & Harrel, 1997) sources. 8. Other empirical work found that when ties are strong, w-o-m receivers are more likely to actively look for information and that the w-o-m information will have a significant influence on the receiver 's purchase decision (Bansal & Voyer, 2000)

The research in weak ties has led to many conclusions. These results have important marketing implications. Marketers attempting to influence word of mouth (w-o-m) spread should distinguish between the two kinds of social interactions that contribute to both positive and negative w-o-m communications. When personal networks are large, weak ties contacts among inter-network individuals are few, or the advertising effect is strong, fostering inter-network ties may be one of the few options available to marketers. More connections within groups (concentrated networks with strong ties) can reinforce a behavior in the groups, but more connections between groups (integrated networks with weak ties) can open up a group to new behaviors and to behavioral changes.

SIX DEGREES OF SEPARATION
A famous study done by Stanley Milgram led to the formulation of the phenomenon of Six degrees of separation. It said that people are all connected to one another by an average of six degrees of separation. (Milgram & Travers, 1969) In continuation with this research Duncan Watts, a network theory physicist at Columbia University, repeated the Milgram study by using a web site to recruit 61,000 people to send messages to 18 targets worldwide in 2003. He successfully reproduced Milgram 's results (the average length of the chain was approximately six links).

THREE DEGREES OF INFLUENCE Social networks obey this rule. Our influence gradually dissipates and eases to have noticeable effect on people beyond the special frontier that lies at three degrees of separation. We are generally influenced by friends within 3 degree but general not by those beyond. This is so because:

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1. The intrinsic-decay explanation When information reaches your friend’s friend’s friend’s friend’s, that person may no longer have accurate or reliable information on what you actually did. 2. The network instability explanation: Influence may decline because of an unavoidable evolution in the network that makes the link beyond 3 degrees unstable. Friends stop being friends, etc. 3. The evolutionary-purpose explanation: Biology´s part; in our past, there was no one who was four degrees removed from us. Insight about six degrees of separation has huge implication on how a message becomes viral and how information is disseminated. The extension of this research is Three degrees of influence, which analyses how far the influence of opinions travels in the chain. This concept goes a long way in explaining how the marketer should structure his promotion strategy by quantifying the extent to which initial message is able to travel in the network. Word of mouth also tends to spread 3 degrees as well. This requires marketers to structure their advertising and promotion in such a manner so as to sustain word of mouth spread in the market by continually reinforcing the w-o-m message being circulated.

STICKINESS FACTOR
The intrinsic nature of the message does bear an impact on how far the message will travel in the social network. (Allocca, 2011) Whether the message is positive or negative also decides its stickiness. Research in consumer behavior indicates that unfavorable information about products tends to carry greater weight with prospective buyers than favorable information. (Mahajan, Muller, & Kerin, 1984) When messages stick for long enough they can come to define the clique or the entire network itself. This is how culture comes into existence. The norms that form a culture are formed because of their stickiness. A norm is a shared experience about what is appropriate that spreads from person to person. People can reinforce particular norms so that directly and indirectly connected people share an idea about something without realizing that they are being influenced by one another. More connections within groups (concentrated networks) can reinforce a behavior in the groups, but more connections between groups (integrated networks) can open up a group to new behaviors and to behavioral changes. In the book Contagious: Why Things Catch On, Jonah Berger uses each of these viral marketing sensations to help explore the question of why some products, ideas and behaviours succeed while others fail (a question at the heart of all marketing). While there isn’t a formula to ensure your marketing content will be widely shared, there are six key ingredients that make up a recipe for contagious content. Collectively, Berger calls these the STEPPS.

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1. Social Currency According to Berger, social currency manifests when “people share things that make them look good to others.” If you’ve never heard of New York bar Please Don’t Tell, you’ll want to check this out. From a marketing perspective, social currency is created by delivering information that will make prospects look good when they share it with others, such as members of the clique or wider network. 2. Triggers Triggers are “stimuli that prompt people to think about related things.” When thinking about triggers for marketing content, carefully consider context. Successful marketing content is designed for every prospect’s unique environment, situation and problem, helping to make things personal and keep the brand top of mind. 3. Emotion Marketing is often thought of as less personal, but marketing content can still be rooted in emotion. However, as the book notes, content that is physiologically arousing (such as anger or excitement) tends to outperform content that evokes other types of emotion. From a marketing perspective, focusing on highlighting core offering and intimating how one’s expertise can solve a problem by providing the product one is selling. 4. Public As highlighted in the book, the late great Steve Jobs understood better than most that observability matters (hence why the Apple logo faces outwards on the top of its laptops). Designing the marketing content so that it’s powerful enough to stand alone and leave a lasting impression is crucial. People tend to imitate and share because the choices of others help provide information, known as “social proof”. A great way to lend social credibility to the content is to include brief case study features that highlights the succ esses of a product’s customer base. (Berger, 2013) 5. Practical Value The simple idea here is that people like to help others and are more than willing to spread great content of practical value. Be sure to keep your marketing content concise yet detailed, and remember to “package the message so that people can easily pass it on.” 6. Stories Embedding marketing content into stories can help to turn virality valuable. Stories, like ancient Greek tales, help to carry information in ways that straight content can’t. The STEPPS framework is a good tool for marketers to validate and develop marketing content ideas. It provides pointers as to how the any message should be structured and what all are the various points to keep in fact while creating a viral video or ad.

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CONTAGION
The development of emotions in humans, the display of emotions and the ability to read others’ emotions helped coordinate group activity. In short, existence of emotions and the ability to share them makes human a social animal. Thus, most of the contagion in social networks can be understood in the context of emotions. (Christakis & Fowler, Connected, 2009) Also, emotional appeal plays a huge role in the extent to which a message goes viral as has already been stated in STEPPS framework. The most fundamental concepts explaining contagion and its causes are listed below. These concepts answer the question that why word of mouth and viral marketing are so effective in the first place. Emotions origin Early humans had to rely on one another for survival. Their interactions with the physical environment (weather, predators etc.) were modulated or affected by their interactions with their social environment. Emotions for groups The development of emotions in humans, the display of emotions, and the ability to read the emotions of others helped coordinate group activity by three means:  Facilitating interpersonal bonds  Synchronizing behavior  Communicating information Emotional contagion Emotions spread from one person to another because of two features of human interaction: 1. Humans are biologically hardwired to mimic others outwardly, and in mimicking thei r outward displays, we come to adopt their inward states. 2. Reading the expressions of others was probably a key step on the way toward synchronizing feelings and developing the emotional empathy that underlies the process of emotional contagion. Affective afference People imitate the facial expressions of others and as a direct result, they come to feel as others. Mirror neuron system Mirror neuron system makes emotions contagious. Our brains practice doing actions we merely observe in others, as if we were doing them ourselves.

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Mass psychogenic illness When an emotion spread from person to person and affects a large number of people. A single reaction in one person can sometimes cause many others to feel the same thing, creating an emotional stampede. The Proust phenomenon Memories invoked by smell induce stronger emotions than those evoked by verbal descriptions of the same odor. Words are powerful, but one familiar whiff can jolt the mind into the past with more emotional intensity than can a signal from any other sense. Social affirmation Humans tend to confirm to social norms. This is so because a person derives his/her sense of worth from the perception of his worth that his ties about him. Here more the number of people confirming to the norm, higher is the chance that the individual shall also confirm to it. Psychologist Stanley Milgram’s famous sidewalk experiment illustrates the importance of reinforcement from multiple people. On two cold winter afternoons in New York City in 1968, Milgram observed the behavior of 1,424 pedestrians as they walked along a fifty-foot length of street. He positioned “stimulus crowds,” ranging in size from one to fifteen research assistants, on the sidewalk. On cue, these artificial crowds would stop and look up at a window on the sixth floor of a nearby building for precisely one minute. There was nothing interesting in the window, just another guy working for Milgram. The results were filmed, and assistants later counted the number of people who stopped or looked where the stimulus crowd was looking. While 4 percent of the pedestrians stopped alongside a “crowd” composed of a single individual looking up, 40 percent stopped when there were fifteen people in the stimulus crowd. Evidently, the decisions of passersby to copy a behavior were influenced by the size of the crowd exhibiting it. An even larger percentage of pedestrians copied the behavior incompletely: they looked up in the direction of the stimulus crowd’s gaze but did not stop. While one person influenced 42 percent of passersby to look up, 86 percent of the passersby looked up if fifteen people were looking up. More interesting than this difference, however, was that a stimulus crowd of five people was able to induce almost as many passersby to look up as fifteen people did. That is, in this setting, crowds larger than five did not have much more of an effect on the actions of passing individuals.(Milgram, Bickman, & Berkowitz, 1969) All aspects of contagion especially social affirmation have played a role in marketing. Howard Schultz has been known for predicting brand disconnect when Starbucks stores lost their signature aroma of coffee. This exemplifies the role of The Proust Phenomenon in marketing. Similarly, mirror neuron network and the property of emotional contagion shows how new products get adopted and what role is played by contagion. In certain cases, word of mouth

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marketing has been found to be responsible for almost half of the sales of an enterprise. Thus, the impact of contagion and w-o-m cannot be understated.

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CONCLUSION
From the analysis done so far some very significant inferences can be drawn. Firstly, social network determines how and when products and ideas are adopted and how do they sustain in a society. On combining the study of influencers, mavens and persuaders with the knowledge of network structure, a marketer can get a representative network of real life human networks. By identifying the network and the structure a marketer will not only be able to predict the spread of message and influence but predict how and when changes might occur in the graph. Such a network is a powerful tool as it allows marketer to predict the trajectory of product adoption and therefore optimize his/her business’s operations accordingly. This knowledge shall also bring down the cost of promotion and advertising and increase effectiveness of the communication as the target audience will be known. Also, since network paths would already be known a marketer can just target a small audience and then let the network diffuse the message on its own. This type of focus and influence is unprecedented in marketing where John Wanamaker’s quote (“Half the money I spend on advertising is wasted. The trouble is, I don’t know which half.”) has come to characterize efficiency and effectiveness of advertising. A huge potential remains undiscovered in this field and I believe that along with rest of Information Technology apparatus, Social Network Analysis will continue to contribute to breakthroughs in advertising and marketing.

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BIBLIOGRAPHY
Albert, R., & Barabasi., A. (2002). Statistical mechanics of complex net works. Rev. Modern Phys., 47-97. Allocca, K. (2011, November 19). Why videos go viral. Why videos go viral. Manhattan, United States of America: TEDxYouth. Aral, S., & Walker, D. (2012). Identifying Influential and Susceptible Individuals in Social Networks: Evidence from a Randomized Experiment. Science, 337-341. Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The Effects of the Social Structure of Digital Networks on Viral Marketing Performance. Information Systems Research, Vol. 19, No. 3, The Interplay Between Digital andSocial Networks, 273-290. Bansal, H. S., & Voyer, P. A. (2000). Word-of-Mouth Processes Within a Services Purchase Decision Context. Journal of Service Research, 166-177. Barabasi, A. R. (1999). Emergence of scaling in random networks. Science, 509-512. Berger, J. (2013). Contagious Why Things Catch On. New York: Simon and Schuster. Christakis, N. A., & Fowler, J. H. (2007, July 26). The Spread of Obesity in a Large Social Network over 32 Years. The new england journal of medicine, pp. 370-379. Christakis, N. A., & Fowler, J. H. (2009). Connected. New York: Back Bay Books / Little, Brown and Company. Dorogovtsev, S. N., & Mendes, J. (2003). Evolution of Networks: From Bio logical Nets to the Internet and WWW. Oxford, UK: Oxford University Press. Duhan, D., Johnson, S., Wilcox, J., & Harrel, G. (1997). Influences on Consumer Use of Word -ofMouth Recommendation Sources. Journal of the Academy of Marketing Science, 283-295. Ellison, G., & Fudenberg, D. (1995). Word-of-Mouth Communication and Social Learning. The Quarterly Journal of Economics, 93-125. Erdos, P., & Renyi, A. (1959). On random graphs. Pub. Math. Debrecen 6, 290-297. Gladwell, M. (2000). The Tipping Point: How Little Things Can Make A Big Difference. United States of America: Little Brown.

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Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Social Theory, 201-233. Iain, D. C., Jens, K., Nigel, R. F., & Simon, A. L. (2005). Effective leadership and decision making in animal groups on the move. Nature, 513-515. Jacob, G., Barak, L., & Eitan, M. (2001). Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. Marketing Letters, 211-223. Mahajan, V., Muller, E., & Kerin, R. A. (1984). Introduction Strategy for New Products with Positive and Negative Word-of-Mouth. Management Science, 1389-1404. Mark, G. (1983). The Strength of Weak Ties: A Network Theory Revisited. Sociological Theory, 201-233. Milgram, S., & Travers, J. (1969). An Experimental Study of the Small World Problem. Sociometry, 425-443. Milgram, S., Bickman, L., & Berkowitz, L. (1969). Note on the Drawing Power of Crowds of Different Size. Journal of Personality and Social Psychology , 79–82. Porter, M. (2001, March). Strategy and Internet. Harvard Business Review. Rogers, E. (1995). The Diffusion of Innovations, 4th Edition. New York: Free Press. Sinek, S. (2009, September 17). Why? TEDx. Stewart, D., Ewing, M., & Mather., D. (2004). e-Audience estimation: Modelling the spread of viral advertising using branching theory. Annual Meeting (pp. 24-27). Denver: Institute for Operations Research and the Management Sciences. Watts, D. J. (2003). Six Degrees: The New Science of Networks: The Science of Connected Age. New York: W.W. Norton & Company. Watts, D., & Reiley, D. (2011, March 23). Which Half of My Advertising is Wasted. New York City, New York, United States of America. Watts, D., & Strogatz, S. (1998). Collective dynamics of "small-world" networks. Nature, 440-442.

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Bibliography: Albert, R., & Barabasi., A. (2002). Statistical mechanics of complex net works. Rev. Modern Phys., 47-97. Allocca, K. (2011, November 19). Why videos go viral. Why videos go viral. Manhattan, United States of America: TEDxYouth. Aral, S., & Walker, D. (2012). Identifying Influential and Susceptible Individuals in Social Networks: Evidence from a Randomized Experiment. Science, 337-341. Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The Effects of the Social Structure of Digital Networks on Viral Marketing Performance. Information Systems Research, Vol. 19, No. 3, The Interplay Between Digital andSocial Networks, 273-290. Bansal, H. S., & Voyer, P. A. (2000). Word-of-Mouth Processes Within a Services Purchase Decision Context. Journal of Service Research, 166-177. Barabasi, A. R. (1999). Emergence of scaling in random networks. Science, 509-512. Berger, J. (2013). Contagious Why Things Catch On. New York: Simon and Schuster. Christakis, N. A., & Fowler, J. H. (2007, July 26). The Spread of Obesity in a Large Social Network over 32 Years. The new england journal of medicine, pp. 370-379. Christakis, N. A., & Fowler, J. H. (2009). Connected. New York: Back Bay Books / Little, Brown and Company. Dorogovtsev, S. N., & Mendes, J. (2003). Evolution of Networks: From Bio logical Nets to the Internet and WWW. Oxford, UK: Oxford University Press. Duhan, D., Johnson, S., Wilcox, J., & Harrel, G. (1997). Influences on Consumer Use of Word -ofMouth Recommendation Sources. Journal of the Academy of Marketing Science, 283-295. Ellison, G., & Fudenberg, D. (1995). Word-of-Mouth Communication and Social Learning. The Quarterly Journal of Economics, 93-125. Erdos, P., & Renyi, A. (1959). On random graphs. Pub. Math. Debrecen 6, 290-297. Gladwell, M. (2000). The Tipping Point: How Little Things Can Make A Big Difference. United States of America: Little Brown. Page 25 Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Social Theory, 201-233. Iain, D. C., Jens, K., Nigel, R. F., & Simon, A. L. (2005). Effective leadership and decision making in animal groups on the move. Nature, 513-515. Jacob, G., Barak, L., & Eitan, M. (2001). Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. Marketing Letters, 211-223. Mahajan, V., Muller, E., & Kerin, R. A. (1984). Introduction Strategy for New Products with Positive and Negative Word-of-Mouth. Management Science, 1389-1404. Mark, G. (1983). The Strength of Weak Ties: A Network Theory Revisited. Sociological Theory, 201-233. Milgram, S., & Travers, J. (1969). An Experimental Study of the Small World Problem. Sociometry, 425-443. Milgram, S., Bickman, L., & Berkowitz, L. (1969). Note on the Drawing Power of Crowds of Different Size. Journal of Personality and Social Psychology , 79–82. Porter, M. (2001, March). Strategy and Internet. Harvard Business Review. Rogers, E. (1995). The Diffusion of Innovations, 4th Edition. New York: Free Press. Sinek, S. (2009, September 17). Why? TEDx. Stewart, D., Ewing, M., & Mather., D. (2004). e-Audience estimation: Modelling the spread of viral advertising using branching theory. Annual Meeting (pp. 24-27). Denver: Institute for Operations Research and the Management Sciences. Watts, D. J. (2003). Six Degrees: The New Science of Networks: The Science of Connected Age. New York: W.W. Norton & Company. Watts, D., & Reiley, D. (2011, March 23). Which Half of My Advertising is Wasted. New York City, New York, United States of America. Watts, D., & Strogatz, S. (1998). Collective dynamics of "small-world" networks. Nature, 440-442. Page 26