Effect of Social Networks in Marketing Essay Example

These impacted students returned to their respective villages and towns.

Ten days later in Anamosa village where some of these students had gone, an uncontrollable outbreak of laughter affected a grand total of 217 people. Similarly, other girls who had returned to their village near Arrangement Girls' Middle School caused the epidemic to propagate there in mid-June when 48 out of 154 students experienced uncontrollable laughter necessitating school closure.

On June 18th in Kangarooed village, another outbreak transpired when a girl went home; it began with her immediate family and subsequently spread to two nearby boys' schools which also shut down as a consequence. After several months, the epidemic eventually subsided.

Both the villagers and scientists investigating this outbreak acknowledged that it was not solely a matter for amusement. This particular epidemic involved epidemic hysteria, capitalizing on humans' inclination toward emotional contagion. Emotions, whether positive or negative, have the ability to disseminate among individuals and larger groups.
The origin of emotions is not individual but collective (Christians & Fowler, Connected, 2009). These emotions are influenced by our connections with others, both close and distant, which also impact our purchasing behavior. This has important implications for marketers in terms of product development and reaching out to consumers. It is worth noting that online and offline communities play a role in shaping our overall behavior and buying decisions (Watts & Reilly, 2011).

Marketing using networks, or promoting products through social networks, has been used long before advertising itself existed as traders and businessmen heavily relied on word of mouth to boost sales. Marketing using networks goes by various names like viral marketing, word of mouth marketing, referral programing community marketing and influencer

marketing. All these approaches involve studying the nature and principles of networks and how they influence the spread of information and formation of opinions among consumers.

The literature review and analysis focus on various aspects related to people within network contexts such as ties, influence, contagion effects, and the role of the internet in networks. On page 5, the text introduces research insights and their implications for current and future marketing practices.Before diving into the chapters, it's important to explain certain concepts that will be frequently mentioned. One such concept is a social network, which consists of nodes (representing people) and edges (representing connections) between them. Essentially, a network is a collection of nodes connected by edges. The attributes and characteristics of these nodes and edges play a crucial role in shaping the nature and dynamics of the network's interactions.

Different networks can have nodes representing diverse actors or participants (as shown in fig.1). Nodes can represent various entities such as trade relationships between nations or ingredients in a recipe. These nodes can possess weights indicated by their size or color. Networks often assign weights to nodes based on factors like degree or community affiliation.

Edges are shown as lines connecting nodes and illustrating their interactions. Similarly, these edges may also carry weights that signify different levels of interaction.

Within a network, centrality measures determine the importance of a node. Centrality refers to how deeply embedded a node is within the overall structure of the network. There are three primary measures used for assessing centrality: degree centrality, betweenness centrality, and shortest paths centrality. Degree refers to the number of edges a node has, while betweenness measures how many shortest

paths pass through a node.
Closeness in a network is determined by the number of steps it takes for a node to reach any other node. This means that even if a node has low betweenness or degree, it can still have high closeness if it is connected to a node with high betweenness or degree. To understand this concept, think about having only a few friends but being able to gather information about everyone else if one of your friends is connected to many people. This indirect connection allows you to be linked to the rest of the network.

In figure X, Node 1 is connected to the entire network through Node Y. Research on social networks shows how our position in the network affects our lives similar to how our economic, cultural, and social surroundings shape our path. This chapter explores recent research on social networks and their impact on individuals. It also examines characteristics that define people's positions in the network and how promotional strategies can use this information.

Furthermore, we have control over shaping our own network by associating with individuals who are similar to us (homophily). We actively seek out those who share our interests, histories, and aspirations. The text discusses the importance of word of mouth in achieving success and highlights how individuals can control their social connections.

There is an ongoing debate regarding whether people become like each other or if similar individuals naturally gravitate towards each other.Research has shown that word of mouth marketing can have a significant impact on an individual's network and overall lifestyle (Caldwell, Page 8). Within this process, different roles play a crucial part. For instance, "connectors"

are individuals who know many people and frequently make introductions. These connectors act as hubs with wide-ranging connections across various circles due to their traits like curiosity, sociability, and energy (Caldwell, Page 8).

On the other hand, "mavens" are information specialists whom we rely on for new knowledge. They possess marketplace knowledge and freely share it with others while also serving as information brokers (Caldwell, Page 8). Mavens have the ability to initiate "word-of-mouth epidemics" through their expertise, social skills, and communication abilities.

Additionally, there are "salesmen," who are charismatic individuals with exceptional negotiation skills. Their quality convinces others to agree with them (Caldwell, 2000). Understanding these different actors involved in word of mouth marketing is crucial for strategically sending messages to target audiences and ensuring viral spread (Local, 2011).

The phenomenon of videos going viral on platforms like Youth illustrates the viral nature of word of mouth marketing. Our social network greatly influences us; even having an additional friend can provide various health benefits without them actively doing anything for us. Furthermore, our position within the network determines our susceptibility to circulating information (Christians & Fowler, Connected 2009).The content flowing through social networks has a significant role as it transports various things between individuals. A crucial factor in this flow is the tendency for people to influence and imitate each other. Each connection with others presents opportunities for both influence and being influenced (Christians & Fowler, Connected, 2009). The transmission of effects from person to person, even beyond direct social ties, typically follows a hypocycloid pattern. In simpler terms, certain effects can spread from one individual to another and continue propagating through multiple connections (Christians & Fowler,

Connected, 2009).

When considering contagion, we often assume that if one person possesses something and comes into contact with another person, the second individual will also acquire it—similar to contracting a germ. However, norms and behaviors may not easily spread. This text stresses the significance of social reinforcement in complex processes such as quitting smoking or purchasing luxury goods. Instead of isolating someone and expecting them to change on their own, it is more effective to surround them with nonsmokers or people from their social network. Strong ties play an important role in these situations as explained in chapter 3. Additionally, consumer demands often arise due to societal pressure rather than actual needs.The measurement of personal success extends beyond one's wealth or consumption; individuals also assess their achievements by comparing themselves to others they know. In order to attract a desirable partner, it is not necessary to be the most beautiful or wealthy person; rather, one must simply be more attractive than others within their network. Gender can impact a person's ability to influence others in their network, as well as the effect of homophily.

Asian Oral conducted research using data from Backbone.Com and uncovered several significant findings: males generally have less influence compared to those who do not disclose their gender, relationship status has minimal influence except for those who claim it's complicated negatively affecting adoption within their local network by peers. It is important to note that significant influencers are not always the ones driving change but rather those who are susceptible to it.

The research also reveals that males who disclose their gender exhibit lower susceptibility to influence compared to those who do not disclose

it. Susceptibility increases non-linearly with the number of notifications received. Additionally, individuals become more susceptible as they progress in their reported relationship status towards marriage, in comparison to those who do not disclose their relationship status. Moreover, susceptibility increases as individuals transition from being single to being in a relationship and eventually getting engaged.Interestingly, married individuals do not seem to be significantly influenced by others in regards to their relationship status (Oral & Backbone.Com). Walker (2012) examines the impact of social networks such as Backbone and other social media platforms on marketing strategies. In order to optimize the reach and effectiveness of their messages, marketers must comprehend the properties and functions of networks. It is essential for marketers to analyze the entire group and its structure rather than solely focusing on individual members (Christians & Fowler, Connected, 2009). This chapter investigates these properties and how marketers can utilize this understanding to better cater to their customers. The study also explores how network structures affect viral marketing and how the internet has transformed our perception of networks. Stewart, Ewing, and Matter (2004) introduced a viral marketing model based on Orders' and Rennin's (1959) random network model described by Albert and Barabbas (2002). This article discusses the creation and characteristics of random networks. According to Albert and Barabbas (2002), a random network is formed by starting with isolated nodes and allowing each node a probability of connecting with other nodes. The degree distribution of nodes in a random network follows a binomial distribution with parameters N ?1 and 0. Each node can potentially connect with up to N ?1 other nodes, resulting in an average of 1/CNN

links per node.In viral marketing networks with a large N and small 0, the average degree is moderate. Thus, the degree of a node approximately follows a Poisson distribution with mean network connectedness A. Scale-free networks were introduced by Barabbas (1999) and Albert and Barabbas (2002), and have been extensively studied. These networks represent various systems such as the World Wide Web, citation patterns in scientific publications, and the electrical power grid of western United States.

The key characteristic of scale-free networks lies in the probability distribution of node degrees which determines the number of communication links or edges each member has. According to the text, this degree follows a Power-law distribution defined by P(k), where P(k) represents the probability of a node being connected to k other nodes. Unlike the declining probabilities in a Poisson distribution, this Power-law distribution has "fat-tailed" probabilities that decline at a slower rate as the number of connections (k) increases.

This unique distribution allows for some nodes to have many connections while most nodes have few connections, resulting in a low average number of connections. Regardless of its size, these highly connected nodes known as hubs contribute to a small average distance (L) between any two nodes in the network.The scale-free network structure emerges due to dynamic growth and preferential attachment, which are important features of social networks. In these networks, new members are added over time and connected based on their existing connectivity, resulting in a Power-law distribution of the degree or number of connections per node. These networks, known as scale-free networks, maintain statistical properties despite growth (Wallace, 2008).

Small World Networks were initially introduced by Watts & Castrato (1998)

as models for social networks with high clustering and short average distance between nodes. Clustering measures the connectivity within a neighborhood and is considered a local property. The clustering coefficient C represents the fraction of neighbors linked to each other for a specific node.

In Attica networks, it is common to observe high clustering and long average distances. These networks can be represented as points in a multidimensional space with edges connecting nearby points. Conversely, small world networks have short average distances between nodes.

To convert lattice networks into small world networks, a rewiring procedure is used. This involves removing arcs that connect neighboring nodes within clusters and replacing them with random links outside of the cluster.As the probability of rewiring increases, the average distance decreases rapidly, resulting in a network structure with low node separation similar to random networks but with strongly connected neighborhoods characteristic of regular networks. Eventually, as the probability of rewiring continues to increase, the graph becomes more akin to a random network.

Small world networks have potential applications in viral marketing due to their inclusion of connections formed through physical interactions. Tightly knit communities represent social structures based on friendships or professional relationships developed within a limited physical area. For example, Albert and Barabbas (2002) discuss a social system where individuals are well-connected with their neighbors and coworkers but have fewer connections to those living far away in another state or country. On the other hand, random links play a role in depicting connections between local networks and represent more distant acquaintances.

By increasing the number of rewired links, the viral message can spread more rapidly within the network, resulting in faster saturation. This

suggests that there are only a few individuals who are closely connected to everyone else within a few steps while others rely on these special individuals as their connection to the wider world (Caldwell, 2000).

However, small world networks also have their limitations. For instance, a small-world internet is efficient but vulnerable to malicious hackers.Small-world electricity networks, like the one that experienced major blackouts in northeastern US in August, exhibit both efficiency and fragility (Caldwell, 2000). The unique characteristics of these networks, such as virility and resilience, are a result of their underlying structure.