Social network business model pdf

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Think of traditional advertising. As an example, there are limited TV advertising spots for which the big brands bid prices up. These often come from verticals with a large market size e. Many verticals are priced out of TV ads because their market is too small. Customer lifetime value in relation to acquisition costs plays a role but for small verticals, this is not enough due to insufficient volume and lack of targeting.

We can make similar observations for the geographical dimension. Local companies are often priced out of many forms of advertising. Search and Social platforms add economic value to these companies by allowing them to reach the right consumers.

There are many ways of ad targeting. We will use the vertical and geographic dimensions as our examples. On the next diagram, you see that the average cost per click CPC can vary by a factor of 10 depending on the vertical.

This means that the lower cost verticals would be priced out if they had to compete for the same ad space. Equally, if you look at the average revenue per user ARPU across different geographies Facebook , you see a factor of The fascinating truth is that you can basically rattle down a laundry list of microeconomic others call them strategic concepts that Google, Facebook, Twitter, Snapchat and Pinterest and other successful social media and search engines provide as additional value propositions to their advertisers.

I am sorry to throw this at you like this, but rest assured I have a lot more explanation on each of these within the premium resources. This is on top of the multitude of value propositions for businesses. Where in traditional industries would you see this? Often margins are eroded by powerful middlemen who interface with the customers. It is amazing how similar search engines and social media are in their value propositions for advertisers.

You will notice how the value proposition to users also follows important economic concepts, in this case from the area of search and transaction costs. We can actually find a level that is not superficial where all these platforms are comparable which is part of the detailed resources.

The great value of doing this is that we can branch down from there into our own ideas. Many of you share with me their ideas.

This is why I know that doing this is of enormous value. Network effects are often referred to as the most important competitive advantages of platform businesses. Network effects NWE are the effects that incremental participants and participation have on the value of the network to other participants.

Positive and negative network effects: Network effects can be positive or negative. Network effects on Social among close connections tend to be mostly positive. But they can also turn negative bullying, harassment, etc. Enhancing positive network effects and reducing negative ones is the most important activity of a platform business.

Then there are wider negative network effects and externalities where e. Negative network effects need to be managed by the platform. Facebook had 30, staff , pdf to manage the multitude of negative network effects of which they say:.

Connections underpin NWE. But they are not NWE as such. I would not say that this is wrong but like to call out that you should not use it as a formula but rather as a guide. It is a correlation that was used in another context and needs to be overlaid with many other factors, firstly, with who is actually connected to whom. Platforms, by no means, try to bluntly maximise the number of connections.

That in itself will lead to confusion and overload, burying relevant content among irrelevant content. Platforms try to do quite the opposite, by aiming to algorithmically provide well-matched connections which is why you see me use this phrase so often. Closer connections drive more engagement. Well-matched connections are one of the contributing factors to achieve high network effects. Comparing our five platforms, I have identified 8 fundamental factors that I am explaining in detail..

Finding best friends is a different problem than finding more friends, so we need to think about new ways to help people find the friends they care most about. Furthermore, often more than one of the above four methodologies are combined in a study of an influence model of collective behavior. The analyses in these studies are based on three categories of assumptions concerning the ways processes of social influence are formalized in these models. In what follows in this introduction, we are going to describe the common theoretical options about these assumptions and to specify the ones that we are following in the model of social influence, which we are discussing in the subsequent sections of the present paper.

As we have already explained, the social space of influence processes is usually conceived in relational structural terms. As it is the case in social network analyses, a directed or undirected valued graph is a typical formal representation of a configuration of a social influence system.

The vertices or nodes of this graph represent actors, who are trying to influence each other according to the strength of their ties with others.

The strength of such influence ties is represented by certain values that the links edges or arcs of the social influence network can take. However, we have treated them separately in the above list because they constitute a discrete methodology of stochastic systems. Furthermore, the references of the statistical mechanical systems that we have given refer almost exclusively to social impact systems analyzed by mean field theories.

Symmetric influence relationships among actors give rise to a directed graph, while non-symmetric non-reciprocated relationships correspond to an undirected graph.

We should add that in certain models of social influence developed in the tradition of statistical mechanics in econophysics and sociophysics and interacting particle systems but often also in the context of game theory , more regular types of graphs, like lattices, are considered to constitute the social space of the sites that actors dwell upon and through the corresponding regular grid of links they tend to influence and be influenced by each other.

Here we have to stress that social influence outcomes are considered to be disaggregated and distributed over the actors of the social influence network, who may change their individual outcomes according to the dynamics of the social influence process they are sustaining. In other words, there is a dynamic pattern of values the social influence outcomes distributed over the vertices of the graph representing the underlying social space, which is dynamically updated according to the strength of the influences that are exerted over the existing pattern of linkages among actors.

Given that a graph representing the social influence space is typically assumed to be connected, we can understand that it is a complex adaptive process what shapes the social influence outcomes that actors possess during the different instances of the time evolution of such collective phenomena.

Moreover, we are assuming that there is a certain structure in the set of all possible social influence outcomes. In fact, we consider the case that the influence outcomes are embedded in a certain graph. This assumption is not a technical one but it arises in a natural way in the specific context of the social influence processes that we are studying in this paper. As we are going to explain in the next section, this is exactly the theoretical formalization used in theories of discrete social choice Arrow, ; Sen, ; Mas-Colell et al.

However, by subsuming our analysis inside the perspectives of a social influence model, we are disengaged away from one of the fundamental assumptions of social choice theory, according to which the aggregation of individual choices should be devoid of any influences or manipulations that actors might exert to each other.

In return to the abandonment of the main focus of social choice, we are gaining the insight and the methodological tool-kits that theories of social influence can provide.

What is more interesting is that nonhomogeneous equilibrium positions can emerge too. These are equilibrium configurations composed of diverse influence outcomes, which are distributed over the vertices actors of the underlying social influence network. However, not many such results exist in the literature up to now. Roughly speaking, what determines the absorbing states of an interacting system as the social influence system that we are going to discuss in the sequel of this paper depends upon under which conditions actors halt their interactions.

In principle, we could think of two plausible ways of interaction halt. One is when all actors have reached the same outcome, after having shifted their initial outcomes, because of the influences they have received and they have exerted in their social influence network.

Obviously, this is the case of a unanimous equilibrium outcome that all actors may adopt and subsequently they would be locked-in.

However, there is a second scenario of interaction halt. This is when two interacting actors reach two bridgeless opposite outcomes which constitute the two uncompromising antipodes of a polarized situation. From this point of view, we can understand where an appropriate relational structure of the set of influence outcomes might lead us to. If this structure affords the existence of polarized outcomes for instance, antipodal positions , then when actors move inside this structure by alternating their positions among all possible outcomes, as they are subject to an influence process, then chances are that a pair of actors seizes two antipodal outcomes, in which case these two actors cannot further interact with each other.

When this happens for all pairs of interacting actors, then the social influence network is polarized across two antipodal positions of outcomes, which means that the whole system is trapped or locked-in in a nonhomogeneous equilibrium state.

In fact, the last polarization scenario can be realized in the specific social influence model that we are developing here. Since the relational structure triggering polarization that we would like to consider here comes from the theoretical setting of social choice theory, in the following first section we intend to present the fundamental concepts of this theory. As a matter of fact, since our focus is on social networks and those working in the sociological field of social network analysis have rather limited exposure to theories of social choice, our presentation will be sufficiently detailed at the cost of repeating very elementary facts from theoretical economics and formal political theory.

In the third section, we are going to discuss the exact rules of interaction, upon which our social influence model is based. In fact, we are postulating the type of interactions which is followed in stochastic theories of interacting particle systems. Furthermore, in this section, we are going to classify the equilibrium states and to state a theorem which sets the conditions under which the equilibrium outcomes orderings will be unanimous or polarized.

It turns out that these conditions refer to how the initial orderings are spread over the graph of orderings. Of course, a technical proof of our main theorem escapes the scope of the present presentation.

In the fourth section, we are going to present the results of computer simulations that we have done in order to understand how probable the emergence of polarization is under this model of social influence. Furthermore, we are going to outline a number of possible extensions of the present work that we intend to pursue in the future. Fundamental Concepts from Social Choice Theory We start with a presentation of the fundamental definitions and concepts of the theory of social choice in the discrete case Mas-Colell et al.

By this, it is meant that R is a complete and transitive binary relation. The set of all weak orderings on X is denoted by O X. Completeness implies that R is reflexive too, i. By this we mean that in order to transform one ordering into the other, we need just one single elementary or minimal change of preferences: either to change a strict preference xPy into an indifference xIy or the other way around. In social choice theory, this metric is often called Kemeny distance, as it was originally introduced by Kemeny In these pairs, one of the two orderings should include a chain of three or more indifferent alternatives,9 while the other should be composed of only strict preferences or it could also include disjoint pairs of indifferent alternatives.

Then there is no single demi-transposition between such pairs of weak orderings. Essentially, this is because transitivity would necessitate at least two demi-transpositions to transform one of these orderings into the other, while the previous relation was defined through a single demi- transposition. In other words, all orderings including chains of indifferent alternatives of length larger than two i. Here L X denotes the set of all linear orderings or strict-total preferences Mas-Colell et al.

Equivalently Sen , p. In other words, a weak ordering is linear if and only no distinct alternatives can be indifferent to each other. We have already said that each individual or voter is assigned to some ordering of preferences among all the alternatives or candidates. Interactions on a Social Influence Network We first start with a description of the social influence network that we are going to consider in our model.

For the sake of simplicity, we are going to stick to the undirected graph case: a more general model of social influence based on a valued directed graph could be easily constructed by doing some direct modifications in the definition of the social space of the present model. Typically, this process eventually reaches an equilibrium state. Because of the two conditions of interaction halt, it is obvious which configurations of orderings should survive in an equilibrium state: In equilibrium, all adjacent actors should necessarily have either the same or antipodal orderings.

Thus, using the terminology of Brian Arthur , we might say that the two possible equilibrium lock-ins are either a single unanimous ordering or a dipole of two antipodal orderings. Of course, which one of these two configurations is eventually going to emerge in equilibrium depends on the starting initial distribution of orderings over actors.

Then our main result describing the way this process of social influence is stabilized reads as follows: Theorem 1. Simulation Results We have run a number of different computer simulation14 experiments in order to understand statistically the structure of the equilibrium states of this social influence process.

These simulations were implemented for the case of three alternatives i. In each simulation experiment, we have started from a randomly or arbitrarily chosen initial profile of orderings and have proceeded with random selections of interacting dyads of actors until reaching an equilibrium interaction halt. Furthermore, each simulation converges to an equilibrium profile of orderings consisting of either a single unanimous ordering or a dipole of two antipodal orderings.

After having run a simulation N times where N is a large number , let BN be the total number of dipoles produced throughout these simulations. Next, we have conducted a simulation experiment with the ring topology of the social influence network.

In particular, we have found that homopolar initial profiles are always stabilized into a monopolar equilibrium state representing a unanimous ordering by all actors in the social influence network. We have one vision in the industry which is very simple.

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Singles Mingle, LLC is a social network site which will not take with levity anything that will help in achieving the business vision. As stated earlier, our business vision is to be among the leading social network sites in the world. We will not be able to accomplish this vision if we do not do the needful, especially as it concerns our business structure. This is why we will take very seriously the recruitment process of our business.

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