Influencing the influencers: Marketing Strategies and Social Networks

Advertising and Networks

Galeotti and Goyal (2009) develop a simple model where a firm can advertise in the presence of local network effects. Network effects can take the form of classical consumption externalities as well as of social learning via word of mouth communication. Galeotti and Goyal (2006) show that the optimal use of social networks leads to higher sales and greater profits. However, an increase in the level and dispersion of social interaction can increase or decrease the optimal influence strategy and profits of the firm, depending on the content of the interaction. Optimal influence strategies will target individuals with low or high connections, depending on the content of interaction. Finally, the returns to investing in market research on social networks are greater in more unequal networks.

Perhaps the best-known example of the power of social network marketing is Hotmail. Individuals who received an email message from someone with a Hotmail account could themselves sign up for Hotmail by clicking on the link at the bottom of the message. Almost 12 million people signed up for Hotmail within 18 months of its launch, where the advertising budget was a mere $500,000. Can the use of social networking lead to smaller budgets and more effective advertising?

The spread of information through word of mouth in social networks makes advertising more attractive to firms, as any information they send to one person becomes available to more people. But by making it possible for consumers to be indirectly informed, social networks also render direct advertising by firms less necessary. Thus the effects of social networks on advertising budgets are unclear. Galeotti and Goyal (2009) demonstrate that when the current level of advertising is already extensive, many people are being informed directly. More active use of social networks will then lead to more people getting information from their informed friends. So an increase in network connectivity reduces the need for direct advertising. The converse is true if a firm initially undertakes little or no advertising. Since the network multiplier makes advertising more profitable, a greater reliance on networks could encourage greater spending.

Pricing and Networks

Consider a product that has a collaborative component, such as membership to a social club, an online game, a social network website, or a file sharing tool. A potential consumer decides whether and how much to consume of the product based on her expectations of the consumptions of other consumers. Depending on the product of interest, such network externalities can take different forms: an individual may be affected by the consumption of the entire population–global network externalities–or by the consumption of a subset of the population, like for example his friends or colleagues– local network externalities. Moreover, recent empirical studies reveal that consumers may be heterogeneous in terms of influence. There are consumers who are very influential, and there are consumers who are very susceptible to influence. In general, these may or may not be the same consumers.

Fainmesser and Galeotti (2014) develop a framework where a monopoly sells a network good and may price discriminate using network information: who influences whom. They model the influence of a consumer by her in-degree and out-degree. The former parameterizes the level of influence of the consumer, while the latter parameterizes the susceptibility to influence of the consumer. By comparing the case where the monopolist incorporates information on in-degree and out-degree in his pricing strategy with the uniform price benchmark, the authors clarify how the information that the monopoly has about the pattern of network externalities shape the profits the monopoly can hope to earn, and consumers’ welfare.

There is a current debate about whether antitrust authorities should re-focus their present investigations about Google on how Google’s control of large data set about consumers’ behavior may entrench monopoly power and harm consumers’ welfare, see, e.g., Newman (2013). This issue is part of a more general debate about to what extent law should facilitate informational privacy given the technological development leading private firms to enjoy unprecedented abilities to collect personal data, see, M. Froomkin (2000). An important aspect for antitrust authorities is then to understand: How would consumers’ welfare change if firms incorporate information on consumers’ influence in their marketing strategies?

Fainmesser and Galeotti (2014) demonstrate that discrimination on in-degree and on out-degree have different implications for both aggregate consumer surplus and payoff distribution across consumers. In particular, moving from no- discrimination to price discrimination on out-degrees increases the welfare of all consumers, only if the out-degree distribution is sufficiently dispersed. Otherwise, it increases the welfare of consumers with low out-degrees and decreases the welfare of consumers with high out-degrees and the overall effect on aggregate consumer surplus is ambiguous. On the other hand, a move from no discrimination to price discrimination on in-degrees increases unambiguously overall consumer surplus, but some consumers may be worse off.

Concluding Remarks

The study of how the availability of detailed information on social networks shapes firms’ incentives informs practitioners on possible profitable network based marketing strategies, and informs competition authorities on possible consumers’ welfare losses or gains.

References

Bloch, F. and N. Querou, 2013, “Pricing in social networks”, Games and Economic Behavior, 80, 263-281.
Campbell, A., 2013, “Word of Mouth and Percolation in Social Networks”, American Economic Review. Forthcoming.
Candogan, O., K. Bimpikis and A. Ozdaglar, 2012, “Optimal Pricing in Networks with Externalities”, Operations Research.
Domingos, P. and M. Richardson, 2001, Mining the network value of customers, Proceedings of the 7th Conference on Knowledge Discovery and Data Mining, 57-66.
Fainmesser and Galeotti, 2014, “The value of network information”, Working Paper.
M. Froomkin, 2000, “The Death of Privacy?”, Standard Law Review, 52(5):1461-1543.
Galeotti and Goyal, 2009, “Influencing the Influencers: A Theory of Strategic Diffusion”, RAND Journal of Economics, 40(3), 509-532.
Goyal, S. and M. Kearns, 2012, Competitive Contagion in Networks. STOC.
N. Newman, 2013, “The cost of lost privacy: search, antitrust and the economics of the control of user data”, SSRN Working paper.