Companies Are Using Your Social

¶ … Companies Are Using Your Social Media Data

The ubiquity of social networks is now globally recognized as one of the most potent economic, social and political catalysts of change affecting people's lives every day. In the midst of the complete re-ordering of how people, groups and companies communicate, each person participating on a social network is leaving a digital footprint (Bernoff, Li, 2008). How the contents of this digital footprint are used, and for what purposes, will dictate the growth, direction and trajectory and most importantly, the trust these social networks retain over time. The use of social media data as a means to more precisely align marketing and selling strategies to the needs, wants and preferences of consumers is realigning marketing, selling, pricing, advertising and analytics (Fisher, 2009). The effectiveness of social media in the context of communications, marketing, selling, pricing and customer service is the focus of this proposal.

Background

Each social network has its own unique benefits and value to potential members or users of these applications and collaborative communities, and the ability to discern those differences is the basis of social media and digital marketing strategies that are becoming so pervasive today. The intent of this research proposal is to discuss how social networking suites are using their member's data to define audiences for given products or services with a much greater level of accuracy and granularity than has been possible in the past (Fisher, 2009). As consumers or users of social media are (Bernoff, Li, 2008). Of the hundreds of social networks that exist today, Facebook has by far been the most aggressive user of customer data, claiming that it is already in the public domain by being on their application platform (Zimmer, 2010). This has raised some significant issues about transparency and trust of social networks as a result (Casteleyn, Mottart, Rutten, 2009). There are also the technological aspects of using social media data, specifically the use of unstructured content analysis techniques that use linguistic models to replicate how customers think (Wei, Yang, Lin, 2008). Latent semantic indexing is one of the essential technologies used by Google to construct their PageRank algorithm (Li, Kwong, 2010). This technology is also used within many of the social networking companies to more effectively manage unstructured customer data (Cochrane, 2008). As can be seen from the Facebook philosophy of using customer data at their own discretion to drive their advertising model, and the use of unstructured content to construct linguistic models that provide insights into consumer behavior, the issue of trust is pivotal in how data from social media applications is used (Porter, Donthu, 2008).

Theoretical Framework

The balancing of social media data for optimizing the customer experience online vs. its use for advertising and selling, including the reselling of the data to other companies anchor the two continuums of the ethical spectrum. For the continuum to stay in balance there must be collaboration and disclosure of just what the data is being used for and how its success is being measured over time. An equilibrium model or framework emerges from this analysis that defines how the use of social media data needs to be managed not just from an execution standpoint but also from a governance perspective as well. For any social network to retain and grow trust the equilibrium of the model must be preserved or their credibility will be lost. Figure 1 shows the proposed Social Media Data Equilibrium Model.

Figure 1: Social Media Data Equilibrium Model

Research Methodology

A multi-phased methodology will be used for creating the model and also for providing insights into how trust in social networks is driven as a direct result of how well the model stays balanced. If there is a perception that the model is shifting in any way, then social network users begin to doubt the transparency and authenticity of a given network and unsubscribe from it, deleting their accounts. Too much of this type of attrition and the business model of selling data si gone, too little and the social network will not survive financially and gain enough capital to re-invest.

Phase 1 of the methodology is to determine the relative levels of spending by area. This will be accomplished with public social networks by reviewing their financials. For private social networks the focus will shift to evaluating their released information on this area including data on their income. Facebook for example ha released much of their financial data even though they are still private (Zimmer, 2010).