How Social Networks Use Your Data to Build CRM Systems

Introduction

The decision social networks face regarding how to monetize their content and fuel new growth is predicated on data mining and business intelligence techniques, the ethics of how customer data is used, and their strategies for long-term growth. How social networks use subscriber data is the central area of analysis this paper addresses. In evaluating how social media data is being used by social networks today, the concepts of advanced analytics — including latent semantic indexing, data mining, and the eventual development of data services — are explored.

The use of social network customer data for improvement of online applications is, however, secondary to the creation of advertising-driven business models. In addition, the use of social media data for the development of entirely new approaches to Customer Relationship Management (CRM) is also discussed. Of the many potential directions that social network providers could pursue strategically, the most probable is the development of Social CRM (SCRM)-based applications and services, delivered entirely online. SCRM will be the catalyst for redefining relationships in both the B2B and B2C marketing contexts, attaining the goal of achieving a true 360-degree view of the customer for the first time.

The greater value of social media data lies not in advertising models but in the customer-driven business processes that companies globally face daily as they work to grow their businesses. The ethical use of social media data will be debated for decades to come, as the founders and leaders of these companies regard any information shared as their asset or property, arguing that it exists in the public domain. Ownership of data will ultimately redefine business models.

Trust, Ethics, and Social Media Data

The principles of reciprocity, responsiveness, transparency, and trust — among the greatest virtues in human interaction — are what lead people to connect and communicate online. The phrase "joining the conversation" has become ubiquitous in the area of social networks (Bernoff & Li, 2008). Many entrepreneurs who launched social networks were previously community managers who received formal educations in techniques for creating high levels of collaboration and communication in diverse groups, and their start-ups reflect those values (Fischer & Reuber, 2011). Social networks were initially seen as a powerful catalyst that would elevate key influencers in every industry from thought leaders to rock stars of their fields, with exponentially greater impact on purchasing decisions (Mui, Mohtashemi, & Halberstadt, 2002). Today, however, the opinions of trusted friends and associates carry more weight online than those of influencers. This shift is leading to a revolution in how people use, trust, and create businesses from social media data.

The ecosystems emerging from social networks operate with an arbitrary and often continually evolving concept of trust. What often happens as a result is that trust can be betrayed as users move from one ecosystem to another — or, as Mark Zuckerberg has done on occasion, privacy settings are redefined across hundreds of millions of accounts unilaterally (Collins, 2010). As trust functions as the new currency in all social transactions, the role of ethics is critically important in navigating the strategic decisions social networks must make (Mui, Mohtashemi, & Halberstadt, 2002). The use of social network data needs to be governed by a systematic methodology that applies insights gained from unstructured analysis, presents findings back to the user, and shows what their personal opt-in data and activity reveal about them from a digital footprint standpoint (O'Hara, Alani, Kalfoglou, & Shadbolt, 2004).

Defining decision frameworks that seek to promote the shared greater good over individual gain, while mitigating losses across all members of a community, is what John Stuart Mill had in mind when he defined utilitarian ethics (Mill, 1861). There are many parallels between utilitarian ethical theory and the trajectory of trust on social networks (Jonsson, 2011). The development of ad hoc trust-based networks and the defining of utilitarian-based workflows are, in theory, what the founders of social networks considered their ethical and moral compass (Fischer & Reuber, 2011). Left to its own trajectory of growth, social networks would over time arbitrate trust through balkanization and "walled gardens" — virtual, gate-guarded communities. Semantic analysis would be used to validate whether someone was telling the truth, and the ethics of disclosing whether trust was warranted (Mui, Mohtashemi, & Halberstadt, 2002).

The areas of latent semantic indexing, linguistic analysis, and semantic analysis are all rapidly converging with federated trust networks and sentiment analysis of social networks (Mui, Mohtashemi, & Halberstadt, 2002). This rapid pace of innovation is fueled, in part, outside of social networks — by the need to analyze massive amounts of unstructured content that government security agencies monitor to evaluate the risk of terrorist attacks, potential leaks of classified information, and threats to national security (Rishel, Perkins, Yenduri, & Zand, 2007). These same technologies are being applied to define semantic grids, using latent analysis, to determine the veracity and trustworthiness of content across all social networks (Olmedilla, Rana, Matthews, & Nejdl, 2005). For the commercialization of data on social networks to succeed without causing segregation online, utilitarianism needs to be combined with linguistic modeling to ensure authenticity and trust.

Analytical Methods: LSI and Sentiment Analysis

The majority of social media data is unstructured and resists easy categorization into taxonomies or any form of organized classification. Despite challenges being addressed through initial efforts in Latent Semantic Analysis (LSI) (Wei, Yang, & Lin, 2008) and sentiment analysis (Fan & Chang, 2010), a significant gap remains between the analytical tools and techniques available to monetize social media content and those capable of personalizing the user experience. For Facebook, the dilemma is stark: increasing privacy settings to create a more stable, secure, and protected platform conflicts directly with the need to capture more data for its advertising-driven business model and unique product development strategies (Collins, 2010).

Using Latent Semantic Indexing (LSI) technologies and techniques (Wei, Yang, & Lin, 2008) to determine linguistic models within unstructured social media data provides a benchmark for evaluating how effectively social networking sites are utilizing their data. The application of LSI for sentiment analysis of contextual update data — primarily tweets — establishes a baseline indicator of users' intention to purchase additional services, either from the social network itself or from its advertisers (Thelwall, Buckley, & Paltoglou, 2011).

Conclusion

The use of social media data from an ethical, legal, and marketing standpoint is going to be continually debated for decades, with intellectual property attorneys arguing that consumers deserve the right to know when their profile data is sold for profit. Social network companies will conversely argue that the data has been shared on an open forum, making it public data — an asset they can resell, package, analyze, and use as the basis for business models. Between these two extremes lies reality.

The intent of this paper is to illustrate that for social networking companies, the greatest profits are to be made in providing automated, highly targeted data sets for use in Social CRM (SCRM) systems. As every company struggles with how best to manage its customer relationships, the central challenge is gaining maximum insight at the lowest possible cost. Social network providers recognize this and are working to create APIs, data sets, advanced analytical tools, and programs that add the greatest value to their data without degrading the latency and speed at which it can be delivered.

All of these factors are taken into account in this analysis. Despite social networking sites' stated purpose of using data to improve their own applications, the reality is that advertising business models generating over $1 billion in revenue for Facebook alone are crucial for their survival. This will continue, and the data sets, analytical frameworks, and accuracy of data capture will increase further, driving sales. The concept of CRM will also change, as a true 360-degree view of the customer emerges for the first time.

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