This paper examines the emerging field of social marketing analytics delivered via Software-as-a-Service (SaaS) and cloud platforms as a component of Business Intelligence (BI). It evaluates tools such as the SAS Social Marketing Service, which integrate with major social networks to parse and interpret unstructured content at scale. The paper explores techniques including latent semantic indexing, semantic analysis, and sentiment analysis, discussing their role in transforming unstructured social data into actionable decision-making models. It also addresses the benefits, data quality considerations, privacy and security factors, and the key challenges and risks organizations face when adopting these advanced analytics applications.
The paper demonstrates applied technology evaluation: it introduces a new BI tool category, explains the underlying analytical methods (LSI, semantic and sentiment analysis), assesses real-world product entries, and weighs benefits against risks. This structure mirrors a standard technology assessment framework used in business and information systems writing.
The paper opens with a contextual introduction situating social marketing analytics within the broader BI landscape and identifies a specific market entrant. The body section evaluates the software category in detail, covering analytical techniques, benefits, data quality, security, and adoption challenges. A reference list in APA format supports all major claims. The concise, focused structure suits an exploratory overview of an emerging technology area.
The emerging area of social marketing analytics, today delivered on the Software-as-a-Service (SaaS) or cloud platform (Lawrence, Melville, Perlich, Sindhwani, Meliksetian, Hsueh & Liu, 2010), is used for interpreting and analyzing all messages on social networks relating to a company or product (Raab, 2010). This is one of the newest areas of Business Intelligence (BI), with several new application suites still in the R&D stage, yet with several significant new product introductions already on the market. The most noteworthy is the SAS Social Marketing Service (Marketing Weekly News, 2010), which is entirely web-based and supports integration with Facebook, FriendFeed, Twitter, and many other social networks. The intent of this analysis is to evaluate the use of social marketing analytics and assess the newest entrants into this market, including SAS (Marketing Weekly News, 2010).
The purpose of using social marketing analytics software is to gain greater insights into the unstructured content that is growing exponentially on social networks. Advanced unstructured content management techniques and systems — including latent semantic indexing (LSI), semantic analysis, and sentiment analysis — are used to interpret and organize unstructured content into models that can support decision-making (Tyagi, 2010). This new area of social marketing analytics, for the first time, gives companies the ability to quickly parse through unstructured content both internally and externally, and gain new insights into how customers perceive their products and services (Tsai, 2009). From this standpoint, social marketing analytics complements and strengthens existing BI platforms, the majority of which analyze only structured content.
It is estimated that 90% of a company's content is unstructured, and the majority of insights for devising more effective strategies can be obtained from this content through more effective BI methods (Marketing Weekly News, 2010).
The benefits of social marketing analytics include the ability to quickly parse and analyze literally terabytes of unstructured content obtained from social networks, and then succinctly define a linguistic model showing a company's strengths and weaknesses from a customer-perception standpoint. These linguistic models encapsulate unstructured data and make it possible to devise queries and complete further analysis. In addition, companies gain a baseline measurement of how their customers perceive them from both positive and negative standpoints. The downside of social marketing analytics is the cost of integrating these BI systems with legacy systems (Raab, 2010) and the ramp-up time required to learn their advanced features (Tsai, 2009).
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