The following is an excerpt from “Strategy for Social Engagement & Monitoring: Transforming Noise to Signal” an exclusive whitepaper brought to you by Oracle. Download the complete whitepaper now and learn about the requirements necessary for an optimal SRM solution that will provide organizations with real-time actionable insights from social media through both base and advanced analytical functions.
An Optimal Listening Platform
While listening or collecting the millions of conversations that occur daily is a critical function, it is not sufficient to gain true insights into your customers. In order to gain truly actionable insights and real knowledge, you need to analyze the data. Even though all social media conversations are unstructured, the optimal analytical solution must be able to incorporate and analyze internal enterprise data, often referred to as semistructured data, so that internal conversations such as support calls and call center logs can be included in the overall analysis. This approach provides for a more comprehensive assessment of consumers’ behaviors, attitudes, and perceptions regarding their lifestyles, your category, brand, product, or campaign. It enables you to conduct more-advanced analytics looking for correlation and causality as it pertains to shifts in consumer trends. A complete and robust social media analytics solution includes a blend of qualitative and quantitative analysis based on millions of conversations collected annually, as well as a robust historical archive. When evaluating social media analytics solutions, it is recommended that you understand each solution’s ability to function across the following three essential process steps:
Figure 2. Social media analytics solutions need to function across three process steps.
The optimal social media analytics solution will have both the base functions of data refinement and more-advanced analytical functions of data associations.
Data refinement is required to structure and clean the data for more-in-depth analysis, focusing only on conversations that are relevant.
Data associations enable deeper analysis, often involving a combination of advanced sentiment definition, semantic analysis, topic creation, and influencer identification, augmenting core data sets with derived values and attribute correlations.
A key inherent feature of any social media analytics solution is that it does not restrict or unnecessarily limit your accessibility to the data. In fact, a solution should provide key themes by highlighting where large clusters of relevant conversations are occurring. Theme analysis is an open-ended, white space discovery that identifies and classifies the clusters of conversations that organically emerge from natural consumer interaction. Additionally, a social media analytics solution platform should be able to “understand” when a posting is referring to multiple topics and then break down these topics into subsets of postings, or snippets. These snippets will enable the user to more accurately capture advanced consumer sentiment across any topic, theme, dimension, or term.