Part one of this post helps to explain the foundation of enterprise class social data analytics platforms (SDAP). Below is a list of the type of analytic functions you should demand from the platform you are working with. Not all end users require all of these functions but if you are planning to support a wider array of users within your organization its critical to have a strong and flexible analytic foundation within your SDAP application.
Platform Analytic Functions
Geospatial Analysis - Leveraging the location data of customers, prospects and communities is a critical capability of a strong social data analytics platform. Understanding the location of a social data elements opens the door for creative applications in marketing and service for innovative companies. Examples would include Geo targeted add delivery, regionally focused next best offers, supply chain insight and more.
Sentiment Analysis - Understanding sentiment within social data is a sophisticated analytic function. Its dependent on the natural language processing (NLP) capabilities of the platform and can provide a candid view of how a social community or specific user "feels" about your product, brand or service. This insight can drive marketing and customer care strategies as well as alert you to growing trends around your service both positive and negative.
Influence Analysis - Influence is the currency of social data and its critally important to understand where influence is within your company's social sphere. SDAP's with influence analytic capabilities can surface who is seen as a community or topic leader and give a company insight into their own influence on topics important to their customers. Identifying influencers and monitoring corporate influence are both critical to successful social data analytical strategies.
Machine Data Analysis - Machine data is often defined as information that is produced by computer systems as processes or functions are executed. An example would be the log file entries that track which pages of a website a browser or group of browsers interact with. This machine data can provide the behavioral insights of a social network user helping to create a view of what content they are interested in, how long they interact with information or what advertisement they react to. Leveraging machine data for insight into social interaction and behavior is an exciting point of insight for a company striving to better understand customers and prospects.
Demographic Analysis - Simple demographic analysis is important especially to marketing professionals. Some solutions utilize available data via API's from the social networking platforms supplying the data others go beyond to leverage NLP technology to determine demographic details based on content and linguistic analysis. Capturing information on gender, age, ethnicity creates added value in social data. Many originating systems tie the social data to account information for the authors. when this is available for analysis it possible to build highly detailed master data on customers and prospects who are socially active.
Brand Affinity - Is a metric utilized by marketing professionals to measure the good will created with customers via a companies branded products. Its similar to sentiment data in that it measure how people feel towards a brand but in this case is more focused on marketing processes and campaigns. For many marketing professions Brand Affinity analytics is a foundational function to build strategies upon.
Text Analytics - Is a capability that builds on the output from Natural Language Processing. Once the unstructured social data has been parsed and organized text mining solutions can analyze the information to identify patterns and trends within the data. Text analytics isn't terribly different that data mining in that it strives to organize the data for analysis and once organized can produce insights otherwise too difficult for people to do manually. Text analytics opens the door for innovative analysis of social data.