Social Media Based Predictive Analytics

Jeff LoCastro, Founder and CEO of Neener Analytics based in United States participates in Risk Roundup to discuss “Social Media Based Predictive Analytics”.   Overview Social media, is becoming as common as air, food and water. Irrespective of nations, almost everyone is using social media today. So, why is social media gaining enormous popularity and usage? Is it because social media allows social connectivity? Perhaps! Social media seems to have made it easier for individuals and entities across nations: its government, industries, organizations and academia (NGIOA) to make a personal connection with friends, family, followers and fans. It has changed the way we communicate within, between and across nations boundaries in cyberspace, geospace and space (CGS). It is because of the social media that the communication is now personal and direct. In addition to the communication becoming personal and direct, the feedback is also personal and direct. Why is that important? It is important because role of middleman is disappearing. As a result, entities are seeing their initiatives, products and services in ways they imagined, was not possible before. So how does individuals being connected on social media help entities across NGIOA in seeing things differently or understand things better or take more effective decisions. It is by using social media analytics. Social Media Analytics has made it important for entities across NGIOA to understand or listen to what is being said on social media. What is being said about entities or business and its products/services was important before. However, what is being said about business, product or services in a digital global age has become a matter of survival and sustainability. While social media listening or monitoring is intended to help entities across NGIOA understand what is being said about them on the social web at any given time, understanding it in real time is now a survival necessity. Over the years, businesses across many industries have been monitoring their customers’ posts on their websites. This has been more of an attempt to identify, contain and manage some angry or unhappy customers.  Is that approach sufficient today in a digital global age? Perhaps not.  When we evaluate “Lending Industry”, especially how lenders evaluated their lending decisions before digitization, what stands out is, how decisions are taken today, is strikingly different from how lending decisions were made yesterday. It is important to evaluate: * Why is there a need for different approach? * Why is there a need for social media analytics? * How can lenders across nations get to know more via social media analysis? * What would social media data analysis point to? * When to use social media based predictive analytics and more importantly when not to? * Can social media be a stand-alone solution for the complex challenges facing lenders across nations today? * What is a social media analytics process? * What are key social media analytic techniques? * What are the core technologies on which the analytic techniques are based? * What kind of questions social media analytics tools addresses? * Are social media analytic tools mature? * Can we predict the future with social media analysis? * How powerful can predictive analytics tools be for lending purposes? * Who’s using social media based predictive analytics today? Social media and predictive analytical tools have given entities across NGIOA an opportunity and platform to understand and connect with customers, citizens, followers, fans directly. While having that connection, and being able to reach fans and followers immediately and directly is working wonders for branding and understanding concerns and complains,