Last month in New York, KRC Senior Vice President Derek Richer presented to business and technology professionals at the Sentiment Analysis Symposium.  The topic: A Predictive Model of Social Content Effectiveness. See below for a synopsis of KRC’s latest capabilities in social media analytics and the deck from that presentation.

KRC Research has developed a state-of-the art, proprietary social and digital media analytics method which combines human intelligence with advanced analytical tools. By randomly sampling social media comments, custom coding the sentiment into categories, and building statistical models from the results, our work for a range of clients has uncovered important strategic insights into the why behind social media posts.

Too often social media analytics is limited to listening and monitoring – counting likes, mentions, retweets, and shares. While these tools may be useful for quickly discovering customer service problems, their capacity to uncover insights to inform strategy is limited. In addition, the human element in our approach eliminates the coding errors and automation’s inability to read language nuances of content. Our “smart analytics” method provides the depth and accuracy of information that companies need to achieve their goals.

KRC has helped several companies better engage consumers with this methodology. We developed a framework to code 1,500 pieces of Twitter and LinkedIn content for the nonprofit Life Happens, a life insurance trade association. We then created a predictive model to show which elements of posts best engaged the company’s audience to help optimize their messaging.

KRC utilized a similar framework for Verizon’s content creation work, based on several variables including timing, topic and tone, which helped us develop a model to show which components of posts best drive engagement. The result was actionable guidance on how to continually improve Verizon’s communications strategy. These are only two examples of how organizations can use and learn from our dynamic approach.