Last week at the CRM evolution conference in NYC I presented an updated discussion on socialytics in a social CRM context. The conference itself was excellent, here's conference chair Paul Greenberg's follow up post. Anyway, the concept of socialytics (a trend we started covering last year) or deploying an analytics platform to capture and analyze data generated by the social web is gaining momentum and getting a lot of attention from social software vendors and more importantly from end users. The diagram at the left represents what I call the "intersection of highest value information", the point where social data intersects with existing enterprise data. The mashup of social data and enterprise data opens up a long list of potential opportunities for enhanced decision making to drive significant business value. Here are my slides form the presentation and what follows are a few comments about the content, although not the entire presentation voice over of course:
The process of socialytics is fairly straight forward, listen and/or capture social data from defined public and private data sources and pass that data through analytics applications. The socialytics platform should provide the foundation for the capture process and the analytics applications. The social CRM application of socialytics is in effect the intersection of CRM, web analytics and social data. From listening on the social web companies can capture compliments, complaints, questions, problems, competitive information, crisis, influencer, voice fo the crowd and opportunities / needs from prospects, influencers and customers. From that listening companies can then track things like positive mentions, length or span of travel of information, conversations, synchronization, tallies of all mentions, sentiment and activation. Each of these tracked items could relate to an analytics app or series of apps. Overall socialytics in the SCRM context is about social activity, engagement and about driving new business.
Most enterprise analytics provide information that are reactive, something happened in the past and because of it the business needs to take some action to get back on track or get some additional value. In socialytics there is an additional promise or potential to turn analytics into a predictive tool. So from the monitoring and decision support process the information could actually be used to take action on events that are in the future versus reacting to history. Predictive analytics could be applied to define customer future buying behavior, or community behavior. How powerful would it be to learn that a customer was close to defecting before it happens or that a community member was a future influencer. While reactive analytics are useful the real opportunity, especially in dealing with customers is predicting behavior and taking action on that information.