In the last couple of years, a complete new business area has arisen to help brands deal with the new communication paradigm in which each individual is a potential mass communicator and influencer through social media. This area is called social media monitoring and analytics and while it is changing the way market research has been done so far, it poses new problems concerning the high volume of information, challenges in the analysis of subjective and disperse data and the requirement of better visualization techniques.
Even though hundreds of tools and processes have been created to monitor and analyse social media data, it may be suggested that none of them has truly been able to allow the mass analysis of high volumes of data in a reasonable amount of time.
That´s why we decided to focus our efforts in developing sophisticated algorithms to automatically detect the most important conversations using clustering techniques and a methodology to score those conversations, providing more precise metrics and better reasoning on social media data. In addition, we noticed that it is equally important to display the insights gathered in an intuitive manner, using visualizations techniques for clearer translation of data into context.
It will be shown how to diminish the time required for analysis considerably using clustering techniques, while delivering better metrics and contextualization. Metrics like sentiment, reach and influence will be exposed in an innovative way, in which clusters are analyzed and have different weights. It will also be shown how these clustering techniques, along with other mathematical tools for processing social media data, should lay ground to the next generation of social media analysis, brand valuation and market research.