Data from social media is increasingly being utilized to better understand consumer preferences and prospective future demand. In this paper, the Interpretive Structural Modeling (ISM) and the Decision Making Trial and Evaluation Laboratory (DEMATEL) approaches are used to identify the interdependencies and cause-effect between social media attributes centered around better understanding the impact of attributes on product sales. The methodology is demonstrated on a the social media and sales data from a large food and beverage company. Results show that the “followers” and “comments” are interdependent and influenced by the “posts”, “impressions” and “videos”. The ISM and DEMATEL results are validated with Pearson's correlation coefficient.