The increasing amount of content created in Social Media platforms is calling for sophisticated filters that separate relevant from non-relevant content. Social Media Analytics (SMA) is a field that addresses this challenge by the development of strategies, methods and technologies to automate this filtering process. This work in progress paper presents an experiment, which examined two Social Media (SM) applications as well as two Business Intelligence (BI) applications for the analysis of tweets. The overall goal is to identify differences of these tool categories with regard to the analytics process itself as well as the obtained results. Using the scenario of a fitness tracking application for smartphones, data from Twitter was collected and analyzed with applications of both categories. The findings show (1) differences between BI and SM application, (2) challenges resulting from the different analytics processes, and (3) hints for decision makers as well as data analysts when to use which category for analyzing social content.