Online profiling and clustering of Facebook users
In a relatively short period of time, social media have acquired a prominent role in media and daily life. Although this development brought about several academic endeavors, the literature concerning the analysis of social media data to investigate one's customer base appears to be limited. In this paper, we show how data from the social network site Facebook can be operationalized to gain insight into the individuals connected to a company's Facebook site. In particular, we propose a data collection framework to obtain individual specific data and propose methodology to explore user profiles and identify segments based on these profiles. The proposed data collection framework can be used as an identification step in an analytical customer relationship management implementation that specifically focuses on potential customers. We illustrate our methodology by applying it to the Facebook page of an internationally well-known professional football (soccer) club. In our analysis,we identify four clusters of users that differ with respect to their indicated "liking" profiles.
|Keywords||Cluster analysis, Correspondence analysis, Customer relationship management, Facebook, Online profiling, Social networks|
|Persistent URL||dx.doi.org/10.1016/j.dss.2014.12.001, hdl.handle.net/1765/90250|
|Series||ERIM Top-Core Articles|
|Journal||Decision Support Systems|
Van Dam, J.W, & van de Velden, M. (2015). Online profiling and clustering of Facebook users. Decision Support Systems, 70, 60–72. doi:10.1016/j.dss.2014.12.001