Mining and Visualizing Social Data to Inform Marketing Decisions

Most of today's commercial companies heavily rely on social media and community management tools to interact with their clients and analyze their online behaviour. Nonetheless, these tools still lack evolved data mining and visualization features to tailor the analysis in order to support useful marketing decisions. We present an original methodology that aims at formalizing the marketing need of the company and develop a tool that can support it. The methodology is derived from the Cross-Industry Standard Process for Data Mining (CRISP-DM) and includes additional steps dedicated to the design and development of visualizations of mined data. We followed the methodology in two use cases with Swiss companies. First, we developed a prototype that aims at understanding the needs of tourists based on Flickr and Instagram data. In that use case, we extend the existing literature by enriching hashtags analysis methods with a semantic network based on Linked Data. Second, we analyzed internal customer data of an online discount retailer to help them define guerilla marketing measures. We report on the challenges of integrating Facebook data in the process. Informal feedback from domain experts confirms the strong potential of such advanced analytic features based on social data to inform marketing decisions.