Marketing analysis of wineries using social collective behavior from users' temporal activity on Twitter. Issue 5 (September 2020) - Bello-Orgaz, Gema; Mesas, Rus M.; Zarco, Carmen; Rodriguez, Victor; Cordón, Oscar; Camacho, David
Highlights: This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users' temporal activity Time series of mentions made by individual users to each company's Twitter account are aggregated to obtain collective activity data for the companies Classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, are used to extract collective temporal behavior patterns and models of the dynamics of customers over time The methodology is validated in a case study from the wine market using data gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley) The findings presented show that the proposed methodology provides winery companies with new collective knowledge that can be very valuable Abstract: Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users' temporal activity. Time series of mentions made by individual users to each company's Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company's and other users' actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.