Automatic detection of potential customers by opinion mining and intelligent agents

Customer acquisition is an issue that continues to receive attention from companies worldwide. Various marketing campaigns using psychological methodologies have been designed to address this issue. However, once a campaign is launched, it is highly complicated to detect which sets of customers are most likely to purchase an offered product. This fact is key since it allows companies to focus their efforts on specific clients and discard others. Several selection techniques have been implemented, but most of them are usually very demanding in terms of time and human resources for the companies. Artificial Intelligence techniques appear to help to simplify the process. Thus, companies have started to use Machine Learning (ML) models trained to efficiently detect those clients with certain proneness to purchase. Toward this goal, this paper presents a novel purchase propensity detection ML system based on Sentiment Analysis techniques able to consider customer comments regarding the offered products. The tourist domain was selected for the case study, where the obtained product was successfully embedded in an initial prototype.