Proceedings of the 17th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2022F131

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 93–101 (2022)

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Abstract. Nowadays, customer acquisition is an open issue that has a special interest in all companies over the world. Very different marketing campaigns using psychological methodologies are 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 a key objective 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 simplifying the process. Thus, companies have started to use Machine Learning (ML) models trained to efficiently detect those clients with certain proneness to purchase. In this sense, this paper presents a novel purchase propensity detection ML system based on the Sentiment Analysis techniques able to consider the customer comments regarding the offered products. The tourist domain has been selected for the case study, where the obtained product was successfully embedded in an initial prototype.

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