Support decision system based on invoices data mining to estimate commercial pent-up demands. (15th September 2022)

• Record Type: Journal Article
• Authors: dos Santos Neto, Ademir Batista; Moras Batista, Maria da Conceição; Ferreira, Tiago A.E.
• Is Part Of: Expert systems with applications. Volume 202(2022)
• Publication Date: 2022-09-15
• Subjects: 68U01; Pent-up demand -- Support decision system -- Clusterization -- Data mining; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117204
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: In the world, all the time, commercial transactions are carried out. A large proportion of those commercial transactions are stored in some way from the companies. The data presented in those databases have the potential to enable several analyses about those transactions, for example, where customers came from to pick up the product. Therefore, liking stores and customers, it is possible to observe some patterns in their relationship. However, if the customer's unfulfilled wish is not registered, how can pent-up demands be identified? Here, we aim to present a methodology developed to identify pent demand by analyzing commercial invoices. In this regard, we are going to use Brazil as a case study to apply our methodology. Most of the commercial transactions in Brazil are informed to the government by electronic invoices (NFe). These invoices are an electronic version of the register of Brazilian commercial transactions. In these invoices, there is information about the commercial transaction carried out. Using the information collected in the electronic invoices, it is possible to quantitatively evaluate the existence of pent-up demand about some product in a specific region and then create decision support mechanisms. Our experiment observed that 13, 6% of products presented a strong indication of pent-up demand according to our methodology. This analysis shows the decision-makers opportunities to explore the potential in increasing the sales of some products. Graphical abstract: Highlights: Companies spending a lot of money in subjective market analysis. It is hard empirically found Pent-up demand about a specific product. Using artificial intelligence it is possible found potential costumers for a product.