## 2 Study Background
Among the models mentioned above, RFM is the easiest to apply in several areas of commerce, retail, and supermarkets, since only transaction data (sales) of customers are required, from which the attributes of recency (R), frequency (F), and monetary (M) are obtained. Based on these data, according to [PERSON] and [PERSON] [42], it is possible to detect customers from the best RFM scores. If the customer has recently made a purchase, their \(R\) attribute will be high. If they buy many times during a given period, their F attribute will be higher. Finally, if their total spending is significant, they will have a high \(M\) attribute. By categorizing the customer within these three characteristics, it is possible to obtain a hierarchy of importance, with customers who have high RFM values at the top, and customers who have low values at the bottom. Despite these possibilities for segmentation, the original standard model is somewhat arbitrary, segmenting customers into quintiles, five groups with 20% of the customers, and not paying attention to the nuances and all the interpretations that the customer base can have. In addition, the method can also produce many groups (up to 125) that often do not significantly represent the customers of an establishment. Table 1 summarizes the main characteristics listed from related works. [PERSON], [PERSON], and [PERSON] [43] grouped products from a database using the standard RFM model. [PERSON], [PERSON], and [PERSON] [44] opted for the development of a new model, considering the periodicity (LRFMP). [PERSON] et al. [45] also developed a new model, to which the recency feature was modified and separated (R + FM). [PERSON], [PERSON], and [PERSON] [43] aimed to improve inventory management, valuing a more conclusive segmentation of products, since the standard RFM model arbitrarily defines segments without adapting to the peculiarities of the data, while the model applied through K-means achieved a segmentation with highly similar data in each cluster. On the other hand, [PERSON], [PERSON], and [PERSON] [44] and [PERSON] et al. [45] aimed to manage customer relationships through strategies focused on segments, aiming to increase the income they provided to the company. All authors used the K-means algorithm, as it is reliable and widely used. It is noteworthy that in the work by [PERSON], [PERSON], and [PERSON] [43], the algorithm had a greater methodological focus, since eight validation indexes were used for \(k\) clusters, aiming to optimize the organization of the segments. The amount of segmented data varied greatly between the three works due to the different application contexts. [PERSON], [PERSON], and [PERSON] [43] had 2043 products in the database to segment, resulting in three clusters. They had a record of 16,024 customers of a bakery chain, with five segments specified, obtained through analysis by three validation indices (Silhouette, Calinski-Harabasz, and Davies-Bouldin). Finally, [PERSON] et al. [45] grouped data from 3 million customers belonging to a Middle East e-commerce database, resulting in 10 clusters, 3 belonging to the recency characteristic and the other 7 distributed between frequency and monetary characteristics. It is noteworthy that [PERSON] et al. [45] tested the model in production, setting up a campaign that focused on the active