## 4 Results and Discussion
### Cluster Profile  
Once the desired solution is obtained, it is necessary to analyze the clusters contained therein, so that their profile is easily understood; and which characteristics are really relevant. [PERSON], [PERSON], and [PERSON] (2007) state that before benefiting from the results, an analyst needs to understand the essence of each cluster, that is, what are the characteristics shared among the customers of a cluster that differentiate them from others.  
A bar chart was created that presents the average of each RFM characteristic of each cluster contained in the \(k=6\) solution, presented in Figure 9. Each bar represents an RFM attribute, and its height is defined by the average of the attribute in question in the cluster. In this representation, each attribute has a black dot referring to the average of the entire solution, allowing you to compare whether the attribute of the cluster stands out in relation to all the others.  
Figure 8: SLSa results of K-medoids and MiniBatch K-means.  
By analyzing Figure 9 considering each RFM attribute, it is possible to have the following interpretations:  
1. Clusters 2 and 5 have a recency, frequency, and monetary attribute below the overall average, possibly indicating a type of customer who no longer frequents the store (cluster 2) or is in the process of stopping frequenting (cluster 5). Clusters 2 and 5 have 390 and 338 customers, respectively, representing about 41% of all registered customers;
2. Clusters 1 and 3 have high recency, but low frequency and monetary, indicating a new type of customer who is not yet familiar with the store, or is in the process of developing a frequent visiting relationship, or even an old customer who frequented the store recently. Either way, these clusters may represent the flow of customers who have recently purchased from the store. Clusters 1 and 3 have 474 and 379 customers respectively, representing about 48% of all registered customers;
3. clusters 4 and 5 have above-average RFM attributes, indicating loyal customers who buy frequently and spend high total money relative to others. Cluster 6 has the highest values among all clusters, representing the store's best customers. Its RFM attributes are expressively higher, yet this cluster contains only 28 customers. Cluster 4 also has fewer customers than the other clusters, with 139 in total. The two clusters together represent a total of 167 customers, about 11% of all registered customers.  
Figure 9: Profile plot of the clusters of the \(k=6\) solution.  
With the information generated by the cluster profiles, it is possible to obtain a succinct summary of the types of customers who frequent the company, these being lost customers (with low recency, frequency, and monetary), customers in the process of being lost (with below-average recency; low frequency and monetary), recent customers (with high recency but low frequency and monetary), less recent customers (with high recency but lower than recent customers, and a lower frequency and monetary than recent customers), loyal customers (high recency, frequency, and monetary), and finally, the best customers (the best possible RFM attributes).