Abstract
Contemporary businesses must evaluate the performance of their sales personnel and refine their sales strategies. In this context, a variety of approaches are employed to develop strategies, including combining sellers based on their respective sales characteristics, to increase sales. Clustering, a machine learning approach, is used to derive inferences from sales data. The results are then used to inform future sales planning and determine priorities. To achieve this, the sellers are initially grouped (clustered) by similar characteristics based on specific criteria (such as sales volume and product information). This enables the identification of the typical strengths and weaknesses of sellers within each cluster. To illustrate, while sellers in a cluster with high sales volume and customer satisfaction scores may assume a pioneering role in the introduction of new products, it may be beneficial to investigate which products could be preferred in the region where sellers in a low-performing cluster are located, and what measures could be taken to increase sales of these products. By examining the sales performance of clustered sellers, it is possible to ascertain the relationships among the best-selling products across different applications. This approach enables the identification of products sold in conjunction, products that stimulate each other's sales, and products that appeal to disparate customer segments. Following the cluster analysis, an association analysis enables a more comprehensive investigation of the interrelationships among products. The results of this analysis permit the identification of product preferences among specific customer profiles. Based on the information mentioned above, more effective product recommendations and personalized marketing strategies can be formulated. An examination of sales within the identified clusters reveals pertinent information.