Dynamic Sales Commission Prediction Using Machine Learning

To achieve better revenue performance, to motivate the sales team and to increase the general performance of the business, it is crucial to match sales commission in the highly competitive market of today. However, the traditional commission structures often fail to adequately accommodate the dynamics of product demands that lead to inefficiencies, lost opportunities, and even injustice among the salespeople. To solve these issues, the proposed approach utilizes artificial intelligence and machine learning techniques. [13] is a paper which seeks to improve dynamic sales commission prediction using semi-supervised learning technique by combining both limited labeled data and large amounts of unlabeled data to achieve high accuracy and less annotation costs. It adjusts commission rates based on the changes in the product demand by using the historical sales data, real-time market information and analytical models. This paper [19] combines decision analysis and predictive modeling and uses linear regression for accurate sales prediction for dynamic sales commission rates. Thus, the strategy is effective in ensuring that sales executives are motivated to sell the products with slow moving inventory. thus enhancing the possibility of stock clearance while minimizing holding costs for inventories and at the same time maximizing revenue prospects.