Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing
This work addresses the need for improved sales performance in direct marketing for businesses, but it appears incremental as it builds on existing RFM-analysis and boosting tree methods.
The paper tackled the problem of identifying potential customers in direct marketing by proposing a data mining preprocessing method for customer profiling, segmentation, and sales prediction, resulting in the creation of a customer profile and sales forecast.
In an increasingly customer-centric business environment, effective communication between marketing and senior management is crucial for success. With the rise of globalization and increased competition, utilizing new data mining techniques to identify potential customers is essential for direct marketing efforts. This paper proposes a data mining preprocessing method for developing a customer profiling system to improve sales performance, including customer equity estimation and customer action prediction. The RFM-analysis methodology is used to evaluate client capital and a boosting tree for prediction. The study highlights the importance of customer segmentation methods and algorithms to increase the accuracy of the prediction. The main result of this study is the creation of a customer profile and forecast for the sale of goods.