Intelligent System Comparing Clustering Algorithms to Recommend Sales Strategies - Arévalo-Huaman, Gianella (2024)
The context of the pandemic has accelerated the growth of electronic commerce in recent years. Consequently, there is intense competition among companies to boost sales and achieve success in a market environment where the failure rate stands at 80%. Motivated by this reason, an Intelligent System is proposed to recommend a sales campaign strategy within an e-commerce platform, automating the analysis of customer data by employing machine learning algorithms to segment (K-means) customers into groups based on their information. Additionally, the system recommends (Decision Tree) a specific sales strategy for each group. Therefore, the objective of this study is to analyze all the relevant aspects that arise in the relationship between an e-commerce business and its customers, as well as the effectiveness of generating strategies based on specific groups through Customer Segmentation. As a result, the system achieved a significant increase in Web Traffic, Click-through Rate, and Sales Revenue by 14%, 5%, and 10%, respectively, indicating a monetary growth and improved engagement after the utilization of our tool.