TY - GEN
T1 - Intelligent System Comparing Clustering Algorithms to Recommend Sales Strategies
AU - Arévalo-Huaman, Gianella
AU - Vallejos-Huaman, Jose
AU - Burga-Durango, Daniel
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Customer Segmentation
KW - E-commerce
KW - K-means
KW - Marketing Strategies
UR - https://www.scopus.com/pages/publications/85180760992
U2 - 10.1007/978-3-031-48858-0_17
DO - 10.1007/978-3-031-48858-0_17
M3 - Contribución a la conferencia
AN - SCOPUS:85180760992
SN - 9783031488573
T3 - Communications in Computer and Information Science
SP - 209
EP - 219
BT - Advanced Research in Technologies, Information, Innovation and Sustainability - 3rd International Conference, ARTIIS 2023, Proceedings
A2 - Guarda, Teresa
A2 - Portela, Filipe
A2 - Diaz-Nafria, Jose Maria
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023
Y2 - 18 October 2023 through 20 October 2023
ER -