| dc.description.abstract | Identifying customer churn plays a vital role in the operation and expansion of any
business. By identifying non-returning consumers, a company can learn why customers leave
and can then tailor their marketing campaigns to maximize business growth. business. This
study aims to detect and implement a machine learning model that can almost accurately
analyze and have customer churn prediction in the E-commerce Retail industry.
With the increasing recognition and rapid development of the e-commerce industry, e commerce retail began to compete more fiercely (Zhang, 2015). Today, the most important
option is how to retain consumers, which can be implemented by providing good services and
benefits at a reasonable price. Therefore, with the growing e-commerce platform in Vietnam
and increasing competition among companies when meeting the needs of customers, having a
strategy and implementing a variety of service can attract customers and keep them stay. In
addition, the non-return of customers on the e-commerce platform represents unbalanced
consumer categories and difficult-to-identify changes. This imbalance is due to the fact that the
number of customers leaving is much larger than the number of customers not leaving. This
study analyzes the causes and proposes methods by building a supervised machine learning
model to identify the main factors in customer abandonment, thereby helping businesses to
predict ahead of time customer behaviors, quickly have plans to improve and retain customers. | en_US |
