| dc.description.abstract | As people pay more attention to their health, society′s demand for health supplements has become stronger. The demand has also brought about more intense industrial competition, which means that customers have more diverse merchant choices. Customer churn refers to the customer no longer purchasing products from the company in a specific period. For companies, retaining customers is very important because it has a lower cost than acquiring new customers. The cost difference is as much as 20 times.
Among the many channels for selling health supplements, telemarketing is the most common. Traditionally, telemarketers need to make a large number of calls to customers on the list to promote purchase. However, blind calling is an unnecessary waste of labor costs. Compared with customers who will continue to remain loyal, companies should target customers who will churn in the future, but some of them are determined to churn and cannot be retained. In other words, companies should target and take action to the customers who will churn in the future but are more likely to be retained.
This study reveals how to parse available features from real company transactions and call data, to the process of adding features, filtering features, and deciding the number of periods to adopt. It combines predictive models and six SMOTE methods to find churn customers, among which L2 regularization using Logistic regression and ADASYN under different k, the average precision can reach 82.224%, which means that more than 80% of the customers predicted to be churn are actually churn. Then, cluster the customers predicted to be churn through K-MEANS method, and find out the feature to identify the customer cluster that is more possible to be retained. | en_US |