Qian Tong

Bio: Qian Tong is an academic researcher from Kunming University of Science and Technology. The author has contributed to research in topics: Loyalty business model & Customer relationship management. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

Abstract: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

Customer Churn Prediction Using AdaBoost Classifier and BP Neural Network Techniques in the E-Commerce Industry

Abstract: In customer relationship management, it is important for e-commerce businesses to attract new customers and retain existing ones. Research on customer churn prediction using AI technology is now a major part of e-commerce management. This paper proposes a churn prediction model based on the combination of k-means clustering and AdaBoost classifier algorithm, allowing the segmentation of customers into three categories. Important customer groups can also be determined based on customer behavior and temporal data. Customer churn prediction was carried out using AdaBoost classification and BP neural network techniques. The results show that the research method of clustering before prediction can improve prediction accuracy. In addition, a comparative analysis of the results suggests that the AdaBoost model has better prediction accuracy than the BP neural network model. The research results of this paper can help B2C e-commerce companies develop customer retention measures and marketing strategies.

Competing Risk Model for Mobile Phone Service

Abstract: Since Korean government has implemented the "Number Portability System" in the domestic mobile communications market, mobile communication companies have been striving to hold onto existing customers and at the same time to attract new customers. This paper presents a competing risk model that considers the characteristics of a customer in order to predict the customer's life under the "Number Portability System." Three competing risks considered are pricing policy, quality of communication, and usefulness of service. It was observed that the customers who pay more are less sensitive on pricing policy younger people are less sensitive than older people to the quality of communication and women are more sensitive than men to the degree of usefulness of service. We expect that the result of this study can be used as a guideline for effective management of mobile phone customers under the Number Portability System.

A Model Analysis of Relationship between Customer Information and Cross-selling

Abstract: Cross-selling is a kind of selling practice providing value for both customers and enterprises.The upgrade of IT and market and customer positioning technology brought by that provides enterprises with more opportunities of cross-selling.Providing customers with customized products,meeting customers' requirements and occupying more market by cross-selling will be beneficial to the enterprises to gain the absolute advantages by reducing prices.

Design of Customer Churn Early Warning System Based on Mobile Communication Technology Based on Data Mining

Abstract: Customer churn is a fundamental problem faced by enterprises and an important factor affecting the operation of enterprises. Due to current market conditions and changing consumer behavior, it analyzes potential customer behavior trends by mining customer behavior data. This allows companies to set targets for looming market changes so that market movements can be predetermined. The rapid development of modern mobile communication technology makes the way of life need more new ways to adapt to the development of the new era. At the same time, with the rapid development of mobile communication technology, information management systems have been widely used. If a large amount of data can support decision-making information through data mining technology, it can drive the process of enterprise decision-making. It conducts purposeful and differentiated retention efforts on these customers. It increases the success rate of high-value customer retention, reduces the likelihood of customer churn, and reduces maintenance costs. It does this to achieve preset goals and minimize losses due to customer exit. This paper proposes and establishes a customer churn early warning system based on data mining. It uses this to find the customer trends behind a large amount of customer data. It uses the decision tree algorithm to participate in the decision-making process of the enterprise with this algorithm model. The RFT model proposed in the experiment and its results show that customer value is a key factor in the decision-making process of a firm. The accuracy rate is about 6% higher than that of the control group using the logistic regression model directly.

Machine learning based customer churn prediction in home appliance rental business

Abstract: Abstract Customer churn is a major issue for large enterprises. In particular, in the rental business sector, companies are looking for ways to retain their customers because they are their main source of revenue. The main contribution of our work is to analyze the customer behavior information of actual water purifier rental company, where customer churn occurs very frequently, and to develop and verify the churn prediction model. A machine learning algorithm was applied to a large-capacity operating dataset of rental care service in an electronics company in Korea, to learn meaningful features. To measure the performance of the model, the F-measure and area under curve (AUC) were adopted whereby an F1 value of 93% and an AUC of 88% were achieved. The dataset containing approximately 84,000 customers was used for training and testing. Another contribution was to evaluate the inference performance of the predictive model using the contract status of about 250,000 customer data currently in operation, confirming a hit rate of about 80%. Finally, this study identified and calculated the influence of key variables on individual customer churn to enable a business person (rental care customer management staff) to carry out customer-tailored marketing to address the cause of the churn.