With the rapid development of mobile network, the use of mobile phones has become popular. People use mobile phones every day to surf the Internet, shop, socialize, work, etc. In the process of using mobile web services, users may be dissatisfied with the service perception, such as voice connectivity, Internet access, Slow Internet access and other common problems. If the customer is not satisfied with the communication service, the customer can usually complain about the quality of the communication service, so the frequency of the customer complaint has become an important evaluation index for the management of the operator. The quantity and frequency of customers ‘complaints about telecommunication service are increasing gradually, which brings challenges to the service quality and efficiency of telecommunication operators. This paper presents a methodology for customer complaints. The analysis system is based on the data of Horizontal pull- through, combined with big data analysis model, focus on the user’s response to the Internet slow, Internet access, voice access issues such as real-time positioning analysis, to provide customers with the first time solutions.

The feedback of service quality comes from customers is an important information for mobile operators, and the value of different user contacts varies greatly. This paper tries to integrate this contact information with telecom operator’s business and signaling data, and then realize the digital mapping from user experience to operation and network problems. Our aim is diagnose the root cause of the problem and then provide the systematic solution. This paper proposes a systematic and package solution for collaborative improvement of user experience and network quality, driven by user’s contact information with operators. Meanwhile, this paper also proposes three method to help telecom operators to repair user’s stickiness step by step, and improve the mobile network quality synchronously.

With the rapid progress of the telecom industry and fierce competition among telecom operators, telecom companies pay more attention to customer retention. Telecom companies developed multiple solutions to predict churn customers before customers move to another telecom operator. However, the existing prediction solutions have some disadvantages in the real-world use cases. For example, churn definition is limited to moving from one telecom operator to another, which is too late for preventing customer churn. The main contribution of the paper is to introduce the new definition of customer chum for the telecom industry, and to propose a Half Termination Dynamic Label (HTDL) that improves the churn prediction solution with XGBoost. Experiment results showed that the proposed solution improved the model performance, which significantly outperforms traditional solution, in terms of churn prediction on F1-score. The new solution also sidelines more active customers for retention.

Social network marketing is a very promising topic in the data operation work of telecom operators. Based on the big data collection and analysis of telecom operators, this paper presents a content recommendation scheme which considering both users' social relationships and users' personal preferences. Regarding users' personal preference analysis, this scheme uses DPI (Deep Packet Inspection) technology to obtain the user's personal preference tag and evaluate the user's preference index. In terms of user social relations, it integrates the analysis of mobile users' communication behaviors, temporal and spatial relationships, interaction circles and other related indicators. Logistic regression algorithm is used to illustrate the influence from a user to another. The preference recommendation scheme based on the mobile network user social circle proposed in this paper expands the value scenarios of operators' big data, integrates resources and channels, improves operators' data insight capabilities, and realizes the value mining and enhancement of operators' big data.