Research of customer churn warning model for a mobile internet company

Mobile Internet is a fast growing, dynamic field, and has wide-ranging application prospects. Electronic commerce is an important application of mobile Internet and it is increasingly changing people’s way of life in the information era. For the present, electronic commerce business is confronted with such challenges as homogeneous profit pattern, customer churn, tenuous loyalty, single channel and so on. Compared to traditional electronic commerce, mobile electronic commerce has incomparable advantages in location, urgency and anytime, anywhere access. On the foundation of association rules in data mining, the architecture of cell phone photograph data mining system and recommendation system model is totally researched. It used the association rule mining algorithm to bulid a cell phone photograph data mining system model. It is very effective for cell phone photograph data mining problem in mobile electronice commerce.

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Predicting the Quality of Service Experienced By the Users Based on the Measurement History of the Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b6703.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2286-2290

Keyword(s):

Mobile Phone ◽

Network Performance ◽

Mobile Network ◽

Mobile Internet ◽

Mobile Phone Network ◽

History Of ◽

Set Up ◽

Mobile Network Operators ◽

Internet Company

Growing from one generation to the following, wireless networks have frequently been enhancing their efficiency in various methods and also for varied purposes. The increase in the usage of different mobile phone world broad web functions may result in degeneration in the mobile network performance. Such destruction often drops the efficiency of the mobile network solutions that can influence the mobile World broad web consumer's experience, which can create the world wide web individuals switch between different mobile network operators to get excellent customer experience. In this particular situation, the effectiveness of mobile phone network operators primarily relies on the capability to make a certain top quality of experience (QoE), which is a procedure of consumers' identified quality of mobile Internet company. The goal is actually to exploit the information made by and already accessible in the network to appropriately release, set up, and optimize network nodules.

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Warning model of customer churn based on emotions

2015 International Conference on Logistics, Informatics and Service Sciences (LISS) ◽

10.1109/liss.2015.7369683 ◽

2015 ◽

Author(s):

Xiaowei Zhang ◽

Juanqiong Gou

Keyword(s):

Customer Churn ◽

Warning Model

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On Customer Churn and Early Warning Model of Telecom Broadband

2010 Third International Symposium on Information Processing ◽

10.1109/isip.2010.100 ◽

2010 ◽

Author(s):

Ding Pan

Keyword(s):

Early Warning ◽

Customer Churn ◽

Early Warning Model ◽

Warning Model

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Analysis of imbalanced data set problem: The case of churn prediction for telecommunication

Artificial Intelligence Research ◽

10.5430/air.v6n2p93 ◽

2017 ◽

Vol 6 (2) ◽

pp. 93 ◽

Cited By ~ 4

Author(s):

Chun Gui

Keyword(s):

Feature Selection ◽

Relative Weight ◽

Minimum Cost ◽

Mobile Internet ◽

Classification Method ◽

Data Set ◽

Customer Churn ◽

Original Dataset ◽

Combined Models ◽

Feature Selection Techniques

Class-imbalanced datasets are common in the field of mobile Internet industry. We tested three kinds of feature selection techniques-Random Forest (RF), Relative Weight (RW) and Standardized Regression Coefficients (SRC); three kinds of balance methods-over-sampling (OS), under-sampling (US) and synthetic minority over-sampling (SMOTE); a widely used classification method-RF. The combined models are composed of feature selection techniques, balancing techniques and classification method. The original dataset which has 45 thousand records and 22 features were used to evaluate the performances of both feature selection and balancing techniques. The experimental results revealed that SRC combined with SMOTE technique attained the minimum value of Cost = 1085. Through the calculation of the Cost on all models, the most important features for minimum cost of telecommunication were identified. The application of these combined models will have the possibility to maximize the profit with the minimum expenditure for customer retention and help reduce customer churn rates.

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