Customer Churn, Telecom, Churn Management, Data Mining, Churn Prediction, Customer retention Abstract Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn.
This writing summarizes and reviews the first reported work on deep learning for churn the loss of customers because they move out to competitors.
Acquiring new customers costs five to six times more than retaining existing ones. The current focus is to move from customer acquisition towards customer retention. Being able to predict customer churn in advance, provides to a company a high valuable insight in order to retain and increase their customer base.
Tailored promotions can be offered to specific customers that are not satisfied. Deep learning attempts to learn multiple levels of representation and automatically comes up with good features and representation for the input data.
To investigate and consider the application of deep learning as a predictive model to avoid time-consuming feature engineering effort and ideally to increase the predictive performance of previous models. Using deep learning for predicting churn in a prepaid mobile telecommunication network Previous works: Most advanced models make use of state-of-the-art machine learning classifiers such as random forests [ 6 ], [ 10 ].
Use graph processing techniques [ 8 ]. Predict customer churn by analyzing the interactions between the customer and the Customer Relationship Management CRM data [ 9 ] Base their effectiveness in the feature engineering process.
The feature engineering process is usually time consuming and tailored only to specific datasets. Machine learning classifiers work well if there is enough human effort spent in feature engineering. Having the right features for each particular problem is usually the most important thing. Features obtained in this human feature engineering process are usually over-specified and incomplete.
Introduce a data representation architecture that allows efficient learning across multiple layers of detailed user behavior representations. This data representation enables the model to scale to full-sized high dimensional customer data, like the social graph of a customer.
The first work reporting the use of deep learning for predicting churn in a mobile telecommunication network. Churn in prepaid services is actually measured based on the lack of activity Infer when this lack of activity may happen in the future for each active customer. A four-layer feedforward architecture.
Autoencoders, deep belief networks and multi-layer feedforward networks with different configurations. Billions of call records from an enterprise business intelligence system. Churn rate is very high and all customers are prepaid users, so there is no specific date about contract termination and this action must be inferred in advance from similar behaviors.
There are complex underlying interactions amongst the users. Churn prediction is viewed as a supervised classification problem where the behavior of previously known churners and non-churners are used to train a binary classifier.
During the prediction phase new users are introduced in the model and the likelihood of becoming a churner is obtained.
Depending on the balance replenishment events, each customer can be in one of the following states: Customer churn is always preceded by an inactive state and since our goal is to predict churn we will use future inactive state as a proxy to predict churn. Two main sources of information: Each CDR provides a detailed record about each call made by the customer, having at least the following information: Id of the cell tower where the call is originated.The main objective of this paper is to accuracy and time complexity.
Telecommunication churn dataset is used for the analysis. The obtained results revealed that MLP algorithm outperformed in terms of accuracy and C algorithm customer churn prediction. Phase 2:During this phase the comparative study was . Analysis of Customer Churn prediction in Logistic on case study of churn analysis.
This paper discusses the use of Mining Mart, a churn analysis tool. It mainly discusses Analysis of Customer Churn prediction in Logistic Industry using Machine Learning. The focus of this paper is churn prediction in MMORPGs.
networks and how it differs from churn in the telecommunication networks. A wide-array of techniques has been used for churn analysis. For example, logistic regression models [11, ], or extension to customer churn prediction [4. choosing effective input variables for telecommunication customer churn prediction model input variables for the churn prediction model.
This paper Telecommunications customer churn prediction process Telecommunication customer churn prediction models. The term customer churn is used in telecommunication industry to define customers who change their supplier or provider to a new one offering same service  .
churn prediction is an important element in making an acc urate and effective decision . In this paper, we propose three hybrid data mining models for predicting customer.
This paper tries to fill this gap by empirically comparing two techniques: Customer churn - decision tree and view of how decision tree and logistic regression models can be used to analyse and understand customer churn in the mobile telecommunication market; and (b) explores the pros and cons of these techniques.
Prediction is made by.