scholarly journals Customer Churn Prediction in Telecommunication Industry Having Data Certainty

2020 ◽  
pp. 113-122
Author(s):  
V R Reji Raj ◽  
Rasheed Ahammed Azad .V
Keyword(s):  
Binary Classification ◽  
Decision Makers ◽  
Churn Prediction ◽  
Operating Characteristics ◽  
Evaluation Measures ◽  
Customer Churn ◽  
Customer Churn Prediction ◽  
Roc Area ◽  
Data Certainty ◽  
Distance Zone

Customer Churn Prediction is a challenging activity for decision makers because most of the time, churn and non-churn customers have similar features. It is one of the major concerns for large companies, especially in the field of telecommunication field. Churn can be considered as a binary classification. The classifiers shows different accuracy levels at different zones of data. In such cases, a correlation can easily be observed in the level of classifier's accuracy and certainty of its prediction. So a mechanism to estimate the classifier’s certainty for different zones within the data is needed so that the expected classifier’s accuracy can be estimated. Here the classifier’s certainty estimation is done using six sigma rule of normal distribution applied on the correlation values of all features in the dataset. Based on this the dataset is grouped into two categories such as (i) data having high certainty, and (ii) data having low certainty. Based on these criteria, classifier accuracy is estimated in the high distance zone. From the different evaluation measures like accuracy, f-measure, precision, recall and Receiving Operating Characteristics (ROC) area, the performance of classifier is evaluated. Then by applying a k fold approach the certainty of the classifier decision is estimated.

Hybrid ensemble learning approaches to customer churn prediction

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sara Tavassoli ◽  
Hamidreza Koosha
Keyword(s):  
Binary Classification ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Bootstrap Sample ◽  
Learning Approaches ◽  
Ensemble Classifiers ◽  
Churn Prediction ◽  
Content Type ◽  
Customer Churn ◽  
Customer Churn Prediction

PurposeCustomer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.Design/methodology/approachIn this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.FindingsTo examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.Originality/valueIn this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.

scholarly journals Customer churn prediction in telecommunication industry using data certainty

2019 ◽  
Vol 94 ◽  
pp. 290-301 ◽  
Author(s):  
Adnan Amin ◽  
Feras Al-Obeidat ◽  
Babar Shah ◽  
Awais Adnan ◽  
Jonathan Loo ◽  
...  
Keyword(s):  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction ◽  
Using Data ◽  
Data Certainty

Customer Churn Prediction with Feature Embedded Convolutional Neural Network: An Empirical Study in the Internet Funds Industry

2019 ◽  
Vol 18 (01) ◽  
pp. 1950003 ◽  
Author(s):  
Chongren Wang ◽  
Dongmei Han ◽  
Weiguo Fan ◽  
Qigang Liu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Demographic Data ◽  
Support Vector ◽  
The Internet ◽  
Churn Prediction ◽  
Operating Characteristics ◽  
Customer Churn ◽  
Static Data ◽  
Customer Churn Prediction

In this paper, we investigated the customer churn prediction problem in the Internet funds industry. We designed a novel feature embedded convolutional neural networks (FE-CNN) method that can automatically learn features from both the dynamic customer behavioral data and static customer demographic data and can utilize the advantage of convolutional neural networks to automatically learn features that capture the structured information. Our results show that our FE-CNN model outperforms the other traditional machine learning models with hand-crafted features, such as logistic regression (LR), support vector machines (SVM), random forests (RF) and neural networks (NN) in terms of accuracy, area under the receiver operating characteristics curve (AUC) and top-decile lift. Furthermore, we found that after adding the demographic data feature to the basic CNN model, the performance of the FE-CNN model improved. Overall, we found that the FE-CNN is the most powerful way to solve the problem of customer churn prediction in the Internet funds industry. Our FE-CNN method can also be applied to other fields that have both dynamic data and static data.

scholarly journals Customer Churn Prediction in Telecommunication Industry Using Data Certainty

2020 ◽  
pp. 252-258
Author(s):  
V R Reji Raj ◽  
Rasheed Ahammed Azad .V
Keyword(s):  
Machine Learning ◽  
Market Value ◽  
Machine Learning Algorithms ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
The Past ◽  
Customer Churn Prediction ◽  
Using Data ◽  
Data Certainty

Customer churn is a major problem affecting large companies, especially in telecommunication field. So the telecom industries have to take the necessary steps to retain their customers, to maintain their market value. So companies are seeking to develop methods that predict potential churned customers. We have to find out the factors that increase customer churn for making necessary actions to reduce churn. In the past, different data mining techniques have been used for predicting the churners. Here the most popular machine learning algorithms used for churn predicting are analysed. The conclusions are stated with the help of suitable tables.

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