Telecommunication subscribers' churn prediction model using machine learning

Qureshi, Saad Ahmed; Rehman, Ammar Saleem; Qamar, Ali Mustafa; Kamal, Aatif; Rehman, Ahsan

doi:10.1109/icdim.2013.6693977

Cited by 105 publications

(48 citation statements)

References 4 publications

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“…The use of re-sampling method in order to solve the problem of class imbalance has also been discussed. The results show that in case of the data set used, decision trees is the most accurate classifier algorithm on identifying potential churners (Qureshi 2013).…”

Section: Discussionmentioning

confidence: 98%

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Kayaalp

2017

KSEJ

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companies are provided by the payments made by these customers periodically. It is very important to be able to keep customers satisfied in order to be able to sustain this revenue with the least expenditure cost. The objectives of this study are: • Reviewing the relevant studies about churn analysis on telecommunications industry presented in the last five years, particularly in the last two years, and introducing these up-to-date studies in the literature, • Determining the data mining methods frequently used in churn implementations, • Shedding a light on methods that can be used in further studies.

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Section: Discussionmentioning

confidence: 98%

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Kayaalp

2017

KSEJ

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“…Moreover, usage of black-box models such as SVM and random forests were declared improper for churn prediction. In oppose to earlier work using decision trees, a research carried out in [23] using data of 106,000 customers with usage behavior of three months on well-known algorithms of Decision Trees Regression Analysis and Artificial Neural Networks (ANNs) to identify possible churners and found decision trees as most accurate classification algorithm for predicting customers churn in telecommunication. In order to identify most appropriate classifier, a research conducted in [24], consisted of three levels, which are classification techniques, oversampling and input selections.…”

Section: Related Workmentioning

confidence: 99%

A fuzzy based churn prediction and retention model for prepaid customers in telecom industry

Azeem¹,

Usman²

2018

IJCIS

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Accurate and timely identification of the potential churner, also known as churn prediction, is crucial to devise effective retention strategies. A number of churn prediction models have been proposed in the past, however, the existing models suffer from a number of limitations due to which these models are not applicable on real world large size telecom datasets. Firstly, the feature selection methods adopted in majority of the previous models neglected the information rich variables present in Call Details Records (CDRs). Secondly, the present models have been validated mainly with benchmark datasets which don't provide a true representation of a large scale real world data in telecom sector. Thirdly, there is very limited amount of work reported in literature that has extended the prediction models towards automatic and intelligent retention mechanisms. Moreover, categorization and severity of the predicted churners has not been focused in the past for targeted and intelligent retention campaigns. Motivated by the aforementioned limitations, we propose a novel churn prediction and retention model for achieving the aim of accurate identification and targeted retention of churners. Primarily, our contribution is the accurate identification of churners at different severity levels using fuzzy based classifiers. Secondarily, our model automatically generates intelligent retention campaigns by mining customer usage and complaints patterns. Experimental results on real world telecom data of a South Asian company revealed the supremacy of fuzzy classifiers in terms of classification by achieving 98% accuracy of churner class. Moreover, the proposed retention strategy based on churner severity and categorization managed to retain 87% of the potential churners.

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“…Due to the competition and the current scenario situation has become worse due to competition. Moreover,improvisation of 1 percent in CRR(Customer Retention Rate) could easily increase the share price by 5% [6]. Hence, in this research work we have proposed a model namely MRF(Modified Random forest) , our method provides several layer for improvising the accuracy and ignores the overfitting.…”

Section: Introductionmentioning

confidence: 99%

Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

P*,

R B

2020

IJITEE

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Customer Churn Prediction has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.

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Telecommunication subscribers' churn prediction model using machine learning

Cited by 105 publications

References 4 publications

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A fuzzy based churn prediction and retention model for prepaid customers in telecom industry

Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

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