Customer Churn Prediction in Telecom Sector: A Survey and way a head

© 2021 International Journal of Scientific & Technology Research. This work is licensed under a Creative Commons Attribution 4.0 International License.The telecommunication (telecom)industry is a highly technological domain has rapidly developed over the previous decades as a result of the commercial success in mobile communication and the internet. Due to the strong competition in the telecom industry market, companies use a business strategy to better understand their customers’ needs and measure their satisfaction. This helps telecom companies to improve their retention power and reduces the probability to churn. Knowing the reasons behind customer churn and the use of Machine Learning (ML) approaches for analyzing customers' information can be of great value for churn management. This paper aims to study the importance of Customer Churn Prediction (CCP) and recent research in the field of CCP. Challenges and open issues that need further research and development to CCP in the telecom sector are exploredPeer reviewe

Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression

Pada sebuah perusahaan, karyawan merupakan aset yang berharga dan dapat menunjang kesuksesan perusahaan tersebut. Namun, hilangnya tenaga kerja dapat merugikan perusahaan. Kondisi ini disebut dengan Employee Churn. Salah satu solusi untuk mengatasi Employee Churn adalah dengan menerapkan model Uplift Modeling. Dalam penelitian ini, penulis menganalisa penerapan Logistic Regression terhadap Uplift Modeling dalam permasalahan Employee Churn. Data yang diteliti adalah data karyawan dari IBM HR Analytics. Hasil prediksi pada penelitian ini mendapat akurasi sebesar 64,40%, sedangkan hasil preskripsi menghasilkan hasil yang cukup baik apabila menerapkan waktu kerja tambahan pada karyawan. Berdasarkan hasil yang didapat, diketahui bahwa para karyawan justru cenderung bertahan di perusahaan apabila diberikan waktu kerja tambahan

Predicción de rotación de clientes en la industria de las telecomunicaciones utilizando métodos de minería de datos

At present, in competitive space between companies and organizations, customers churn is their most important challenge. When a customer becomes churn, organizations lose one of their most important assets, which can lead to financial losses and even bankruptcy.  Customer churn prediction using data mining techniques can alleviate these problems to some extent.  The aim of the present study is to provide a hybrid method based on Genetic Algorithm and Modular Neural Network to customer churn prediction in telecommunication industries and use Irancell data as a sample. The accuracy result of this study which is 95.5% get the highest accuracy rank in comparisons with the result of other methods, which shows using modular neural network with two modules of feedforward neural network and also using genetic algorithm to obtain optimal structure for modules of the neural network are the most important indicators of this method to each the highest accuracy result among the rest of methods.At present, in competitive space between companies and organizations, customers churn is their most important challenge. When a customer becomes churn, organizations lose one of their most important assets, which can lead to financial losses and even bankruptcy.  Customer churn prediction using data mining techniques can alleviate these problems to some extent.  The aim of the present study is to provide a hybrid method based on Genetic Algorithm and Modular Neural Network to customer churn prediction in telecommunication industries and use Irancell data as a sample. The accuracy result of this study which is 95.5% get the highest accuracy rank in comparisons with the result of other methods, which shows using modular neural network with two modules of feedforward neural network and also using genetic algorithm to obtain optimal structure for modules of the neural network are the most important indicators of this method to each the highest accuracy result among the rest of methods

Implementation of Data Mining for Churn Prediction in Music Streaming Company Using 2020 Dataset

Customer is an important asset in a company as it is the lifeline of a company. For a company to get a new customer, it will cost a lot of money for campaigns. On the other hand, maintaining old customer tend to be cheaper than acquiring a new one. Because of that, it is important to be able to prevent the loss of customers from the products we have. Therefore, customer churn prediction is important in retaining customers. This paper discusses data mining techniques using XGBoost, Deep Neural Network, and Logistic Regression to compare the performance generated using data from a company that develops a song streaming application. The company suffers from the churn rate of the customer. Uninstall rate of the customers reaching 90% compared to the customer’s installs. The data will come from Google Analytics, a service from Google that will track the customer’s activity in the music streaming application. After finding out the method that will give the highest accuracy on the churn prediction, the attribute of data that most influence on the churn prediction will be determined

Advanced Algorithm for Prediction of Churn in Various Industries in the Fast Growing World

In the study it has been proven that the cost of acquisition of a new customer is much more then thecost of the retention of the existing customer. Also, it becomes very easy for the organizations if they come to know of the customers that are likely to get churn in advance studying the behavioral aspects so that they can take appropriate measures to keep the customer in their own territory. So, there has been a lot of study on the existing algorithms to understand which can provide the better accuracy in terms of the prediction analysis of their customers. This can be very useful for the service providers to in order to maintain trust and loyaltytowards their customers and in good will against their competitors

Prediction model of algal blooms using logistic regression and confusion matrix

Algal blooms data are collected and refined as experimental data for algal blooms prediction. Refined algal blooms dataset is analyzed by logistic regression analysis, and statistical tests and regularization are performed to find the marine environmental factors affecting algal blooms. The predicted value of algal bloom is obtained through logistic regression analysis using marine environment factors affecting algal blooms. The actual values and the predicted values of algal blooms dataset are applied to the confusion matrix. By improving the decision boundary of the existing logistic regression, and accuracy, sensitivity and precision for algal blooms prediction are improved. In this paper, the algal blooms prediction model is established by the ensemble method using logistic regression and confusion matrix. Algal blooms prediction is improved, and this is verified through big data analysis

Teknik Weighting untuk Mengatasi Ketidakseimbangan Kelas Pada Prediksi Churn Menggunakan XGBoost, LightGBM, dan CatBoost

Churn merupakan kondisi dimana seseorang berpindah dari satu layanan ke layanan yang lain. Churn pelanggan menjadi masalah yang meningkat cukup signifikan dan menjadi tantangan utama yang harus dihadapi banyak perusahaan perbankan karena memiki peran penting terhadap laba perusahaan.  Oleh sebab itu, diperlukan cara untuk memprediksi perilaku churn tepat waktu agar bisa menerapkan retensi pelanggan. Namun, Permasalahan yang dihadapi oleh model prediksi churn adalah ketidakseimbangan kelas sehingga membuat model klasifikasi menghasilkan kinerja yang buruk. Solusi yang paling sering digunakan untuk mengatasi masalah ketidakseimbangan kelas terbagi menjadi tiga pendekatan yaitu pendekatan level data, level algoritma dan  ensemble. Setiap pendekatan  mengalami beberapa masalah yang sulit diprediksi ketika digunakan untuk menangani masalah ketidakseimbangan kelas. Pada penelitian ini, peneliti melakukan eksperimen menggunakan metode ensemble berbasis boosting untuk melakukan prediksi churn pelanggan dan mencoba meningkatkan kinerjanya pada dataset yang tidak seimbang dengan parameter tuning menggunakan scale pos weight. Algoritma klasifikasi yang digunakan yaitu XGBoost (extreme gradient boosting), LightGBM (light gradient boosting machine) dan CatBoost. Hasil eksperimen akan membandingkan kinerja dari ketiga algoritma berbasis boosting tersebut dengan menyesuaikan bobot parameternya sebanyak tiga kali. Dari hasil pengujian, model CatBoost memperoleh nilai recall tertinggi sebesar 0.79. Sedangkan untuk nilai recall terendah adalah model CatBoost default dengan nilai 0.47. Bedasarkan hasil ekperimen dapat disimpulan bahwa model bekerja dengan cukup baik pada data yang tidak seimbang dengan memberikan mekanisme hyperparameter scale pos weightsehingga model dapat lebih fokus pada kelas minoritas yang sulit dideteksi. 

A hybrid naïve Bayes based on similarity measure to optimize the mixed-data classification

In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method

FTA: a novel feature training approach for classification

conference pape

OTT SUBSCRIBER CHURN PREDICTION USING MACHINE LEARNING

Subscriber churn is a critical issue for companies that rely on recurring revenue from subscription-based services like the OTT platform. Machine Learning algorithms can be used to predict churn and develop targeted retention strategies to address the specific needs and concerns of at-risk subscribers. The research questions are 1) What Machine Learning algorithms are used to overcome subscriber churn? 2) How to predict subscribers’ churn in the OTT platform using Machine Learning? 3) How to retain subscribers and improve customer targeting? The dataset was collected from the Kaggle repository and implemented it into the various prediction algorithms used in previous research. Then, evaluate the performance of each algorithm to find out the highest accuracy model. The findings and conclusion for each question are 1) Logistic regression, multi-layer perceptron, random forest, decision trees, and gradient boosting machines were identified as effective algorithms for churn prediction analysis. 2) By sending the test data to a trained model by their historical dataset, customers are likely to leave a company (i.e., churn) based on their characteristics can be predicted. 3) Personalized offers and promotions, improving customer service, developing loyalty programs, and optimizing pricing strategies were suggested strategies for retaining subscribers. The gradient boosting machine model was found to have the highest accuracy and maximum AUROC, making it a powerful tool in the fight against customer churn. Areas for further study include incorporating unstructured data sources, deep learning techniques, and integrating real-time data sources to improve the accuracy and effectiveness of churn prediction models