Predictive analytics of Churn Customers Calling Details Records using Classification by Clustering (CBC) dealing with Supervised Machine Learning Algorithms

Telecom companies generate enormous amounts of data regularly. The telecom Decision makers that obtaining new customers is more challenging than sustaining existing ones. Furthermore, data from existing churn customers may be utilized to detect churn clients and their patterns of behavior. This research develops a model of churn prediction for the telecommunication business, which uses NB, SVM, DT, and RDF to detect churn clients. The proposed model churns customers' data using classification techniques, with the Random Forest (RDF) method performing well (95.94 % correctly categorized instances), the Decision Tree (DTs) providing classification accuracy (95.40 %), the Naïve Bayes (NB) provided classification accuracy (89.58 %), and the Support Vector Machine (SVMs) provided classification accuracy (71.08 %). The four different classification algorithms' predictions and observations are compared, with a percentage of 71 percent equality and 29 percent variation

Research trends in customer churn prediction: A data mining approach

This study aims to present a very recent literature review on customer churn prediction based on 40 relevant articles published between 2010 and June 2020. For searching the literature, the 40 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to six main dimensions: Reference; Areas of Research; Main Goal; Dataset; Techniques; outcomes. The research has proven that the most widely used data mining techniques are decision tree (DT), support vector machines (SVM) and Logistic Regression (LR). The process combined with the massive data accumulation in the telecom industry and the increasingly mature data mining technology motivates the development and application of customer churn model to predict the customer behavior. Therefore, the telecom company can effectively predict the churn of customers, and then avoid customer churn by taking measures such as reducing monthly fixed fees. The present literature review offers recent insights on customer churn prediction scientific literature, revealing research gaps, providing evidences on current trends and helping to understand how to develop accurate and efficient Marketing strategies. The most important finding is that artificial intelligence techniques are are obviously becoming more used in recent years for telecom customer churn prediction. Especially, artificial NN are outstandingly recognized as a competent prediction method. This is a relevant topic for journals related to other social sciences, such as Banking, and also telecom data make up an outstanding source for developing novel prediction modeling techniques. Thus, this study can lead to recommendations for future customer churn prediction improvement, in addition to providing an overview of current research trends.info:eu-repo/semantics/acceptedVersio

Features of using Cox regression in various instrumental environments

The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. The article presents a Cox regression model to solve the problem of customer churn. Cox regression is recognized as a model with high accuracy of predictions in healthcare. Therefore, it is interesting to use the model in other industries. The paper presents the results and comparative analysis of calculations on the Cox model using three tools: Statistical Package for the Social Sciences, programming language R and Russian software – analytical platform Loginom. A distinctive feature of the developed probabilistic model is the determination of the risk of event occurrence in conditions of incomplete data, as well as the identification of indicators that have a significant impact on the degree of its manifestation

Improving Accuracy and Performance of Customer Churn Prediction Using Feature Reduction Algorithms

Prediction of customer churn is one of the most essential activities in Customer Relationship Management (CRM). However, the state-of-the-art of the customer churn prediction approach only focuses on the classifier selection in improving the accuracy and performance of churn prediction, but rarely contemplate the feature reduction algorithms. Furthermore, there are numerous attributes that contribute to customer churn and it is crucial to determine the most substantial features in order to acquire the highest prediction accuracy and to improve the prediction performance. Feature reduction decreases the dimensionality of the information and may allow learning algorithms to function faster and more effectively and able to produce predictive models that deliver the highest rate of accuracy. In this research, we investigated and proposed two (2) different feature reduction algorithms which are Correlation based Feature Selection (CFS) and Information Gain (IG) and built classification models based on three 3) different classifiers, namely Bayes Net, Simple Logistic and Decision Table. Experimental results demonstrate that the performance of classifiers improves with the application of features reduction of the customer churn data set. A CFS feature reduction algorithm with the Decision Table classifier yields the highest accuracy of 92.08% and has the lowest RMSE of 0.2554. This study recommends the use of feature reduction algorithms in the context of CRM for churn prediction to improve accuracy and performance of customer churn prediction

PENINGKATAN AKURASI ALGORITMA BACKPROPAGATION DENGAN SELEKSI FITUR PARTICLE SWARM OPTIMIZATION DALAM PREDIKSI PELANGGAN TELEKOMUNIKASI YANG HILANG

Abstrak: Telekomunikasi adalah salah satu industri, di mana pelanggan memerlukan perhatian khusus, oleh  karena  itu,  manajemen  di  sebuah  perusahaan  telekomunikasi  ingin  kehilangan  pelanggan  model prediksi untuk efisien memprediksi berpotensi kehilangan pelanggan. Jaringan syaraf adalah metode yang sering digunakan untuk memprediksi. Teknik yang paling populer dalam metode adalah saraf algoritma jaringan backpropagation. Namun algoritma backpropagationmemiliki kelemahan pada kebutuhan untuk data  pelatihan  besar  dan  optimasi  yang  digunakan  kurang  efisien.  Particle  Swarm  Optimization (PSO) adalah  suatu  algoritma  optimasi  yang  dapat  memecahkan  yang  efektif  masalah  pada  algoritma  neural network umumnya  menggunakan  algoritma  backpropagation.  Pengujian  model  dengan  berbasis menggunakan  Backpropagation Particle Swarm Optimizationmenggunakan data pelanggan hilang pada telekomunikasi. Model yang dihasilkan diuji untuk memperoleh akurasi dan nilai-nilai AUC dari masingmasing  algoritma  untuk  mendapatkan  tes  menggunakan  nilai  yang  diperoleh  akurasi  Backpropagation adalah 85.48% dan nilai AUC adalah 0.531. Sementarapengujian dengan menggunakan Backpropagation berbasis  Particle  Swarm  Optimization dipilih  atribut  dan  penyesuaian  nilai  parameter  yang  diperoleh 86.05% akurasi dan nilai AUC adalah 0,637. Dengan demikian dapat disimpulkan bahwa data pelanggan uji  hilang  dalam  telekomunikasi  menggunakan  aplikasi  Particle  Swarm  Optimization  Backpropagation dan dalam pemilihan atribut  diperoleh bahwa  metode  ini  lebih akurat dalam prediksi pelanggan  hilang telekomunikasi dibandingkan dengan Backpropagation, ditandai dengan peningkatan akurasi 00:57% dan nilai-nilai AUC dari 0.106, dengan nilai yang dimasukkan ke dalam akurasi klasifikasi cukup.Kata  Kunci:  Telekomunikasi,  Neural  Network,  Backpropagation,  Particle  Swarm  Optimization

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