ANALYSIS OF CUSTOMER CHURN PREDICTION IN TELECOM INDUSTRY USING LOGISTIC REGRESSION

Customer Relationship Management (CRM) is a challenging issue in marketing to better understand the customers and maintaining long-term relationships with them to increase the profitability. It plays a vital role in customer centered marketing domain which provides a better service and satisfies the customer requirements based on their characteristics in consuming patterns and smoothes the relationship where various representatives communicate and collaborate. Customer Churn prediction is one of the area in CRM that explores the transaction and communication process and analyze the customer loyalty. Data mining ease this process with classification techniques to explore pattern from large datasets. It provides a good technical support to analyze large amounts of complex customer data. This research paper applies data mining classification technique to predict churn customers in three variant sectors Banking, Ecommerce and Telecom. For Classification, enhanced logistic regression with regularization and optimization technique is applied. The work is implemented in Rapid miner tool and the performance of the prediction algorithm is assessed for three variant sectors with suitable evaluation metrics.

Download Full-text

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

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8392.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 475-482

Keyword(s):

Data Mining ◽

Comparative Analysis ◽

Random Forest ◽

Prediction Model ◽

Classification Accuracy ◽

Churn Prediction ◽

Customer Churn ◽

Customer Churn Prediction ◽

Telecom Industry ◽

Fierce Competition

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.

Download Full-text

Intelligent Optimal Deep Learning based Customer Churn Prediction Model in Telecom Industry

10.54216/ajbor.040202 ◽

2021 ◽

pp. 57-64

Author(s):

Taif Khalid Shakir ◽

◽

Ahmed N. Al Masri

Keyword(s):

Service Sector ◽

Short Term Memory ◽

Simulation Analysis ◽

Churn Prediction ◽

Short Term ◽

Customer Churn ◽

Long Short Term Memory ◽

Customer Churn Prediction ◽

Telecom Industry ◽

Evaluation Parameters

Customer churn prediction (CCP) is a crucial problem in telecom industry which helps to improve the revenue of the company and prevent the loss of customers. Customer churn is an important issue in service sector with highly competitive services. At the same time, the prediction of users who are probably leaving the company can be identified at an earlier stage to prevent loss of revenue. Several works have used machine learning (ML) techniques for predicting the existence of customer churn in different industries. With this motivation, this paper presents an optimal long, short-term memory with stacked autoencoder (OLSTM-SAE) technique for CCP in telecom industry. The OLSTM-SAE technique encompasses three subprocesses namely preprocessing, classification, and parameter optimization. The OLSTM-SAE technique classifies the preprocessed data into churn and non-churn customers. In addition, the grey wolf optimization (GWO) technique is used to adjust the variables involved in the LSTM-SAE model. For examining the enhanced performance of the OLSTM-SAE technique, an extensive simulation analysis takes place, and the outcomes are inspected with respect to various measures. The experimental results highlighted the betterment of the OLSTM-SAE technique in terms of different evaluation parameters.

Download Full-text