Customer Profiling and Retention Using Recommendation System and Factor Identification to Predict Customer Churn in Telecom Industry

2021 ◽  
pp. 155-172
Author(s):  
Nooria Karimi ◽  
Adyasha Dash ◽  
Siddharth Swarup Rautaray ◽  
Manjusha Pandey
Keyword(s):  
Recommendation System ◽  
Customer Churn ◽  
Customer Profiling ◽  
Telecom Industry

An Electronic Commerce Recommender System Based on Product Character

2011 ◽  
Vol 267 ◽  
pp. 909-912 ◽  
Author(s):  
Shen Bao Chen
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Competitive Environment ◽  
Referral Process ◽  
Personalized Service ◽  
Filtering Algorithm ◽  
Customer Churn ◽  
Collaborative Filtering Algorithm

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

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

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.

scholarly journals Data Mining and Data Analytics for Analysing Customer Churn Rate

2020 ◽  
Vol 4 (4) ◽  
Author(s):  
Sharan Kumar Paratala Rajagopal
Keyword(s):  
Data Mining ◽  
Data Analytics ◽  
Research Paper ◽  
Customer Churn ◽  
Telecom Industry ◽  
Using Data

This research paper describes how to determine the various factors impacting the customers churn rate in telecom industry. And what factors impact customer to move from one telecom source to another. Using data analytics and data mining will analyze the factors for churn rate.

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

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.

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