Telecom Customer Churn Prediction Method Based on Cluster Stratified Sampling Logistic Regression

2014 ◽  
Author(s):  
Peng Li ◽  
Tingting Bi ◽  
Yang Liu ◽  
Siben Li
Keyword(s):  
Logistic Regression ◽  
Prediction Method ◽  
Stratified Sampling ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals Customer Churn Prediction using Logistic Regression with Regularization and Optimization Technique

2020 ◽  
Vol 9 (9) ◽  
pp. 334-339
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Optimization Technique ◽  
Customer Relationship ◽  
Communication Process ◽  
Prediction Algorithm ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Churn Prediction

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.

scholarly journals Deep Learning for Customer Churn Prediction in E-Commerce Decision Support

2021 ◽  
pp. 3-12
Author(s):  
Maciej Pondel ◽  
Maciej Wuczyński ◽  
Wiesława Gryncewicz ◽  
Łukasz Łysik ◽  
Marcin Hernes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Business Performance ◽  
Prediction Method ◽  
Correct Prediction ◽  
Retail Sector ◽  
Learning Tools ◽  
Churn Prediction ◽  
Customer Churn ◽  
History Of ◽  
Customer Churn Prediction

Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.

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