scholarly journals Prediction of Customer Churn on e-Retailing

2020 ◽  
Vol 8 (6) ◽  
pp. 5541-5545
Keyword(s):  
Customer Relationship ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Interaction ◽  
Huge Impact ◽  
Business To Consumer ◽  
Past Data ◽  
Customer Services ◽  
And Behavior

The technology has always been an instigating factor in progress for human civilization which resulted in driving the customer services to a greater need. The enrichment of technology has amplified and embellished the customer interaction among various business to consumer sectors. These technological upgrading have a huge impact on the retail industry which is an ever-growing market with key competitors around the world. In a consortium of multiple competitors in the same business, the re-engagement of disinterested customers is essential rather than winning a new customer. The sustenance of a customer can be figure out by Churn Prediction. Churn prediction is a new promising method in customer relationship management to analyze customer retention in subscription-based business. It is the activity of identifying customer with a high probability to discontinue the company based on analyzing their past data and behavior. It looks at what kind of customer data are typically used, do some analysis of the features chosen, and initiate a churn prediction model. Thus, churn prediction is a valuable approach in identifying and profiling the customers at risk.

scholarly journals One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Jin Xiao ◽  
Bing Zhu ◽  
Geer Teng ◽  
Changzheng He ◽  
Dunhu Liu
Keyword(s):  
Customer Value ◽  
Missing Values ◽  
Credit Scoring ◽  
Customer Relationship ◽  
Classifier Ensemble ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
One Step

Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

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 Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

Algorithms and the Social Organization of Work

2020 ◽  
pp. 803-822
Author(s):  
Ifeoma Ajunwa ◽  
Rachel Schlund
Keyword(s):  
Ethical Issues ◽  
Machine Learning Algorithms ◽  
Customer Relationships ◽  
Customer Relationship ◽  
Customer Data ◽  
Hiring Process ◽  
Customer Interaction ◽  
The Social ◽  
Automated Algorithms ◽  
A Company

This chapter argues that the proliferation of automated algorithms in the workplace raises questions as to how they might be used in service of the control of workers. In particular, scholars have noted machine learning algorithms as prompting a data-centric reorganization of the workplace and a quantification of the worker. The chapter then considers ethical issues implicated by three emergent algorithmic-driven work technologies: automated hiring platforms (AHPs), wearable workplace technologies, and customer relationship management (CRM). AHPs are “digital intermediaries that invite submission of data from one party through preset interfaces and structured protocols, process that data via proprietary algorithms, and deliver the sorted data to a second party.” The use of AHPs involves every stage of the hiring process, from the initial sourcing of candidates to the eventual selection of candidates from the applicant pool. Meanwhile, wearable workplace technologies exist in a variety of forms that vary in terms of design and use, from wristbands used to track employee location and productivity to exoskeletons used to assist employees performing strenuous labor. Finally, CRM is an approach to managing current and potential customer interaction and experience with a company using technology. CRM practices typically involve the use of customer data to develop customer insight to build customer relationships.

scholarly journals Fuzzy particle swarm optimization (FPSO) based feature selection and hybrid kernel distance based possibilistic fuzzy local information C-means (HKD-PFLICM) clustering for churn prediction in telecom industry

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
C. K. Praseeda ◽  
B. L. Shivakumar
Keyword(s):  
Customer Relationship Management ◽  
Relationship Management ◽  
Local Information ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Swarm Optimization ◽  
Customer Churn ◽  
Kernel Distance ◽  
Telecom Industry ◽  
Hybrid Kernel

Abstract Customer churn has been considered as one of the key issues in the operations of the corporate business sector, as it influences the turnover directly. In particular, the telecom industries are seeking to develop new approaches to predict potential customer to churn. So, it needs the appropriate algorithms to overcome the increasing problem of churn. This work proposed a churn prediction model that employs both strategies of classification and clustering, that helps in recognizing the churn consumers and giving the reasons after the churning of subscribers in the industry of telecom. The process of information gain and fuzzy particle swarm optimization (FPSO) has been executed by the method of feature selection, besides the divergence kernel-based support vector machine (DKSVM) classifier is employed in categorizing churn customers in the proposed approach. In this way, the compelling guidelines on retention have generated since the process plays a vital role in customer relationship management (CRM) to suppress the churners. After the classification process, the churn customers are divided into clusters through the process of fragmenting the data of churning customer. The cluster-based retention offers have provided by the clustering algorithm of hybrid kernel distance-based possibilistic fuzzy local information C-means (HKD-PFLICM), whereas the measurement of distance have accomplished through the kernel functions such as the hyperbolic tangent kernel and Gaussian kernel. The results reveal that proposed churn prediction model (FPSO- DKSVM) produced better churn classification results compared to other existing algorithms such as K-means, flexible K-Medoids, fuzzy local information C-means (FLICM), possibilistic  FLICM (PFLICM) and entropy weighting FLICM (EWFLICM). Article highlights Customer churn is a major concern in most of the companies as it influences the turnover directly. The performance of churn prediction has been improved by applying artificial intelligence and machine learning techniques. Churn prediction plays a crucial role in telecom industry, as they are in the position to maintain their precious customers and organize their Customer Relationship Management.

scholarly journals A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ming Zhao ◽  
Qingjun Zeng ◽  
Ming Chang ◽  
Qian Tong ◽  
Jiafu Su
Keyword(s):  
Prediction Model ◽  
Empirical Research ◽  
Customer Value ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Customer Churn ◽  
Data Mining Algorithms ◽  
Customer Churn Prediction ◽  
Telecom Industry ◽  
Logistic Regression Algorithm

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

Enhancing Customer Service Using Chatbot Application Through Artificial Intelligence

2020 ◽  
Vol 17 (4) ◽  
pp. 1633-1637
Author(s):  
M. Prabu ◽  
T. Sai Tarun ◽  
A. Shereef Naina Mohamed ◽  
A. Vijay
Keyword(s):  
Artificial Intelligence ◽  
Customer Service ◽  
Service Sector ◽  
Customer Relationship ◽  
Customer Interaction ◽  
Customer Services

In every service based or product based company customer services is considered to be an important sector to maintain customer relationship. This sector also consumes a lot of resources from the company both labor wise and money wise. In this sector the usage of resources are high due to the demand in the sector, A good company is defined how good is their customer service, Today most of the companies lack a good customer interaction, Hence to ease this process of customer services in this paper we propose to use A.I chatbot in the customer service sector. The result will be faster and more optimal customer service solutions.

scholarly journals Current Directions and Future Research Priorities of Customer Data Analysis

2020 ◽  
Vol 2 (2) ◽  
pp. 300-311
Author(s):  
Mohammed M Mohammed ◽  
Nagi A. Mohamed ◽  
Ali A. Adam ◽  
Shazali S. Ahmed ◽  
Fakhreldeen A. Saeed
Keyword(s):  
Research Priorities ◽  
Customer Segmentation ◽  
Future Research ◽  
Research Directions ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Analytics ◽  
Future Research Directions ◽  
And Behavior ◽  
Shed Light

Customer analysis is receiving special attention from both researchers and professionals. The objective of this paper is to identify the trends of techniques used to address customer’s current problems and shed light on future research directions using a literature review. We reviewed the literature for the last five years. The findings revealed that customer purchase was the most popular technique used by the research community followed by customer satisfaction and visit wit. Whereas customer segmentation and customer churn were the least. However, the regression method was commonly used for predicting customer purchase and behavior. But, social media and big data are still in their early stages for customer analytics research.

scholarly journals PREDIKSI TINGKAT PELANGGAN CHURN PADA PERUSAHAAN TELEKOMUNIKASI DENGAN ALGORITMA ADABOOST

2021 ◽  
Vol 21 (1) ◽  
pp. 34-43
Author(s):  
Iqbal Muhammad Latief ◽  
Agus Subekti ◽  
Windu Gata
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Secondary Data ◽  
Telecommunications Industry ◽  
Customer Relationships ◽  
Training Data ◽  
Churn Prediction ◽  
Customer Data ◽  
Adaboost Algorithm ◽  
Customer Churn

With the rapid advancement of the telecommunications industry, and competition between telecommunications companies is increasing, companies need to predict their customers to determine the level of customer loyalty. One of them is by analyzing customer data by doing a Customer Churn Prediction. Predicting Customer Churn is an important business strategy for the company. To acquire new customers is much higher cost than retaining existing customers. The ease of operator switching is one of the serious challenges that the telecommunications industry must face. By predicting customer churn, companies can take immediate action to retain customers. To retain existing customers, the company must improve customer service, improve product quality, and must know in advance which customers have the possibility to leave the company. Prediction can be done by analyzing customer data using data mining techniques. In line with this, gathering information from the telecommunications business can help predict whether customer relationships will leave the company. The data used in this study are secondary data and amount to 7.403 data customers. The data has 21 variables. This study proposes to use the ensemble method namely adaboost, xgboost and random forest and compare them. Algorithm is validated through training data and testing data with a ratio of 80:20. From the results we got using python tools, it was found that the adaboost algorithm has an accuracy of 80%.Keywords—accuracy, adaboost, churn prediction, compare model, data mining.

New Churn Prediction Strategies in the Telecom Industry

2009 ◽  
pp. 218-235 ◽  
Author(s):  
Dymitr Ruta ◽  
Christoph Adl ◽  
Detlef Nauck
Keyword(s):  
Information Content ◽  
Prediction Methods ◽  
Temporal Data ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Lifetime ◽  
Telecom Industry ◽  
Aggregation Technique ◽  
Different Levels

In the telecom industry, high installation and marketing costs make it six to 10 times more expensive to acquire a new customer than it is to retain an existing one. Prediction and prevention of customer churn is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is a lot of related temporal data generated as a result of customer interaction with the service provider. The major problem with this data is its time discontinuity resulting from the transactional character of events they describe. Moreover, such irregular temporal data sequences are typically a chaotic mixture of different data types, which further hinders its exploitation for any predictive task. Existing churn prediction methods like decision trees typically classify customers into churners and non-churners based on the static data collected in a snapshot of time while completely ignoring the timing of churn and hence the circumstances of this event. In this work, we propose new churn prediction strategies that are suitable for application at different levels of the information content available in customers’ data. Gradually enriching the data information content from the prior churn rate and lifetime expectancy then typical static events data up to decay-weighted data sequences, we propose a set of new churn prediction tools based on: customer lifetime modelling, hidden markov model (HMM) of customer events, and the most powerful k nearest sequence (kNS) algorithm that deliver robust churn predictions at different levels of data availability. Focussing further on kNS we demonstrate how the sequential techprocessing of appropriately pre-processed data streams lead to better performance of customer churn prediction. Given histories of other customers and the current customer data, the presented kNS uses an original combination of sequential nearest neighbour algorithm and original sequence aggregation technique to predict the whole remaining customer data sequence path up to the churn event. On the course of experimental trials, it is demonstrated that the new kNS model better exploits time-ordered customer data sequences and surpasses existing churn prediction methods in terms of performance and capabilities offered.

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