Providing an efficient Customers Churn Prediction Model based on Improvised K-Means Clustering And Non Linear Support Vector Machine (original) (raw)
Related papers
Improved Customer Churn Behaviour by Using SVM
Churn Prediction has been major research problem with the growth of market development as customers asset more valuable persons for growth of company. The proposed Hybrid approach is an integration of two techniques named random forest and Support Vector Machine (SVM) provides better and accurate results in the prediction of churn customers The proposed Hybrid approach is implemented in MATLAB with Statistics toolbox on the dataset of customers having 3333 instances and 21 attributes to evaluate the performance of proposed Hybrid approach. Various parameters are to be considering into experiment in enhancing the performance of Hybrid approach. The experiment results reveal good distinction of churn and loyal customers from the given dataset and provide more accurate and satisfactory results when the Hybrid approach is compared with various classifiers or algorithms.
IAES International Journal of Artificial Intelligence (IJ-AI)
Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has been discovered. A support vector machine is employed as the foundational learner, and a churn prediction model is constructed based on each analysis of variance. The separation of churn data revealed by experimental assessment is recommended for churn prediction analysis. Customer attrition is high, but an instantaneous support can ensure that customer needs are addressed and assess an em...
Applied Sciences
Nowadays, customer churn has been reflected as one of the main concerns in the processes of the telecom sector, as it affects the revenue directly. Telecom companies are looking to design novel methods to identify the potential customer to churn. Hence, it requires suitable systems to overcome the growing churn challenge. Recently, integrating different clustering and classification models to develop hybrid learners (ensembles) has gained wide acceptance. Ensembles are getting better approval in the domain of big data since they have supposedly achieved excellent predictions as compared to single classifiers. Therefore, in this study, we propose a customer churn prediction (CCP) based on ensemble system fully incorporating clustering and classification learning techniques. The proposed churn prediction model uses an ensemble of clustering and classification algorithms to improve CCP model performance. Initially, few clustering algorithms such as k-means, k-medoids, and Random are em...
Customer Churn Analysis in Banking Sector
2020
The role of ICT in the banking sector is a crucial part of the development of nations. The development of the banking sector mostly depends on its valuable customers. So, customer churn analysis is needed to determine customers whether they are at risk of leaving or worth retaining. From organizational point of view, gaining new customers is usually more difficult or more expensive than retaining existing customers. So, customer churn prediction has been popular in the banking industry. By reducing customer churn or attrition, the commercial banks gain not only more profits but also enhancing core competitiveness among the competitors. Although many researchers proposed many single prediction models and some hybrid model, but accuracy is still weak and computation time of some algorithms is still increased. In this research, churn prediction model of classifying bank customer is built by using the hybrid model of k-means and Support Vector Machine data mining methods on bank custome...
Churn Prediction in the Telecommunications Sector Using Support Vector Machines
ANNALS OF THE ORADEA UNIVERSITY. Fascicle of Management and Technological Engineering., 2013
In these days, due to challenges resulted from global competition, customer churn represents one of the significant concerns for companies in different industries. With a churn rate of 30%, the telecommunication sector takes the first place on the list. In order to solve this problem, predictive models need to be implemented to identify customers who are at risk of churning. In this paper, an advanced methodology for predicting customers churn in mobile telecommunications industry is presented. The dataset used, contains call details records and has 21 attributes for each of its 3333 records. We use a Support Vector Machines algorithm with four kernel functions to implement the predictive models. The performance of the models is evaluated and compared using gain measure.
A Hybrid Two-Level Support Vector Machine-Based Method for Churn Analysis
2021 5th International Conference on Cloud and Big Data Computing (ICCBDC), 2021
Customer churn is a central problem in almost every sector. Due to the diversity of the customers, products and services, and a massive amount of data being generated as a result of e-commerce tools and services, (big) data analytics and artificial intelligence-based methods have been developed and used for churn analysis in order to develop a strategy that is expected to understand the reasons behind the customer churn and subsequently to develop an effective and profitable customer retention programme. The analysis based on the data analytics and artificial intelligence methods focuses more on the profiling of customers, the classification of customer churn and identification of features that affect the churn. However, there doesn't seem many studies that would be able to help understand how much a potential customer is likely to (or not likely to) pay for the products or services when churned or not, and to predict how much a particular customer or group of customers may have paid for the products or services. Therefore, in this study, a two-level churn analysis is proposed to (1) classify the customer churn or not, and (1) predict how much the customer has paid for the service. In order to achieve it, a machine learning method, namely support vector machine (SVM), was used for the classification part whereas a monthly service charge was predicted by using support vector regression (SVR) method. In order to select the most appropriate feature subset for both analyses, an unsupervised feature selection method, namely the multi-cluster feature selection method was utilized. The same feature selection method was used for both analyses for the sake of consistency to understand its performance over both analyses. The proposed hybrid approach was then applied in IBM's Telcom data set with over 7000 customers in order to demonstrate the applicability and generalization ability of the proposed two-level approach. The SVMbased classification method has yielded AUC 85.6 and total classification accuracy of 81.5% being higher than those of a recent study where an aggressive set of the supervised classification methods was performed. The SVR-based prediction of the monthly charge has resulted in RMSE of 1.27, which is a reasonably acceptable outcome in the sector given the diversity of the ranges of charges as evidenced in its standard deviation. The approach presented in the study demonstrates that both the churn classification and charge prediction can be performed at the same time with a higher degree of accuracy. As the approach is open for further improvement, future analysis will be carried out to improve the accuracy for both analyses over other data sets to demonstrate its robustness and generalization ability.
Customer Churn Prediction Using Machine Learning Algorithms
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Customer churn is a serious problem in the telecommunications industry and occurs more often. Customer churn is the percentage of customers that stopped using your company's product or service during a certain time frame. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms will be use in this paper are Decision Tree, Support Vector Machine, Random Forest. Also, the performance of support vector were better than other algorithms.
Journal of Physics: Conference Series
This research was intended to know factors that influenced the churn rate significantly in telecommunication company through research of historical billing and profiling data of customers. This study consisted of seven variables which are four billing historical variables and three profiling variables. The data was taken from a telecommunication company in Indonesia by taking active customer with minimum 6 months and historical data form January until March 2018. The data were tested using Support Vector Machine method by taking the result of classification performance either on performance of total variable or performance in each variable. The significance threshold was defined 5%. The results show that there were three variables that influenced the churn rate significantly namely voice usage, data usage and reload with performance percentage less than 5% of total performance. Those three variables were historical billing data.
An Optimal Churn Prediction Model using Support Vector Machine with Adaboost
Customer churn is a common measure of lost customers. By minimizing churn, a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer retention is important for a good marketing and a customer relationship management strategy. In this paper, a detailed scheme is worked out to convert raw customer data into meaningful and useful data that suits modelling buying behaviour and in turn to convert this meaningful data into knowledge for which predictive data mining techniques are adopted. In this work, a boosted version of SVM which is a combination of SVM with Adaboost is used for increasing the accuracy of generated rules. Boosted versions have high accuracy and performance than non-boosted versions. The aim of churn prediction model is to detect the customers with high tendency to leave the firm and also increase the revenue for the firm.
Retail Customer Churn Analysis using RFM Model and K-Means Clustering
2021
In this current world of business, Customer Churn is one of the major concerns for various business owners or the organizations for maintaining existing and attracting new customers. Analysis of various types of customers can be conducted by researching customer relationship management which in turn provides strong support for business decisions. Customer churn occurs when certain customers are no longer loyal or a part of a particular business. Losing customers will not only result in losses but also develop threat to the organization. Because of multiple competitors in the same business, the re-engagement of customers who are less interested is essential rather than engaging a new one. It is observed that acquiring new buyers is costlier than retaining the present customer. Churn prediction is a new promising method in customer relationship management to analyze customer behavior by identifying customers with a high probability to discontinue the company based on analyzing their p...