The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Logistic Regression Using the SAS System: Theory and Application
Logistic Regression Using the SAS System: Theory and Application
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Exploiting structural information for semi-structured document categorization
Information Processing and Management: an International Journal
Prediction in Marketing Using the Support Vector Machine
Marketing Science
Expert Systems with Applications: An International Journal
The evaluation of consumer loans using support vector machines
Expert Systems with Applications: An International Journal
An application of support vector machines for customer churn analysis: credit card case
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Understanding protein structure prediction using SVM_DT
ISPA'05 Proceedings of the 2005 international conference on Parallel and Distributed Processing and Applications
Home photo categorization based on photographic region templates
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Customer churn prediction using improved one-class support vector machine
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Face authentication using one-class support vector machines
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Recognition of Western style musical genres using machine learning techniques
Expert Systems with Applications: An International Journal
Detecting stock-price manipulation in an emerging market: The case of Turkey
Expert Systems with Applications: An International Journal
Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Customer churn prediction by hybrid neural networks
Expert Systems with Applications: An International Journal
Customer Churn Prediction for Broadband Internet Services
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Application of data mining to the spatial heterogeneity of foreclosed mortgages
Expert Systems with Applications: An International Journal
Variable selection by association rules for customer churn prediction of multimedia on demand
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Monitoring and backtesting churn models
Expert Systems with Applications: An International Journal
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Expert Systems with Applications: An International Journal
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Using SVM based method for equipment fault detection in a thermal power plant
Computers in Industry
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A data mining framework for detecting subscription fraud in telecommunication
Engineering Applications of Artificial Intelligence
Using PCA to predict customer churn in telecommunication dataset
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
Tuning metaheuristics: A data mining based approach for particle swarm optimization
Expert Systems with Applications: An International Journal
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
Data augmentation by predicting spending pleasure using commercially available external data
Journal of Intelligent Information Systems
Customer churn prediction in telecommunications
Expert Systems with Applications: An International Journal
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Predicting customer churn through interpersonal influence
Knowledge-Based Systems
A new neural network based customer profiling methodology for churn prediction
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
Expert Systems with Applications: An International Journal
Churn prediction in new users of Yahoo! answers
Proceedings of the 21st international conference companion on World Wide Web
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Probabilistic Approaches For Credit Screening And Bankruptcy Prediction
International Journal of Intelligent Systems in Accounting and Finance Management
Credit card churn forecasting by logistic regression and decision tree
Expert Systems with Applications: An International Journal
Uniformly subsampled ensemble (USE) for churn management: Theory and implementation
Expert Systems with Applications: An International Journal
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Churn management optimization with controllable marketing variables and associated management costs
Expert Systems with Applications: An International Journal
International Journal of Information Retrieval Research
Kernel Factory: An ensemble of kernel machines
Expert Systems with Applications: An International Journal
Hi-index | 12.09 |
CRM gains increasing importance due to intensive competition and saturated markets. With the purpose of retaining customers, academics as well as practitioners find it crucial to build a churn prediction model that is as accurate as possible. This study applies support vector machines in a newspaper subscription context in order to construct a churn model with a higher predictive performance. Moreover, a comparison is made between two parameter-selection techniques, needed to implement support vector machines. Both techniques are based on grid search and cross-validation. Afterwards, the predictive performance of both kinds of support vector machine models is benchmarked to logistic regression and random forests. Our study shows that support vector machines show good generalization performance when applied to noisy marketing data. Nevertheless, the parameter optimization procedure plays an important role in the predictive performance. We show that only when the optimal parameter-selection procedure is applied, support vector machines outperform traditional logistic regression, whereas random forests outperform both kinds of support vector machines. As a substantive contribution, an overview of the most important churn drivers is given. Unlike ample research, monetary value and frequency do not play an important role in explaining churn in this subscription-services application. Even though most important churn predictors belong to the category of variables describing the subscription, the influence of several client/company-interaction variables cannot be neglected.