Journal ArticleDOI
Review: Application of data mining techniques in customer relationship management: A literature review and classification
TLDR
Findings of this paper indicate that the research area of customer retention received most research attention and classification and association models are the two commonly used models for data mining in CRM.Abstract:
Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.read more
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How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study
Samuel Fosso Wamba,Shahriar Akter,Andrew Edwards,Andrew Edwards,Geoffrey Chopin,Denis Gnanzou +5 more
TL;DR: An interpretive framework is presented that analyzes the definitional perspectives and the applications of big data, and a general taxonomy is provided that helps broaden the understanding ofbig data and its role in capturing business value.
Journal ArticleDOI
How 'big data' can make big impact: findings from a systematic review and a longitudinal case study
Samuel Fosso Wamba,Samuel Fosso Wamba,Shahriar Akter,Andrew Edwards,Geoffrey Chopin,Denis Gnanzou +5 more
TL;DR: In this paper, the authors present an interpretive framework that analyzes the definitional perspectives and the applications of big data and provide a general taxonomy that helps broaden the understanding of Big Data and its role in capturing business value.
Journal ArticleDOI
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature
TL;DR: The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
Posted Content
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
TL;DR: This work proposes Seq2 SQL, a deep neural network for translating natural language questions to corresponding SQL queries, and releases WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets.
Journal ArticleDOI
A literature review and classification of recommender systems research
TL;DR: This research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender system research.
References
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Journal ArticleDOI
Managerial Applications of Neural Networks: The Case of Bank Failure Predictions
Kar Yan Tam,Melody Y. Kiang +1 more
TL;DR: Empirical results show that neural nets is a promising method of evaluating bank conditions in terms of predictive accuracy, adaptability, and robustness.
Book
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
TL;DR: Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Book
Decision Support and Business Intelligence Systems
TL;DR: Decision Support and Business Intelligence Systems 9e provides the only comprehensive, up-to-date guide to today's revolutionary management support system technologies, and showcases how they can be used for better decision-making.
Customer Relationship Management: Emerging Practice, Process, and Discipline
Atul Parvatiyar,Jagdish N. Sheth +1 more
TL;DR: In this paper, the authors explore the conceptual foundations of CRM by examining the literature on relationship marketing and other disciplines that contribute to the knowledge of customer relationship management, and propose a CRM process framework that builds on other relationship development process models.
Book
Building Data Mining Applications for CRM
TL;DR: This one-stop guide to choosing the right tools and technologies for a state-of-the-art data management strategy built on a Customer Relationship Management (CRM) framework helps you understand the principles of data warehousing and data mining systems and carefully spell out techniques for applying them so that your business gets the biggest pay-off possible.