A complete and comprehensive handbook for the application of data mining techniques in marketing and customer relationship management. It combines a technical and a business perspective, bridging the gap between data mining and its use in marketing.It guides readers through all the phases of the data mining process, presenting a solid data mining methodology, data mining best practices and recommendations for the use of the data mining results for effective marketing. It answers the crucial question of 'what data to use' by proposing mining data marts and full lists of KPIs for all major industries.Data mining algorithms are presented in a simple and comprehensive way for the business users along with real-world application examples from all major industries.The book is mainly addressed to marketers, business analysts and data mining practitioners who are looking for a how-to guide on data mining. It presents the authors' knowledge and experience from the "data mining trenches", revealing the secrets for data mining success.
Galal M, Hassan G and Aref M Developing a Personalized Multi-Dimensional Framework using Business Intelligence Techniques in Banking Proceedings of the 10th International Conference on Informatics and Systems, (21-27)
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Reviewer: Christoph F. Strnadl
Turning data into action is the aim of this insightful and practice-oriented book; it succeeds in an extraordinary way. It is common knowledge that enterprises constantly collect data on transactions (for example, through loyalty card programs) in order to better manage customers (from the early stages of building a relationship, to cross-selling and up-selling additional products, to the customer retention phase). This management discipline, commonly called customer relationship management (CRM), has gained much traction in the past decade through the introduction of CRM applications (supporting, for example, customer service or front-line processes). At the same time, enterprises collect all sorts of operational data, including data concerning customers and their interactions with the company, and feed it into large data repositories (data warehouses or data marts). Then, sufficiently sophisticated data mining algorithms analyze this data to extract insight and knowledge. This work superbly demonstrates how to use analytical data mining techniques to gain actionable results when analyzing a customer base. In this context, "actionable" means giving statistics-based hints as to what an enterprise should do with a specific category of customer-for instance, in terms of acquisition models, cross/deep/up-selling strategies, and identifying churn or attrition signals. Chapter 1 is a short introduction to CRM and data mining, and their synergies when used together. Chapter 2 gives an overview of data mining techniques, and introduces supervised and unsupervised modeling techniques. The former try to predict an event or estimate the value of a continuous attribute, whereas the latter are used for data reduction, clustering, and association detection. Chapter 3 presents in detail data mining techniques for segmentation, including principal component analysis (for data reduction) and three clustering algorithms ( k -means, TwoStep, and Kohonen networks). The authors explain how to examine and evaluate the quality of identified clusters (for example, using measures such as cohesion or separation); this is a very important stage in any real data mining project since sometimes quite costly decisions are based on the discovered clusters. The authors also explain three decision tree models: classification and regression trees, C5.0, and chi-squared automatic interaction detector (CHAID). Additionally, the authors provide a good discussion of the relative strengths and weaknesses of the individual methods. As a result, prospective users can be well aware of the tradeoffs before choosing one method over another. Chapter 4 covers the data preparation phase, which is typically the most critical and time-consuming phase of a data mining project. Based on the idea of building a so-called "mining data mart"-a central data repository for subsequent and ongoing data mining practices-the authors present the full data model, including field lists and definitions, for a retail bank, a mobile telephony operator, and a retailer data mart. This information is strung together by a really careful exposition of the underlying reasoning and the assumptions that led to the particular choices. Chapter 5 introduces readers to the realm of customer segmentation-the process of dividing customer into distinct, meaningful, and homogeneous groups based on certain properties. It covers behavioral and value-based segmentation in depth, and also gives overviews of propensity-based, loyalty, needs/attitudinal-based, and socio-demographic and life-stage segmentation. At least half of the book's praise is due to the elaborate way that it explains the theoretical methods in the form of three extensive case studies for retail banking (chapter 6), a mobile telephony operator (chapter 7), and a retail company (chapter 8). The authors not only show and thoroughly explain the segmentation strategy and its results, but also include actual data right down to the level of which data fields should be collected and used during the statistical analysis. A reader from any of these industries could literally use the book as a template for a first project. The book is written in a language that is easily accessible to business users who are not fluent in statistical methods and who have no prior exposure to the data mining or customer segmentation domain. In some places, the use of formulas could not be avoided in order to be exact (and I praise the authors for doing so); nevertheless, all of the formulas and their applications are thoroughly explained. Additionally, the book contains many graphics, figures, and screen shots from a real data mining application (SPSS). The methods and examples focus on consumer markets (as opposed to business-to-business markets) and the analysis of internal data. This book is poised to become a standard reference, and I unconditionally recommend it to anyone working in this field. Online Computing Reviews Service
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As an important data mining and knowledge discovery task, association rule mining searches for implicit, previously unknown, and potentially useful pieces of information—in the form of rules revealing associative relationships—that are embedded in the .
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Web Services and Web-based applications are growing at an exponential rate. This is generating a huge amount of Web data having its own peculiar characteristics. This in turn makes research in the area of Web Data Mining more challenging. Web Data .
Data mining, or knowledge discovery in databases, has been popularly recognized as an important research issue with broad applications. We provide a comprehensive survey, in database perspective, on the data mining techniques developed recently. Several .
