Knowledge management and data mining for marketing

Shaw, Michael J.; Subramaniam, Chandrasekar; Tan, Gek Woo; Welge, Michael

doi:10.1016/s0167-9236(00)00123-8

Cited by 431 publications

(221 citation statements)

References 10 publications

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“…The cases are ordered by the offset (X-axis), where cases with the same optimal alternative are shaded by the same color (or gray scale). The emerging role of data visualization has been identified as a separate and important data mining task [19]. Our spectrum falls into the class of pixel-oriented techniques.…”

Section: Methodsmentioning

confidence: 99%

Explaining clinical decisions by extracting regularity patterns

Bielza

Pozo

Lucas

2008

Decision Support Systems

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When solving clinical decision-making problems with modern graphical decision-theoretic models such as influence diagrams, we obtain decision tables with optimal decision alternatives describing the best course of action for a given patient or group of patients. For real-life clinical problems, these tables are often extremely large. This is an obstacle to understand their content. KBM2L lists are structures that minimize memory storage requirements for these tables, and, at the same time, improve their knowledge organization. The resulting improved knowledge organization can be interpreted as explanations of the decision-table content. In this paper, we explore the use of KBM2L lists in analyzing and explaining optimal treatment selection in patients with non-Hodgkin lymphoma of the stomach using an expert-designed influence diagram as an experimental vehicle. The selection of the appropriate treatment for non-Hodgkin lymphoma of the stomach is, as for many other types of cancer, difficult, mainly because of the uncertainties involved in the decision-making process. In this paper we look at an expert-designed clinical influence diagram as a representation of a body of clinical knowledge. This diagram can be analyzed and explained using KBM2L lists. It is shown that the resulting lists provide high-level explanations of optimal treatments for the disease. These explanations are useful for finding relationships between groups of variables and treatments. It is demonstrated that these lists can act as a basis for gaining a deeper understanding of the underlying clinical problem.

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Section: Methodsmentioning

confidence: 99%

Explaining clinical decisions by extracting regularity patterns

Bielza

Pozo

Lucas

2008

Decision Support Systems

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“…2 Organizations have realized that customer knowledge is a key element for supporting the various organizational decisions. 3 On the other hand, the intense competition and increased choices available for customers have created new pressures for marketing decision-makers, and a need has emerged to manage customers in a long-term relationship. Customer relationship management (CRM) requires that the sport organizations tailor their services and interact with their customers (for example, event sponsor or advertiser) based on actual customer preferences and needs.…”

Section: Original Articlementioning

confidence: 99%

“…This makes the study of the knowledge extraction and management particularly valuable for marketing. 3 I address this issue in this article by presenting a framework for knowledge discovery, in the context of sport marketing decisions. Shank 4 defi nes sports marketing as ' the specifi c application of marketing principles and processes to sport services and to the marketing on non-sport services through association with sport ' .…”

Section: Original Articlementioning

confidence: 99%

“…Most organizations have built up databases about their customers and their purchase transactions, 3 but, owing to lack of appropriate tools and techniques to analyze these databases, a wealth of customer information and buying patterns is permanently hidden and unutilized in such databases. Knowledgebased marketing, which uses appropriate tools, addresses this need and helps leverage required knowledge in databases.…”

Section: Marketing and Event Sponsorsmentioning

confidence: 99%

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Marketing DSS architecture for sports sponsorship management

Noori

2012

J Database Mark Cust Strategy Manag

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As sports marketing is an important business activity for sport organizations , precise marketing actions must be developed frequently. This study considers the business-to-business side of sports marketing. Marketing managers should consider features of each specifi c corporation for development of appropriate marketing actions. This article suggests a decision support system for generating a proposal and marketing mix for each corporation as sponsor or advertiser based on a case-based reasoning approach. Past marketing plans, customer information and past proposals are valuable documents containing marketing knowledge and experiences. Marketing managers develop and execute marketing plans and proposals. Therefore, understanding how to systematically keep the past marketing and sales histories in an information system, provide a mechanism to effi ciently retrieve the most suitable plans or proposals, and facilitate adaptation is very necessary and benefi cial to sports marketing managers.

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“…Relevant predictive features typically include historical transactions from the customer with the company, demographic information of the customer and so on. Data mining techniques, and more specifically, classification algorithms, are then deployed to generalize the relationship that exists between a customer's characteristics, and his or her probability to churn [3]. Once built, these models can be used to predict the future behavior of customers and to deliver targeting information for churn-preventing marketing campaigns.…”

Section: Introductionmentioning

confidence: 99%

Ensembles of Probability Estimation Trees for Customer Churn Prediction

Bock

Poel

2010

Trends in Applied Intelligent Systems

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Customer churn prediction is one of the most important elements of any Customer Relationship Management (CRM) strategy. In this study, a number of strategies are investigated to increase the lift of ensemble classification models. In order to increase lift performance, two elements of a number of well-known ensemble strategies are altered: (i) the potential of using probability estimation trees (PETs) instead of standard decision trees as base classifiers is investigated and (ii) a number of alternative fusion rules for the combination of ensemble member's outputs are proposed and compared. Experiments are conducted for four popular ensemble strategies (Bagging, the Random Subspace Method, SubBag and AdaBoost) on five real-life churn data sets. The results demonstrate the value of using PETs over standard decision trees in order to increase lift. Overall, the effect of the proposed strategies heavily depends on the chosen ensemble algorithm in which they are implemented.

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Knowledge management and data mining for marketing

Cited by 431 publications

References 10 publications

Explaining clinical decisions by extracting regularity patterns

Explaining clinical decisions by extracting regularity patterns

Marketing DSS architecture for sports sponsorship management

Ensembles of Probability Estimation Trees for Customer Churn Prediction

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