A soft-computing-based method for the automatic discovery of fuzzy rules in databases:uses for academic research and management support in marketing

The study here highlights the potential that analytical methods based on Knowledge Discovery in Databases (KDD) methodologies have to aid both the resolution of unstructured marketing/business problems and the process of scholarly knowledge discovery. The authors present and discuss the application of KDD in these situations prior to the presentation of an analytical method based on fuzzy logic and evolutionary algorithms, developed to analyze marketing databases and uncover relationships among variables. A detailed implementation on a pre-existing data set illustrates the method. © 2012 Published by Elsevier Inc.

Strategic marketing and e-business

The dramatic growth in e-business is manifest in phenomena such as the surge in internet retailing, the boom in social media based marketing communications, and the centrality of e-commerce to many organizations’ core strategies. Despite this the precise implications of e-business for marketing strategy remain little-understood. In order to guide theory development and practice in the marketing strategy domain, it is of fundamental importance to take stock of the impact that e-business has had upon strategic marketing. Therefore, this chapter develops a conceptual framework in order to explicate the implications of e-business for strategic marketing theory and practice. We find that the impact of e-business on strategic marketing is far-reaching; influencing not only isolated departments, but the organization as a whole. Finally, we conclude that whilst organizations should be alert to the dynamic opportunities and threats posed by e-business, the guiding principle of value creation should not be forgotten.

Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.