Mining marketing maps for business alliances

Authors:
Shu-Hsien Liao;Wen-Jung Chang;Chai-Chen Lee
Affiliations:
Department of Management Science and Decision Making, Tamkang University, No. 151, Yingjung Rd, Dansui Jen, Taipei 251, Taiwan, ROC;Graduate School of Management Science, Tamkang University, No. 151, Yingjung Rd, Dansui Jen, Taipei 251, Taiwan, ROC;Graduate School of Management Science, Tamkang University, No. 151, Yingjung Rd, Dansui Jen, Taipei 251, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

A business can strengthen its competitive advantage and increase its market share by forming a strategic alliance. With the help of alliances, businesses can bring to bear significant resources beyond the capabilities of the individual co-operating firms. Thus how to effectively evaluate and select alliance partners is an important task for businesses because a successful corporation partner selection can therefore reduce the possible risk and avoid failure results on business alliance. This paper proposes the Apriori algorithm as a methodology of association rules for data mining, which is implemented for mining marketing map knowledge from customers. Knowledge extraction from marketing maps is illustrated as knowledge patterns and rules in order to propose suggestions for business alliances and possible co-operation solutions. Finally, this study suggests that integration of different research factors, variables, theories, and methods for investigating this research topic of business alliance could improve research results and scope.