A market basket analysis of the US auto-repair industry. Issue 2 (2nd July 2020)

• Record Type: Journal Article
• Authors: Patron, Hilde; Gomez, Laureano
• Is Part Of: Journal of Business Analytics. Volume 3:Issue 2(2020)
• Page Start: 79
• Page End: 92
• Publication Date: 2020-07-02
• Subjects: Apriori algorithm -- automobile repair data -- market basket analysis; Business intelligence -- Periodicals; Management -- Statistical methods -- Periodicals; Decision making -- Statistical methods -- Periodicals; 658.403
• DOI: 10.1080/2573234X.2020.1838958
• Languages: English
• ISSNs: 2573-234X

Abstract

ABSTRACT: Market basket analysis (MBA), or the mining of transactional data to uncover association rules, is a popular methodology used in managerial decision making. MBA is centered around three key parameters: support, confidence, and lift, and the choice of starting values for these parameters can have a significant impact on the results of the analysis. We develop a procedure in R around the Apriori algorithm to help in identifying lift maximising rules when the support covers a specified proportion. The procedure facilitates the choice of minimum parameters, eliminates redundancies, and organizes the resulting association rules into actionable formats. When applied to the US auto repair data, we find un-exploited bundling packages that can be added to the scheduled maintenance services of traditional marketing campaigns.