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Applying Market Basket Analysis Algorithms to Marketing Campaigns
Hilde PatronLaureano GomezMichael J. Sinkey
其他書名
An Analysis of the US Auto-repair Industry
出版SAGE Publications Limited, 2023
ISBN15296303639781529630367
URLhttp://books.google.com.hk/books?id=Wu_bzwEACAAJ&hl=&source=gbs_api
註釋This case study reflects on how to apply market basket analysis (MBA) algorithms (like the apriori algorithm) to marketing campaigns. MBA is the process of sorting through transactional data to identify bundles of items or services that customers often purchase together. The outcome of MBA is a series of association rules of the form "if A then B". A common example of such a rule is "if (eggs and bread) then (milk)". This rule says that customers who buy eggs and bread together frequently buy milk as well. MBA revolves around three parameters: support, confidence, and lift. Support measures the frequency with which items in the rule show up in the data; confidence refers to the probability with which items in set B are bought together with items in set A; finally, lift refers to the correlation between the sets A and B. In this case study, students will learn what MBA is in more detail, how to select key starting parameters, and how to suggest marketing strategies based on real data. The study is based on the article "An MBA of the US auto-repair industry," which was published in the Journal of Business Analytics in 2020. While we apply the methodology to auto-repair data, MBA can be applied to grocery store, educational, healthcare, transportation, tourism, and human resource data, among many other disciplines.