 It takes two to tango: Statistical modeling and machine learningV. Kumar and Mani Vannan Journal of Global Scholars of Marketing Science, 2021, vol. 31, issue 3, 296-317 Abstract: Statistical methods (SM) have been dominant in generating insights from any type of data for generations. However, with the recent advances in technology, machine learning (ML) has become one of the widely spoken methods to generate insights with more ease of use. While the followers of statistical methods have a differing view point about ML, and the followers of ML have a differing viewpoint about SM, this article isolates the merits of each of these two methods and advances arguments for when to use what method based on the purpose, context, frequency of use, cost, expertise and time. To be specific, the main purpose of SM is for inference and that of ML is prediction. Further, this article goes one step further and creates a scenario where it shows that when we combine the learning from using a statistical method and apply it to machine learning, the ultimate benefit can be greater than the sum of each method’s benefits. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jgsmks:v:31:y:2021:i:3:p:296-317 Ordering information: This journal article can be ordered from DOI: 10.1080/21639159.2020.1808838 Access Statistics for this article Journal of Global Scholars of Marketing Science is currently edited by Seong-Yeon Park More articles in Journal of Global Scholars of Marketing Science from Taylor & Francis Journals |
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