Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications

Francisco Villarroel Ordenes and Rosaria Silipo

Journal of Business Research, 2021, vol. 137, issue C, 393-410

Abstract: Machine learning (ML) promises great value for marketing related applications. However, the proliferation of data types, methods, tools, and programing languages hampers knowledge integration amongst marketing analytics teams, making collaboration difficult. Visual-based programing might help by facilitating the orchestration of ML projects in a more intuitive visual fashion. This article introduces the KNIME Analytics Platform, a leading visual-based programing software, and its potential for implementing ML projects in Marketing. We contribute to the ML literature in marketing by creating a live repository of projects, hosted on the KNIME hub, where users can learn, share and reuse workflows (visual code). We developed a starting set of five annotated projects in the areas of customer churn, sentiment analysis, automated image analysis, search engine optimization, and customer experience. For two of these projects, we offer a step-by-step guide to facilitate learning by marketing practitioners, academics, and machine learning enthusiasts.

Keywords: Machine learning; Computer vision; Deep learning; Text mining; Sentiment analysis; Customer experience (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:137:y:2021:i:c:p:393-410

DOI: 10.1016/j.jbusres.2021.08.036

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