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Practice paper

Text into numbers: Can marketers benefit from unstructured data?

Barry P. Keating
Applied Marketing Analytics: The Peer-Reviewed Journal, 2 (2), 111-120 (2016)
https://doi.org/10.69554/HQBK5905

Abstract

Since much of the data marketers encounter is in the form of text, using predictive analytics techniques requires that the text be in some manner transformed into data that can be effectively used by standard data mining techniques. How exactly does this ‘transformation’ take place? Once transformed, how are the resulting data used in an analytics algorithm? This paper seeks to answer these two questions and to present an example of the process described. In addition, an important and common error that is often encountered in text mining is explained.

Keywords: text mining; target leakage; dimension reduction; natural language processing; k Nearest Neighbor

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Author's Biography

Barry P. Keating is a professor of business economics at the University of Notre Dame. He specialises in understanding how not-for-profit organisations function, more specifically, how they respond to incentives, changes in revenue and cost conditions, and changes in regulatory mechanisms. He frequently works with not-for-profits examining the role and value of volunteers and forecasting blood supplies for the United States. Barry has co-authored a book examining the economics of nonprofit firms and government units (Blackwell) and a book examining the use of cost benefit analysis in nonprofits (BEPress). He has done extensive work in the areas of business forecasting and regulation. Barry’s forecasting and predictive analytics textbook (in its 6th edition; McGraw-Hill) is the best-selling forecasting book for use in colleges and universities. He consults with both forecasting and data mining software producers and firms with forecasting problems (eg Cadbury, Microsoft, Southwest Airlines, Starbucks, Toyota, Wells Fargo, etc). Barry is a Heritage Foundation Fellow (1992–1996), a Heartland Institute Research Fellow, Former President of the Economic Club of Michiana, former Chair of the Department of Finance at Notre Dame, and serves on the Board of Advisors of both the Indiana Policy Review Group and the Institute of Business Forecasting. He has published more than 100 articles for professional journals and trade publications. Barry is the winner of a Kaneb Teaching Award from Notre Dame, the MBA Professor of the Year Award, and is a fellow of the Kaneb Center.

Citation

Keating, Barry P. (2016, June 1). Text into numbers: Can marketers benefit from unstructured data?. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 2, Issue 2. https://doi.org/10.69554/HQBK5905.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 2 / Issue 2
© Henry Stewart
Publications LLP

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