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Text into numbers: Can marketers benefit from unstructured data?

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

Document Type: Research Article

Publication date: June 1, 2016

More about this publication?
  • Applied Marketing Analytics is the major new professional journal publishing in-depth, peer-reviewed articles on all aspects of marketing analytics. Guided by an expert Editorial Board each quarterly 100-page issue - published both in print and online - features detailed, practical articles written by and for marketing analytics professionals on innovative thinking, strategies, techniques, software and applied research showing how major brands are collecting, interpreting and acting on marketing analytics, both around the world and across varied digital and non-digital marketing channels. Learn how to measure the effectiveness of your marketing initiatives more accurately, how this compares to your competitors, identify gaps in your marketing analytics program and what metrics that support sound marketing decision making - and add to the bottom line.
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