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Using Machine Learning to Extract Insights From Consumer Data

Using Machine Learning to Extract Insights From Consumer Data

Hannah H. Chang, Anirban Mukherjee
Copyright: © 2023 |Pages: 15
ISBN13: 9781799892205|ISBN10: 1799892204|EISBN13: 9781799892212
DOI: 10.4018/978-1-7998-9220-5.ch107
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MLA

Chang, Hannah H., and Anirban Mukherjee. "Using Machine Learning to Extract Insights From Consumer Data." Encyclopedia of Data Science and Machine Learning, edited by John Wang, IGI Global, 2023, pp. 1779-1793. https://doi.org/10.4018/978-1-7998-9220-5.ch107

APA

Chang, H. H. & Mukherjee, A. (2023). Using Machine Learning to Extract Insights From Consumer Data. In J. Wang (Ed.), Encyclopedia of Data Science and Machine Learning (pp. 1779-1793). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch107

Chicago

Chang, Hannah H., and Anirban Mukherjee. "Using Machine Learning to Extract Insights From Consumer Data." In Encyclopedia of Data Science and Machine Learning, edited by John Wang, 1779-1793. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-7998-9220-5.ch107

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Abstract

Advances in digital technology have led to the digitization of everyday activities of billions of people around the world, generating vast amounts of data on human behavior. A parallel trend has been the emergence of computational methods and analysis techniques needed to deal with these new sources of behavioral data—which tend to be more unstructured, of much larger scale, and noisier. As they are recent and emerging developments, many behavioral scientists and practitioners may be unaware or unfamiliar with these recent developments and opportunities to extract behavioral insights. The main objective of this article is to discuss machine learning methods for researchers and practitioners interested in addressing customer-relevant questions using new secondary data sources that are publicly available, such as data from crowdfunding, video streaming, crowdsourcing, and social media platforms. This article offers a primer on the application of computational social science for understanding consumer data for researchers and practitioners.

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