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An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing

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  • Venkatesh Shankar

    (Mays Business School)

  • Sohil Parsana

    (Mays Business School
    Oracle)

Abstract

With artificial intelligence permeating conversations and marketing interactions through digital technologies and media, machine learning models, in particular, natural language processing (NLP) models, have surged in popularity for analyzing unstructured data in marketing. Yet, we do not fully understand which NLP models are appropriate for which marketing applications and what insights can be best derived from them. We review different NLP models and their applications in marketing. We layout the advantages and disadvantages of these models and highlight the conditions under which different models are appropriate in the marketing context. We introduce the latest neural autoencoder NLP models, demonstrate these models to analyze new product announcements and news articles, and provide an empirical comparison of the different autoencoder models along with the statistical NLP models. We discuss the insights from the comparison and offer guidelines for researchers. We outline future extensions of NLP models in marketing.

Suggested Citation

  • Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
    • Handle: RePEc:spr:joamsc:v:50:y:2022:i:6:d:10.1007_s11747-022-00840-3
    DOI: 10.1007/s11747-022-00840-3
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    Cited by:

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    2. U. M. Fernandes Dimlo & V. Rupesh & Yeligeti Raju, 2024. "The dynamics of natural language processing and text mining under emerging artificial intelligence techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4512-4526, September.
    3. Schauerte, Nico & Becker, Maren & Imschloss, Monika & Wichmann, Julian R.K. & Reinartz, Werner J., 2023. "The managerial relevance of marketing science: Properties and genesis," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 801-822.
    4. Weibin Lin & Xianli Wu & Zhengwei Wang & Xiaoji Wan & Hailin Li, 2022. "Topic Network Analysis Based on Co-Occurrence Time Series Clustering," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
    5. Jonah Berger & Grant Packard & Reihane Boghrati & Ming Hsu & Ashlee Humphreys & Andrea Luangrath & Sarah Moore & Gideon Nave & Christopher Olivola & Matthew Rocklage, 2022. "Marketing insights from text analysis," Marketing Letters, Springer, vol. 33(3), pages 365-377, September.
    6. Christopher Gerling & Stefan Lessmann, 2024. "Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis," Papers 2411.14463, arXiv.org.
    7. Alin-Gabriel Vaduva & Simona-Vasilica Oprea & Dragos-Catalin Barbu, 2023. "Understanding Customers' Opinion using Web Scraping and Natural Language Processing," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 537-544, August.

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