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Frontiers: Supporting Content Marketing with Natural Language Generation

Author

Listed:
  • Martin Reisenbichler

    (Department of Marketing, Vienna University of Economics and Business, Vienna A-1020, Austria)

  • Thomas Reutterer

    (Department of Marketing, Vienna University of Economics and Business, Vienna A-1020, Austria)

  • David A. Schweidel

    (Goizueta Business School, Marketing Area, Emory University, Atlanta, Georgia 30322)

  • Daniel Dan

    (School of Applied Data Science, Modul University, Vienna, Vienna A-1190, Austria)

Abstract

Advances in natural language generation (NLG) have facilitated technologies such as digital voice assistants and chatbots. In this research, we demonstrate how NLG can support content marketing by using it to draft content for the landing page of a website in search engine optimization (SEO). Traditional SEO projects rely on hand-crafted content that is both time consuming and costly to produce. To address the costs associated with producing SEO content, we propose a semiautomated methodology using state-of-the-art NLG and demonstrate that the content-writing machine can create unique, human-like SEO content. As part of our research, we demonstrate that although the machine-generated content is designed to perform well in search engines, the role of the human editor remains essential. Comparing the resulting content with human refinement to traditional human-written SEO texts, we find that the revised, machine-generated texts are virtually indistinguishable from those created by SEO experts along a number of human perceptual dimensions. We conduct field experiments in two industries to demonstrate our approach and show that the resulting SEO content outperforms that created by human writers (including SEO experts) in search engine rankings. Additionally, we illustrate how our approach can substantially reduce the production costs associated with content marketing, increasing their return on investment.

Suggested Citation

  • Martin Reisenbichler & Thomas Reutterer & David A. Schweidel & Daniel Dan, 2022. "Frontiers: Supporting Content Marketing with Natural Language Generation," Marketing Science, INFORMS, vol. 41(3), pages 441-452, May.
    • Handle: RePEc:inm:ormksc:v:41:y:2022:i:3:p:441-452
    DOI: 10.1287/mksc.2022.1354
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    References listed on IDEAS

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    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2016. "Search Engine Optimization: What Drives Organic Traffic to Retail Sites?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 25(1), pages 6-31, March.
    3. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    4. Carnevale, Marina & Luna, David & Lerman, Dawn, 2017. "Brand linguistics: A theory-driven framework for the study of language in branding," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 572-591.
    5. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    6. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2019. "Modeling Consumer Footprints on Search Engines: An Interplay with Social Media," Management Science, INFORMS, vol. 65(3), pages 1363-1385, March.
    7. Ron Berman & Zsolt Katona, 2013. "The Role of Search Engine Optimization in Search Marketing," Marketing Science, INFORMS, vol. 32(4), pages 644-651, July.
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    Cited by:

    1. Peres, Renana & Schreier, Martin & Schweidel, David & Sorescu, Alina, 2023. "On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 269-275.
    2. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.

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