Frontiers: Supporting Content Marketing with Natural Language Generation

Martin Reisenbichler (), Thomas Reutterer (), David A. Schweidel () and Daniel Dan ()
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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

Marketing Science, 2022, vol. 41, issue 3, 441-452

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.

Keywords: SEO; content marketing; natural language generation; transfer learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (9)

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