Supporting Content Marketing with Natural Language Generation
(A2021-93711)

AUTHORS

Martin Reisenbichler, Vienna University of Economics and Business; Thomas Reutterer, WU Vienna University of Economics and Business; David Schweidel, Emory University Goizueta Business School; Daniel Dan, Modul University Vienna

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 in search engine optimization (SEO). Traditional SEO projects rely on hand-crafted content that is time consuming and costly to produce. To address the costs associated with producing SEO content, we propose a semi-automated methodology using state-of-the-art NLG and demonstrate that the “content writing machine” can create unique, human-like SEO content. Comparing the resulting content with human refinement to traditional human-written SEO texts, we find that the revised, machine-made texts are indistinguishable from those created by SEO experts. We conduct field experiments in two industries and show that the resulting SEO content outperforms that created by human writers (including real SEO experts) in search engine rankings and website engagement, and substantially reduces production costs.