Text-mining 10-K (annual) reports: A guide for B2B marketing research

Advances in the availability and sophistication of software to facilitate the analysis of secondary data have contributed toward the growth of textual analysis. 10-K reports are a particularly salient source of insight into an array of issues in accounting and finance research yet remain underutilized in marketing. Therefore, the purpose of this article is to offer a rationale for such analysis and a method that can be applied in B2B marketing. We draw on a strong tradition of textual analysis in finance to outline a method of text mining 10-K reports. We then discuss the downloading of raw texts, parsing raw text files and linking 10-Ks to various dependent measures. We provide links for downloading parsed 10-K files and suggest software for textual analysis. The framework offers B2B marketers a rich alternative to primary data and proprietary datasets. Ongoing advances in AI-enabled NLP text analysis further increase the future value of the approach for B2B marketers. • We present a finance-inspired prescriptive method for 10-K text mining. • 10-K reports are particularly useful for exploring and understanding B2B marketing challenges. • We provide a blueprint for marketing researchers to access the rich insights offered by these secondary data sources. • We detail the downloading of raw text, parsing text files and linking textual analysis to various dependent measures. • We also reference three empirical examples of our approach.