Language, whether spoken or written, is fundamental to the consumer experience. It is how consumers express their thoughts, articulate choices, negotiate with others, and receive information about products or services. And it is how marketers deliver persuasion attempts, make apologies, and build relationships with consumers.
Language has also long been a powerful research tool. Scholars have used content analysis methods like ethnography in interviews, observational studies, and interpretation of language-based artifacts to advance our understanding of consumer culture (e.g., Arnould & Thompson, 2005; Stern, 1989). Research on the psychology of language has studied how people respond to a range of semantic, syntactic, and rhetorical aspects of verbal communication (Kronrod, 2022).
But something new has emerged in the last decade or so—something that has helped foster a genuine explosion in the analysis of text in consumer research (Packard & Berger, 2023). First, language data has become more accessible. While consumers, companies, and other marketplace actors are constantly producing language, only recently has much of this content become digitized (or digitizable), making it far easier to collect and analyze. Every day, billions of consumers share attitudes and opinions online. Customer service calls, depth interviews, and Zoom meetings can be transcribed with the push of a button, and the shift from paper and pencil surveys to online data collection means open-ended participant responses are ready-made for automated text analysis. Similarly, massive online repositories of human conversations, product reviews, books, movie scripts, newspaper articles, and other cultural content provide easy ways to explore ideas in language.
Second, new tools have changed how language can be analyzed. Previously, language data could only be coded manually. Researchers, or research assistants, would read or listen to language and score it on various dimensions. While manual coding is helpful, it is often subjective and difficult to scale for both lab and field research. Manually reading and carefully evaluating just 10 conversations, online reviews, or thought listings takes a fair amount of time—and reading 1000 takes 100 times as long.
In recent years, though, psychologists and computer scientists have developed tools that allow language data to be processed and analyzed quickly and easily. Dictionaries like Linguistic Inquiry and Word Count (LIWC, Tausczik & Pennebaker, 2010) allow researchers to count the presence of different words linked to psychological constructs and approaches like latent Dirichlet allocation (Blei et al., 2003). Word embeddings (cf. Bakarov, 2018) and large language model approaches (e.g., BERT, GPT) make it possible to measure almost any construct. And these tools are becoming more user-friendly every day.