![Automated Text Analysis in Marketing [Innovation Spotlight]](https://www.ama.org/wp-content/uploads/2019/09/automated-text-analysis.jpg?resize=1360%2C550 "Automated Text Analysis in Marketing [Innovation Spotlight]")
Listen to the authors present their findings (source: October 2019 JM Webinar)
How does Siri get better at answering customer queries and predicting their needs? It applies artificial intelligence and machine learning to voice transcripts. This process, automated text analysis, pervades marketplace interactions. Online reviews, customer service calls, press releases, news articles, marketing communications, and other interactions create a wealth of textual data companies can analyze to optimize services and develop new products. By some estimates, 80-95% of all business data is unstructured, with most of that being text. This text has the potential to provide critical insights about its producers, including individuals’ identities, their relationships, and their goals. This text can be aggregated to create insights about organizations and social institutions and how attitudes vary over cultural contexts, demographics, groups, and time.
But how can marketers best use such data? A new article in the Journal of Marketing provides an overview of automated textual analysis and describes how it can be harnessed to generate marketing insights. Specifically, our team discusses how researchers and managers can use text to better understand the individuals and organizations who produce the text. The article also explores how the content of text affects various audiences. For example, how consumers may be influenced to change their behaviors or brands influenced to attend to issues raised by consumers depends in large part on the content of text.
Given the volume of text data available, automated text analysis methods are critical, but need to be handled carefully. Researchers should avoid over-fitting and weigh the importance of features to glean and use the right predictors from text. Thus, this article also provides an overview of the methodologies and metrics used in text analysis, providing a set of guidelines and procedures for marketing researchers and marketing scholars. Understanding these methods help us understand how text is used and processed. For example, virtual assistants are currently under scrutiny for the fact that humans are listening to the audio recordings. However, this process is necessary to train the machines used for automated text analysis.
The goal of this article is to further our collective understanding of text analysis and how it can be used for insights. Researchers and marketers can use this article to create frameworks, establish and communicate policies, and strengthen cross-functional collaboration with teams and across researchers in the field of marketing working on textual analytics projects.
Read the full article.
From: Jonah Berger, Ashlee Humphreys, Wendy Moe, Oded Netzer, and David Schweidel, “Uniting the Tribes: Using Text for Marketing Insights,” Journal of Marketing, 84 (January).
Read the authors’ slides for sharing this material in your classroom.
From: Jonah Berger, Ashlee Humphreys, Wendy Moe, Oded Netzer, and David Schweidel, “Uniting the Tribes: Using Text for Marketing Insights,” Journal of Marketing, 84 (January).
Go to the Journal of Marketing
Jonah Berger is Associate Professor of Marketing, University of Pennsylvania, USA.
Ashlee Humphreys is Associate Professor of Integrated Marketing Communications, Medill School of Journalism, Northwestern University.
Stephan Ludwig is Professor of Marketing, Monash University, Australia.
Wendy W. Moe is Dean’s Professor of Marketing, University of Maryland, USA.
Oded Netzer is Professor of Business, Columbia Business School, Columbia University.
David A. Schweidel is Rebecca Cheney McGreevy Endowed Chair and Professor of Marketing, Emory University, USA.