Leveraging audio data: A guide to understanding customer-firm conversations

Bitty Balducci (), Bin Pang, Lingshu Hu, Can Li, Wenbo Wang, Yi Shang, Detelina Marinova and Matt Gordon
Additional contact information
Bitty Balducci: Washington State University, Department of Marketing and International Business
Bin Pang: University of Missouri, Department of Computer Science
Lingshu Hu: Washington and Lee University, The Williams School of Commerce, Economics, and Politics
Can Li: University of Missouri, Department of Computer Science
Wenbo Wang: University of Missouri, Department of Computer Science
Yi Shang: University of Missouri, Department of Computer Science
Detelina Marinova: University of Missouri, Department of Marketing
Matt Gordon: University of Missouri, Department of English

Marketing Letters, 2026, vol. 37, issue 1, No 10, 16 pages

Abstract: Abstract Although technological advancements enable communication through diverse mediums, conversations involving back-and-forth dialogue remain a central way in which firms interact with customers. Yet technical barriers, high subscription costs, and data security concerns hinder the extraction of insights from customer-firm conversations. To address these challenges, we create a methodological framework and end-to-end automated program, AudioMatic, designed to process audio recordings of conversations between business agents and customers. This paper details AudioMatic’s workflow, provides access to the corresponding code, and demonstrates its effectiveness using 3,900 sales prospecting calls. A logistic regression-based machine learning model trained on salespersons’ verbal and vocal features extracted via AudioMatic accurately predicts conversation outcomes with 88.08% accuracy. Follow-up analyses further reveal that salespersons’ vocal features account for 39.2% of the model’s predictive value. Overall, AudioMatic enhances the accessibility of audio analysis, highlighting both the practical and theoretical potential of integrating conversational audio data in research and practice.

Keywords: Customer-firm conversations; Audio data; Automated data processing; AudioMatic; LLMs; Feature extraction (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11002-025-09797-z Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:mktlet:v:37:y:2026:i:1:d:10.1007_s11002-025-09797-z

Ordering information: This journal article can be ordered from
http://www.springer. ... etailsPage=societies

DOI: 10.1007/s11002-025-09797-z

Access Statistics for this article

Marketing Letters is currently edited by Joel Steckel and Peter Golder

More articles in Marketing Letters from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().