EXPRESS: Amplifying Insights: Harnessing Voice Technology to Decode Customer Uncertainty in Marketing

Verbal responses are widely collected in marketing through voice surveys and phone calls. Recent advances in voice technology enable firms to go beyond textual content and extract information from voice, such as speaker identity and sentiment. However, little is known about whether voice can be used to infer response uncertainty, a key construct for understanding and reducing bias in customer feedback. Existing marketing measures of uncertainty were developed largely for selected-response surveys and face limitations in cognitive simplicity, predictive robustness, and applicability. We examine two methods that infer uncertainty as expressed in vocal delivery: one based on raw audio data, and the other based on a voice feature (i.e., pitch). We evaluate both methods in a laboratory net promoter score (NPS) study and a field NPS study conducted with a full-service music store chain; the voice feature approach outperforms the raw audio approach in marketing applications while preserving privacy. The collaborating firm suggested two managerial benefits of inferring response uncertainty: improving incentive program effectiveness by targeting customers hypothesized to be more likely to redeem coupons, and identifying brand ambassadors who provide persuasive testimonials. These firm-driven insights inform an actionable framework for using voice-inferred uncertainty in marketing practice.

Authors

Publication Details

Journal
Journal of Marketing
Published
2026-09-06
DOI
https://doi.org/10.1177/00222429261484107
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EXPRESS: Amplifying Insights: Harnessing Voice Technology to Decode Customer Uncertainty in Marketing

Jehoshua Eliashberg, Tongyao Shen, Min Ding, Hye-Jin Kim
Journal of Marketing
AI in Service Interactions
article

EXPRESS: Amplifying Insights: Harnessing Voice Technology to Decode Customer Uncertainty in Marketing

Jehoshua Eliashberg, Tongyao Shen, Min Ding, Hye-Jin Kim
article en

Abstract

Verbal responses are widely collected in marketing through voice surveys and phone calls. Recent advances in voice technology enable firms to go beyond textual content and extract information from voice, such as speaker identity and sentiment. However, little is known about whether voice can be used to infer response uncertainty, a key construct for understanding and reducing bias in customer feedback. Existing marketing measures of uncertainty were developed largely for selected-response surveys and face limitations in cognitive simplicity, predictive robustness, and applicability. We examine two methods that infer uncertainty as expressed in vocal delivery: one based on raw audio data, and the other based on a voice feature (i.e., pitch). We evaluate both methods in a laboratory net promoter score (NPS) study and a field NPS study conducted with a full-service music store chain; the voice feature approach outperforms the raw audio approach in marketing applications while preserving privacy. The collaborating firm suggested two managerial benefits of inferring response uncertainty: improving incentive program effectiveness by targeting customers hypothesized to be more likely to redeem coupons, and identifying brand ambassadors who provide persuasive testimonials. These firm-driven insights inform an actionable framework for using voice-inferred uncertainty in marketing practice.

Journal of Marketing
Openalex Percentile: Top 8%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.