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Brand Voiceprint

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Abstract

Firms endeavor to differentiate their products and brands by applying various elements to their marketing communication tools. One underutilized element is the human voice, which carries much information about the speaker (both static and dynamic) and may serve as the “auditory face” of a brand. In this study, we propose a concept that we have named the “voiceprint,” which identifies the “ideal” voice for promoting a product/brand. In our conceptual framework, we illustrate how different combinations of acoustic features in voices that represent the product evoke certain perceptions and images, which ultimately drive preferences toward the product/brand. To empirically demonstrate that consumers are indeed affected by voices, we conducted a laboratory study wherein subjects evaluated different actors’ voices in radio advertisements for various product categories. The data were analyzed using voice feature extraction methods and by applying a latent class multivariate model. The results showed that different voices indeed have a significant effect on people’s preferences. In addition, heterogeneity in different consumer segments and product categories was found regarding important voice features that drove preferences. Managerially, our findings provide guidance for marketers regarding how to effectively select the right voice for their marketing communications.

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Notes

  1. We also considered the speech rate, which considers pauses in the calculation of speaking speed, but it had a very high correlation with articulation rate; thus, we ended up only considering the articulation rate.

  2. Strictly speaking, pitch represents perceived frequency, like loudness represents perceived signal intensity.

  3. Although the concept of timbre is complex and is determined by a combination of temporal and spectral aspects, for this research, we only consider the spectral centroid.

  4. A similar term used for pitch is jitter, which is defined as the cycle-to-cycle variation of fundamental frequency, i.e., the average absolute fundamental frequency difference between consecutive periods. We did not consider jitter in the analysis because it is highly correlated with shimmer, and previous research shows that people are usually not able to distinguish between these two [44].

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Author information

Author notes

  1. Hye-jin Kim and Yi Wang are equally contributed on this work.

Authors and Affiliations

  1. College of Business, Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Hye-jin Kim

  2. Business School, Central University of Finance and Economics, Beijing, China

    Yi Wang

  3. Smeal College of Business, Pennsylvania State University, University Park, PA, 16802-3007, USA

    Min Ding

Authors
  1. Hye-jin Kim
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  2. Yi Wang
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  3. Min Ding
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Correspondence to Yi Wang.

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Kim, Hj., Wang, Y. & Ding, M. Brand Voiceprint. Cust. Need. and Solut. 8, 123–136 (2021). https://doi.org/10.1007/s40547-021-00120-1

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