EXPRESS: Vocal Similarity, Timbre, and Persuasion in Consumer-Spokesperson Interactions
研究利用机器学习分析消费者与代言人的声音音色相似性，发现声音越相似，消费者越容易被说服，这一效应在缺乏其他可信信号时更强。
Consumers are more easily persuaded by people who are similar to them in looks, behavior, and beliefs. Does similarity’s effect on persuasion extend to similarity in how people sound? We explore how similarity in vocal timbre influences consumer choice. Using machine learning, we generate an objective measure of vocal similarity between an individual consumer and a spokesperson using mel-frequency cepstral coefficients (MFCCs) to capture vocal timbre. First, using data from 7,002 entrepreneur-investor combinations in Shark Tank, we demonstrate the effect of vocal similarity on persuasion in investment pitches. Then, in 2,091 Kickstarter campaigns, we show that a spokesperson’s voice closer to a large audience’s average voice results in higher persuasion, measured by fundraised amount and campaign success – a result driven by vocal similarity. Moreover, these effects are attenuated when external signals of campaign credibility are present. Finally, in four laboratory studies, we show that vocal similarity with a spokesperson or recommender leads to greater trust in their competence and positively influences persuasion. We also show that objective and subjective voice similarity have similar results, with objective similarity mapping on to subjective similarity. We provide a deeper understanding of consumer-spokesperson interactions, including new tools for vocal analytics.