Guide: Design and run an agent-powered A/B test of your AI voices on real calls

Voice application developers often overlook testing the voice itself, which is crucial as it significantly impacts user behavior, as demonstrated by a controlled study conducted by Miravoice. This study, which tested 12 voices across 100,000 calls, found that voice selection alone could reduce the intro hang-up rate by 2.5 percentage points and increase call completion rates, highlighting the importance of choosing the right voice for applications. The study underscores the necessity of using survival-conditioned metrics instead of raw rates to accurately measure the effect of voice on call outcomes, as initial hang-ups often occur before the voice has an impact. Additionally, it is crucial to avoid common pitfalls in experimental design, such as testing multiple variables simultaneously or deciding success criteria post-analysis. Properly designed experiments should control all variables except the one being tested and use metrics that reflect genuine caller engagement. The guide also provides a detailed methodology and prompts to assist developers in setting up their own rigorous tests, ensuring the chosen voice truly enhances user interaction. Rime, one of the study's participants, emphasizes the importance of the testing process over individual results, advocating for a systematic approach to voice selection that aligns with specific application goals.

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