How to Scale Customer Interview Processes Using ElevenLabs Agents

Traditional customer interviews were difficult to scale due to operational limitations such as scheduling coordination and multilingual support. Surveys are easy to scale, but they have the drawback of missing the nuance and emotion in responses.

ElevenLabs solved this problem by building an AI interviewer using ElevenLabs Agents. It chose Gemini 2.5 Flash as the logic engine to secure low latency and high intelligence, and used the friendly-toned 'Hope' voice to have it act as an empathetic researcher.

The main process was as follows:

• Prompting and Guardrails: Designed the AI to ask follow-up questions on ambiguous answers to draw out in-depth insights.
• Data Collection: Used the Analysis feature to convert conversation content into structured data, automatically tracking answers to key questions.

The results were very encouraging. Within 24 hours, they conducted a total of over 36 hours of conversations with more than 230 users, achieving a success rate of 85%. In terms of cost, it was also highly economical at approximately under $1 per 10 minutes ($0.09/minute).

The collected transcripts were analyzed using Claude Opus 4.5 to derive detailed insights such as user segments, navigation feedback, and regional price sensitivity. In addition, an interactive report was created with Claude for internal team sharing, allowing data points to be checked directly.