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Guides
The Crisis in Consumer Insights Research: How Bots, Fraud, and Failing Methodologies Are Poisoning Your Data
AI bots evade survey detection 99.8% of the time. Here's what this means for consumer research.
Scaling Moderated Research With Voice AI: What to Watch For
Voice AI promises to democratize deep customer research, but scaling introduces new challenges around quality, bias, and metho...
The traditional economics of moderated research create an impossible trade-off. Deep, probing customer conversations yield transformative insights, but conducting 100 interviews means coordinating 100 schedules, paying for 100 hours of moderator time, and waiting weeks for transcription and analysis. Most teams settle for 8-12 interviews and hope they've captured representative perspectives.
Voice AI changes this equation fundamentally. Platforms like User Intuition now conduct hundreds of adaptive interviews simultaneously, delivering analysis in 48-72 hours instead of 6-8 weeks. Early adopters report 93-96% cost savings compared to traditional approaches while maintaining qualitative depth. But this transformation introduces new considerations that research teams must navigate carefully.
The question isn't whether to adopt voice AI for scaled research—the efficiency gains make adoption inevitable for competitive teams. The question is how to scale responsibly while preserving the methodological rigor that makes qualitative research valuable in the first place.
The Quality Paradox of Scale
Traditional moderated research achieves quality through scarcity. A skilled moderator conducts perhaps 3-4 interviews per day, bringing full attention and adaptive thinking to each conversation. This scarcity forces careful participant selection and thoughtful protocol design. The bottleneck itself creates quality constraints.
Voice AI removes this bottleneck entirely. You can now conduct 500 interviews as easily as 50. This abundance creates a new challenge: maintaining quality when constraints disappear. Teams accustomed to treating each interview as precious must develop new quality frameworks for scaled research.
The first watch point is conversation depth. Not all voice AI platforms maintain the adaptive, probing nature of skilled moderation. Some follow rigid scripts, missing opportunities to explore unexpected responses. Others struggle with natural follow-up questions that dig beneath surface answers. Advanced voice AI uses conversational laddering—the
