Qualitative Research

CATI

CATI

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Conveo automates video interviews to speed up decision-making.

Definition:

CATI, which stands for Computer-Assisted Telephone Interviewing, is a data collection methodology in which trained interviewers conduct structured or semi-structured interviews by telephone, guided by software that displays questions, enforces skip logic, and records responses simultaneously. Widely used in market research, public opinion polling, and consumer insights programs, CATI enables researchers to reach geographically dispersed respondents at scale while maintaining a degree of conversational depth that self-completion surveys cannot replicate. Within qualitative research, CATI has historically served as a bridge between fully structured surveys and open-ended exploratory interviews, allowing probing follow-up questions within a controlled framework. However, its reliance on telephone infrastructure, trained interviewer availability, and manual scheduling creates operational constraints that limit research speed and continuous deployment.

How Conveo Does It

Conveo moves beyond the operational limits of CATI by replacing telephone-based interviewing with AI-moderated video interviews conducted asynchronously with real participants, not synthetic respondents or AI avatars. Teams can launch a fully configured study in approximately 30 minutes, with hundreds of interviews running in parallel across 50-plus languages, and receive structured, stakeholder-ready findings within days. The platform captures voice, video, tone, and facial cues, delivering the conversational depth CATI aimed for, at enterprise scale and without the scheduling overhead.

Frequently asked questions.
CATI stands for Computer-Assisted Telephone Interviewing. It is a research method where trained interviewers conduct structured surveys or semi-structured interviews by phone, guided by software that manages question flow and records responses in real time. CATI has been widely used in consumer research, political polling, and customer satisfaction studies because it combines human interaction with systematic data capture across large, geographically dispersed samples.
CATI matters in qualitative research because it introduced a degree of conversational flexibility that purely written surveys lack. Interviewers can probe unexpected responses, clarify ambiguous answers, and adapt their tone to keep participants engaged. For decades, this made CATI a practical option for research programs that needed both scale and some depth. Its structured format also made data easier to code and analyze consistently across large interviewer teams and multiple research waves.
CATI, Computer-Assisted Telephone Interviewing, is conducted remotely over the phone, while CAPI, Computer-Assisted Personal Interviewing, involves face-to-face interviews where the interviewer uses a tablet or laptop to guide the session. CAPI typically enables richer data collection, including visual stimuli and observed behavior, but requires physical presence and is more expensive to field. CATI trades that depth for geographic reach and lower cost per interview, making it better suited to large national or multi-market studies.
AI is replacing the core function of CATI, structured conversational interviewing at scale, without the telephone infrastructure or interviewer scheduling that made traditional CATI slow and expensive. AI-moderated platforms can conduct hundreds of simultaneous interviews asynchronously, probe responses dynamically based on what participants actually say, and deliver analyzed findings within days. Unlike CATI, which depended on human interviewers following a script, AI moderation adapts in real time while maintaining consistent methodology across every session.
Enterprise teams still use CATI for specific programs where telephone contact is required, such as B2B research with hard-to-reach professionals or studies targeting older demographics with lower digital adoption. In practice, CATI works best for structured tracking studies, customer satisfaction programs, and large-scale quantitative waves where consistency across interviewers matters more than conversational depth. Many teams now evaluate AI-moderated video interviewing as a faster, more scalable alternative for programs where telephone access is not a strict requirement.
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