Question 1

Can AI reliably simulate responses to choice-based experiments?

Accepted Answer

AI is improving fast, but it's not yet a fully reliable stand-in for real people in choice-based experiments. Advanced models can analyze past respondent data or use machine learning to predict choices, offering a rough simulation of how consumers might respond to certain product or price combinations. However, these simulations often fall short of capturing the true complexity of human decision-making — factors like emotions, context, or completely new preferences that weren't in the training data. In practice, researchers sometimes use AI to augment or pre-test scenarios (because it's quick and cost-effective), but they still validate those findings with actual survey respondents to be sure. So while AI-generated responses can hint at patterns, they're not yet dependable enough to replace the insights gained from real human choices in a conjoint or choice model study.

Question 2

How do I make a credible business case for ethnography versus surveys?

Accepted Answer

To justify ethnography (immersive observational research) versus just surveys, highlight what unique value it provides and how that translates to business outcomes. Emphasize that ethnography uncovers the "why" and unspoken behaviors that surveys often miss. For example, maybe surveys showed decent satisfaction scores, but an ethnographic study revealed customers struggling with a product in their daily context - a hidden pain point that, once solved, significantly increased retention or sales. Frame these insights in dollar terms or KPIs: the improvement might reduce churn by X% or open a new market segment, adding tangible value. Also compare costs: ethnography is more upfront investment, but point out how much a failed product launch or a misread customer need would cost the company (often far more than a few field studies). By providing a concrete success story and a clear ROI argument (value gained vs. research cost), you'll make a compelling, credible case that ethnography isn't a "nice-to-have" but a strategic necessity for deeper insights that drive growth and prevent costly missteps.

Question 3

How do I keep quiet participants engaged without biasing the group?

Accepted Answer

Engaging quiet participants in a group without introducing bias takes gentle, inclusive facilitation. One approach is to use round-robin or structured turns, where everyone gets a chance to speak on a question; you can politely invite input by naming the quieter person ("Alex, what's your perspective on this?") without framing how they should answer. Another tactic is to provide alternative ways to contribute, like written brainstorming or polling during the session, then discussing results, so quieter folks voice ideas without directly jumping into a heated discussion. The key is not to push too hard or lead them - avoid yes/no questions or suggestions that could pressure agreement. By creating a comfortable space (nodding, eye contact, patience) and using neutral prompts, you encourage silent members to share while keeping the overall conversation unbiased.

Question 4

Will agentic interviewing give me comparable depth to human moderation?

Accepted Answer

Agentic interviewing (using an AI interviewer) can deliver a good level of depth on structured topics, but it usually doesn't match the nuanced probing of a skilled human moderator. AI-driven agents are very consistent and can ask follow-up questions based on answers, but they might miss subtle emotional cues or opportunities for empathy and improvisation that humans catch. For example, a virtual agent will diligently cover all scripted questions and even some dynamic prompts, yet it might not notice when a respondent hesitates or needs gentle encouragement to elaborate. In practice, many organizations treat AI interviewing as a complement — using it to cover the basics or scale up the number of interviews — while relying on human moderators for deeper exploration and sensitive topics where personal rapport is key.

Question 5

When is it cost-effective to centralize vs decentralize fieldwork coordination?

Accepted Answer

Centralizing fieldwork coordination is most cost-effective when studies are similar across regions and can benefit from standardized processes and economies of scale. If multiple projects use the same methods or vendors, a central team can negotiate better rates, eliminate duplicate effort, and maintain consistent quality, thus driving costs down. In contrast, decentralizing (having local teams handle their own fieldwork) is economical when each project or market has unique needs that a one-size-fits-all approach would only inflate. For example, highly localized studies might require specific cultural knowledge or on-the-ground flexibility that a distant central team cannot provide efficiently. In those cases, giving autonomy to regional coordinators avoids the overhead of central management. As a rule of thumb: centralize coordination to save money when projects are homogenous and scalable, and decentralize when local agility and customization are critical to avoid waste.

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Market Research FAQs with Expert Insights

Can AI reliably simulate responses to choice-based experiments?

How do I make a credible business case for ethnography versus surveys?

How do I keep quiet participants engaged without biasing the group?

Will agentic interviewing give me comparable depth to human moderation?

When is it cost-effective to centralize vs decentralize fieldwork coordination?