Question 1

How should we test pricing with AI agents before conjoint with people?

Accepted Answer

Before investing in a full conjoint survey with real people, you can run preliminary pricing experiments using AI-driven agents that simulate consumer behavior. These AI agents — trained on past purchase data or decision rules — can quickly explore how virtual shoppers might respond to various price points or bundles. By observing these simulations, you identify promising pricing options and eliminate clearly suboptimal ones. The key is to calibrate your AI models carefully: ensure the agent's preferences or elasticities reflect reality so the insights are meaningful. For example, you might simulate thousands of purchase decisions with an AI model to find the price point where demand drops off, then focus your human conjoint questions around that critical range. Once the AI testing narrows down the field, validate those pricing scenarios with a conjoint study among actual customers to confirm the AI's predictions hold true.

Question 2

When should research ops run refreshers on sampling and bias?

Accepted Answer

Research operations should run refreshers on sampling and bias whenever there are signs the team might need a reminder or when methodologies change - common triggers include new team members coming on board, adopting a new sampling approach, or learning of issues in a recent project. For example, if a study's results raised questions due to a skewed sample or an unnoticed bias (say certain groups were underrepresented or the phrasing led respondents), it's a clear signal to hold a training session to reinforce proper sampling techniques and bias mitigation. Regular proactive refreshers (perhaps annually or at the kickoff of major studies) are also valuable, especially when introducing new tools or data sources. Any time the research process evolves - like bringing in a new panel provider, using AI-based recruitment, or trying a different survey methodology - research ops should revisit sampling and bias best practices with the team, ensuring everyone stays sharp and potential problems are caught early.

Question 3

Will collecting more passive data always increase representative behavior?

Accepted Answer

Collecting more passive data (like tracking behavior via apps or devices) doesn't automatically make your findings more representative of true behavior, though it can help. Passive data does capture what people actually do (bypassing faulty recall or self-report bias)_x000D_, but issues of representativeness remain. If the people whose data you capture aren't a good cross-section of the target population, you'll still have bias. For example, those willing to have their behavior tracked tend to be younger or more tech-comfortable, which might skew results_x000D_. Also, even tons of passive data might miss behaviors that aren't tracked (e.g., offline activities or actions on platforms you can't monitor). In summary, passive data can yield more authentic behavior insights, but it needs careful sampling and often complementing with other data to ensure it truly represents the broader audience.

Question 4

Why does reported interest in playbooks seem to exceed engagement with them?

Accepted Answer

People often express more appetite for playbooks than they actually demonstrate in usage; the idea of having a go-to manual for processes is appealing, but in practice many employees don't consult them regularly due to issues of convenience and relevance. For example, team members might request a playbook believing it will help, but once it exists, they may find it too long or generic to apply to their immediate task, or simply forget it's there when under time pressure. It's common for a playbook to be created and then sit on a shelf (or intranet) not integrated into daily workflows, so despite high reported interest, actual engagement remains low; additionally, if the playbook isn't kept up to date or doesn't reflect current realities, people lose trust in it and revert to asking colleagues or relying on memory. In short, the concept of a playbook is attractive, but actual usage only picks up if the content is very accessible, actionable, and reinforced in the team's routine.

Question 5

When should sensors complement surveys rather than replace them?

Accepted Answer

Use sensors to complement rather than replace surveys whenever you need objective behavior data plus the personal context behind it. Sensors (from wearables, smartphones, IoT devices, etc.) excel at capturing what people do — heart rate spikes, foot traffic patterns, dwell time — but they can't tell you why. For example, a sensor might show a customer lingered in a store aisle for 5 minutes; a follow-up survey is needed to know if they were engaged by a display or frustrated searching for a product. The two methods work best together whenever behavior alone is insufficient: sensors provide accurate, continuous measures at scale, and surveys supply the motivation or satisfaction insights. Completely replacing surveys would mean losing the "why" behind actions, so it's usually better to combine them for a fuller picture.

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Market Research FAQs with Expert Insights

How should we test pricing with AI agents before conjoint with people?

When should research ops run refreshers on sampling and bias?

Will collecting more passive data always increase representative behavior?

Why does reported interest in playbooks seem to exceed engagement with them?

When should sensors complement surveys rather than replace them?