Question 1

How do we encourage stakeholders to use portals before asking for new studies?

Accepted Answer

The best way to get stakeholders using a research portal is to make it their fastest path to answers - in other words, show that checking the portal is easier than commissioning a new study. This starts with keeping the portal well-organized and up to date, so users trust that a quick search will surface relevant past insights. Some companies integrate the portal into everyday workflows, for example by embedding links to it in project request forms or team chat bots, nudging people to look there first. Highlighting success stories can help too ("Look how Product X team saved time by finding existing data in the portal"). The key is to demonstrate value: when stakeholders see that the portal saves them effort and provides credible information on demand, they naturally turn to it instead of reflexively asking for new research.

Question 2

Can AI tools improve efficiency in CATI or online survey administration?

Accepted Answer

Yes, AI tools can streamline both CATI (phone surveys) and online survey administration by automating repetitive tasks and enhancing data quality. In CATI, AI-powered voice agents can handle initial calls or simple survey questions, reducing interviewer workload - for instance, an AI could dial and ask screening questions, then pass the call to a human for complex parts. AI can also assist live interviewers by suggesting follow-up probes based on responses or flagging inconsistent answers in real time. For online surveys, AI chatbots can engage respondents in a more conversational way, potentially boosting completion rates. On the backend, machine learning algorithms quickly clean and categorize open-ended responses (text analytics) and can detect low-quality data (like straight-lining or random responding) faster than manual review. The net effect is faster fieldwork and analysis without sacrificing quality. Researchers just need to ensure these AI systems are well-tested to avoid any bias or robotic interactions that might put off respondents.

Question 3

When should product analytics calibrate survey findings in journeys?

Accepted Answer

Product analytics should calibrate survey findings at points in the customer journey when user behavior and feedback don't align, such as when metrics and survey responses tell different stories. Combining usage data with survey feedback at these moments reveals whether a drop-off is genuine friction or just a data quirk. For example, a survey might indicate customers love a new feature, but analytics show it's rarely used-calibration can uncover if users liked the idea but struggled with the interface. This practice is especially valuable after product changes or at key journey milestones to ensure improved metrics actually reflect customer sentiment. Aligning survey insights with real usage patterns prevents teams from chasing misleading data and directs focus to journey improvements that truly matter.

Question 4

How can AI-assisted synthesis dull critical thinking skills?

Accepted Answer

Over-relying on AI to synthesize research findings can indeed dull analysts' critical thinking if they're not careful. When an AI quickly produces a summary or report, researchers might be tempted to take those conclusions at face value and skip their own deep dive. If that becomes a habit, the team's ability to question data, spot nuances, or generate original insights can atrophy over time. For example, an AI might summarize survey feedback in broad strokes, but a human analyst would catch subtle contradictions or an outlier comment that could be a game-changer. To prevent skill fade, many organizations treat AI-driven synthesis as a first draft: the human experts then critically review, verify, and build on it. This way, they get efficiency from the AI without sacrificing the thoughtful analysis that leads to truly valuable insights.

Question 5

What privacy concerns arise from unifying cross-channel data?

Accepted Answer

Unifying cross-channel data can raise serious privacy concerns because it creates a comprehensive profile of individuals that they never explicitly consented to. When data from web browsing, mobile apps, in-store visits, and other sources all gets linked, information that was anonymous in one channel can suddenly identify someone when combined with others. For example, linking an anonymous online browsing account with an in-store loyalty program purchase could unmask a person's identity and reveal their shopping habits. Such aggregation might also expose sensitive behavior patterns or personal details that people assumed were private. There's a higher risk if this rich dataset leaks or is misused - it could expose everything from a person's purchasing habits to location history. To address these concerns, companies must ensure they have clear consent for each use, anonymize data where possible, and strictly limit who can access the unified dataset.

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Market Research FAQs with Expert Insights

How do we encourage stakeholders to use portals before asking for new studies?

Can AI tools improve efficiency in CATI or online survey administration?

When should product analytics calibrate survey findings in journeys?

How can AI-assisted synthesis dull critical thinking skills?

What privacy concerns arise from unifying cross-channel data?