Question 1

How do I show ROI from high-cost research with niche B2B audiences?

Accepted Answer

Demonstrate return on investment (ROI) for expensive research in a niche B2B (business-to-business) market by tying its insights to tangible business outcomes that carry high value. In specialized B2B contexts, a single insight can influence a major contract or product decision, so you'd quantify how the research drove revenue, cost savings, or risk reduction. For example, if the research unveiled a customer pain point that led your company to win a new $1M account or avoid a costly feature mistake, document that narrative with numbers. Translate the study's findings into metrics executives care about - like faster sales cycles, higher client retention, or expansion into a new niche market - directly attributed to the research. By presenting these case studies or pilot outcomes, you make it clear that even though the study was high-cost, it enabled decisions or opportunities worth many times its price tag, which is the essence of positive ROI.

Question 2

Will panel-plus Big Data finally fix cross-device deduplication?

Accepted Answer

Combining a panel with big data (sometimes called a "panel-plus" approach) makes cross-device deduplication much more feasible, but it's not a magic bullet that perfectly resolves it in every case. By using a panel of known individuals or households, researchers can calibrate and validate big data algorithms that match devices to people, greatly reducing duplicate counts across phones, tablets, and TVs. For example, a panel might tell you that a certain cookie ID and a mobile device belong to the same person; feeding that ground truth into a big data model helps identify similar links at scale. This hybrid method has indeed closed a lot of gaps in cross-device audience measurement - it's far more accurate than relying on big data signals or panels alone. However, challenges remain: not all users are represented in panels, and privacy changes (like restrictions on tracking technologies) can limit data quality. So while the panel-plus-big-data strategy goes a long way toward solving cross-device duplication by leveraging the strengths of both, it's an improved solution rather than a flawless "final fix."

Question 3

How should we recruit silent attritors without biasing journey insights?

Accepted Answer

To learn from "silent attritors" (customers who leave quietly), fold them into your normal feedback collection instead of singling them out, so their insights emerge naturally alongside other customers' feedback. For example, in your next customer survey or interview round, include a segment of recently inactive users as if they're just part of the sample, asking the same questions you'd ask any customer about their experience and needs. This way, silent attritors can reveal why they disengaged in response to neutral, open-ended questions (e.g., "How has your experience with our service been? Any suggestions?") rather than feeling interrogated with a pointed "Why did you stop using us?" survey. By comparing their responses to those of active customers, patterns will emerge — perhaps attritors cite lacking features or poor follow-up as reasons for drifting away, whereas active users do not. The key is that you approached them as part of the routine, yielding a genuine read on where the customer journey failed some people. As a result, you can identify how to win them or others like them back without biasing the insights by treating them too differently.

Question 4

How can AI draft summaries for CIO briefings without hallucinations?

Accepted Answer

Use a grounded, controlled approach so the AI only summarizes facts you've given it. In practice, that means providing the AI with a curated briefing document or data points (e.g. key project updates, metrics, and bullet points) and instructing it to summarize from that information alone. Modern AI tools allow you to use techniques like retrieval-augmented generation - the AI can pull specifics from an internal knowledge base or recent reports, then draft the CIO summary based strictly on those verified sources. Also, set clear guidelines in your prompt (for example, "Only use the data provided. If something isn't in the input, do not invent it."). By constraining the AI's knowledge and style upfront and then doing a quick human fact-check on the output, you can get concise, useful CIO briefings without the risk of made-up "hallucinated" details.

Question 5

Will instant payments change service expectations in retail banking research?

Accepted Answer

Yes - the rise of instant payments is raising customer expectations for speed and convenience in banking, and that shift is something retail banking research is starting to capture. When people can send or receive money in seconds, they begin to expect similarly immediate service in other areas like customer support, account updates, or resolving issues. For example, a customer who instantly transfers funds might feel frustrated if a card dispute or loan approval still takes days; their baseline for "fast service" has changed. Research surveys and interviews now often probe perceptions of timeliness and real-time responsiveness, reflecting this new standard. In practice, banks may need to explore offering 24/7 support, faster turnaround on requests, and proactive alerts - and consumer research will reveal how much these factors impact satisfaction and loyalty in an era of instant expectations.

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Market Research FAQs with Expert Insights

How do I show ROI from high-cost research with niche B2B audiences?

Will panel-plus Big Data finally fix cross-device deduplication?

How should we recruit silent attritors without biasing journey insights?

How can AI draft summaries for CIO briefings without hallucinations?

Will instant payments change service expectations in retail banking research?