Question 1

How is connected health technology changing medical device research priorities?

Accepted Answer

Connected health technology (like smart medical devices, wearables, and health apps) is shifting medical device research toward user experience and data integration priorities. Traditionally, research focused on a device's clinical effectiveness and safety in isolation, but now teams must also prioritize how the device connects to other systems and fits into patients' daily lives. For example, a modern glucose monitor study doesn't just test accuracy; it also evaluates the device's mobile app interface, real-time data sharing with healthcare providers, and patient adherence when using continuous monitoring. Data privacy and security have become key research angles as well, since these devices transmit sensitive health information. Additionally, connected devices generate continuous real-world data, so researchers increasingly analyze usage patterns and outcomes over time to ensure the technology delivers sustained value. In essence, connected health is broadening the scope: successful medical device development now demands excellence in digital features, interoperability, and patient engagement, not just the core medical function.

Question 2

How can I quantify ROI from cross-channel attribution research?

Accepted Answer

Treat your attribution research as an investment and measure what it changed. Start by identifying the optimizations or shifts in strategy that came out of the cross-channel attribution findings - for example, reallocating budget from one channel to another or cutting spend in low-ROI placements. Then track the results of those changes in hard numbers. If moving spend from Channel A to Channel B increased sales or lowered customer acquisition cost, quantify that impact in dollars. Compare those gains (or savings) to the cost of doing the attribution study. For instance, if the research cost $100K but led to adjustments that generated an extra $500K in revenue or saved $200K in wasted spend, you can confidently report a strong return on investment (in this case 5x revenue gain or net $400K benefit). In short, link the attribution insights to actual performance improvements and calculate the difference - that difference is the ROI attributable to the research.

Question 3

How are remote and hybrid approaches changing fieldwork logistics?

Accepted Answer

Remote and hybrid research approaches are transforming fieldwork logistics by minimizing travel and onsite requirements while adding new technology coordination and flexibility. Instead of sending researchers physically to each site, many studies now use video conferencing, mobile diaries, or online communities to gather data, which cuts down on travel time and costs. This shift means logistics focus more on tech setup (like ensuring participants have the right apps or internet access) and scheduling across time zones rather than booking facilities and travel. Hybrid approaches — mixing in-person sessions with remote methods — require coordinating both physical and virtual elements. For example, a product test might involve shipping samples to participants' homes and then conducting follow-up interviews via video call, blending the convenience of remote data collection with the richness of seeing real-life context. Overall, fieldwork planning has become more flexible and scalable, though researchers must now manage technology reliability and keep participants engaged remotely as part of logistics.

Question 4

Do bigger language models guarantee higher research accuracy?

Accepted Answer

Not necessarily. Larger language models (with more parameters and training data) often produce more fluent and knowledgeable-sounding outputs, but that doesn't automatically equate to greater accuracy on research tasks. In fact, big models can still "hallucinate" facts or confidently give wrong answers if the information isn't in their training data. What matters more is how relevant and up-to-date the model's knowledge is and whether it's augmented with real data. A well-tuned smaller model that's fed curated research information (or used with a live database search) can outperform a huge model working off of generic training alone. Also, bigger models can sometimes be too generalized, missing niche details that a domain-specific tool would catch. In short, while size brings potential, you still need to verify and fact-check AI outputs — treat any model's answer as a starting point that benefits from human validation and subject matter context.

Question 5

How can we avoid overweighting rare journeys when sampling edge cases?

Accepted Answer

To avoid overweighting rare customer journeys when studying edge cases, include them deliberately but keep their influence proportional - either by weighting them to reflect how infrequent they truly are or by analyzing them separately from your main sample. For example, if only 5% of users take an unusual path through your product, you can still recruit some of those users to learn from their experience, but ensure that in any overall metrics or conclusions, their data is weighted around 5% rather than treated equally with the majority. One practical approach is stratified analysis: examine insights from mainstream journeys versus edge-case journeys in parallel, then contextualize the edge cases by emphasizing their low incidence. This way, you capture valuable insights from anomalies (which might reveal hidden opportunities or issues) without letting those outlier behaviors distort the big picture or drive decisions more than they should.

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Market Research FAQs with Expert Insights

How is connected health technology changing medical device research priorities?

How can I quantify ROI from cross-channel attribution research?

How are remote and hybrid approaches changing fieldwork logistics?

Do bigger language models guarantee higher research accuracy?

How can we avoid overweighting rare journeys when sampling edge cases?