Question 1

Can AI tools improve forecasting for new drug launches?

Accepted Answer

Yes, AI tools can make new drug launch forecasts more sophisticated by analyzing complex patterns across many data sources, but they complement rather than completely replace traditional forecasting. Forecasting a novel drug's uptake is tricky, especially with limited analogs. AI and machine learning can help by processing vast amounts of data — beyond what an analyst could handle — such as patient demographics, physician prescribing behaviors, formulary and insurance data, and even social media or key opinion leader sentiment. For example, an AI model might detect that a drug with a certain efficacy profile and marketing mix follows a launch trajectory similar to past drugs in related categories, adjusting predictions as real-world prescription data starts coming in. This often yields a more nuanced forecast that updates in near real time. However, because every new drug has unique aspects (a novel mechanism, different competition, regulatory nuances), AI predictions need expert oversight. Companies typically use AI-driven forecasts alongside traditional analog-based methods and expert input. The AI improves accuracy by uncovering patterns and early indicators, while human experts ensure the predictions make sense given any one-of-a-kind factors.

Question 2

When do enterprise buyers prefer in-depth interviews to surveys?

Accepted Answer

Enterprise buyers typically prefer in-depth interviews over surveys when they need to explore complex decisions or nuanced feedback in detail. For example, if a company is evaluating a high-stakes B2B solution or an intricate service offering, buyers often opt for one-on-one interviews so they can discuss context, requirements, and concerns in depth — things a multiple-choice survey might gloss over. Interviews allow for probing follow-up questions and storytelling, which helps uncover the 'why' behind responses and surface insights that would be lost in a checkbox format. In essence, whenever the topic is complicated, sensitive, or variable across organizations (such as understanding internal decision dynamics or specialized needs), enterprise stakeholders lean toward qualitative interviews to get richer insight, using surveys mainly when they need quick, quantifiable input at scale.

Question 3

How are patient expectations evolving in the era of telehealth?

Accepted Answer

In the telehealth era, patients expect healthcare to be as convenient and responsive as other digital services, yet still personal and trustworthy. They now assume routine check-ins or consultations can happen via video or app on short notice, often without the need to travel — essentially healthcare on-demand. This shift, accelerated by the pandemic, means patients also look for seamless technology experiences: intuitive apps, quick follow-ups (like getting digital prescriptions or test results promptly), and confidence that their data is private. At the same time, they haven't dropped their desire for a human touch; patients want clinicians who listen and show empathy over the screen. Overall, telehealth has raised expectations for access and speed, forcing providers to blend tech convenience with the quality and care of an in-person visit.

Question 4

Do experimental designs always assume more sample is always better?

Accepted Answer

No — good experimental design is about the right sample size, not simply the biggest sample size. While having more respondents generally increases statistical power (making it easier to detect a true effect), it doesn't mean "more is always better" in every situation. Once you have enough sample to reliably detect the differences or effects you care about (determined by a power analysis), piling on more data yields diminishing returns and can even be counterproductive. Extremely large samples might flag tiny differences as "significant" even if they're not practically meaningful, which can be misleading. There's also the reality of cost and time: testing on 10,000 people when 1,000 would have given a clear answer is inefficient and could divert resources from other tests. In some cases — like medical trials or any test involving inconvenience to participants — ethics dictate using the minimum sample necessary to achieve valid results, to avoid exposing more people than needed. So, an experiment should recruit enough participants to be robust and credible, but not so many that you're just inflating numbers. The goal is to determine an optimal sample size based on expected effect size and confidence levels, rather than assuming that a bigger N inherently makes for a better study.

Question 5

How do I compare ROI from mobile ethnography with classic in-person methods?

Accepted Answer

To compare the return on investment (ROI) of mobile ethnography versus classic in-person ethnography, evaluate both the costs and the type of insights each yields for the same research goals. Mobile ethnography (participants using smartphone apps to record diaries, photos, or videos) generally has lower fieldwork costs and scales more easily - you can engage many participants at once across locations without travel expenses. Traditional in-person ethnography requires a researcher on-site with each participant, which is expensive and time-intensive but can produce very deep, contextual insights that might be missed remotely. In assessing ROI, consider metrics like cost per insight or actionable finding: a mobile study might deliver a higher volume of observations per dollar, while an in-person study might provide more profound revelations per participant. Also factor in speed (mobile studies often finish faster) and overall impact (did one method uncover key insights that the other didn't?). By weighing total spend against the relevance and depth of insights gained, you can judge which approach gave better ROI for your needs.

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Market Research FAQs with Expert Insights

Can AI tools improve forecasting for new drug launches?

When do enterprise buyers prefer in-depth interviews to surveys?

How are patient expectations evolving in the era of telehealth?

Do experimental designs always assume more sample is always better?

How do I compare ROI from mobile ethnography with classic in-person methods?