Question 1

How do I reduce drop-off in multi-week online qual studies?

Accepted Answer

To keep participants from dropping out of a multi-week online qual study, you have to keep them engaged without overburdening them. Plan a mix of activities so it doesn't feel repetitive or too time-consuming--one week might involve a quick photo upload or poll, the next a short discussion prompt or diary entry. Consistent communication is key: send friendly reminders before each task and share occasional updates or highlights from the findings to show participants their input matters. Also, structure incentives to reward ongoing participation, not just completion, so people have a reason to stick around (for instance, small weekly rewards or a bonus for hitting certain milestones). Finally, make participants feel valued; a simple thank-you message or shout-out for great contributions can boost motivation. By keeping the experience varied, manageable, and rewarding, you'll see far fewer people drop off before the study concludes.

Question 2

Which decisions benefit most from shop-alongs or in-store observation?

Accepted Answer

Shop-alongs and in-store observation are most useful for decisions that hinge on real-world shopper behavior — especially things like store layout, shelf placement, packaging design, and point-of-sale marketing. For example, if a product is underperforming, observing shoppers in the aisle can reveal whether the item is hard to find, poorly merchandised, or losing attention to a competitor's display — insights that surveys or sales data alone might miss. These methods excel at uncovering the "why" behind shopping choices, informing decisions on everything from signage and store navigation to assortment mix and in-store promotions. In short, any decision aimed at improving the in-store customer experience or shopper journey benefits from the firsthand perspective that shop-alongs provide.

Question 3

Do high NPS scores always predict lower churn in banking?

Accepted Answer

High Net Promoter Scores (NPS) in banking usually correlate with lower customer churn, but they don't guarantee it. NPS captures how likely customers are to recommend the bank, which often reflects satisfaction and loyalty - factors that tend to reduce churn. However, churn can still happen for reasons NPS doesn't capture. For instance, a customer might love their bank's service (giving a high NPS) but leave anyway because a competitor offers a significantly better interest rate or because of a personal relocation. Banks have learned to use NPS as one predictive signal among many. Combining NPS with other data - like product usage, complaints, or life events - gives a more reliable forecast of who might actually leave. In short, a high NPS tilts the odds in favor of retention, but it's not a fail-safe predictor on its own.

Question 4

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 5

How can we increase participation rates among hard-to-reach executives?

Accepted Answer

To engage hard-to-reach executives, take a highly personalized, value-focused approach — make it clear that participating will be worth their limited time. Start with an exclusive, relevant invitation: for example, an email endorsed by a respected industry peer or association can lend credibility. In the invite, explain briefly why their insight matters and promise something in return, like a summary of the study's findings or benchmarking data they can use. Keep the engagement convenient: if it's a survey, ensure it's very short and high-level; if it's an interview, schedule at their convenience and stick to the promised time. Tailor incentives to what execs value — often thought leadership or networking over gift cards — so perhaps offer early access to results or a peer roundtable. Finally, guarantee confidentiality and a professional process. When executives see that participating is easy, prestigious, and offers real benefits, participation rates go up.

Fieldwork Dallas

Why choose Us

What is Fieldwork Dallas?

Research Facility

Research Facility Details

Facility Rooms

Services and Expertise

Market Research FAQs with Expert Insights

How do I reduce drop-off in multi-week online qual studies?

Which decisions benefit most from shop-alongs or in-store observation?

Do high NPS scores always predict lower churn in banking?

Can AI tools improve forecasting for new drug launches?

How can we increase participation rates among hard-to-reach executives?