Question 1

When should I use an online board to explain unexpected survey findings?

Accepted Answer

Use an online discussion board when a survey yields a puzzling or unexpected finding that numbers alone can't explain, and you need the participants' own words to clarify "why." An online board (a moderated forum or chat with selected respondents) is ideal for post-survey follow-up because it lets people reflect on the surprising result and have a conversation around it at their convenience. For example, if a survey shows a niche feature scored unexpectedly high interest, you could invite respondents to an online board to discuss what appealed to them about it - their stories might reveal a hidden need or misunderstanding. The asynchronous format is less rushed than a live focus group, so participants can provide thoughtful input. In short, whenever you have data that begs for context or hypotheses to unpack it, an online board is a timely, efficient way to get qualitative depth straight from the source of your survey data.

Question 2

How can pharma research better capture patient experiences outside clinical?

Accepted Answer

Pharma research can capture patient experiences outside the clinic by embracing real-world data collection methods that follow patients in their daily lives. Instead of just relying on clinic visits and surveys, companies now use mobile apps, wearable sensors, and patient diaries to gather ongoing insights into symptoms, medication adherence, and quality of life. For example, a patient might log pain levels or side effects in an app each day, providing context that a monthly doctor visit might miss. In-home interviews or community forums also let researchers hear patients' stories about managing their condition day-to-day. By integrating these patient-driven data streams with traditional clinical trial data, pharma gets a much richer, more holistic view of how treatments impact people beyond the exam room.

Question 3

When should we cap frequency to avoid creative fatigue and waste?

Accepted Answer

Cap campaign frequency when key engagement metrics plateau or turn negative, indicating creative fatigue has set in. Too many ad exposures after this inflection point waste budget and can even irritate your audience. Monitoring diminishing returns on metrics — like a drop in click-through rate or conversion lift beyond a certain number of impressions — helps pinpoint when additional frequency stops adding value. For example, if brand recall or favorability scores stagnate despite more impressions, it's a clear signal the ad's effectiveness has peaked. Imposing a frequency cap at that stage preserves media efficiency and ensures your message stays fresh, balancing necessary repetition with audience tolerance. Many campaigns find that capping around 3-5 impressions per user per week is effective once performance tapers, though optimal levels vary by context and creative strength.

Question 4

What evidence connects UX task success to conversion rate improvements?

Accepted Answer

There's solid evidence that better UX task success translates to higher conversion rates. When users can easily complete key tasks on a website or app (find a product, fill out a form, checkout), they're far more likely to convert, and case studies back this up. For example, Expedia streamlined their booking process and saw a 12.5% increase in hotel bookings_x000D_, directly tying a usability improvement to more sales. Similarly, Walmart improved its mobile app experience and nearly doubled mobile order conversions_x000D_. These outcomes occur because reducing friction (errors, confusion, steps) means fewer users give up mid-process. In short, making UX improvements that boost task success (high completion rates, low abandonment) has been proven to lift conversions, as more satisfied users finish what they set out to do.

Question 5

What caveats should we be aware of when scaling AI-assisted qual?

Accepted Answer

When scaling AI-assisted qualitative research, remember that automation can miss nuance or introduce bias, so human oversight and data care are crucial. For instance, AI-generated summaries of focus groups might overlook subtle emotions or context that a human moderator would catch. Bias is another caveat: if the AI model wasn't trained on diverse data, it might skew interpretations or underrepresent minority viewpoints when analyzing open-ends at scale. Data privacy is at stake too; feeding large volumes of transcripts or videos into cloud-based AI tools requires ensuring confidential handling and compliance with privacy standards. Finally, scaling up doesn't eliminate the need for human insight. Researchers should always review the AI's findings, validate any surprising patterns, and be prepared to manually delve deeper if something looks off. In short, AI can greatly speed up qual analysis, but it must be treated as a helpful assistant rather than an infallible expert, with checks in place at each step.

Sago Dallas

Why choose Us

What is Sago Dallas?

Research Facility

Research Facility Details

Facility Rooms

Services and Expertise

Market Research FAQs with Expert Insights

When should I use an online board to explain unexpected survey findings?

How can pharma research better capture patient experiences outside clinical?

When should we cap frequency to avoid creative fatigue and waste?

What evidence connects UX task success to conversion rate improvements?

What caveats should we be aware of when scaling AI-assisted qual?