Question 1

When should exposure logs feed directly into effectiveness testing pipelines?

Accepted Answer

Feeding exposure logs (records of who saw an ad or message) directly into effectiveness testing pipelines makes sense when speed and continuous learning are critical. If you need real-time or near-real-time measurement of an intervention's impact - such as monitoring an ad campaign's performance as it runs - automatically piping the exposure data into your testing or analytics system allows immediate updates. This is most useful in large-scale digital programs where exposure and outcome data flow constantly and decisions (like adjusting spend or targeting) can be made on the fly. However, it should be done only after the data is cleaned and properly structured, because direct feeds of messy log data can lead to faulty conclusions. In short, use direct integration when timely feedback is valued and your data processes are mature enough to handle continuous input without compromising accuracy.

Question 2

How do I build a business case to fund an always-on online community?

Accepted Answer

Building a business case for an always-on online community means quantifying its value in terms of faster, cheaper insights and better decisions. Start by highlighting how a dedicated community can replace or streamline many one-off studies — leading to cost savings over time — and provide instant access to customer feedback whenever stakeholders have questions. Use concrete examples or pilot data: for instance, show that the community could cut down concept testing time from weeks to days, or that continuous engagement helped another brand spot a market shift early. Include an ROI estimate in your proposal (e.g. how many traditional surveys or focus groups the community will offset annually) and mention qualitative benefits like closer customer relationships and more informed innovation. By framing the community as an asset that accelerates insights and reduces research spend long-term, you create a compelling, evidence-backed argument for funding.

Question 3

How do we capture unmet needs without leading or priming?

Accepted Answer

To uncover unmet needs without leading participants, use open-ended and observational research methods that let people reveal their challenges in their own words. For example, instead of asking a leading question like "Would feature X help you?", you ask them to describe a recent difficult experience or a workaround they use in the relevant area. Techniques such as in-depth interviews, diary studies, or ethnographic observation encourage participants to talk about their routine and pain points naturally, bringing up needs you didn't explicitly mention. Moderators remain neutral and probe gently with follow-ups ("Can you tell me more about that?" or "Why was that frustrating?") rather than suggesting solutions. By giving people the space to explain problems and desires on their own, you capture authentic unmet needs without priming or biasing them with your assumptions.

Question 4

How should we measure reach for short-form vertical video surfaces?

Accepted Answer

Traditional reach metrics need a tweak for short-form vertical video (like TikTok, Instagram Reels, YouTube Shorts) - you'll want to focus on unique viewers and meaningful views rather than raw impression counts. Platforms will report massive view numbers for a catchy 15-second video, but since these formats autoplay and loop, a better gauge of reach is how many distinct individuals saw it. Marketers typically define a view threshold (say a 2 or 3-second view or the video unmuted) to count as real exposure, and then look at unique reach alongside engagement rates (shares, likes, comments) to judge impact. Using these measures, often combined with frequency caps in ad campaigns, ensures you're assessing whether the content genuinely reached a broad audience and held their attention long enough to matter. In summary, prioritize unique reach and engaged views for short-form videos to capture true audience exposure.

Question 5

How do we know if learning value justifies prototype costs at each stage?

Accepted Answer

We determine if a prototype's learning value justifies its cost by evaluating the decision impact of that prototype at each development stage. In practice, this means asking what critical uncertainty or design question the prototype will resolve and comparing that value to the prototype's expense. For example, an early-stage low-fidelity prototype might be inexpensive but provide vital feedback on the concept direction — if it prevents the team from pursuing a flawed path, its value far outweighs the small cost. Conversely, a later-stage high-fidelity prototype is much more expensive, so you'd only justify it if it addresses a high-stakes issue (like fine-tuning user experience before production or securing stakeholder buy-in for launch). At each stage, weigh the cost of building the prototype against the potential cost of not learning those insights (like rework or a failed launch). If the knowledge gained significantly reduces risk or improves outcomes, the prototype is worth the cost; if not, it can be skipped; by checking this cost-benefit at each stage gate, teams ensure they only invest in prototypes when the insight clearly merits it.

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Market Research FAQs with Expert Insights

When should exposure logs feed directly into effectiveness testing pipelines?

How do I build a business case to fund an always-on online community?

How do we capture unmet needs without leading or priming?

How should we measure reach for short-form vertical video surfaces?

How do we know if learning value justifies prototype costs at each stage?