Question 1

How can I be sure of AI object-/scene-detection in ethnography footage?

Accepted Answer

To trust AI object and scene detection in ethnography videos, you need validation steps that confirm the system's accuracy in context. Researchers typically start by testing the AI on sample footage where the true contents are known, comparing its tags to human-coded results. High agreement (or precision/recall metrics) gives confidence, and any mismatches highlight where the model needs tuning. Additionally, using AI tools that provide confidence scores or flag uncertain detections lets you or a human reviewer double-check critical moments, ensuring the final analysis of ethnographic footage is reliable rather than blindly accepted from the AI. Also, ethnographic contexts can be unusual for off-the-shelf models, so fine-tuning the AI on footage similar to yours or using a human-in-the-loop for tricky cases helps ensure nothing important is missed.

Question 2

How can I use a small set of groups to pressure-test a segmentation?

Accepted Answer

You can take a handful of focus groups (or mini-groups) and use them as a quick reality check for your segmentation. The approach is to recruit a small number of people representative of each segment and then observe how well their attitudes and behaviors align with what the segment is supposed to represent. For example, if Segment A is defined as "budget-conscious millennials," you can have a group discussion with a few Segment A members to see if they indeed share similar views on spending and differ from another group (say Segment B "luxury seekers"). If the discussions show that within-group consistency is high and between-group differences are clear, your segmentation passes the test. On the other hand, if everyone across groups sounds the same or if the supposed Segment A group actually splits into divergent viewpoints, that tells you the segments might need refining. Using just a few well-chosen groups in this way helps you pressure-test whether each segment is meaningful and distinguishable in practice before you fully commit to it.

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 reliably detect sarcasm and nuance in social data?

Accepted Answer

AI still struggles with reliably detecting sarcasm and subtle nuance in social media data, even as language models improve. Sarcasm often relies on context, tone, or cultural references - a human reader picks up the eye-roll implied by "Yeah, that was just great ðŸ™„," but an algorithm might take those words at face value as positive. Modern Natural Language Processing (NLP) techniques, including Large Language Models (LLMs), have narrowed the gap a bit by analyzing surrounding context and even emoji cues, but they're far from foolproof. They can misclassify sarcastic remarks as genuine sentiment or miss understated humor, especially when dealing with niche slang or irony that requires real-world knowledge. In practice, firms using AI for social listening often incorporate human review or specialized training sets to handle sarcasm and nuance. The bottom line: AI can flag potential sarcasm or complex tone, but you shouldn't rely on it alone for high-stakes interpretation of social data - a human touch is usually needed to confirm what the AI "hears."

Question 5

Does high website traffic always indicate strong purchase intent?

Accepted Answer

No — high website traffic doesn't automatically mean strong purchase intent. You can attract a large number of visitors who are merely browsing, researching, or drawn by content without any immediate intention to buy. For example, a viral blog post or broad marketing campaign might spike your site visits, but many of those people could leave after a quick look or bounce from your homepage without engaging further. What really signals purchase intent are quality metrics: how long visitors stay on product pages, whether they add items to cart, download information, or fill out inquiry forms. In short, a smaller volume of highly engaged, qualified traffic is far more valuable than a huge influx of unqualified clicks, so marketers should look beyond raw traffic numbers to gauge true intent.

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Market Research FAQs with Expert Insights

How can I be sure of AI object-/scene-detection in ethnography footage?

How can I use a small set of groups to pressure-test a segmentation?

Do high NPS scores always predict lower churn in banking?

Can AI reliably detect sarcasm and nuance in social data?

Does high website traffic always indicate strong purchase intent?