Question 1

How do I know if AI has reliably coded thousands of short mobile entries?

Accepted Answer

You can determine if an AI has reliably coded thousands of short mobile survey entries by spot-checking and benchmarking its results against human judgment. A practical approach is to take a random sample of the AI-coded responses (say 5-10% of them) and have experienced human analysts recode those independently. If the AI's categories or tags largely match what people would assign, that's a good sign of reliability; significant discrepancies indicate problems. Also examine consistency and outliers: for example, check if the AI put an unusual number of entries into a single category or created any nonsensical groupings, which might reveal errors. Whenever possible, compare the AI's output to known results (like a previous study's hand-coded data or a predetermined ground truth subset) to quantify accuracy. By auditing the AI's work with human eyes and simple statistics, you'll gain confidence in whether its coding is trustworthy at scale.

Question 2

How do location-triggered mobile surveys provide uniquely actionable insights?

Accepted Answer

Location-triggered mobile surveys (sent when a person is at or leaves a specific place) deliver extremely actionable insights because they capture feedback in context. By pinging someone's smartphone the moment they exit a store, attend an event, or visit a location, you get their impressions while the experience is fresh. This immediacy means responses are tied to specific details - for example, a customer might mention "the checkout line in the downtown store was too slow" or "the display in aisle 3 was really eye-catching" because the memory is recent. Such pinpointed input tells you exactly where and what the issue or success was, so you can act on it (fix that slow checkout in that particular store, or replicate the good display elsewhere). In contrast, traditional surveys often come much later and yield more generic feedback; location-triggered surveys give you real-time, place-specific insights that map directly to operational improvements.

Question 3

What's a robust plan to test NPS cadence changes?

Accepted Answer

Treat NPS cadence changes as an experiment: design a test where you compare the current survey frequency to a new one on a subset of customers. For example, keep one customer group on the usual NPS schedule (say quarterly) while another group receives NPS surveys at the new cadence (say monthly). Track metrics like response rate, score stability, and any signs of survey fatigue in each group over multiple cycles. By randomizing customers and running the test for a few rounds, you can confidently see if more frequent surveys yield better insights or just irritate respondents. A robust plan also means having a clear success criterion (e.g., if monthly NPS significantly improves trend sensitivity without hurting responses), so you know whether to roll out the cadence change company-wide.

Question 4

What ethical considerations apply when recording shoppers in-store?

Accepted Answer

When recording shoppers in-store for research, you must consider privacy, consent, and data handling ethics. Ethical best practice is to inform shoppers (often via signage or announcements) that research recording is happening, giving them the chance to opt out or avoid being recorded if they wish. It's crucial to comply with local privacy lawsâ€"some jurisdictions and store policies require explicit consent for video or audio recording, and special care to avoid or get permission for recording minors. Additionally, researchers should minimize personal data capture (for instance, blur faces or avoid collecting identities) and securely store any recordings to prevent misuse. Use the data only for its intended research purpose and restrict access to it. By being transparent and protecting shopper privacy at every step, you maintain trust and meet ethical standards while gathering in-store insights.

Question 5

Why do NPS-defined promoters defect without complaining or contacting support?

Accepted Answer

Even loyal-seeming customers can leave quietly because being a "promoter" in NPS terms doesn't lock in their future behavior. A customer might give you a 9 or 10 on an NPS survey (meaning they'd recommend you) and still defect later without a peep if something changes for them or a better option appears. Often, these promoters don't complain because, in their eyes, nothing dramatic went wrong - they simply found a product that suits them better or their needs evolved. Many people won't contact support unless they hit a serious problem; if their switch was due to convenience, price, or a feature elsewhere, they just move on silently. It highlights that NPS captures sentiment at a moment in time, but loyalty can be fragile. That's why it's important to also monitor behavioral signals (usage drop-off, purchase frequency changes, etc.) and proactively check in with high-value customers. Promoters defecting silently remind us that companies must continuously deliver value and pay attention to subtle signs of waning engagement - not just rely on high survey scores.

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Market Research FAQs with Expert Insights

How do I know if AI has reliably coded thousands of short mobile entries?

How do location-triggered mobile surveys provide uniquely actionable insights?

What's a robust plan to test NPS cadence changes?

What ethical considerations apply when recording shoppers in-store?

Why do NPS-defined promoters defect without complaining or contacting support?