Question 1

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 2

When should local qual be used to resolve conflicting global quant signals?

Accepted Answer

You should turn to local qualitative research when your global quantitative data shows puzzling or conflicting results that you can't fully explain with numbers alone. If a global survey or dataset has outliers or opposite trends in specific markets (for instance, one country's satisfaction scores dropped while others rose), it's a sign that local context needs to be explored qualitatively. In such cases, conducting focus groups or in-depth interviews in the particular region helps uncover cultural nuances or specific issues behind the anomaly. Local qual is also useful when stakeholders are unsure why a metric is underperforming in one area despite overall positive global averages. By hearing directly from local customers or employees, researchers can find the "why" that the spreadsheet data is missing. In summary, whenever the what of the data (the numbers) doesn't line up across regions, local qual should be deployed to clarify the why.

Question 3

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 4

Does higher app engagement always indicate durable payments loyalty?

Accepted Answer

Not always. High engagement in a payments app--such as frequent logins or transactions--shows the user finds value now, but it doesn't guarantee long-term loyalty. Users can be very active in the short term and still switch if a competitor offers a better deal or if a single bad experience erodes their trust. Durable loyalty in the payments space comes from deeper factors like trust, network effects (for example, having friends or favorite merchants on the platform), and rewards or conveniences that make it hard to leave. So while strong engagement is a positive signal and engaged users are more likely to stick around than inactive ones, you should still track retention and satisfaction over time. In short, high app engagement is encouraging but needs to be reinforced with ongoing value and positive experiences to truly indicate lasting loyalty.

Question 5

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.

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Market Research FAQs with Expert Insights

Do high NPS scores always predict lower churn in banking?

When should local qual be used to resolve conflicting global quant signals?

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

Does higher app engagement always indicate durable payments loyalty?

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