Question 1

What's the cleanest way to test banking kiosk versus teller journeys?

Accepted Answer

The cleanest way to compare a bank's self-service kiosk experience with a human teller experience is to run a controlled test where customers are randomly assigned to each option for the same task. For instance, you might have a group of customers perform a common transaction — like depositing a check or opening an account — one group using the kiosk and a similar group working with a teller. By measuring outcomes such as transaction time, error rates, and customer satisfaction immediately afterward, you isolate the differences attributable to the channel. Random assignment is key here: it ensures that any differences in results (say, kiosk users finished two minutes faster on average, or teller users felt more reassured) are due to the method of service, not because one group had inherently easier cases or more tech-savvy individuals. This A/B test style approach provides clear, unbiased evidence of how kiosks stack up against tellers for key banking journeys.

Question 2

Do we risk partisan bias when AI classifies verbatims in public opinion research?

Accepted Answer

There's a real risk of partisan bias when using AI to classify verbatim comments in public opinion research, especially if the algorithm's training data or assumptions skew toward one political perspective. For example, a natural language model trained mostly on one media ecosystem might label phrases common to the opposite political camp as more negative or extreme than they are, simply because it hasn't learned those expressions in a neutral way. These classifiers pick up on subtle linguistic patterns, so without careful balance they can unintentionally tilt sentiment or topic coding left or right. To avoid this, researchers need to train and fine-tune AI tools on diverse, balanced data and regularly validate the AI's coding against human judgment - ensuring any partisan bias is detected and corrected early.

Question 3

How should we calibrate sensor data with diaries to reduce drift?

Accepted Answer

To keep sensor data accurate over time (and correct any drift), researchers calibrate it by regularly cross-referencing it with participants' diary entries. In practice, this means aligning the timing and values of sensor readings with the real-world events people log in their diaries. For example, if a wearable sensor records physical activity levels, the participant's diary might note "went for a run at 7 PM." If the sensor data doesn't show a spike at that time, it may indicate drift or miscalibration, and the researchers can adjust the sensor's baseline or algorithms accordingly. By periodically checking sensor output against diary-reported actions or feelings (like stress level notes matching heart rate data), any discrepancies can be caught early. This iterative calibration ensures the sensor data stays aligned with reality and improves overall data quality throughout the study.

Question 4

How can I quantify ROI from cross-channel attribution research?

Accepted Answer

Treat your attribution research as an investment and measure what it changed. Start by identifying the optimizations or shifts in strategy that came out of the cross-channel attribution findings - for example, reallocating budget from one channel to another or cutting spend in low-ROI placements. Then track the results of those changes in hard numbers. If moving spend from Channel A to Channel B increased sales or lowered customer acquisition cost, quantify that impact in dollars. Compare those gains (or savings) to the cost of doing the attribution study. For instance, if the research cost $100K but led to adjustments that generated an extra $500K in revenue or saved $200K in wasted spend, you can confidently report a strong return on investment (in this case 5x revenue gain or net $400K benefit). In short, link the attribution insights to actual performance improvements and calculate the difference - that difference is the ROI attributable to the research.

Question 5

How can we prove whether CX dashboard use drives decisions and outcomes?

Accepted Answer

To show that a CX dashboard is influencing decisions, track and document instances where its use correlates with positive changes in the business. For example, measure whether teams or regions that log in to the dashboard frequently also see improvements in customer metrics like satisfaction or churn over time, implying that acting on the data makes a difference. You can supplement this with case studies: gather stories of managers who spotted an issue on the dashboard — say a drop in a service quality metric — took action, and then saw that metric rebound or complaints drop. Some organizations even conduct A/B style pilots (one group uses the dashboard, another doesn't) to directly observe differences in outcomes. By combining usage analytics with performance results and concrete anecdotes, you build compelling evidence that the dashboard isn't just a passive report, but a driver of decisions and improvements.

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Market Research FAQs with Expert Insights

What's the cleanest way to test banking kiosk versus teller journeys?

Do we risk partisan bias when AI classifies verbatims in public opinion research?

How should we calibrate sensor data with diaries to reduce drift?

How can I quantify ROI from cross-channel attribution research?

How can we prove whether CX dashboard use drives decisions and outcomes?