Question 1

Will panel-plus Big Data finally fix cross-device deduplication?

Accepted Answer

Combining a panel with big data (sometimes called a "panel-plus" approach) makes cross-device deduplication much more feasible, but it's not a magic bullet that perfectly resolves it in every case. By using a panel of known individuals or households, researchers can calibrate and validate big data algorithms that match devices to people, greatly reducing duplicate counts across phones, tablets, and TVs. For example, a panel might tell you that a certain cookie ID and a mobile device belong to the same person; feeding that ground truth into a big data model helps identify similar links at scale. This hybrid method has indeed closed a lot of gaps in cross-device audience measurement - it's far more accurate than relying on big data signals or panels alone. However, challenges remain: not all users are represented in panels, and privacy changes (like restrictions on tracking technologies) can limit data quality. So while the panel-plus-big-data strategy goes a long way toward solving cross-device duplication by leveraging the strengths of both, it's an improved solution rather than a flawless "final fix."

Question 2

Is an RCT always the gold standard for public programs?

Accepted Answer

Randomized controlled trials (RCTs) are often considered the gold standard for evaluating public programs because they can show cause and effect clearly, but they are not always feasible or appropriate. In real-world public interventions, strict random assignment may be unethical, too costly, or impractical — if an initiative must serve an entire community or it's urgent, you can't withhold the program from a control group just to run an RCT. For example, a city-wide education reform or disaster relief effort can't randomly exclude some residents; instead, evaluators might use quasi-experimental methods like matched comparisons or pre-post studies to assess impact. In essence, an RCT is ideal when conditions allow, but many public sector evaluations rely on alternative rigorous designs to get credible results when an RCT isn't possible.

Question 3

Which decisions benefit most from shop-alongs or in-store observation?

Accepted Answer

Shop-alongs and in-store observation are most useful for decisions that hinge on real-world shopper behavior — especially things like store layout, shelf placement, packaging design, and point-of-sale marketing. For example, if a product is underperforming, observing shoppers in the aisle can reveal whether the item is hard to find, poorly merchandised, or losing attention to a competitor's display — insights that surveys or sales data alone might miss. These methods excel at uncovering the "why" behind shopping choices, informing decisions on everything from signage and store navigation to assortment mix and in-store promotions. In short, any decision aimed at improving the in-store customer experience or shopper journey benefits from the firsthand perspective that shop-alongs provide.

Question 4

Does higher ad attention always lead to increased sales or renewals?

Accepted Answer

Not always. Higher ad attention is generally a good thing, but it doesn't guarantee more sales or renewals unless that attention translates into persuasive impact. Ads need to be noticed to work — so attention is a critical first step — but a viewer could pay close attention and still decide not to buy. Maybe the ad was entertaining or provocative (hence the attention) but the product or offer didn't resonate. In such cases, you get eyeballs but no action. Sometimes attention metrics can even be misleading; for example, an eye-catching gimmick might draw focus, but if it's not tied to the brand message, it won't drive results. Ultimately, increased sales or renewals happen when an attentive audience also finds the ad relevant, believable, and motivating. Think of attention as the door-opener — what happens after, in terms of message and product experience, determines if that attention converts into business outcomes. So while more ad attention usually helps, it's not a guarantee on its own without the right follow-through.

Question 5

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.

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Market Research FAQs with Expert Insights

Will panel-plus Big Data finally fix cross-device deduplication?

Is an RCT always the gold standard for public programs?

Which decisions benefit most from shop-alongs or in-store observation?

Does higher ad attention always lead to increased sales or renewals?

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