Question 1

When should implicit precede qual to uncover hidden associations?

Accepted Answer

It makes sense to conduct an implicit test before qualitative research when you suspect people have hidden biases or gut-level associations that they can't or won't articulate directly. By using implicit methods (like reaction time tasks or implicit association tests) first, you can surface subconscious attitudes — say, a consumer's unspoken negative feeling about a brand element — that might not come up in a standard interview. Those results then guide the follow-up qualitative phase: researchers know what sensitive or subtle topics to probe in depth. In practice, topics involving deep-seated preferences, brand imagery, or socially sensitive attitudes are prime candidates for this sequence — implicit measures reveal the underlying pattern, and one-on-one interviews or focus groups afterward help explain the 'why' behind those hidden associations.

Question 2

How do usability findings translate into executive decisions and priorities?

Accepted Answer

For usability findings to influence executive decisions, you need to frame them in terms of business outcomes or risks that leadership cares about. In practice, this means translating a user experience issue into dollar impacts or key performance metrics: for example, instead of saying "users find feature X confusing," you might show that "feature X's complexity causes 30% of customers to drop out before purchase, costing an estimated $Y in lost sales each month." By quantifying the impact of UX problems (or the benefit of fixing them) on things like revenue, customer retention, or support costs, you elevate them to strategic importance. This approach turns what could be seen as minor UI tweaks into clear opportunities or threats for the business. When executives see a direct line from a usability insight to financial gain or loss, they're much more likely to prioritize it. In short, speaking the language of ROI and risk, backed by data, ensures that UX findings inform high-level priorities and resource allocation.

Question 3

How should we set latencies and cutoffs for reaction-time tasks?

Accepted Answer

Setting latency cutoffs for reaction-time tasks involves analyzing pilot data and statistical norms to filter out implausible responses without losing true signals. Researchers typically eliminate extremely fast reactions (possible accidental key presses) and very slow ones (likely distraction or confusion). For example, if most responses fall between about 500 milliseconds and 3 seconds, you might set those as bounds and treat anything outside as outliers; some researchers simply drop the fastest and slowest 5% as a rule of thumb. The key is to study the distribution of reaction times in a trial run and choose cutoffs that remove obvious noise while retaining genuine responses. It's also important to document these thresholds and the rationale for transparency and easier replication in cognitive or UX studies.

Question 4

Will privacy changes limit UX telemetry for consented in-product studies?

Accepted Answer

Yes, evolving privacy rules and platform changes are likely to narrow what user experience (UX) data you can collect, even when users consent in-app. Recent moves by companies and regulators aim to limit silent background tracking and ensure users explicitly know what's collected. For instance, Apple's iOS now requires apps to get permission for any kind of tracking, and even if a user opts in for an in-product study, the operating system might still restrict certain data points (like precise location or cross-app identifiers). Meanwhile, regulations like GDPR and the upcoming ePrivacy directive push for data minimization, meaning you're expected to collect only what's truly necessary and often need to anonymize it. All of this means UX researchers will need to plan with a leaner dataset - focusing on key metrics and possibly using more in-session surveys or user interviews to supplement telemetry. In short, even with consent, the days of capturing every click and scroll are fading; you'll be working with smaller but hopefully more meaningful data streams.

Question 5

How should we adapt pricing research for markets with cash economies?

Accepted Answer

Adapting pricing research for cash-dominant markets means mirroring how people actually handle money day to day. In cash economies, consumers think in terms of immediate out-of-pocket cost, so frame survey questions or experiments around scenarios like "Would you pay X in cash right now for this product?" rather than abstract pricing or long-term payment plans. Use price points that make sense locally — common round numbers or typical small purchase sizes — because many shoppers operate within those practical limits (like only carrying a certain amount of cash). It also helps to gather qualitative insights by observing or interviewing shoppers: you might find they routinely bargain or save up over time for larger buys, which affects their price sensitivity. With that knowledge, you can adjust your research design to be realistic (for example, including a bargaining element in a hypothetical purchase scenario or using visuals of local currency for context). The goal is to align the research with everyday cash behaviors so that what people tell you about pricing matches what they'd actually do with money in hand.

MARS Research

What is MARS Research?

Recognition

Research Facility

Research Facility Details

Facility Rooms

Research Software and Platform Details

Services and Expertise