Question 1

How hard is it to recruit Gen Z for observational studies today?

Accepted Answer

Recruiting Gen Z for observational studies is challenging today, but achievable if you adapt to their communication style and values. This generation is often inundated with requests and protective of their time and privacy, so old recruitment methods (like long phone calls or generic email blasts) yield poor results. Instead, researchers see more success through social media outreach, short authentic messages, and emphasizing how the study aligns with something Gen Z cares about (such as social impact or personal growth). Offering flexible participation options also helps - many Gen Z respondents prefer remote observation via their smartphone or a brief video diary over a stranger shadowing them in person. With a meaningful incentive and a relatable approach, Gen Z can and does participate; the key is making the experience convenient and relevant from their point of view.

Question 2

How should loyalty insights influence assortment and pricing strategies?

Accepted Answer

Loyalty program insights should directly guide what products you carry (assortment) and how you price them by revealing which items and deals matter most to your best customers. Analyze your loyalty data to see which products your high-value customers buy frequently or value highly - those are candidates to keep in stock, possibly expand, or feature prominently since they drive repeat business. Conversely, if certain products are rarely bought by loyal members, you might trim or rethink those offerings. On pricing, loyalty insights show where you can differentiate: for instance, if loyal customers have a higher willingness to pay for a premium line, you can maintain healthy margins there, but if they're very price-sensitive on everyday goods, you might implement targeted discounts or bundle deals to keep them happy. In short, use loyalty insights to double down on what your most engaged customers love - shaping your assortment around those preferences - and to set prices or offers that reward loyalty while still protecting profitability. This ensures your strategy caters to your core customers' tastes and value perceptions, which in turn drives further loyalty and sales.

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

When should panel data be combined with transactional or CRM data?

Accepted Answer

Combine panel survey data with customer transaction or Customer Relationship Management (CRM) records when you need to link self-reported attitudes with actual behavior to gain more actionable insights. This integration is especially useful for validating survey responses (like stated purchase intent or satisfaction ratings) against real purchase or usage patterns, enabling more accurate segmentation and predictive models of customer value. It's best done when you have proper consent and data governance in place, and when a clear analysis question demands it — for example, merging a panel's brand perception scores with CRM purchase history to see if high brand favorability translates to repeat sales or loyalty. However, this step is not needed for every study — only merge datasets when the added insight clearly justifies the extra data integration effort and complexity.

Question 5

When are eye-tracking studies most valuable in in-store environments?

Accepted Answer

Eye-tracking is most valuable in-store when visual attention is directly linked to the outcome you care about - usually in contexts like shelf layout optimization, packaging design, or point-of-sale signage. For example, if you're concerned that a new product or promotion isn't being noticed on the shelf, an eye-tracking study can confirm exactly what shoppers see (and ignore) as they scan the display. It's particularly useful before major in-store changes: if you're about to reset a category or introduce new packaging, eye-tracking can validate whether your key items and messages catch the eye in the few seconds shoppers spend in front of that section. Essentially, use it when you need hard evidence of visibility: Are shoppers actually looking at the end-cap display? Do they read that shelf tag or promotional sign? Because eye-tracking requires special hardware and setup, it delivers the best value when those specific questions of "What draws attention?" will inform a big decision (like layout revisions or packaging tweaks). That's when the investment in eye-tracking is justified by the clarity it provides.

C & C Market Research

Why choose Us

Features and attributes

What is C & C Market Research?

Services and Expertise

Market Research FAQs with Expert Insights

How hard is it to recruit Gen Z for observational studies today?

How should loyalty insights influence assortment and pricing strategies?

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

When should panel data be combined with transactional or CRM data?

When are eye-tracking studies most valuable in in-store environments?