Question 1

What practices ensure path-to-purchase studies capture omnichannel behavior?

Accepted Answer

You have to design the study to follow customers across all touchpoints, not just one channel. In practice, that means using multiple data collection methods and breaking down silos. For example, combine surveys (or mobile diaries) that ask shoppers to recount every step of their buying journey with hard data from different channels (web analytics, app usage, in-store purchase records). This way, if a consumer searches online, clicks an ad, then buys in a physical store, your study captures each link in that chain. Make sure to prompt respondents about various channels ("Which of these did you use to research? Store visit, website, social media, etc.") so they remember less obvious steps. Also, ensure collaboration between teams - e-commerce, retail, and marketing data should be integrated so you're not missing parts of the journey. By triangulating self-reported behavior with actual transaction or click-stream data, and by intentionally looking at interactions between channels, your path-to-purchase study will reflect true omnichannel behavior rather than a siloed view.

Question 2

How should in-store research adapt to declining foot traffic?

Accepted Answer

In-store research should adapt to declining foot traffic by becoming more flexible and omnichannel. One approach is to complement the shrinking in-person sample with digital outreach. If fewer people are walking in, engage them through their smartphones or computers instead: send quick mobile surveys via your store's app, or follow up with online shoppers to ask about their experience (and why they maybe skipped a store visit). You can also use loyalty or sales data to identify customers who haven't visited lately and reach out via email or text for their feedback. For those who do come into the store, make each interaction count by gathering deeper insights from the smaller pool. Conduct detailed shop-alongs or exit interviews to understand their behavior and needs more thoroughly. Additionally, consider moving some research outside the store - for instance, set up a feedback booth at a community event or near a store entrance to catch input from passersby who might not be shopping inside. By blending limited in-store observations with online surveys, mobile feedback, and outreach in alternative venues, you can still gather meaningful shopper insights even as traditional foot traffic declines.

Question 3

What practices prevent panel fatigue and improve long-term respondent quality?

Accepted Answer

To prevent panel fatigue and maintain high-quality responses over the long run, research teams should avoid over-surveying participants and keep participation rewarding. Effective practices include capping how often each panelist is invited to studies, designing shorter surveys that respect respondents' time, and varying topics or formats to sustain interest. For example, a panel program might rotate members for certain surveys or periodically give top participants a break, so no one feels burned out. Regularly refreshing the panel with new members and offering meaningful incentives or feedback (like sharing study findings) also signal that respondents are valued, not just data sources. When people have positive, engaging experiences at a reasonable cadence, their attention and honesty remain high - improving data quality in every successive study.

Question 4

How are loyalty programs evolving in response to inflation and digital-first?

Accepted Answer

Loyalty programs are evolving by offering more immediate value and personalized digital experiences to counter inflation and serve a digital-first customer base. With inflation squeezing budgets, many programs have shifted to provide instant rewards — like cash-back credits, fuel discounts, or same-day coupons — rather than making members wait to accumulate points, so customers feel real savings on everyday purchases. At the same time, loyalty has gone fully digital: mobile apps and online portals now deliver personalized offers in real time, and programs use data to tailor rewards (for example, suggesting discounts on items a particular customer buys often). Gamification features such as challenges, tiers, and badges on these apps keep members engaged, while partnerships (e.g., linking a grocery store loyalty program with a gas station) add extra value to help offset rising costs. Overall, flexibility and relevance have increased — members can easily access and use their perks through digital channels, and the rewards are structured to meaningfully help with inflation-driven price concerns.

Question 5

Is eNPS enough to guide action, or do we need more employee data?

Accepted Answer

Employee Net Promoter Score (eNPS) by itself is a limited gauge; it provides a pulse of employee sentiment, but guiding action usually requires more detailed data. While eNPS can flag whether employees overall would recommend the workplace, it won't tell you why or pinpoint specific pain points. Companies should pair eNPS with other feedback sources — full engagement surveys, pulse checks on topics like career growth or manager support, and even open-ended comments — to understand the drivers behind the score. Also, consider objective data like turnover rates or productivity metrics to see if they correlate with eNPS trends. Using this broader set of employee insights, leadership can confidently design interventions (e.g., improving training if growth opportunities are lacking) rather than chasing a single metric.

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Market Research FAQs with Expert Insights

What practices ensure path-to-purchase studies capture omnichannel behavior?

How should in-store research adapt to declining foot traffic?

What practices prevent panel fatigue and improve long-term respondent quality?

How are loyalty programs evolving in response to inflation and digital-first?

Is eNPS enough to guide action, or do we need more employee data?