Question 1

How can cross-channel research address fragmentation in digital ecosystems?

Accepted Answer

Cross-channel research combats the fragmentation of digital ecosystems by stitching together data and insights from all the platforms and devices a customer uses. It does this by tracking and linking interactions across channels — websites, mobile apps, social media, email, and even in-store systems — often using common user IDs or analytics techniques to connect the dots. For instance, integrated tracking might reveal that a customer who ignored an email offer later converted after seeing a related social media ad--a link that would be missed if each channel were viewed in isolation. By designing studies to follow the entire customer journey (for example, combining web analytics with mobile usage data and follow-up surveys), researchers can see how each touchpoint influences the next. This integrated approach yields a holistic view of customer behavior: instead of siloed channel snapshots, you get a continuous narrative that bridges gaps between platforms and reduces blind spots.

Question 2

Does higher click-through rate always mean higher campaign success?

Accepted Answer

Not necessarily - a higher click-through rate (CTR) is encouraging, but it doesn't guarantee overall campaign success. CTR tells you people are clicking, but you have to ask: what happens after the click? It's possible to have a sky-high CTR yet see those visitors bounce without converting, meaning lots of interest but no sales or sign-ups. For example, a flashy banner might attract many clicks (boosting CTR) but if it's misleading or not targeting the right audience, those clicks won't translate into meaningful results. In contrast, another ad with a modest CTR could be attracting fewer - but highly qualified - prospects who actually convert at a high rate. Ultimately, success metrics like conversions, cost per acquisition, or revenue need to be weighed alongside CTR. A high CTR is only truly good news if those clicks align with your campaign goals; otherwise, it can be a vanity metric.

Question 3

How should in-store insights feed into packaging and planogram design?

Accepted Answer

In-store shopper insights should directly inform how products are packaged and arranged on shelves (planograms) so designs align with real customer behavior. For example, if observations show customers overlooking a product because its packaging blends in, the design team can update the packaging with bolder colors or clearer labels to stand out. Likewise, if insight reveals that certain items get more attention at eye level or next to complementary products, those details should feed into the planogram layout to optimize placement. The idea is to loop actual store findings - like traffic patterns or shopper comments - back into design decisions. By adjusting packaging and shelf plans based on how people navigate and decide in-store, companies ensure their product presentation is grounded in reality, leading to better visibility and sales.

Question 4

How do I compare ROI from mobile ethnography with classic in-person methods?

Accepted Answer

To compare the return on investment (ROI) of mobile ethnography versus classic in-person ethnography, evaluate both the costs and the type of insights each yields for the same research goals. Mobile ethnography (participants using smartphone apps to record diaries, photos, or videos) generally has lower fieldwork costs and scales more easily - you can engage many participants at once across locations without travel expenses. Traditional in-person ethnography requires a researcher on-site with each participant, which is expensive and time-intensive but can produce very deep, contextual insights that might be missed remotely. In assessing ROI, consider metrics like cost per insight or actionable finding: a mobile study might deliver a higher volume of observations per dollar, while an in-person study might provide more profound revelations per participant. Also factor in speed (mobile studies often finish faster) and overall impact (did one method uncover key insights that the other didn't?). By weighing total spend against the relevance and depth of insights gained, you can judge which approach gave better ROI for your needs.

Question 5

Which concept screening KPIs best predict in-market performance?

Accepted Answer

Metrics like strong purchase intent (especially the top-box "definitely would buy" response) and high uniqueness or relevance scores in concept screening tend to be the best predictors of how a product will perform in-market. When a concept's survey results show a large share of respondents saying they are very likely to buy, that's usually a positive signal for trial. But purchase intent alone isn't foolproof — so researchers also look at whether the concept is seen as unique and if it addresses a real need (i.e. relevance). In practice, concepts that go on to succeed typically score well on a combination of KPIs: a top-tier purchase intent percentage coupled with high ratings for appeal and differentiation. For example, a concept that not only generates a strong "would buy" score but also stands out as novel and solves a clear customer pain point is much more likely to translate into solid in-market performance. No single metric guarantees success, but those key indicators together provide the best gauge of a concept's potential.

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