Question 1

What practices improve accuracy in scenario planning in research?

Accepted Answer

Scenario planning becomes more reliable when researchers use diverse data inputs and actively challenge assumptions to craft plausible futures. Best practices include incorporating interdisciplinary perspectives and historical analogues so scenarios aren't just reflections of current biases. Quantifying key drivers — for example, using trend data or simulations for each scenario's assumptions — adds rigor to these what-if narratives. It's also important to engage subject matter experts to review and stress-test each scenario, which can expose blind spots or overly optimistic thinking. Defining clear signposts (early indicators for each scenario) further improves accuracy by letting teams monitor which future is unfolding and adjust plans accordingly. By regularly updating scenarios as new information emerges — and combining data-driven modeling with creative foresight — scenario planning yields more credible, actionable guidance for strategic decisions.

Question 2

Do high SUS scores always predict strong long-term retention?

Accepted Answer

High System Usability Scale (SUS) scores are a positive sign of an easy-to-use product, but they don't guarantee long-term user retention. A highly usable app could still struggle to retain users if it lacks compelling content or ongoing value beyond the initial ease of use. Retention often hinges on factors like the product's relevance to user needs, fresh features, and engagement strategies; an interface might score well on usability surveys, yet if users don't find long-term benefit or interest, they will drift away. For example, many novelty apps launch with great usability and receive high SUS ratings initially, but months later usage drops because the core offering wasn't strong enough. In short, usability is crucial, but sustaining user loyalty also requires continuous value delivery, evolving features, and understanding deeper user motivations.

Question 3

How do we defend price changes with evidence customers accept?

Accepted Answer

Defend a price change by presenting customer-driven evidence that the new price aligns with their value perceptions. That means using data on willingness to pay and actual purchase behavior to prove customers will accept the change. For example, pricing research (surveys or conjoint experiments) can show that most buyers would still purchase at the proposed price point_x000D_. If you piloted a price increase or have past data from similar adjustments, highlight that churn was minimal and customer satisfaction held steady. By grounding your case in concrete consumer insights and market testing, you reassure stakeholders that the price change is justified by demand.

Question 4

How are health trends affecting consumer attitudes toward packaged goods?

Accepted Answer

Health and wellness trends are making consumers more scrutinizing of packaged goods, favoring products they see as natural, nutritious, and aligned with their wellbeing goals. Shoppers increasingly read labels for things like sugar content, artificial additives, and sourcing, often avoiding products with too many chemical-sounding ingredients. Popular lifestyle trends such as plant-based diets, low-carb or keto plans, and functional foods (e.g., probiotics for gut health) are shifting demand toward alternative ingredients and fortification. For example, a consumer might choose a snack bar with organic ingredients and added protein over a traditional candy bar, even if the latter is a longtime favorite. This heightened health awareness means packaged goods brands must reformulate or clearly communicate benefits — simply relying on convenience or legacy brand loyalty isn't enough as more consumers shun items seen as overly processed or unhealthy.

Question 5

How can e-commerce research better track abandoned carts and missed conversions?

Accepted Answer

E-commerce teams can sharpen their view of cart abandonment and missed conversions by marrying funnel analytics with direct user feedback. First, ensure your web analytics are tracking the checkout funnel in detail to pinpoint exactly where drop-offs occur (e.g., at shipping, payment, etc.). Then layer on the why: trigger a quick exit survey or follow-up email when someone abandons a cart to ask what stopped them from buying. Loyalty or account data can help connect the dots too - for instance, see if those who abandon online end up purchasing in-store later, indicating a channel switch rather than a true loss. Additionally, review qualitative clues (like session recordings or heatmaps) to spot any UX issues causing frustration. By combining data on where people leave with insight into why, you can zero in on fixes that recapture those missed conversions.

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Market Research FAQs with Expert Insights

What practices improve accuracy in scenario planning in research?

Do high SUS scores always predict strong long-term retention?

How do we defend price changes with evidence customers accept?

How are health trends affecting consumer attitudes toward packaged goods?

How can e-commerce research better track abandoned carts and missed conversions?