Question 1

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 2

Does longer time spent in-store always signal greater engagement?

Accepted Answer

Not necessarily — spending more time in-store doesn't automatically equate to greater engagement. Extra time can be positive (browsing multiple aisles, trying demos, asking questions) or negative (searching for a product due to poor signage, waiting in a long checkout line). For example, a shopper lingering by choice to compare options is likely engaged, but one stuck waiting for assistance is experiencing frustration, not delight. Context and companion metrics matter: if longer visits coincide with higher conversion rates or interaction with displays, that suggests genuine engagement. But if baskets stay empty or complaints rise with longer store time, the time is reflecting friction. In short, more time in a store is only a good sign when it's spent meaningfully — otherwise it may signal issues rather than enthusiasm.

Question 3

How should we test pricing with AI agents before conjoint with people?

Accepted Answer

Before investing in a full conjoint survey with real people, you can run preliminary pricing experiments using AI-driven agents that simulate consumer behavior. These AI agents — trained on past purchase data or decision rules — can quickly explore how virtual shoppers might respond to various price points or bundles. By observing these simulations, you identify promising pricing options and eliminate clearly suboptimal ones. The key is to calibrate your AI models carefully: ensure the agent's preferences or elasticities reflect reality so the insights are meaningful. For example, you might simulate thousands of purchase decisions with an AI model to find the price point where demand drops off, then focus your human conjoint questions around that critical range. Once the AI testing narrows down the field, validate those pricing scenarios with a conjoint study among actual customers to confirm the AI's predictions hold true.

Question 4

What practices improve reliability in price elasticity studies?

Accepted Answer

To improve reliability in price elasticity studies, design the research to mimic real-world buying conditions and force realistic trade-offs. Use scenarios that simulate actual purchase environments: for instance, show products alongside competitors or within a budget, so respondents react as they would while shopping. Advanced techniques like choice-based conjoint or simulated purchase experiments yield more stable data than simply asking "Would you buy at $X?" because they capture true trade-offs, especially if you randomize price presentations to avoid order bias. Finally, whenever possible, cross-validate survey findings with real sales or past data to ensure the elasticity estimates align with actual behavior. By grounding the study in realism and validating the output, you'll get price elasticity insights you can trust for decision-making.

Question 5

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.

Blue Yonder Research

Features and attributes

What is Blue Yonder Research?

Services and Expertise

Market Research FAQs with Expert Insights

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

Does longer time spent in-store always signal greater engagement?

How should we test pricing with AI agents before conjoint with people?

What practices improve reliability in price elasticity studies?

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