Question 1

How should we test ESG preference capture without biasing choices?

Accepted Answer

You should test ESG (Environmental, Social, Governance) preferences in a way that hides the "right" answer and forces real trade-offs, so people's choices aren't swayed by wanting to look virtuous. One effective approach is to embed ESG factors into a choice experiment or conjoint analysis: for example, present products or investments with varying attributes (price, quality, an ESG feature) and see which options people pick. This way, instead of asking directly "How important is sustainability to you?" (which can bias people to say it's important), you observe whether they actually choose a greener option when it costs more or has some trade-off. Also, keep wording neutral and specific — mention concrete attributes like "made with recycled materials" rather than emotionally charged labels like "eco-friendly," so respondents aren't led by the phrasing. By designing the study around implicit or indirect choices, you capture how much ESG truly matters in decision-making without tipping off respondents to the answer that sounds good.

Question 2

What practices keep innovation pipelines aligned with consumer needs?

Accepted Answer

Keeping an innovation pipeline aligned with consumer needs means weaving customer feedback into every stage of development and validating ideas early and often. In practice, that might start with exploratory research or social listening to spot emerging pain points, then co-creating solutions with consumers through workshops or concept tests. As ideas progress, iterate on them with real users--build quick prototypes or beta versions and let target customers try them out, so you catch misalignments before a full launch. Also, keep an eye on real-world data and trends continuously; if consumer behavior or preferences shift, be ready to pivot your pipeline priorities accordingly. In short, it's an ongoing loop: every major idea gets shaped by direct consumer input, from initial concept to final product, so you're consistently developing solutions that address genuine customer needs rather than internal hunches.

Question 3

How can we show how new positioning improves acquisition and retention?

Accepted Answer

Demonstrate that a new market positioning is improving acquisition and retention by linking it to measurable changes in customer metrics through tests or before-and-after analysis. One approach is to run an A/B test or pilot launch: introduce the new positioning (messaging, branding, value proposition) to one segment or region and compare the customer acquisition rate and retention (or churn) against a control group still using the old positioning. If an experiment isn't feasible, analyze trends over time - do you see higher sign-ups, increased conversion from leads to customers, or longer customer lifespans after the repositioning rollout compared to prior periods (adjusting for seasonality or other market changes)? You can also gather qualitative evidence, like customer surveys indicating better brand perception or reasons for purchase that align with the new messaging. By combining these data points - such as a boost in monthly new users and a drop in churn post-rebrand - you can show a cause-and-effect link between the new positioning and improved acquisition and retention.

Question 4

How do we decide between CX dashboard templates & market-specific CX KPIs?

Accepted Answer

Choose your customer experience (CX) metrics approach by balancing the need for consistency with the need for local relevance - often a hybrid model works best. Start with a core dashboard template of universal CX KPIs (Key Performance Indicators) that every market tracks, ensuring everyone speaks the same language on fundamental measures like NPS or overall satisfaction. Then, allow for market-specific KPIs to be added in regions where unique drivers of customer experience exist (for example, a delivery speed metric in one market with logistical challenges, or a cultural preference metric in another). This way you maintain comparability across markets on the basics while still capturing what matters most in each locale. Deciding factor should be whether a local metric reflects something truly critical to customer loyalty in that market - if so, include it alongside your global dashboard, rather than forcing a strict one-size-fits-all or fragmenting everything by market. The result is a layered CX measurement system: common benchmarks company-wide, plus tailored insights that keep local teams engaged and focused on the right priorities.

Question 5

Can AI pricing models outperform traditional conjoint in predicting purchase?

Accepted Answer

AI-driven pricing models can sometimes predict actual purchase behavior better than a traditional conjoint study, especially when they have rich real-world data to learn from. Conjoint analysis relies on what people say they'd choose in a hypothetical scenario, which isn't always how they behave when it comes time to buy. An AI model can analyze large amounts of actual sales data and customer behavior, picking up nuanced patterns (like how demand shifts with time of day, or how different customer segments respond to price changes) that a conjoint might miss. In areas like e-commerce or retail with dynamic pricing, such AI models can continuously adjust and often outperform the more static conjoint-based predictions. However, AI models need that data history. For a new product with no sales track record, conjoint is still very useful to estimate price sensitivity. In practice, these approaches can complement each other: conjoint might guide initial pricing, and then AI refines it once the product is in market. So under the right conditions, AI pricing tools can outdo conjoint in accuracy, but both have their place.

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Market Research FAQs with Expert Insights

How should we test ESG preference capture without biasing choices?

What practices keep innovation pipelines aligned with consumer needs?

How can we show how new positioning improves acquisition and retention?

How do we decide between CX dashboard templates & market-specific CX KPIs?

Can AI pricing models outperform traditional conjoint in predicting purchase?