Question 1

What challenges arise when running quant experiments across multiple markets?

Accepted Answer

Running quantitative experiments across multiple markets introduces cultural, logistical, and analytical challenges. One big hurdle is ensuring the survey or test is equivalent in each country - translations and local context can inadvertently change the meaning of questions or tasks, affecting results. Additionally, sampling must be carefully balanced: you might face differing response rates or panel quality by region, which can skew comparisons. Operationally, coordinating timing (time zones, launch dates) and data collection across markets adds complexity. Even after collecting data, analysts must account for structural differences (e.g. one market's consumers might generally give higher ratings than another's). All of this means you need rigorous planning: use expert local input to adapt materials appropriately, standardize procedures as much as possible, and apply statistical techniques (like normalization or separate analysis by market) to ensure valid cross-market comparisons.

Question 2

When are conjoint results more convincing than sales team's pricing instincts?

Accepted Answer

Conjoint analysis results become more convincing than the sales team's gut instincts when you need objective, customer-backed evidence to settle pricing debates. Sales instincts are valuable, but they often come from a handful of big deals or personal impressions, whereas a well-run conjoint study quantifies preferences across a broad market sample. If conjoint data shows, for example, that customers are willing to pay a premium for certain features (or that a mid-priced bundle is optimal), you can use those numbers to challenge or confirm a sales team's hunch in a concrete way. It's especially powerful in situations where instincts conflict or stakes are high - like launching a new product or entering a new segment - because conjoint provides a simulation of market choices rather than anecdotes. By illustrating trade-offs (perhaps revealing that customers would drop an ancillary service to get a lower price, contrary to what sales assumed), the conjoint results give a fact-based narrative that can sway skeptics.

Question 3

How should we set global templates without stifling local creativity?

Accepted Answer

Design global templates with a flexible framework: establish the core elements that must be consistent worldwide but leave room for local adaptation. For example, define standard metrics, questions, or brand elements that every market's version must include for consistency, while allowing regions to add custom sections, examples, or language nuances to fit their audience. In practice, many organizations use an 80/20 rule--around 80% of the template is standardized for global consistency, while 20% is open for local teams to tailor. Make it clear which parts of the template are mandatory and which are optional or modifiable, and even invite local teams to contribute ideas to the global template guidelines. This approach maintains a common structure for comparability but empowers local creativity, ensuring the template guides quality without becoming a one-size-fits-all straitjacket.

Question 4

What kinds of expertise are professionals willing to share with researchers?

Accepted Answer

Most professionals are willing to share broad expertise and industry knowledge with researchers, especially when questions respect boundaries around confidential information. They'll often discuss general best practices, emerging trends, and challenges in their field, as well as personal experiences that led to insights. For instance, a tech manager might explain how they approach a common problem or evaluate solutions, without revealing their company's proprietary strategy. Professionals also tend to share opinions on what works or doesn't in the market and unmet needs they observe. The key is trust: if researchers clearly state the purpose and ensure anonymity or confidentiality, experts usually open up about their know-how and lessons learned. Avoid pressing for trade secrets, and you'll find most are eager to contribute their perspective to support the research.

Question 5

How are teams using observational methods to understand consumer use of AI?

Accepted Answer

Teams are using direct observation - both in-person ethnography and digital tracking - to understand how consumers actually use AI in their daily lives. For example, researchers have watched families use voice assistants in their homes, which reveals unspoken habits or frustrations. This kind of direct observation uncovers real behaviors - maybe noticing that people rely on a workaround or only use 10% of an AI tool's features - which would never come up through self-reported questionnaires. Another approach is to use user-recorded diaries or screen videos - for example, having people record their screen and narration while using a new AI app - to capture their step-by-step reactions. Watching real usage reveals pain points (confusion, mistrust) and unexpected benefits that surveys might miss. Those insights feed directly into refining the AI product.

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Market Research FAQs with Expert Insights

What challenges arise when running quant experiments across multiple markets?

When are conjoint results more convincing than sales team's pricing instincts?

How should we set global templates without stifling local creativity?

What kinds of expertise are professionals willing to share with researchers?

How are teams using observational methods to understand consumer use of AI?