How to Run Multi-Market Research Without Losing Comparability

Multi-market research sounds straightforward — until it isn’t.

What begins as a single brief can quickly turn into a patchwork of methods, translations, timelines, and interpretations that don’t quite line up. And when comparability slips, so does confidence in the insight.

So how do you run international research that stays locally relevant and globally consistent?

Here’s what actually works.

Why Multi-Market Research Often Goes Wrong

Most issues don’t come from data collection — they come from design decisions made too late.

Common pitfalls include:

• Markets adapting questions independently
• Inconsistent recruitment criteria
• Translation that changes meaning, not language
• Different fieldwork approaches by country
• Local timelines drifting out of sync

Individually, these seem minor. Collectively, they undermine comparability.

Step 1: Design for Comparability From Day One

Comparability isn’t something you “fix in analysis”.
It has to be designed in at the start.

That means:

• Agreeing core objectives before local nuances
• Locking down non-negotiable questions
• Defining which elements can flex by market

A strong global framework gives local teams freedom — without fragmenting the data.

Step 2: Get Recruitment Right (Not Just Fast)

Sample quality is one of the biggest drivers of variation across markets.

Key principles:

• Use equivalent screening logic, not just similar questions
• Align quotas around behaviour, not demographics alone
• Pressure-test criteria across cultures

What counts as a “decision-maker” or “heavy user” can differ dramatically by market if you’re not careful.

Step 3: Treat Translation as a Research Task, Not Admin

Poor translation is one of the most common — and costly — causes of non-comparable data.

Best practice includes:

• Conceptual translation, not literal wording
• Back-translation for key questions
• Researcher review, not just linguist sign-off

If a question doesn’t land the same way emotionally, it won’t measure the same thing.

Step 4: Standardise Methodology, Not Experience

A frequent mistake is forcing identical execution everywhere.

Instead:

• Standardise method (e.g. LOI, structure, stimulus exposure)
• Adapt experience (e.g. moderation style, examples, probes)

This protects the integrity of the data while respecting cultural norms.

Step 5: Keep Fieldwork Tight and Centralised

Staggered fieldwork creates interpretation risk.

Where possible:

• Field markets in parallel
• Use centralised quality control
• Monitor early data for drift

Real-time oversight is often the difference between clean comparisons and messy explanations later.

Step 6: Analyse Globally, Interpret Locally

The strongest international programmes combine:

• A single global narrative
• Clear market-level context
• Explicit explanation of differences

Not all variation is a “problem” — but you need to know why it exists.

What Good Multi-Market Research Looks Like

When done well, international research delivers:

• Confidence in global decisions
• Clear understanding of local nuance
• Faster stakeholder buy-in
• Fewer “but is this really comparable?” questions

And crucially — fewer caveats in the final presentation.

The Bottom Line

Multi-market research doesn’t fail because markets are different.
It fails when those differences aren’t planned for.

Design for comparability early, control the fundamentals, and let nuance live where it belongs — in interpretation, not methodology.

Planning a Multi-Market Study?

At Skopos, multi-market research is built into how we work — from design and recruitment through to analysis and delivery.

If you’re planning research across regions, categories, or customer types, we’re happy to sanity-check your approach before fieldwork begins.

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