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The Rise of In-App Shopping in Vietnam: Research Blind Spot?

21/05/2025

Vietnam’s e-commerce market has transformed at breakneck speed in recent years. From cautious digital browsing to daily online purchasing, the shift has been remarkable. But alongside this rapid growth, a new behavior is emerging—one that’s exciting for brands and concerning for researchers. It’s the rise of in-app shopping, particularly through social platforms and livestreams. And while it’s convenient for consumers and profitable for platforms, it’s quietly becoming one of the biggest blind spots in modern market research.

Marketing vs. Reality: What Vietnamese Shoppers Say Online vs. What They Do In-Store

21/05/2025

In Vietnam’s fast-growing consumer landscape, brands are becoming increasingly reliant on social media and digital platforms to track trends, plan product launches, and craft marketing strategies. TikTok videos, Facebook groups, YouTube reviews, and Instagram stories are now treated as vital data sources. Consumers appear eager to share their opinions publicly, engaging with products through unboxings, haul reviews, “get ready with me” clips, and more. This creates the impression that the voice of the market is now louder, clearer, and more real-time than ever before. But as digital access widens, a new gap is emerging—one that quietly divides what Vietnamese shoppers say online from what they actually do in-store.

How to Spot a Fake Respondent (Before It’s Too Late)

21/05/2025

In market research, your insights are only as good as the people you talk to. You can have a flawless questionnaire, a great moderator, and top-tier analysis—but if your respondents aren’t real, genuine, and qualified, your entire study is at risk.

Unfortunately, fake respondents—people who misrepresent themselves, cheat screeners, or participate solely for incentives—are more common than you might think. Especially in urban studies with open recruitment, online panels, or high incentives, the risk increases dramatically.

So how do you spot them? And more importantly, how do you stop them before their data damages your conclusions?

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