The Marketplace That Made Brands Stop Chasing Follower Counts

Based on the research ofShi, He, and Ba, "Navigating the Influencer Marketplace: Long-Tail Effects and Content Strategies," Information Systems Research, 2026

Standard platform economics has a well-worn prediction for what happens when you make it easier to compare sellers: attention and money concentrate further on whoever is already winning. Superstar economics, network effects, discoverability algorithms that reward existing popularity, all of it points toward the same outcome, a market that gets more top-heavy as friction disappears. A new study of RedNote's influencer marketplace, published in *Information Systems Research*, finds the reverse. When the Chinese lifestyle and social-commerce platform introduced centralized tools for brands to search and compare creators, sponsorship money moved away from the biggest names and toward the middle of the market.

The Old Default: When in Doubt, Pick the Famous One

Before platforms like RedNote built formal marketplaces, brands picking influencers were working with limited information. A creator's true audience quality, engagement authenticity, and brand fit are hard to verify from the outside, and follower count became the obvious stand-in, a number anyone could see, correlated loosely with reach, and cheap to act on. Research on influencer marketing effectiveness has repeatedly flagged this as a blunt instrument: a meta-analytic review in the *Journal of the Academy of Marketing Science* synthesizing the influencer-marketing literature notes how inconsistently follower size predicts actual campaign outcomes, even though it remained a dominant heuristic for years.

That heuristic had a side effect. When verifying quality is expensive, buyers default to famous names because fame itself is a signal, however imperfect, that reduces risk. The Federal Reserve Bank of St. Louis has described the resulting influencer labor market in stark terms: a small number of creators capture disproportionate earnings and attention while the large majority earn comparatively little, a pattern consistent with winner-take-all dynamics found in other talent markets. That is the baseline this study interrupts.

RedNote as a Natural Experiment

RedNote, known in Chinese as Xiaohongshu, or "Little Red Book", is a Shanghai-based platform that blends Instagram-style visual sharing, Pinterest-like discovery, and embedded shopping. It has built its user base, now in the hundreds of millions of monthly active users, largely around lifestyle, beauty, and travel content, skewing toward a young, female audience. The platform's profile jumped internationally in January 2025, when the looming U.S. TikTok ban sent a wave of self-described "TikTok refugees" onto RedNote within days, briefly making it the most-downloaded app in the U.S. App Store, a migration substantial enough that it has since been documented in its own academic research. That moment made RedNote newly visible to Western observers, but the platform had already been running a structural experiment relevant well beyond China: what happens to sponsorship allocation when a platform builds formal search-and-match infrastructure connecting brands to creators.

The researchers, Lanfei Shi, Shu He, and Sulin Ba, used the launch of that infrastructure as a natural experiment. Comparing more than 152,000 posts from 1,161 influencers before and after the marketplace's introduction, they could isolate what changed once brands gained centralized tools to search, filter, and compare creators directly, rather than relying on reputation and follower counts as rough proxies for quality.

Why Cutting Search Costs Flattens Instead of Concentrates

The mechanism the paper identifies runs through search costs and quality uncertainty, drawing on the classic economic logic of costly search: when it is expensive to evaluate every option, buyers rationally settle for a trusted default rather than exhaustively comparing alternatives. Fame served as that default in the influencer market. Once RedNote's marketplace let brands filter creators by verified engagement data, audience demographics, and content history, the cost of evaluating a relatively unknown micro- or nano-influencer dropped sharply. Brands no longer needed to lean on follower count as a stand-in for trustworthiness, because the platform now supplied more direct evidence.

The result documented in the paper is that medium-tier influencers received significantly more sponsorship after the marketplace launched, relative to top-tier creators. This is the part that cuts against standard platform-economics intuition: reducing friction and increasing transparency in a matching market is usually expected to reinforce existing advantages, since well-known incumbents benefit most from wider reach and lower discovery costs, too. Here, transparency did the opposite job. It didn't make the famous more findable, it made the previously unverifiable *safe*, and safety had been the top tier's main proprietary asset.

Transparency didn't make the famous more findable. It made the unknown safe to bet on, and that was the top tier's actual advantage all along.

Two Creator Strategies, One Redistributive Shock

The paper also traces how influencers themselves responded, and the two groups adapted in opposite directions. Medium-tier creators, suddenly facing more sponsorship demand and more scrutiny that comes with it, shifted toward posting more organic, diverse content, protecting the perceived authenticity that had become their competitive asset now that brands could actually verify it. Top-tier creators, meanwhile, responded to relatively softer demand growth by improving the quality of their sponsored posts, defending their premium positioning rather than competing on volume.

That divergence matters for anyone modeling creator-economy dynamics. It's not just that dollars moved; the content ecosystem itself became more differentiated, with mid-tier creators doubling down on trust-building organic content precisely because the marketplace now made that trust economically valuable and verifiable, rather than something only reputation could convey.

What This Means Beyond RedNote

The finding has a specific scope worth respecting: it describes one platform, one marketplace launch, and one measurement window, and the paper's own framing is about influencer marketplaces specifically, not platform search generally. But the mechanism, cheap verification substituting for costly reputation as a risk-reduction device, is not unique to Chinese social commerce. It echoes older platform-economics findings that better information can undercut the safety premium enjoyed by incumbents in other two-sided markets, from freelance marketplaces to online lending. What RedNote's case adds is a clean before-and-after comparison inside a single large platform, with actual sponsorship behavior rather than survey intentions.

For platform operators building or expanding brand-creator marketplaces, the practical implication is that "make search better" is not a neutral infrastructure choice, it is a redistributive one. It can widen the base of creators who can plausibly monetize, which supports platform health and content diversity, but it will also reallocate demand away from the biggest names the platform has spent years cultivating relationships with, a tradeoff worth planning for rather than discovering after the fact.