Nike leads the US sneaker market, Skechers and Jordan convert best

Edited by Avi Yashchin, CEO and Co-Founder · August 17, 2026

A brand strategist deciding where to point next quarter's marketing budget needs one fact fast: Nike still leads the US sneaker category on the metrics buyers actually track. Nike holds the top spot in aided awareness, consideration, and preferred-brand share among major US sneaker brands, while Skechers is closing the gap and Jordan converts a small footprint into an outsized preference rate. None of those three facts tells the strategist which lever, price, style, an endorsement, or distribution, would actually move a buyer tomorrow.

• Nike leads major US sneaker brands on aided awareness, consideration, and preferred-brand share, though Skechers is gaining ground on Nike specifically in consumer consideration (YouGov).
• Nike's global athletic-footwear share fell to roughly 22.9 percent in 2025, a third straight year of decline (Front Office Sports). A separate measure, Heuritech's tally of the Nike-Adidas-New Balance grouping, put Adidas at about 26 percent of that three-brand group in 2025, versus Nike's 34 percent and New Balance's 14 percent (SGB Online).
• Skechers posted the steadiest growth in social visibility of any major sneaker brand in 2025, up 22 percent year over year, per Heuritech's Global Footwear Insights study (SGB Online).
• Jordan's high preferred-brand ratio among a small aware base looks like efficient conversion, but a funnel ratio can't separate real preference strength from who was left in the denominator.
• The decision a buyer actually needs to make, whether to move on price, style, an endorsement deal, or distribution, requires a randomized experiment, not a self-report funnel, to identify which lever moves people and by how much.

What "Nike leads the sneaker market" actually measures

Nike's lead is a brand-tracking result: highest aided awareness, highest consideration, highest self-reported "preferred brand" share among major US sneaker brands, per YouGov's ongoing tracking, which also shows Skechers gaining consideration share against Nike specifically (YouGov). That lead sits inside a US sneaker market projected near $25.6 billion in 2025, with running and walking categories driving most of the growth, categories where Nike has not kept pace (Footwear Magazine). That figure is a forecast made before the year closed, not an audited total, so it can shift as final 2025 data comes in. The erosion shows up globally too: Nike's athletic-footwear share fell to about 22.9 percent in 2025, a third consecutive annual decline, while GlobalData forecasts Adidas continuing to take share through the year (Front Office Sports; GlobalData). Leading a funnel and losing share are not contradictory. They are two different measurements, and only one of them describes what a buyer would do if something changed.

Why do Skechers and Jordan convert their aware base better than Nike?

Because the ratio's denominator is smaller, not necessarily because either brand is stronger. Nike's awareness base includes nearly everyone who owns a television or a phone, so its "preferred brand" percentage has to clear a huge, unfiltered pool of casual, indifferent, and even hostile respondents. Skechers and Jordan have smaller aware bases, and the people who remain in that smaller pool have already self-selected toward the brand, so a higher share of them say they prefer it. Heuritech's Global Footwear Insights study shows Skechers gaining the steadiest ground in social visibility of any major brand in 2025, a different metric from the preferred-brand ratio, but a real signal (SGB Online). Visibility and conversion ratios track different things, and a rising ratio on a smaller base and a rising ratio on a larger base are not directly comparable without knowing who dropped out along the way.

What the funnel can't tell a buyer

A funnel percentage is recall and self-report, not behavior. A respondent who names Skechers as their preferred brand hasn't told anyone whether that preference comes from price, comfort positioning, an athlete tie-in, retail distribution, or just a recent ad they happened to see. The funnel has no counterfactual built in: it cannot say what happens to Nike's preference share if Nike cuts price 15 percent, what happens if Jordan adds a mid-tier SKU, or what happens if Skechers increases media spend. Those are the actual questions a strategist is trying to answer before committing budget, and a stacked bar of aware, considered, purchased, preferred has no mechanism for attributing movement to a specific, changeable lever.

Which lever would actually move a buyer? Randomized experiments, not more survey questions

A randomized experiment answers this by varying the attributes that matter, price, style, endorsement, distribution, across simulated buyers and observing which changes shift choice. The causal identification comes from the randomized manipulation itself, not from the statistical model used to analyze the results. McFadden's discrete choice model, Mixed Logit, and ICLV are estimators that turn randomized choice data into attribute-level effects; they are not causal methods on their own, and a study is only as trustworthy as the randomization behind it. One caveat matters for any substitution question, such as which brand a Nike buyer would move to: a flat logit model carries the independence of irrelevant alternatives assumption, meaning it treats a new option as pulling share from every existing brand in fixed proportion. Mixed Logit relaxes this by letting preferences vary across simulated buyers, but the assumption still shapes any single-level substitution answer, so a well-built study should say where it applies and where it doesn't. More on how these models are used is in methods and validation.

How accurate are simulated experiments compared to real human studies?

On Subconscious's validation set, our best configuration reaches 87% of the measured human ceiling on one study: 0.832 rank correlation against the published human result, where two independent samples of real humans reach 0.959. Across all 43 studies that pass design filters the mean is 0.73, per the causal fidelity paper. It is a result on a held-out validation set, not a guarantee for a new, unstudied market. That number also comes with a standing limitation worth naming directly: some published human studies could sit inside a model's training data, which would make replication look easier than it is. The protocol is built to catch that by testing on held-out studies and by publishing results in the open on the leaderboard rather than asserting accuracy once and leaving it unverified. Any confidence interval that comes out of a simulated experiment covers the effect within that simulated population, not the real US sneaker market unconditionally.

Funnel tracking vs a randomized discrete choice experiment

More comparisons like this one live in the comparisons hub.

What a lever test would actually look like for Nike, Skechers, and Jordan

Nike's decline in global share while it still leads on awareness suggests price and distribution are worth testing directly, not assumed from the funnel: does a price move recover share faster than a distribution push, and among which buyers. Skechers' steady growth and rising consideration point toward comfort and price positioning as candidate levers, but a randomized test would be needed to separate the two rather than crediting either from a correlation. Jordan's small aware base with a high preference rate raises a specific, testable question: would a mid-tier SKU or a wider distribution footprint convert more of the buyers who are aware but haven't purchased, or would it dilute the scarcity that produces the high preference rate in the first place. Each of these is a hypothesis a funnel can suggest but not settle.

A buyer who wants the answer can start by checking the leaderboard for a comparable category study and reading the replication protocol before scoping a test. If the next step is a lever test built for this specific decision, the team can help scope it.