The OOTD is losing steam. At least, that’s what the data suggests. 

When we analyzed 340K fashion creators across tiers, one pattern was hard to ignore: the classic outfit post (the try-on, the fit check, the "here's what I'm wearing") barely registers among top-performing creators anymore. 

That means the real question isn’t just “who are the top fashion influencers?” It’s “what are they doing that works?”

That's why this year's rankings look different. Instead of a single leaderboard, we segmented influencers by tier—nano, micro, mid, macro, and mega—to show how fashion influence actually scales. What drives performance at the nano level (insider access, in-store intel) looks different from what works at mega (luxury POV, street-style entertainment). Understanding those differences is the key to building a smarter strategy.

Our rankings use VIT (Brand Vitality Score)— Traackr’s proprietary metric designed to measure how effectively your content captures attention. VIT goes beyond surface-level metrics by combining Visibility (how many people saw the content), Impact (how many people engaged), and Trust (amount of attention centered on the brand) into a single, holistic score. 

‍Looking for top creators in other industries? We've also provided the same analysis for beauty, spirits, and food & beverage.

The 2026 Fashion Influencer Landscape: Key Insights

Before we dive into the rankings, here’s what’s shaping fashion influencer marketing in 2026:

OOTD is no longer the default performance engine.

The classic fashion staple (OOTDs, fit inspo, try-ons) barely show up among top creators across tiers. Instead of “here’s my outfit,” winning content is built around a repeatable format (shopping intel, POV coverage, subculture storytelling) that gives audiences a reason to come back.

Lower tiers are retail-led: product is the protagonist.

From nano through mid-tier, fashion performance is dominated by shopping finds, new drops, and in-store browsing, especially at value and mass retailers. These creators aren’t positioning themselves as style authorities as much as inventory insiders.

Higher tiers flip the model: creator-first, product-second.

As you move into macro and mega tiers, product-led “finds” fade and performance shifts to creator-centric worlds: fitness culture, luxury access, runway POVs, street-style interviews, and celebrity sightings.

Fitness is one of the clearest scale paths because it’s built on frequency. 

Fitness creators show up heavily in macro + mega because the content loop supports high output and repeated commerce adjacency (affiliate-style posting, recurring gym formats). While multiple creative lanes win here, the common thread is consistency and a clear persona.

Top fashion influencers to watch in 2026

Top Mega Fashion Influencers (1M – 5M Total Followers)

Market Overview: 14,700 influencers | Avg VIT: 694 | Avg ER: 1.5% 

Top 10 US Mega Fashion Influencers

1. Ayiah Soufi (@ayiahsoufi_): 130,000 VIT
2. Jack Scalise (@jack.scalise): 97,200 VIT
3. Michal Blank (@mickmicknyc): 67,300 VIT
4. Jacob Arabo (@jacobarabo): 58,400 VIT
5. Kate Bartlett (@katee.bartlett): 55,200 VIT
6. Anisia Martinez (@anisiamartinezz_): 47,200 VIT
7. Ziv Tamir (@ziv.tamir): 46,700 VIT
8. Jason Bjarnson (@bjarnson_fitness): 42,100 VIT
9. Nella Grabowski (@nell.grabowski):  38,200 VIT
10. Marta Ojeda (@by.martao): 37,900 VIT

Winning Strategies

• Aspirational + status cues drive mega performance: Designer styling, runway proximity, luxury accessories, and high-polish “main character” moments anchor attention.
• Format > outfits: Top creators lock into repeatable formats (street interviews, event coverage, nightlife recaps), turning fashion into a show, not a one-off post.
• Fashion rides along bigger attention engines: Many top mega creators are fitness-, lifestyle-, or personality-first. Fashion enhances their world rather than replacing it.

Top Macro Fashion Influencers (250K – 1M Total Followers)

Market Overview: 33,300 influencers | Avg VIT: 253.2 | Avg ER: 1.3%

Top 10 US Macro Fashion Influencers

1. Kayla Stanley (@kaylastnly): 55,100 VIT
2. Clara Afua (@khlara_styled_it): 42,190 VIT
3. Marc Griffin (@gcfctraining): 24,800 VIT
4. Dylan Shannon (@_dylanshannon): 23,400 VIT
5. Aly Henry (@alykima_): 20,500 VIT
6. Mona Said (@artofstylemona): 17,900 VIT
7. Cameron Kissel (@ckisselc): 16,000 VIT
8. Abe Kikirov (@abekikirov): 16,000 VIT
9. Ana (@bodiedbyana): 15,900 VIT
10. Cole Jernstedt (@colejernstedt): 15,700 VIT

Winning Strategies:

• Gym culture is a dominant fashion lane: Fitness-first creators are driving macro-scale fashion performance. Clothing functions as identity signaling inside a larger lifestyle narrative.
• Two winning content routes: Within that gym lane, you can see two distinct types of content winning: creators who lean into humor + personality (lighthearted commentary, relatable gym moments) versus creators who win through educational, technique-led content (more instructional/“how-to” training POV).
• Integrated brand mentions outperform standalone ads: Fashion works best when embedded into self-improvement arcs, dating POVs, seasonal refreshes, and lifestyle storytelling.

Top Mid Fashion Influencers (50K – 250K Total Followers)

Market Overview: 74,000 influencers | Avg VIT: 76.8 | Avg ER: 2% 

Top 10 US Mid Fashion Influencers

1. Lauren (@totargetigo): 23,200 VIT
2. Elizabeth Dillner (@everythingelizabeth_): 20,600 VIT
3. Samantha Bousquet (@sammbousquet): 18,100 VIT
4. Kevin (@kevinfooty_): 16,200 VIT
5. Dylan Rubinstein (@thedylanjader): 15,900 VIT
6. Emily Boschi (@skin.illustrated): 13,500 VIT
7. Eric McIntyre (@raddadbodtv): 13,100 VIT
8. Suzanne (@targettakesmypaycheck): 12,500 VIT
9. Jess McKay (@jkmckay): 11,000 VIT
10. Molly (@fearlesslymol): 9,470 VIT

Winning Strategies:

• Shopping intel scales into engagement loops: “New at Target,” seasonal refreshes, and comment-to-shop prompts turn content into a two-way channel.
• Single-lane authority drives differentiation: Creators win by owning a tight niche — footwear reviews, sport/style POVs, or specific retailer coverage.
• Personality is the breakout differentiator: Humor, POV framing, and mini-series formats elevate product-led posts into watchable content.

Top Micro Fashion Influencers (10K – 50K Total Followers)

Market Overview: 126,000 influencers | Avg VIT: 20.2 | Avg ER: 3.2% 

Top 10 US Micro Fashion Influencers

1. Tania (@keeping.up.with.tania): 12,100 VIT
2. Gina (@iiheart_gina): 12,000 VIT
3. Cynthia (@she.found.what): 10,900 VIT
4. Shelby Vignal (@shelbyvignal11): 7,690 VIT
5. Jess (@findssbyyjess): 7,330 VIT
6. Meagan Ghadoushi (@meaganghadoushi): 7,240 VIT
7. Ashley Jazmine (@ashleyjazmine): 7,020 VIT
8. Misha Rai (@misha_rai): 6,690 VIT
9. Maegan Bellassai (@maeganbella): 5,590 VIT
10. Gloria Ruppert (@gloria_ruppert): 5,530 VIT

Winning Strategies:

• “Shopping desk” creators dominate: Micro fashion is overwhelmingly driven by new finds / new drops / new sale content at value + mass retailers. These creators aren’t selling a personal style POV as much as a recurring service: what’s worth buying right now, before it’s gone.
• Product-first editing wins: Show the rack. Show the price. Show the reason to buy. The product stays centered.
• Platform-native commerce behavior is built in: TikTok-first, affiliate links, and frequent posting loops create always-on shopping momentum.

Top Nano Fashion Influencers (1K – 10K Total Followers)

Market Overview: 98,600 influencers | Avg VIT: 6.7 | Avg ER: 7.2% 

Top 10 US Nano Fashion Creators

1. Ava Tamborella (@avatambo): 2,990 VIT
2. Grace Riley (@graaceriley): 2,890 VIT
3. Amy Deng (@amydeng__): 2,270 VIT
4. Michaela Gadawski (@michaelagadawski): 1,980 VIT
5. Macartney Bridges (@macartney___bridgess): 1,960 VIT
6. Victoria Rivero (@vvictoriarrivero): 1,850 VIT
7. Ama (@amamissya): 1,670 VIT
8. Avery Pearl (@averyypearl): 1,520 VIT
9. AmandaLove (@amandallove): 1,430 VIT
10. Kaye Bassey (@KayeBassey): 1,400 VIT

Winning Strategies:

• Access + intimacy beats styling advice: The best nano creators feel like a friend’s camera roll — less tutorial, more proximity.
• In-store shopping is the core entertainment format: “Come shopping with me” turns browsing into narrative content.
• Insider access drives credibility: Employee creators and event coverage make fashion feel closer and more authentic.

The Methodology for Finding Top Fashion Influencers

This report was created by Traackr, the decision engine for creator marketing that transforms overwhelming data into strategic clarity. With 15 years of training data and multimodal content analysis, Traackr reveals exactly what's working, what's not, and what to do next—then enables agentic or human-in-the-loop execution from strategy to scaled activation. Trusted by the world's biggest brands: L'Oréal, Diaego, Unilever, Sephora, Samsung, Lacoste, and more

For the purpose of this “top fashion influencers”  study, we evaluated over 4 million post by 340K influencers located in the United States who mentioned fashion products. Influencers of most tiers (nano, micro, mid, and mega) were included. 

We analyzed the content produced and shared by these influencers from January 2025 - December 2025. 

What is Traackr’s Brand Vitality Score (VIT)?

The Brand Vitality Score (VIT) by Traackr is a performance metric that provides a clear, actionable way to measure and improve creator marketing performance.

Developed by Traackr’s Data Science Team, VIT helps marketers:

• Understand what drives performance by combining visibility, engagement, and trust into one score.
• Identify the levers (creator volume, frequency, average audience size, and content performance) that will have the biggest impact, so you can adjust your strategy with confidence.
• Benchmark and optimize your creator program to achieve better results over time.

Why VIT matters:

• Simple: By aggregating multiple data points into one, VIT provides a single metric for success that can be used to benchmark and track trends.
• Transparent: The VIT methodology is easy to understand and highly actionable in uncovering where to invest to drive impact.
• Meaningful: VIT is based on known brand levers and has been optimized through machine learning to correlate with business metrics at the brand level.

Learn more about VIT here