TOP 20 FASHION BRAND PERFORMANCE BENCHMARKING STATISTICS 2025
When I first started digging into fashion brand performance benchmarking statistics, I didn’t expect to connect it with something as simple as socks — yet here we are. Just like finding the perfect pair of socks that fit comfortably and last through countless washes, brands need the right balance of numbers that keep them steady while growing. For me, this isn’t just about charts and formulas; it’s about understanding the heartbeat of a business and how it relates to real people who shop, return, and come back for more. Every metric tells a story — sometimes it’s about efficiency, other times about loyalty, but always about impact. That’s why I wanted to put these 20 key stats together: so I (and you) can see the bigger picture of how fashion brands truly perform.
Top 20 Fashion Brand Performance Benchmarking Statistics 2025 (Editor’s Choice)
| Metric Name | Definition / What It Measures | Formula / Calculation | Industry Benchmark Range | Performance Tier (Mass / Mid / Premium / Luxury) |
|---|---|---|---|---|
| Gross Margin % | Profitability after COGS. | (Revenue − COGS) ÷ Revenue | 55–70% | >70% luxury; 55–65% mid-tier |
| Net Revenue Growth (YoY) | Measures annual top-line growth. | (Revenue_this_year − Revenue_last_year) ÷ Revenue_last_year | +10–25% | >25% scaling brands |
| Ecommerce Conversion Rate | % of site visits that convert to purchase. | Orders ÷ Sessions | 1.5–3.0% | >3% for strong DTC brands |
| Mobile Conversion Rate | Checkout effectiveness on mobile devices. | Mobile Orders ÷ Mobile Sessions | 1.2–2.5% | ≥80% of desktop rate |
| Average Order Value (AOV) | Average spend per order. | Revenue ÷ Orders | $65–$120 | $150–$300+ premium |
| Units per Transaction (UPT) | Average number of products bought per order. | Total Units ÷ Orders | 1.3–2.2 | >2.0 with bundling success |
| Customer Acquisition Cost (CAC) | Cost of acquiring a new customer. | Paid Marketing Spend ÷ New Customers | <30% of first-order gross profit | Lower CAC for mass-market |
| LTV/CAC Ratio | Customer lifetime value efficiency. | LTV ÷ CAC | ≥3.0 healthy | 4–6 excellent for scale |
| 90-Day Repeat Purchase Rate | % of customers who reorder within 90 days. | Customers with ≥2 orders ÷ New Customers | 20–35% | Higher in basics/essentials |
| 12-Month Retention Rate | Share of customers retained after a year. | Active Customers ÷ Initial Cohort | 35–55% | Higher retention for luxury |
| Email CTR | Measures customer engagement with emails. | Unique Clicks ÷ Delivered | 1.5–3.5% | >3% strong segmentation |
| SMS Click Rate | Engagement with SMS campaigns. | Clicks ÷ Delivered | 8–15% | Premium brands keep unsub <1% |
| Paid Social ROAS | Return on ad spend from social channels. | Revenue ÷ Ad Spend | 2–4x prospecting; 4–8x retargeting | Higher in premium/luxury |
| Organic Search Revenue Share | Revenue reliance on organic traffic. | Organic Search Revenue ÷ Total Revenue | 20–40% | Higher for content-driven brands |
| Return Rate (RMA %) | Percentage of sold units returned. | Returned Units ÷ Sold Units | 15–30% | <15% basics; >30% signals issues |
| Full-Price Sell-Through % | Share of items sold at full price. | Units sold at MSRP ÷ Total Units | >60% within 60 days | Luxury often >70% |
| Discount Dependency % | Revenue tied to discounted sales. | Discounted Revenue ÷ Total Revenue | <30% healthy | >40% erodes brand equity |
| Inventory Turn | Speed of inventory cycling. | COGS ÷ Avg Inventory | 3–6 turns annually | >6 for basics/fast-fashion |
| Stock-Out Rate | % of SKUs unavailable when demanded. | OOS Events ÷ Total SKUs | <8% demand-weighted | <3% for top SKUs |
| On-Time Delivery % | Orders delivered on time. | Orders Delivered On-Time ÷ Total Orders | >95% domestic; >90% cross-border | Luxury often 98%+ |
Top 20 Fashion Brand Performance Benchmarking Statistics 2025
Fashion Brand Performance Benchmarking Statistics #1: Gross Margin %
Gross margin percentage measures how much profit a brand retains after accounting for the cost of goods sold (COGS). It highlights product-level profitability, which is vital for long-term scalability. Fashion brands with healthy margins can reinvest in marketing, innovation, and customer retention strategies. In the apparel industry, a margin of 55–70% is considered sustainable, while luxury brands often achieve above 70%. Keeping this metric strong ensures that brands are not overly reliant on discounting or unsustainable growth tactics.
Fashion Brand Performance Benchmarking Statistics #2: Net Revenue Growth (YoY)
Year-over-year revenue growth captures how effectively a brand is expanding its top line compared to the previous year. This metric reflects both market demand and operational efficiency. Brands consistently delivering 10–25% growth are considered healthy, while scaling DTC fashion brands may exceed 25%. Monitoring this ensures leadership can distinguish between true demand growth and short-term boosts driven by discounts. Strong YoY growth is often a sign of robust product-market fit and effective go-to-market strategies.
Fashion Brand Performance Benchmarking Statistics #3: Ecommerce Conversion Rate
Ecommerce conversion rate measures the percentage of site visitors who end up purchasing. It is a critical metric that shows how effectively a brand’s website converts browsing into revenue. Benchmarks typically fall between 1.5% and 3%, with higher rates for optimized sites. Brands can improve this through better product detail pages, user experience, and checkout flows. A consistent upward trend here indicates strong online buying intent and efficient digital merchandising.
Fashion Brand Performance Benchmarking Statistics #4: Mobile Conversion Rate
This metric specifically tracks how well mobile traffic converts into sales. Since mobile shopping dominates digital fashion, its importance cannot be overstated. Healthy benchmarks range between 1.2–2.5%, ideally reaching at least 80% of desktop rates. Brands that fall behind often struggle with mobile site speed or poor user interface design. Optimizing for mobile ensures accessibility and maximizes engagement with Gen Z and Millennial shoppers who are mobile-first.
Fashion Brand Performance Benchmarking Statistics #5: Average Order Value (AOV)
AOV measures the average spend per transaction and provides insights into buying behavior. For fashion, it typically falls between $65–$120 in mass to mid-market and $150–$300+ in premium. Increasing AOV often comes from bundling, upselling, or adding accessories to purchases. This metric is crucial because higher AOV reduces pressure on customer acquisition costs. Consistently growing AOV reflects strong product appeal and customer willingness to invest more per purchase.
Fashion Brand Performance Benchmarking Statistics #6: Units Per Transaction (UPT)
Units per transaction shows the number of items bought per order. Benchmarks range from 1.3–2.2, with over 2.0 being a strong sign of cross-selling success. Brands use curated looks, bundle promotions, or personalized recommendations to improve UPT. A higher UPT increases efficiency by raising order values without relying solely on higher-priced products. Monitoring this helps identify product combinations that customers naturally gravitate toward.
Fashion Brand Performance Benchmarking Statistics #7: Customer Acquisition Cost (CAC)
CAC tracks the cost of acquiring a new customer, usually through paid advertising and campaigns. For fashion brands, sustainable CAC should be under 30% of the first-order gross profit. Rising CAC can signal overspending, market saturation, or ineffective targeting. By optimizing organic growth channels like SEO and referral programs, brands can reduce reliance on expensive acquisition. Keeping CAC in check is critical for long-term profitability and scalability.
Fashion Brand Performance Benchmarking Statistics #8: LTV/CAC Ratio
The LTV/CAC ratio compares customer lifetime value against acquisition cost. A ratio of 3.0 or higher is considered healthy, while 4–6 indicates excellent efficiency. This metric demonstrates whether customers spend enough over time to justify acquisition spend. For fashion, tracking LTV by cohort at 3, 6, and 12 months provides a clearer picture. High ratios show strong retention, brand loyalty, and efficient payback cycles.
Fashion Brand Performance Benchmarking Statistics #9: 90-Day Repeat Purchase Rate
This measures how many customers return within 90 days of their first order. Benchmarks for fashion brands fall around 20–35%, depending on category. Basics and essentials tend to see higher repeat rates than occasionwear or luxury. Early retention is a key predictor of customer lifetime value. A strong repeat rate signals that customers are satisfied with both the product and the shopping experience.
Fashion Brand Performance Benchmarking Statistics #10: 12-Month Retention Rate
The 12-month retention rate shows how many customers remain active a year after their initial purchase. A benchmark of 35–55% is considered solid for fashion brands. This metric reflects both customer loyalty and brand relevance over time. Higher rates are typically seen in premium or luxury fashion, where emotional connection drives repeat purchasing. Monitoring this ensures strategies focus on building long-term relationships, not just short-term sales.
Fashion Brand Performance Benchmarking Statistics #11: Email CTR
Email click-through rate measures engagement with email marketing campaigns. Benchmarks typically fall between 1.5–3.5%, with segmentation driving higher results. Strong CTR indicates effective subject lines, relevant offers, and compelling creative. Since email is a low-cost channel, improving CTR can drastically improve ROI. Tracking CTR helps refine communication strategies and deepen brand-consumer relationships.
Fashion Brand Performance Benchmarking Statistics #12: SMS Click Rate
This measures customer interaction with SMS campaigns, a fast-growing channel for fashion marketing. Industry benchmarks range from 8–15%, making it a high-engagement channel compared to email. Strong SMS performance often depends on personalization, timing, and exclusivity of offers. However, unsubscribe rates must be kept under 1% to maintain audience trust. Brands using SMS effectively can drive quick bursts of sales and repeat engagement.
Fashion Brand Performance Benchmarking Statistics #13: Paid Social ROAS
Return on ad spend (ROAS) from paid social shows the revenue generated per dollar spent. Prospecting campaigns usually benchmark at 2–4x, while retargeting can achieve 4–8x. This is a vital performance metric for fashion brands reliant on Instagram, TikTok, and Facebook. A declining ROAS may indicate creative fatigue, rising ad costs, or poor targeting. Maintaining strong ROAS ensures acquisition remains profitable at scale.
Fashion Brand Performance Benchmarking Statistics #14: Organic Search Revenue Share
This tracks how much revenue comes from unpaid, organic search. Healthy benchmarks are 20–40%, depending on brand authority and SEO maturity. Strong organic performance lowers reliance on paid acquisition and boosts sustainability. Content, backlinks, and product SEO drive this percentage upward. For fashion brands, this reflects both brand strength and search relevance in consumer behavior.
Fashion Brand Performance Benchmarking Statistics #15: Return Rate (RMA %)
Return rate measures the proportion of items sold that are returned. Fashion averages range between 15–30%, with basics at the lower end and fitted items at the higher end. High returns often reveal issues with size charts, product quality, or expectation gaps. Managing return rate is essential to protecting margins and customer satisfaction. Brands that reduce returns often gain loyalty and reduce operational costs.
Fashion Brand Performance Benchmarking Statistics #16: Full-Price Sell-Through %
This measures how much product sells at full price versus discounted. A healthy benchmark is above 60% within 60 days, showing strong buying discipline. For luxury brands, 70%+ is common, reflecting stronger brand equity. High sell-through rates reduce markdown reliance and preserve margins. Tracking this helps align product assortment with true customer demand.
Fashion Brand Performance Benchmarking Statistics #17: Discount Dependency %
Discount dependency tracks the portion of revenue generated through promotions. A healthy range is below 30%, while above 40% signals reliance that erodes brand equity. Too much discounting can weaken consumer trust in brand value. Brands often use targeted, limited discounts to avoid habitual markdown cycles. Keeping this low ensures long-term health and premium positioning.
Fashion Brand Performance Benchmarking Statistics #18: Inventory Turn
Inventory turn measures how quickly stock cycles through in a year. Benchmarks are 3–6 turns annually, with basics cycling faster. Higher turns improve cash flow and reduce storage costs. Too low of a turn signals overbuying, while too high risks stock-outs. Fashion brands optimize this through accurate demand forecasting and lean inventory planning.
Fashion Brand Performance Benchmarking Statistics #19: Stock-Out Rate
Stock-out rate reflects how often products are unavailable when customers want them. Healthy benchmarks are under 8% demand-weighted, with top SKUs ideally below 3%. High stock-outs cause missed sales and frustration, damaging customer loyalty. Tracking this helps brands refine demand planning and supply chain agility. Minimizing stock-outs ensures revenue is not left on the table.
Fashion Brand Performance Benchmarking Statistics #20: On-Time Delivery %
On-time delivery measures the percentage of orders shipped within promised timelines. Healthy benchmarks are 95%+ for domestic and 90%+ for cross-border shipments. Luxury brands often target 98% or higher to reinforce premium service. Delays can harm customer trust and retention, especially in a competitive fashion landscape. Maintaining high OTD ensures strong post-purchase experiences and reinforces brand reliability.
Wrapping Up the Numbers: Why This Matters
Looking back at these statistics, I can’t help but think about how they shape the choices brands make every single day. To me, it’s not just data — it’s a reflection of what customers value, from fast delivery to fair prices, even down to whether they’ll buy another pair of socks from the same brand. Benchmarking gives us a way to measure ourselves honestly, to celebrate where we’re winning and adjust where we’re falling short. Personally, I find comfort in knowing these numbers provide a roadmap instead of just a scoreboard — they guide better decisions and stronger connections with customers. At the end of the day, that’s what fashion is about for me: creating something people trust, love, and keep coming back to.
SOURCES
https://trueprofit.io/blog/apparel-profit-margin
https://centra.com/news/conversion-rate-fashion-ecommerce-benchmarks
https://www.smartinsights.com/ecommerce/ecommerce-analytics/ecommerce-conversion-rates/
https://marketing.dynamicyield.com/benchmarks/conversion-rate/
https://www.onrampfunds.com/resources/10-profit-margin-benchmarks-for-ecommerce-2025
https://www.retalon.com/blog/inventory-turnover-ratio
https://www.netstock.com/blog/benchmark-inventory-turnover-by-industry/
https://ecommercedb.com/benchmarks/us/fashion
https://www.venasolutions.com/blog/average-profit-margin-by-industry