Clothing and clothing access. stores
Total Sector Spending
Overall spending trends across an industry sector
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What's the story behind the data?
The clothing sector is showing divergent momentum signals depending on consumer cohort and brand position. While total sector spending has been volatile, demand patterns suggest a bifurcation by both price tier and shopper profile. Recent months (through June ’25) show a modest rebound, though not uniformly distributed.
Strategic Takeaways by Audience
FP&A / Strategy Teams
- Budget with volatility in mind: Clothing is rebounding after a soft Q1, but patterns remain seasonal and asymmetric. Plan for elevated demand in December and April, and reforecast in February and September as soft months.
- Align assortments with Gen Z signals, but don’t assume they’ll drive revenue alone. Their purchases may shape fashion cycles more than bottom lines.
- Track AOV, not just units: High-AOV brands like Lululemon are winning on mix and loyalty, not just price. Use AOV as a proxy for category health and promotional elasticity.
- Avoid overstocking premium SKUs: As high-income consumers pull back, focus on flexible pricing strategies and bundling to drive mid-tier sell-through.
Marketing and Brand Teams
- Gen Z is your trendsetter, not your spender: Use their behavior for campaign tone and timing, but retarget higher-income cohorts for conversion.
- Leverage AOV shifts in creative: Higher AOV may reflect bundled purchases or gifting periods (e.g., holidays, graduation)—sync messaging accordingly.
- Mobile-first is table stakes: Growth leaders (Shein, Lululemon) are dominating mobile-native and influencer-driven channels.
- Price elasticity varies by income: Mid-income households are still responsive to promotions; high-income cohorts may be indifferent or fully paused.
Investors
- Don’t chase luxury exposure blindly: The <$125K income bracket is showing positive growth; >$150K households are in retreat. Macy’s and Nordstrom earnings validate this trend.
- Favor brands with strong AOV and stable share: Shein and Old Navy are showing resilience via pricing flexibility; Lululemon is holding margin.
- Etsy’s share loss is a red flag: Watch for continued contraction in long-tail or artisanal apparel categories.
- Use Facteus AOV + share trendlines as an early indicator of Q2 earnings surprises—particularly for mixed-format retailers like Gap Inc. and Urban Outfitters.
Policy Makers
- Clothing data shows middle-income stabilization, but upper-income pullback may signal future contraction in discretionary categories.
- Youth-driven segments (Gen Z) are engaged, but require targeted job and wage support to sustain demand. Apparel spend may be a proxy for youth confidence.
- Retail employment outlook should focus on value and digital-first formats, as high-AOV and brick-and-mortar luxury may shrink.
Top Brands by Market Share
Leading brands ranked by market share within sector
Trends + Insights
1. Sector Volatility & Seasonal Peaks
- Total spend rose sharply in December 2024, climbing over 13% YoY, suggesting a strong holiday cycle.
- Post-holiday softness followed, with Jan–Feb 2025 showing negative YoY growth.
- Rebound began in April 2025, but overall sector momentum remains choppy.
- External calibration: This tracks closely with broader retail seasonality patterns reported by the U.S. Census Bureau and Mastercard SpendingPulse.
2. Demographic Spending Patterns
- Gen Z (+3.6%) and Baby Boomers (+3.1%) are driving disproportionate growth in the sector.
- Millennials are retrenching (-2.5%), despite traditionally being a core apparel-buying cohort.
- Nuance on Gen Z: While growth is strong, their total spending power remains lower than other generations. According to McKinsey and Pew Research, Gen Z contributes significantly less to total discretionary spend than Gen X or Boomers. Brands should view this as an early signal opportunity, not a high-volume growth driver.
3. Income-Based Differences
- Growth is concentrated in the middle-income brackets ($75K–$125K), each posting ~0.7% YoY growth.
- Highest-income households (>$150K) are reducing spend (-0.5%).
- This aligns with industry commentary from companies like Macy’s and Nordstrom noting softness at the high end.
Brand-Level Signals
4. Top Brands by AOV
- Lululemon and Zara lead in AOV, sustaining ~$120–$130 average order sizes across 2024–2025.
- Old Navy, Shein, Burlington, and DSW anchor the value-tier (~$40–$70 AOV).
- These price tiers help benchmark relative positioning—but we interpret price indirectly through AOV, which includes product mix, not just item-level pricing. Trends are corroborated with third-party references such as edited.com and RetailDive.
5. Market Share Dynamics
- Etsy has lost significant share (from 26% → 16%), suggesting either a consumer pivot away from artisanal brands or seasonality/fulfillment challenges.
- Shein and Lululemon are gaining ground—Shein by undercutting pricing, Lululemon through brand strength and loyalty.
- Zara has been flat to declining, despite high AOV—suggesting a ceiling on premium fast fashion.
- Old Navy remains stable, likely buoyed by middle-income spending stability.
Citations & Sources
- Facteus transaction data (MIMIC™) from Aug ’24 – Jun ’25
- McKinsey Gen Z Spending Power (2023): https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/gen-z-the-next-wave-of-consumers
- Mastercard SpendingPulse Reports (2024–2025): https://www.mastercardservices.com/en/solutions/spendingpulse
- Pew Research: Household income by generation (2024): https://www.pewresearch.org/social-trends/
- RetailDive Apparel Industry Recaps (Q1–Q2 2025): https://www.retaildive.com
Top Brands by AOV
Leading brands ranked by average order value within sector
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This macro sector analysis provides detailed insights into economic trends and consumer behavior patterns. The visualizations below are derived from real-world transaction data and economic indicators.
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Sector Spending by Income Bracket
Industry sector spending patterns by household income level
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Sector Spending by Generation
Industry sector spending patterns by generational cohort
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