Reducing Returns and Unsold Inventory: What Consumer Data Tells Us About Fit & Quality

Muslimah Akinjole

• 2 min read

In fashion retail, returns are not just a logistical hassle—they're a profit killer. A 2024 report by Narvar found that more than 60% of fashion returns are due to poor fit or unmet quality expectations, costing global brands billions each year in lost revenue and surplus inventory (source). Meanwhile, unsold inventory often leads to heavy markdowns, or worse—waste. But there’s good news: consumer data is unlocking powerful insights into how fashion brands can fix these problems at the source.

The Cost of Returns and Excess Inventory

Why Returns Hurt More Than You Think:

  • Restocking costs (handling, repackaging, resale markdowns)
  • Inventory inaccuracies, leading to poor forecasting
  • Customer churn due to repeated dissatisfaction

Unsold Inventory Risks Include:

  • Tied-up capital that limits product development or marketing
  • Excessive discounting that hurts brand equity
  • Sustainability concerns, with garments often ending up in landfills

What Consumer Data Reveals About Fit & Quality

Customer reviews, star ratings, and feedback loops provide invaluable clues into the reasons behind returns and unsold items. Here are common insights extracted from high-volume data analysis:

  • “Runs small” or “Inconsistent sizing” tags appear frequently in reviews with high return rates
  • Mentions of fabric disappointment (e.g., “feels cheap,” “too sheer”) correlate strongly with markdown cycles
  • Design feature complaints (e.g., sleeve tightness, zipper issues) show up before a style underperforms
  • Low average ratings across a product line predict slow sell-through and high return risk

By quantifying and organizing this feedback, brands can take action before performance dips.

Real-Life Example: Levi’s and Fit Data

Levi’s uses AI to personalize size recommendations online and analyze returns by style and body type. This data-led approach helped them reduce return rates by tailoring recommendations, refining cuts, and introducing inclusive sizing based on real-world feedback (source).

How WovenInsights Helps Brands Tackle Fit & Quality Challenges

WovenInsights' Consumer Insights module is purpose-built to help fashion brands reduce return rates and unsold inventory by decoding what customers really think about fit, fabric, and function.

Key features include:

  • Sentiment Analysis by Product Attribute: Discover patterns in complaints or praise for sizing, construction, comfort, etc.
  • Keyword Clouds: Highlight recurring issues like “tight waistband” or “low-quality seams” that signal early warning signs.
  • Ratings Distribution Dashboards: Visualize product satisfaction across styles, colors, or size ranges.
  • Attribute-Linked Returns Insight: Connect poor performance to specific design or material flaws.

Best Practices to Reduce Returns Using Data

  • Pre-launch testing: Use consumer sentiment from past products to avoid repeating sizing or material missteps.
  • Dynamic product pages: Provide size-fit guidance based on return patterns (e.g., “Customers say this runs small”).
  • Cross-functional visibility: Ensure product development, design, and CX teams all have access to customer insight reports.
  • Continuous feedback loops: Monitor sentiment post-launch to adjust production, pricing, or messaging in real time.

Conclusion
Reducing returns and unsold inventory isn’t just about logistics—it’s about listening. Fashion brands that harness consumer data to optimize fit and quality stand to improve profitability, customer satisfaction, and sustainability outcomes. With WovenInsights, those insights aren’t hidden in scattered reviews—they’re actionable, structured, and ready to inform your next move.