Reducing Returns and Unsold Inventory: What Consumer Data Tells Us About Fit & Quality
In fashion retail, returns are not just a logistical hassle, they're a profit killer.70 percent of returns were caused by poor fit or style, suggesting that shopping tools are a key lever for preventing returns and improving consumer experience, costing global brands billions each year in lost revenue and surplus inventory. 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.
Levi’s and Fit Data
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.
About WovenInsights
WovenInsights is a comprehensive market analytics solution that provides fashion brands with real-time access to retail market and consumer insights, sourced from over 70 million real shoppers and 20 million analyzed fashion products. Our platform helps brands track market trends, assess competitor performance, and refine product strategies with precision.
WovenInsights provides you with all the actionable data you need to create fashion products that are truly market-ready and consumer-aligned.
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