Reducing Returns with Myntra AND AJIO Customer Review Datasets

In today’s fast-moving fashion e-commerce marketplace, data is the backbone of every smart sizing and product decision. High garment return rates due to sizing issues increase logistics costs and reduce customer trust. To solve this problem, brands need structured review data instead of manual checks and assumptions.

With Product Data Scrape, fashion brands can analyze customer review datasets from Myntra and AJIO to identify real sizing issues directly from customer feedback. This approach helps brands make data-driven improvements that lead to up to an 8% reduction in garment return rates.

Why Choose Product Data Scrape for Fashion Review Analysis

• End-to-End Fashion Data Services:
  From large-scale review extraction to Custom eCommerce Dataset Scraping, Product Data Scrape manages the entire data lifecycle—collecting, cleaning, structuring, and delivering ready-to-use datasets.
• Extract Fashion & Apparel Data at Scale:
  Brands can Extract Fashion & Apparel Data such as customer reviews, ratings, fit comments, size complaints, and product attributes from Myntra and AJIO.
• Actionable Sizing Intelligence:
  Analyzing review data helps identify common issues like “runs small,” “tight fit,” or “length mismatch,” allowing brands to improve size charts and product descriptions.
• Customizable Review Datasets:
  With Custom eCommerce Dataset Scraping, datasets can be customized by category, brand, gender, or product type to match specific business goals.
• API & Analytics Integration:
  Structured datasets integrate easily into BI dashboards, analytics tools, and internal systems for faster decision-making.
• No-Code Data Extraction:
  Product Data Scrape delivers clean, structured fashion review datasets without any coding—ideal for merchandising, product, and CX teams.
• Proven Business Impact:
  By using Product Data Scrape to Extract Fashion & Apparel Data through Custom eCommerce Dataset Scraping, fashion brands reduce returns, lower reverse logistics costs, and improve customer satisfaction.