Myntra Retail Listings and Pricing Dataset

Columns

• uniq_id: The unique identifier assigned to each product.
• crawl_timestamp: The specific date and time when the data was captured (crawled).
• product_id: The distinct ID identifying the product within the dataset.
• link: The direct URL link to the product page on the Myntra website.
• size: The size of the product (e.g., L, XL).
• variant_sku: The Stock Keeping Unit code for the specific product variant.
• brand: The brand name associated with the product (e.g., Anouk, Biba).
• care_instructions: Recommendations for product care, such as washing instructions.
• dominant_material: The primary material used in the product's manufacturing (e.g., Cotton).
• title: The descriptive title of the product listing.
• actual_color: The specific colour of the product.
• dominant_color: The primary colour group to which the product belongs.
• product_type: The classification of the product (e.g., Straight Kurta).
• images: Links to image assets associated with the product.
• body: The marketing text or body description provided on the website.
• product_details: detailed attributes and features of the item.
• size_fit: Information regarding the sizing and fit (e.g., model height and size worn).
• complete_the_look: Suggestions for other items to pair with the product.
• type: Hierarchical category path or type classification.
• variant_price: The current selling price of the selected variant.
• variant_compare_at_price: The original or comparison price before discounts.
• ideal_for: The target gender or demographic (e.g., Women, Men).
• is_in_stock: Current stock status (In Stock or Out of Stock).
• inventory: The quantity of items remaining in stock.
• specifications: detailed technical specifications of the product.

Distribution

• Format: CSV (Comma Separated Values)
• Size: Approximately 36.26 MB
• Structure: 15,000 records (rows) and 25 columns.

Usage

• Price Elasticity Analysis: Examining pricing strategies and discount patterns across different brands.
• Fashion Trend Forecasting: Identifying popular materials, colours, and styles for the 2019 season.
• Competitor Analysis: Benchmarking product assortments and stock levels against market leaders.
• Text Analysis & NLP: Utilising product descriptions and titles to train categorisation models.
• Image Recognition: Using image URLs to train computer vision models for fashion item detection.

Coverage

• Geographic Scope: India (Myntra.com marketplace).
• Time Range: 1st June 2019 to 31st August 2019.
• Demographic Scope: primarily Women (61%) and Men (26%), with some coverage for children.

License

Who Can Use It

• Retail Analysts: For market research and competitive intelligence.
• Data Scientists: For building recommendation engines or pricing models.
• E-commerce Managers: For understanding inventory distribution and catalogue structure.
• Fashion Designers: For researching material and colour trends.

Dataset Name Suggestions

• Myntra Fashion Product Catalogue Q3 2019
• India E-commerce Apparel and Accessories Data 2019
• Myntra Retail Listings and Pricing Dataset

Attributes