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E-commerce Fashion Customer Trends
Product Reviews & Feedback
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset offers a valuable look into fashion products listed on Amazon, including details on ratings, prices, and customer reviews. It is derived from a subset of a much larger dataset, initially extracted from Amazon. The primary purpose of this data is to enable an understanding of customer trends and behaviours, which can then be used to predict future market shifts. By analysing this information, you can gain insights into what customers seek, what they favour or dislike, and their spending habits. This can significantly aid in refining product selection, pricing strategies, and marketing efforts for fashion items sold on the Amazon platform.
Columns
- product_name: The name of the product. (String)
- manufacturer: The manufacturer of the product. (String)
- price: The price of the product. (Float)
- number_available_in_stock: The quantity of the product currently in stock. (Integer)
- number_of_reviews: The total count of reviews for the product. (Integer)
- number_of_answered_questions: The total count of answered questions for the product. (Integer)
- average_review_rating: The average rating given to the product. (Float)
- amazon_category_and_sub_category: The specific Amazon category and subcategory the product belongs to. (String)
- customers_who_bought_this_item_also_bought: Information on other items purchased by customers who bought this product. (String)
- description: A general description of the product. (String)
- product_information: Detailed product specifications and information. (String)
- product_description: Another field for the product description. (String)
- items_customers_buy_after_viewing_this_item: Information on items customers purchase after viewing this product. (String)
- customer_questions_and_answers: Questions posed by customers and their corresponding answers. (String)
- customer_reviews: The textual content of customer reviews. (String)
- sellers: Information about the sellers offering the product. (String)
- index: An index identifier for the record.
- uniq_id: A unique identifier for each product record.
Distribution
The dataset is provided in a CSV file format, specifically
amazon_co-ecommerce_sample.csv, and is approximately 35.35 MB in size. It contains 10,000 unique records, offering a detailed snapshot of Amazon fashion products. While a subset of a much larger collection exceeding 7 million items, this particular file focuses on specific data points. Not all columns have data for every record; for instance, some columns like 'customer_questions_and_answers' have a significant number of missing values (91%), and 'price' has 14% missing values, while 'number_available_in_stock' has 25% missing data.Usage
This dataset is ideal for:
- Analyzing customer preferences: Understand what features are most important to customers to inform product design.
- Identifying product issues: Examine the relationships between reviews and ratings to pinpoint potential problems.
- Improving product selection: Use insights from customer behaviour to refine inventory choices.
- Optimising pricing strategies: Analyze similar product prices to set competitive pricing.
- Enhancing marketing efforts: Develop more effective strategies based on customer likes and dislikes.
- Gaining product feedback: Manufacturers can use reviews and ratings to improve their offerings.
- Refining product descriptions: Utilise customer questions and answers to create more informative product listings.
Coverage
The data is collected from Amazon, implying a broad, global e-commerce context, with some specific examples referencing amazon.co.uk. The dataset includes information on various fashion products, customer reviews, ratings, and pricing. There is no specified time range for data collection, and the dataset is not expected to be updated frequently.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
- E-commerce Businesses and Sellers: To enhance product offerings, optimise pricing, and improve marketing effectiveness on Amazon.
- Product Manufacturers: To gather direct customer feedback, refine product design, and identify areas for improvement.
- Data Analysts and Researchers: For studying customer behaviour, market trends, and predictive analytics in the fashion e-commerce sector.
- Marketing Professionals: To develop targeted campaigns and strategies based on customer insights.
Dataset Name Suggestions
- Amazon Fashion Product Insights
- E-commerce Fashion Customer Trends
- Amazon Product Review Analytics
- Fashion Retail Data Snapshot
Attributes
Original Data Source: E-commerce Fashion Customer Trends
QUALITY