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E-commerce Fashion Review Analysis Data
Product Reviews & Feedback
Tags and Keywords
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"No reviews yet"
Free
About
This collection of data contains feedback from women customers regarding dresses purchased through e-commerce platforms. It captures various aspects of their shopping experience, including ratings, written reviews, and product classifications. The purpose of assembling this information is to analyse customer satisfaction patterns, which can help online retailers improve their products and services. By understanding customer feedback, businesses can identify trends and address areas for enhancement.
Columns
- s.no: A sequential index number for each entry.
- age: The age of the customer providing the review.
- division_name: The name of the clothing division the item belongs to.
- department_name: The specific department for the clothing item.
- class_name: The particular class name of the garment.
- clothing_id: A unique identifier assigned to each type of clothing.
- title: The title of the customer's review.
- review_text: The full text of the customer's feedback.
- alikefeedbackcount: The count of other customers who agreed with the review.
- rating: The star rating given to the product by the customer.
- recommend_index: A binary indicator (0 for No, 1 for Yes) of whether the customer would recommend the product.
Distribution
The data is provided in a single CSV file with a size of 8.51 MB. It is structured into 11 columns and contains approximately 23,500 rows or records.
Usage
Ideal applications for this data include:
- Sentiment analysis of customer reviews to gauge product satisfaction.
- Market basket analysis to understand purchasing patterns.
- Developing recommendation systems for e-commerce platforms.
- Identifying key drivers of customer ratings and recommendations.
- Demographic analysis of customer feedback to tailor marketing strategies.
Coverage
The data covers customer reviews for women's dresses sold via e-commerce. There is no specific geographical scope mentioned. The age of customers ranges from 18 to 99 years. Some records may have missing values in columns such as
title and review_text.License
CC BY-SA 4.0
Who Can Use It
- Data Scientists: For building predictive models on customer satisfaction and recommendation likelihood.
- E-commerce Analysts: To derive insights into product performance and customer behaviour.
- Marketing Professionals: To understand customer demographics and sentiment for targeted campaigns.
- Product Managers: To identify areas for product improvement based on direct customer feedback.
Dataset Name Suggestions
- Women's E-commerce Dress Reviews
- Customer Feedback on Women's Apparel
- Online Retail Women's Clothing Reviews
- E-commerce Fashion Review Analysis Data
Attributes
Original Data Source: E-commerce Fashion Review Analysis Data
QUALITY