Dark Mode
Online Retail Delivery Prediction Dataset
Retail & Consumer Behavior
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset aims to facilitate the prediction of e-commerce shipping sales. It provides valuable insights from an international e-commerce company's customer database, which sells electronic products. The data is designed to support the application of advanced machine learning techniques to study customer behaviour and identify key patterns relating to product delivery and customer satisfaction.
Columns
- Customer_care_calls: Represents the number of calls made by a customer to customer care.
- Customer_rating: The rating provided by the customer for their experience.
- Prior_purchases: Indicates how many prior purchases the customer has made on the site.
- Discount_offered: The percentage or value of discount offered on the product.
- Weight_in_gms: The weight of the product in grams.
- Warehouse_block: Specifies the warehouse block where the product is stored (e.g., F, D).
- Mode_of_Shipment: Describes the method used for shipping the product (e.g., Ship, Flight).
- Product_importance: Denotes the perceived importance of the product (e.g., low, medium).
- Gender: The gender of the purchaser (F for female, M for male).
- Class: A binary indicator, with 1 signifying the product was delivered on time and 0 meaning it was not.
Distribution
The dataset is typically provided in a CSV format, with a sample file updated separately to the platform. It contains 11,000 records across 10 distinct columns. The file size is approximately 341.54 kB. All listed columns have 100% valid entries, indicating no missing data within these fields.
Usage
This dataset is ideal for:
- Developing and testing machine learning models to predict e-commerce sales.
- Uncovering key business insights from customer purchasing and delivery data.
- Analysing customer behaviour patterns related to product importance, discounts, and customer support interactions.
- Studying the impact of shipping methods and warehouse logistics on delivery timeliness.
- Regression analysis and classification tasks to understand factors influencing on-time delivery.
Coverage
The dataset originates from an international e-commerce company, focusing on customer interactions and shipping logistics for electronic products. It includes demographic information such as gender and metrics on customer service engagement and purchasing history. Specific geographic or time range details are not available; however, all records across the provided columns are complete and valid.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
- Machine Learning Engineers and Data Scientists: For building predictive models for e-commerce sales and delivery performance.
- Business Analysts: To derive actionable insights into customer satisfaction, operational efficiency, and sales strategies.
- E-commerce Strategists: For optimising pricing, discounts, shipping logistics, and customer service.
- Students and Researchers: For educational purposes and academic studies in business analytics, logistics, and data science.
Dataset Name Suggestions
- E-commerce Shipping Performance Data
- Online Retail Delivery Prediction Dataset
- Customer E-commerce Logistics Insights
- Global E-commerce Sales & Delivery
- Product Delivery Timeliness Analysis
Attributes
Original Data Source: Online Retail Delivery Prediction Dataset
QUALITY