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Customer Yearly Expenditure Data
E-commerce & Online Transactions
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About
Contains customer data collected from an e-commerce platform. This resource is specifically designed for machine learning initiatives, allowing analysts and data scientists to build predictive models. The primary objective is to estimate the Yearly Amount Spent by customers based on observed features such as time spent on the application and website, and the duration of their membership. This offers crucial insight into factors driving high-value customer spending.
Columns
- Email: Textual field containing unique email addresses (500 unique values, 100% valid).
- Address: Textual field detailing the physical address of the customer (500 unique values, 100% valid).
- Avatar: Textual field representing a feature of the customer's avatar or profile picture (138 unique values recorded).
- Time on App: Numerical metric tracking the time a customer spent interacting with the mobile application. The mean time is approximately 12.1 units.
- Time on Website: Numerical metric tracking the time a customer spent interacting with the e-commerce website. The mean time is approximately 37.1 units.
- Length of Membership: Numerical metric indicating the duration of the customer's membership, ranging from 0.27 up to 6.92 units. The mean duration is about 3.53 units.
- Yearly Amount Spent: Numerical target variable representing the total amount, in currency, spent by the customer over a year. Values range from 257 to 766, with a mean of 499.
Distribution
The data is provided in CSV format, named
data.csv, and has a file size of 53.38 kB. The structure consists of 7 distinct columns and 500 individual records or entries. All columns exhibit 100% data validity with no missing values.Usage
This data is highly suitable for building and evaluating regression models aimed at predicting customer lifetime value (CLV). Other key applications include customer segmentation, analysing the correlation between platform engagement (app vs. website time) and spending habits, and developing marketing strategies focused on increasing membership longevity.
Coverage
The dataset captures customer information pertaining to an e-commerce platform. The reference date associated with the data description is 2022-01-19. Demographic or specific geographic coverage details are not explicitly itemised beyond general customer characteristics.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For developing and testing machine learning algorithms to predict future customer expenditure.
- Business Analysts: For identifying key performance indicators (KPIs) related to customer engagement and revenue generation.
- E-commerce Strategists: For understanding the impact of platform design (app versus website) on consumer purchasing behaviour.
Dataset Name Suggestions
- E-commerce Spending Predictor
- Customer Yearly Expenditure Data
- Online Retail Customer Feature Set
- Member Lifetime Value Analysis Data
Attributes
Original Data Source: Customer Yearly Expenditure Data
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