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Customer Purchase Dynamics Dataset
E-commerce & Online Transactions
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About
This dataset provides details on customer purchase behaviour across various attributes, designed to assist data scientists and analysts in understanding the key factors influencing purchase decisions [1]. It incorporates demographic information, purchasing habits, and other relevant features [1]. The dataset is synthetic and was created solely for educational purposes, making it perfectly suited for data science and machine learning projects [2]. It is an original dataset, aimed at exploring consumer trends and unveiling patterns in buying behaviour [1, 2].
Columns
- Age: Represents the customer's age, ranging from 18 to 70 years, with an average of 44.3 years [1, 3].
- Gender: Indicates the customer's gender, where '0' denotes Male and '1' denotes Female. The dataset includes 743 males and 757 females [1, 4].
- Annual Income: The customer's yearly income in pounds. It spans from £20,000 to £150,000, with an average income of £84,200 [4-6].
- Number of Purchases: The total count of purchases made by the customer. Values range from 0 to 20, averaging 10.4 purchases per customer [5, 6].
- Product Category: Specifies the category of the purchased product, encoded as '0' for Electronics, '1' for Clothing, '2' for Home Goods, '3' for Beauty, and '4' for Sports [5-7].
- Time Spent on Website: The duration a customer spent on the website, measured in minutes. This ranges from 1.04 to 60 minutes, with an average of 30.5 minutes [5, 7, 8].
- Loyalty Program: A binary indicator ('0': No, '1': Yes) showing whether the customer is a member of a loyalty programme. There are 1,010 non-members and 490 members [5, 8].
- Discounts Availed: The number of discounts a customer has utilised, ranging from 0 to 5. The average number of discounts availed is 2.56 [5, 8].
- PurchaseStatus (Target Variable): Represents the likelihood of a customer making a purchase, with '0' indicating no purchase and '1' indicating a purchase [5, 9].
Distribution
The dataset is provided as a CSV file named
customer_purchase_data.csv, with a file size of 78.56 kB [3]. It consists of 9 columns and contains 1500 valid records, with no missing or mismatched entries across any feature [3, 4, 6-9]. The target variable, PurchaseStatus, is well-balanced, with a distribution of 48% 'No Purchase' (0) and 52% 'Purchase' (1) outcomes [10]. This dataset is not expected to be updated [3].Usage
This dataset is highly suitable for various analytical tasks, including classification, clustering, and regression [10]. Its primary applications involve predicting customer purchase behaviour and gaining insights into the underlying factors that drive purchasing decisions [10]. It is also valuable for developing and testing machine learning models related to consumer analytics [2].
Coverage
The dataset includes diverse demographic information such as customer age (18-70 years) and gender (Male/Female) [1, 3, 4]. It also covers customers with annual incomes ranging from £20,000 to £150,000 [4-6]. Purchased product categories span Electronics, Clothing, Home Goods, Beauty, and Sports [5]. Information regarding specific geographic regions or time ranges is not available in the provided material.
License
Attribution 4.0 International (CC BY 4.0) licence
Who Can Use It
This dataset is ideal for:
- Data scientists and analysts: For understanding and predicting customer purchase patterns, and identifying key influencing factors [1].
- Machine learning engineers: For building and evaluating predictive models related to consumer behaviour, such as purchase likelihood classification [2, 10].
- Students and researchers: For academic projects, learning exercises, and exploring data analysis techniques in a practical context [2].
- Marketing strategists: For gaining insights into consumer demographics and habits to inform marketing campaigns.
Dataset Name Suggestions
- Customer Purchase Dynamics Dataset
- Consumer Buying Propensity Data
- Online Retail Customer Behaviour
- Purchase Decision Factors Analysis
- E-commerce Customer Activity Dataset
Attributes
Original Data Source: Customer Purchase Dynamics Dataset
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