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Customer Purchase Prediction Dataset
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About
A marketing campaign, detailing key engagement metrics. It follows customers through the marketing funnel, from opening an email to making a purchase. The information is structured to be particularly suitable for building and testing logistic regression models, allowing users to predict purchase behaviour based on campaign interactions and demographic data.
Columns
- Customer id: A unique identifier for each customer.
- Age: The age of the customer.
- Gender: The customer's gender, represented numerically (e.g., 0 and 1).
- Location: The geographical location where the customer lives.
- Email Opened: A binary indicator showing whether the customer opened the marketing email.
- Email Clicked: A binary indicator showing whether the customer clicked a link within the email.
- Product page visit: The number of times the customer visited the product page.
- Discount offered: A binary indicator showing whether a discount was offered to the customer.
- Purchased: A binary indicator showing whether the customer purchased the item.
Distribution
The data is provided in a single CSV file named
Marketingcampaigns.csv with a size of 621 B. It contains 20 records and 9 columns.Usage
Ideal applications for this dataset include:
- Developing predictive models to forecast customer purchase probability.
- Training and evaluating logistic regression algorithms.
- Analysing the effectiveness of different stages in a marketing campaign.
- Segmenting customers based on their engagement and demographic profiles.
Coverage
The dataset covers a sample of 20 customers with diverse ages and locations, including Perth and Sydney. There are no specific time ranges or demographic limitations mentioned in the provided data.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building and validating predictive models, particularly logistic regression.
- Marketing Analysts: To understand customer behaviour and measure campaign performance.
- Students and Educators: As a simple, clean dataset for teaching machine learning and data analytics concepts.
- Business Analysts: To derive insights into the customer journey and identify key conversion drivers.
Dataset Name Suggestions
- Marketing Campaign Customer Engagement
- Email Marketing Conversion Analysis
- Customer Purchase Prediction Dataset
- Logistic Regression Marketing Funnel
Attributes
Original Data Source: Customer Purchase Prediction Dataset
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