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Segmented Vehicle Purchaser Data
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About
Data detailing existing and potential automobile customers, intended to facilitate customer segmentation. The context involves an automobile company planning entry into new markets with five distinct products (P1 through P5). The strategy is based on the behaviour observed in their existing market, where customers were successfully classified into four segments (A, B, C, D) for targeted communication and outreach. The data allows analysts to gain insight into customer preferences, needs, and spending habits to accurately tailor marketing efforts toward specific groups.
Columns
- CustomerID: A unique identifier for the customer.
- Gender: The sex of the customer.
- Married: The marital status of the customer.
- Age: The age of the customer.
- Graduated: Specifies whether the customer holds a graduate degree.
- Profession: The customer’s specific occupation.
- WorkExperience: The customer's duration of work experience in years.
- SpendingScore: A measure of the customer's purchasing frequency, often categorised as Low, Average, or similar descriptors.
- FamilySize: The total number of family members, including the customer.
- Category: An anonymised category assigned to the customer.
- Segmentation: The target variable indicating the customer segment (A, B, C, or D).
Distribution
The data is provided in a CSV file (
test-set.csv). The sample includes details for 2627 potential new customers. Key demographic features include Age, ranging from 18 to 89. Gender distribution is roughly balanced, with 54% Male and 46% Female. Approximately 61% of customers are graduates. SpendingScore is heavily skewed toward the 'Low' category, representing 62% of the records. Note that the WorkExperience column contains a significant number of missing values (around 10%). The dataset is static, with no anticipated future updates.Usage
Ideal applications involve performing customer segmentation analyses, which can be applied to marketing strategies for new market entry. It is perfect for developing predictive models to classify new customers into the established segments (A, B, C, D). Use cases include optimising outreach efforts and tailoring communication messages based on segmented insights to improve sales effectiveness. This dataset is suitable for practicing data analytics, exploratory data analysis, and clustering techniques.
Coverage
The scope focuses on customer details relevant to the purchase history of an automobile company's existing products (P1-P5). The data allows researchers to draw parallels between an existing successful market and the 2627 identified potential customers in a new market. Demographics covered include Age, Gender, Marital Status, Education, Profession, and Family Size.
License
CC0: Public Domain
Who Can Use It
- Data Analysts and Scientists: For practicing clustering algorithms, exploratory data analysis, and building segmentation models.
- Marketing Teams: To understand how customer attributes (age, profession, spending score) drive segment membership for highly targeted campaigns.
- Automobile Companies: To implement effective segmented outreach strategies when planning entry into new regions.
Dataset Name Suggestions
- Automobile Customer Segmentation Profile
- Segmented Vehicle Purchaser Data
- Targeted Auto Customer Characteristics
Attributes
Original Data Source: Segmented Vehicle Purchaser Data
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