Customer Segmentation Data For Marketing Analysis

This dataset contains simulated customer data that can be used for segmentation analysis. It includes demographic and behavioral information about customers, which can help in identifying distinct segments within the customer base. This can be particularly useful for targeted marketing strategies, improving customer satisfaction, and increasing sales.

Columns:
id: Unique identifier for each customer.
age: Age of the customer.
gender: Gender of the customer (Male, Female, Other).
income: Annual income of the customer (in USD).
spending_score: Spending score (1-100), indicating the customer's spending behavior and loyalty.
membership_years: Number of years the customer has been a member.
purchase_frequency: Number of purchases made by the customer in the last year.
preferred_category: Preferred shopping category (Electronics, Clothing, Groceries, Home & Garden, Sports).
last_purchase_amount: Amount spent by the customer on their last purchase (in USD).
Potential Uses:
Customer Segmentation: Identify different customer segments based on their demographic and behavioral characteristics.
Targeted Marketing: Develop targeted marketing strategies for different customer segments.
Customer Loyalty Programs: Design loyalty programs based on customer spending behavior and preferences.
Sales Analysis: Analyze sales patterns and predict future trends.