This interactive plot segments over 2000 shopping mall customers based on their annual income and spending score. By hovering over the data points, you can view individual customer details. The Shopping Mall Customer Segmentation Dataset, often used in clustering and customer segmentation analysis, includes attributes like gender, age, annual income, and spending score. These variables reflect customer purchasing behaviour and are perfect for unsupervised learning tasks like K-Means clustering. Businesses can leverage this data to identify distinct customer groups, enabling more effective marketing strategies, personalized product recommendations, and tailored services.
Low Income, Middle Spending
Customers with low income levels but moderate spending habits.
Business Strategies:
Offer value-for-money products and targeted promotions to encourage increased spending.
Middle to High Income, High Spending
Customers with middle to high income levels who are high spenders.
Business Strategies:
Provide premium products and exclusive offers to maintain their loyalty and spending levels.
Low Income, High Spending
Customers with low income levels who have high spending tendencies.
Business Strategies:
Introduce flexible payment options and promotions to encourage continued spending.
Middle to High Income, Low Spending
Customers with middle to high income levels who are low spenders.
Business Strategies:
Offer personalized services and incentives to encourage increased spending.
Low Income, Low Spending
Customers with low income levels and low spending habits.
Business Strategies:
Focus on affordable products and promotions to attract their interest and increase foot traffic.