Customer segmentation is a crucial process in marketing that involves dividing a company’s customer base into groups with similar characteristics. By grouping customers based on traits like demographics, behavior, or purchasing patterns, businesses can tailor marketing strategies and improve product offerings, leading to increased customer satisfaction and loyalty.
In this project, we aim to perform customer segmentation using the K-Means Clustering algorithm on the Mall Customers Dataset. The dataset contains information about customers, including their age, gender, annual income, and spending score. The goal is to identify distinct customer groups based on their income and spending behavior.
Key steps in the project include:
- Data Preprocessing: Converting categorical data and scaling numerical features to ensure consistent results.
- Clustering with K-Means: Applying the K-Means algorithm to divide customers into distinct segments.
- Optimal Number of Clusters: Using the Elbow Method to determine the ideal number of clusters for accurate segmentation.
- Cluster Analysis: Interpreting the characteristics of each customer segment based on the clustering results.
- Visualization: Plotting customer groups to visualize the segmentation based on their annual income and spending score.
Use Cases:
- Targeted Marketing: Businesses can use this segmentation to create personalized marketing campaigns for different customer groups.
- Product Recommendations: Tailoring product offerings based on the purchasing patterns of specific segments.
- Customer Retention: Focusing retention efforts on high-value customers identified through segmentation.
This project provides a hands-on example of how K-Means clustering can be applied to real-world datasets to derive valuable insights for business decision-making.
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