Customer Segmentation

This workflow segments customers by analyzing transaction, spending, and demographic data. It combines and preprocesses the data, determines the optimal number of customer groups visually using the Elbow method, applies k-Means clustering, and assigns new customers to the identified segments. The results are visualized and can be exported for further use.

• Identify meaningful customer groups based on behavioral and demographic patterns
• Determine the optimal number of segments using a visual, data-driven approach
• Assign new customers to the most relevant segment automatically
• Visualize and assess the quality of customer segments for actionable insights

The workflow merges transaction and demographic data to create comprehensive customer profiles. It then partitions the data into existing and new customers, ensuring that each group is ready for further analysis.

The data undergoes several preprocessing steps, including handling missing values, detecting and managing outliers, and normalizing numerical features. These actions ensure that the clustering algorithm works with consistent and reliable input.

The workflow uses the Elbow method to help estimate the best number of customer segments. By visually inspecting how the clustering quality changes with different numbers of clusters, you can select a value that balances simplicity and accuracy.

With the optimal number of segments set, the workflow applies the k-Means algorithm to group existing customers. It then assigns new customers to the most appropriate segment based on their profiles, ensuring consistent segmentation across both groups.

The segmented customer groups are visualized using interactive plots. You can inspect the characteristics of each segment, evaluate clustering quality, and export the results for reporting or further analysis.