Customer Segmentation

Customer Segmentation Using Unsupervised Learning

In the realm of retail and wholesale industries, understanding customer behavior and preferences is crucial for targeted marketing and personalized services. Customer segmentation, the process of dividing customers into groups based on shared characteristics, enables businesses to tailor their strategies effectively. In this case study, we explore the process of customer segmentation using various unsupervised machine learning techniques.

Introduction

The primary objective of this project is to segment wholesale customers based on their spending patterns using unsupervised machine learning algorithms. The dataset consists of 440 wholesale customers and their expenditures across different product categories, such as Fresh, Milk, Grocery, Frozen, Detergents_Paper, and Delicatessen. By clustering customers into distinct segments, businesses can gain valuable insights into their purchasing behaviors and preferences.

Technologies Used

Challenges Faced During Model Training

Feature Selection and Preprocessing: Deciding which features to include in the analysis and preprocessing the data to handle scaling and dimensionality reduction posed initial challenges. The removal of irrelevant features (Channel and Region) and scaling of data were essential steps for accurate clustering.
Determining Optimal Clustering Parameters: Selecting the appropriate number of clusters and evaluating the performance of clustering algorithms posed challenges. Techniques such as the Elbow Method for K-Means and Silhouette Coefficients for Agglomerative and Gaussian Mixture Clustering were used to determine optimal parameters.
Interpreting and Characterizing Clusters: Interpreting the characteristics of each cluster and deriving meaningful insights from them required careful analysis. Utilizing box plots to visualize spending patterns within each cluster helped in understanding customer behaviors effectively.

How we train our model

Feature Engineering and Dimensionality Reduction: Features irrelevant to the segmentation task, such as Channel and Region, were removed to focus solely on customer spending behavior. Data scaling and dimensionality reduction using PCA were employed to ensure that all features contributed equally to the clustering process.
Model Evaluation and Selection: Multiple clustering algorithms, including K-Means, Agglomerative, Gaussian Mixture, and DBSCAN, were employed to segment customers. Evaluation metrics such as SSE, Silhouette Coefficients, and log-likelihood were utilized to select the optimal number of clusters for each algorithm.
Visualization and Interpretation: Box plots were used to visualize spending patterns within each cluster, allowing for a deeper understanding of customer behaviors. These visualizations facilitated the characterization of clusters and the identification of distinct customer segments.

Featured Images

Results

Customer segmentation revealed three distinct clusters: one with high spending on Fresh, Grocery, and Milk (Cluster #0), another with significant expenditure on Fresh products (Cluster #1), and a third with balanced spending on Grocery, Milk, and Detergents_Paper (Cluster #2). Insights from clustering enable targeted marketing and personalized services to enhance customer satisfaction and drive business growth.

Conclusions

Through the application of unsupervised machine learning techniques, we successfully segmented wholesale customers into distinct clusters based on their spending behaviors. These clusters provide valuable insights for businesses to tailor their marketing strategies, product offerings, and customer service approaches to better meet the diverse needs and preferences of their customer base. By leveraging customer segmentation, businesses can enhance customer satisfaction, loyalty, and ultimately, drive business growth.