<![CDATA[This study investigates the application of Gaussian Mixture Models (GMM) for customer segmentation and targeted pricing strategies in the retail industry to maximize gross income. Using a dataset of 1000 transaction records, the analysis focused on attributes such as unit price, quantity, total amount, and payment methods. The dataset was preprocessed to handle missing values, encode categorical features, and scale numerical features. The optimal number of components for the GMM was determined using the Bayesian Information Criterion (BIC), resulting in the selection of 10 clusters. Model training was conducted using the Expectation-Maximization (EM) algorithm, achieving convergence after 18 iterations. Customer segments were identified and analyzed based on their purchasing behaviors and demographic traits. For instance, Segment 0 preferred bulk purchases of lower-priced items, while Segment 1 favored higher-priced items in smaller quantities, resulting in a higher average purchase value of 2274.19. Conversely, Segment 2 showed a high frequency of returns, indicated by a negative average purchase value of -2608.40. Targeted pricing strategies were developed for each segment, aiming to maximize gross income. The effectiveness of the segmentation and pricing strategies was evaluated using metrics such as the silhouette score, with training and testing scores of 0.175 and 0.015 respectively, highlighting areas for improvement in clustering quality. This study underscores the potential of GMM in uncovering distinct customer segments and tailoring pricing strategies to enhance profitability. Future research should explore alternative clustering techniques and extend the analysis to other retail domains and larger datasets to validate and improve the findings. The practical implications for retail businesses include the need for iterative testing and refinement of pricing strategies based on customer segmentation to achieve sustainable growth and customer satisfaction.]]>
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