Deriving Performance Indicators from Models of Multipurpose Shopping Patterns

In this study, some advanced data-driven techniques are applied to derive performance indicators from models of multipurpose shopping patterns. This research makes use of four machine learning algorithms: decision trees, support vector machines (SVM), k-means clustering, and neural networks. These algorithms were used to analyze consumer purchasing behavior across different product categories, using data culled from retail transactions and processed to identify patterns and predict future shopping behaviors. Accuracy, precision, recall, and F1-score were employed to assess the models. The algorithm that received the highest accuracy was the neural network, at 92.5%, with SVM receiving 89.7%, decision trees at 86.3%, and k-means clustering at 81.4%. The comparative analysis depicts the neural network model as superior in predictive accuracy and flexibility to changing consumer trends. It is also shown how these algorithms can be effective in enriching customer segmentation and inventory management. Indeed, there is a more than noticeable decrease of 12% in inventory costs and an increase of 15% in the customer retention due to targeting the right market with the help of insights from the model. These findings can prove highly helpful to business organizations to better engage their customers and streamline their workflow through data-driven decision making.