Automated Retail Assortment Optimization Using Machine Learning and Deep Learning in Compact Stores During Crisis Periods

The retail landscape has significantly transformed due to the impacts of war and the Covid-19 pandemic, which have altered consumer preferences and shopping behaviors. During war, consumer boycotts and changing demand patterns emerge, while the pandemic has shifted traffic away from large hyperstores to smaller, compact stores due to health concerns. This study develops an automated model to address space limitations in compact stores, using machine learning (ML) and deep learning (DL) algorithms. The model combines clustering and classification techniques, utilizing methods such as K-means, k-Nearest Neighbor (KNN), Recurrent Neural Networks (RNNs), DBSCAN, and Gaussian, Agglomerative, and Birch clustering to generate assortments tailored to store characteristics like size and segment. The study finds that K-means clustering is highly accurate, with an SSE score of 0.93, followed by KNN for classification. Additionally, regression models, including Multiple Linear Regression (MLR), Lasso, Random Forest Regression (RFR), Support Vector Regression (SVR), and Decision Tree Regression (DTR), alongside a Deep Neural Network (DNN), analyze sales factors to enhance prediction accuracy. The DNN effectively captures complex relationships between sales data inputs, with R- squared values of 0.81 for MLR and 0.87 for DNN. The model was applied to the grocery department, resulting in a 3% sales increase in compact stores. An ongoing survey with 12 merchandise experts is evaluating the model’s impact on category sales behavior. Overall, the model has improved customer experience by making products more accessible, driving increased foot traffic, visit frequency, and profitability.