Enhancing Retail Promotional ROI Through AI-Driven Timing and Targeting: A Data Decision Framework for Multi-Category Retailers

In this study, we use AI to create a solution to a problem that is not easily resolved using traditional methods. The basic approach has been to divide the entire year into normal, promotional, and post-promotional periods as targets for sales forecasts. This has resulted in serious errors that degrade inventory management effectiveness and cut down on operating efficiency too. A new decision-making framework integrates machine learning models for time optimization, customer segmentation and cross-category impact forecasting. We demonstrate the validity of introducing such a framework in this paper using completed empirical judging. Our tests indicate that operates with average increases over traditional methods of 38.7% in basket value and 53.7% complementary sales at one time, exceeding promoted items. The framework produces incremental ROI enhancements of 35.5% to 57.6% of across multiple retail formats in most scenarios tested. The success of online grocery operations continues to be the standard in this area with the highest performance gains. rear. Now we are told that indeed such an approach is possible; just recently we were not be able to stomach it. Cross-category analysis reveals categories that have not been coordinated before offering extensive promotional opportunities. The sales increase among category pairs in complementary parts of this data set ran from 6.4% to 15.8%. The framework proves effective in predicting the post-promotional impact, with forecast errors reduced to only 57.3% on average during this most critical of sales periods compared to univariate predictions. This modular, scalable architecture will make a significant practical contribution for better inventory management systems and improved operations efficiency in any retail environment.