Predictive Modeling for Marketing Strategies: A Case Study of a Superstore’s Gold Membership Offer Using Advanced Analytics and Machine Learning Techniques

The research article showcases an in-depth examination of a large retail superstore’s scenario, where they introduce a novel gold membership proposition. This initiative involves the strategic utilization of advanced analytics and machine learning (ML) techniques to not only identify potential customers but also to gain insights into their preferences. This study aims to investigate the available data to establish the elements that influence a customer’s reaction to a new supermarket offer and then construct a predictive model that can accurately anticipate the likelihood that a consumer will respond favorably. In order to enhance marketing strategies and bolster sales figures, the research employs an array of ML methodologies. These include Decision Tree, Support Vector Machine (SVM), Random Forest, and XGBoost. To further elevate their effectiveness, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) techniques are incorporated into these machine learning models. This integration furnishes robust search mechanisms for refining hyperparameters, thus facilitating the discovery of optimal solutions. This iterative tuning process significantly amplifies the models’ classification performance, especially in tackling the intricate challenges presented by the retail superstore context. As per the research results, the utilization of Grey Wolf Optimization yielded notable outcomes. Specifically, when applied to the Random Forest model, it achieved a remarkable accuracy of 95%. Moreover, through the fine-tuning enabled by Grey Wolf Optimization, the Decision Tree model demonstrated the most substantial enhancement in terms of accuracy. Overall, the results suggest that the metaheuristic strategy used to tune hyperparameters has a considerable impact on the performance of all ML models.