Unveiling Gold Membership Classification Using Machine Learning

The main challenge in loyalty programs is selecting customers with limited funding. To address it, we explore various machine learning-based classification models. This study aims to enhance the effectiveness of a marketing strategy that promotes gold membership to customers with prior transaction history. Previously, much research applied decision trees, random forests, and logistic regression for classification, but gradient boosting is still unpopular. However, in this study, the Gradient Boost algorithm exhibits the best performance among these models, achieving an impressive accuracy of around 88%. This result underscores the model's capability to classify customers, thereby suggesting its potential to significantly enhance the marketing strategy's effectiveness. The analysis identifies crucial features that influence the model's predictive capabilities. Notably, the recency of the last visit, the number of transactions involving wine and meat, marital status, and the number of offline store transactions are identified as influential factors. Leveraging machine learning techniques enables the automation of the customer selection process, facilitating the attraction of a more extensive customer base. By targeting those customers most likely to respond positively to the gold membership offer, efficient resource allocation can be achieved. This research provides valuable insights and practical recommendations for implementing an effective marketing strategy under resource constraints. Combining machine learning algorithms and feature identification enables efficient targeting of potential customers, maximizing the impact of the gold membership offering. Implementing the findings of this study could lead to increased customer acquisition and improved overall business performance.