A Hybrid Deep Learning Approach for Predicting Campaign Success

Predicting the success of marketing campaigns is a critical challenge in the fast-moving business world. This challenge requires advanced models that can help deal with complicated consumer behavior and tell us what actions to take. The goal of this study is to create and test a hybrid deep learning model that predicts campaign success using feature embeddings and dense neural networks. The hybrid model uses TabNet in its analysis to identify key features. The analysis identifies NumWebPurchases, MntGoldProds, Teenhome, Income, and Recency as some of the key predictors. Moreover, through which this knowledge is integrated into its learning frameworks. The hybrid model performed better with $91.24 \%$ accuracy, $89.76 \%$ precision, $89.32 \%$ recall, $89.54 \%$ F1-score compared to Multi-Layer Perceptron (MLP), TabNet and Deep Belief Networks (DBN). Presently, the model’s capability to reduce false negatives assists target differentiations in ways that present-day methods do not. Highlighting the relevance of examining feature importance, this exploration also gives marketers a chance to grasp consumer behavior and market trends which could be seen as beneficial.