In this research, we have introduced a framework that integrates advanced feature engineering approaches and machine learning techniques to enhance decision-making in omni-channel retail for precision marketing and improved customer experience. The proposed system comprises several key steps. First, data were collected and preprocessed using various techniques. Next, the most significant features were chosen through the integration of feature selection algorithms. For handling data imbalance, oversampling strategies were employed, and a large customer persona was built through the extraction of a large range of data features including fundamental and consumption attributes. Following this, a response model was developed, and diverse evaluation metrics were employed to assess its performance. The finding of this study illustrates that the proposed system attained desirable outcomes.