Propensity score (PS) is the probability of treatment assignment conditional on ob served baseline characteristics. PS is a balancing score, that is, conditional on the PS, the distribution of observed baseline covariates can be similar between treated and un treated subjects. Propensity Score Matching (PSM) refers to the pairing process of treated and untreated units with similar PS. There is a lack of systematic research in the current literature comparing the matching methods in PSM. On the other side, there are few applications of PSM methods to design and analyze marketing treatments, de spite its powerful ability in advertising to target particular customers that could led to decrease the huge financial losses. This study primarily focuses on evaluating the PSM mechanisms and its application to personalized digital marketing in Banking in stitutions within Oman and beyond. The six most popular PSM approaches (Nearest Neighbor, Nearest Neighbor with Caliper, Optimal, Genetic, and Subclass) have been evaluated based on three di↵erent schemes, using primary survey data that were col lected in the course of this study. Moreover, a new approach, the Paired-samples t test (PStT) has been applied to assess the quality of matching for the PSM. Further more, matched data from PSM' preferable method has been utilized to develop statis tical model for banking customer conversion behavior. Findings of this study, based on bank customers' survey data, showed that the use of PStT with treatment group covariance gives a more precise conclusion in line with previously used assessment methods. Further, using various performance indicators, the Nearest Neighbor with Caliper (NNWC) outperformed other PSM methods. The resulting predictive model using NNWC has a high accuracy, and can be used to predict bank customers' conversion behavior. We recommend the banks to use this method to analyze marketing campaigns and to predict customers behaviors.