Intelligent Customer Analytics and Personalized Banking Services for Profit Maximization
Аннотация
The application of intelligent analytics to customer behavior during financial interactions might enable precise profiling and might furthermore increase profitability by ensuring a sufficient alignment of products for a specific market segment. Due to its data-driven complexity, it is necessary and significant to evaluate the effectiveness of personalization in a specific banking environment. With the objective of improving understanding of behavioral differences in the financial domain, we first identify treatment and control groups and estimate in real-time variation by using the Propensity Score Matching model. This analysis focuses on sentiment-based segmentation at the customer level and reveals that the satisfaction, retention, and engagement of clients can be influenced by a sentiment-driven model on transaction behavior in a predictive framework. Key outcome measures for the personalized banking model were the accuracy for the classification process, the matching balance, and the consistency of the four analytical indicators used: emotional tone, purchase intention, churn likelihood, and profitability index. Through the multiple regressions of the covariates in the context of digital banking, we analyze the magnitude and direction of the predictors in the formation of personalized services. The results showed that there were three types of customer responses: from positive to mixed/neutral (47%), neutral/negative without bias (32%), and negative only (21%). The matched model accounted for up to 89% of the variation in outcomes; the predictive power of the sentiment variables ranged from approximately 0.63 to 0.87, the behavioral variables ranged from 0.48 to 0.75, and the demographic variables ranged from 0.39 to 0.61. These results could provide valuable implications for managers and policymakers.