A Systematic Review of the Literature on Customer Analytics to Predict Dynamic Purchase behavior of Consumers through Credit Cards

Abstract

Abstract: The Credit card industry is flooded with customer data and also their diversified purchasing behavior. These multifariousness analysis helps different industry to maximize revenue through data and to identify right cluster of audience based on the products and services they offer. To analyse this data efficiently and erect efficient co-relation and streamline process execution, expertise in customer segmentation specifically through customer analytics, is required. This domain of analytics uses a wide variety of concepts related to data mining, Passive and active data collection, Media planning, Regression analysis, Customer based corporate-valuation, Market-structure, Probability Models, Optimization, visualization and Implementation of model spreadsheet for predictions on data set. Moreover, customer analytics is the subset of a larger range of business analytics techniques used for better statistical and predictive analysis to transform and make smarter, data-driven business decisions. Keywords: Regression analysis, Market-structure, Optimization, Business analytics, Model spreadsheet.