• Segmentation of Bank Consumers for Artificial Intelligence Marketing

    Author(s):
    Omri Raiter (see profile)
    Date:
    2021
    Subject(s):
    Machine learning
    Item Type:
    Article
    Tag(s):
    AI marketing, bank, Card holders, cluster analysis
    Permanent URL:
    http://dx.doi.org/10.17613/q0h8-m266
    Abstract:
    Banks can offer more personalized products and services by using segmentation solutions. By gaining a deeper understanding of client characteristics, marketers can Choose the appropriate promotional content to deliver, choose the right marketing channels for the target market, identify new and profitable market sectors, and introduce new products and services. Artificial intelligence marketing uses artificial intelligence concepts and models such as machine learning and Bayesian networks. Cluster analysis is a machine learning method for classifying entities into groups that are homogenous in terms of observable characteristics. This study included K-means cluster analysis, Elbow, and silhouette approaches to segment the data for cardholders of various banks. According to the results from Elbow and the silhouette, the ideal number of clusters seems to be five. Based on their income and shopping frequencies, which are supposedly the greatest attributes to establish the segments of the customers, this research identified five distinct consumer segments: Savers, General, Targets, and Big spenders. This research recommends leveraging machine learning techniques to devise various marketing tactics and policies to maximize the bank’s efficiency, customer satisfaction, and quality of services.
    Notes:
    How to Cite: Raiter, O. . (2021). Segmentation of Bank Consumers for Artificial Intelligence Marketing . International Journal of Contemporary Financial Issues, 1(1), 39–54. Retrieved from https://scholar-publica.space/index.php/fin/article/view/9
    Metadata:
    xml
    Published as:
    Journal article     Show details
    Publisher:
    Scholar-publica.space
    Pub. Date:
    2021-12-03
    Journal:
    International Journal of Contemporary Financial Issues
    Volume:
    1
    Issue:
    1
    Page Range:
    39 - 54
    Status:
    Published
    Last Updated:
    1 year ago
    License:
    Attribution-NonCommercial

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