Data Visualization Technologies in Customer Relationship Management: A Methodological Review

Abstract

Data visualization is integral to customer relationship management (CRM), facilitating comprehension, interpretation, and presentation of customer data effectively. It aids in under-standing customer interactions, identifying behavioral trends, and informing business decisions. This paper conducts an extensive literature review synthesizing insights from 22 carefully chosen journal articles, categorizing them into data visualization management stages: collection, preprocessing, storage, processing, analysis, and visualization. Various tools, techniques, and processes identified in the literature offer a comprehensive view of current methodologies and technologies. Beginning with data collection from diverse sources, including databases and surveys, the process progresses to preprocessing, involving techniques such as data normalization and cleansing. Storage emphasizes data confidentiality and integrity, utilizing encryption and digital watermarking. Real-time manipulation occurs through an Extract-Transform-Load process in data warehousing. Python and R are prominent tools for data analysis, employing techniques like data mining and opinion mining. In data visualization, real-time multidimensional analysis software and 3D methods cater to terminal devices, enhancing data comprehension through spatial relationships. Future research avenues include exploring real-world applications, advanced predictive analytics, interactive visualization techniques, integrating WiFi sensors, tag cloud diagrams, and leveraging business intelligence in data visualization technologies. Such endeavors promise to advance CRM practices and deepen insights into customer behavior.