Deep Learning Computer Vision Algorithms, Customer Engagement Tools, and Virtual Marketplace Dynamics Data in the Metaverse Economy
Deep Learning Computer Vision Algorithms, Customer Engagement Tools, and Virtual Marketplace Dynamics Data in the Metaverse Economy

Summary/Abstract: Based on an in-depth survey of the literature, the purpose of the paper is to explore deep learning computer vision algorithms, customer engagement tools, and virtual marketplace dynamics data in the metaverse economy. In this research, previous findings were cumulated showing that visual analytics is pivotal in improving customer engagement and immersive virtual experiences in digital commerce in terms of tailored shopping recommendations while raising brand awareness during customer journey, and I contribute to the literature by indicating that homogenizing online and offline operations by integrating sensor data, customer engagement tools, predictive maintenance, and spatial analytics configure branded digital experiences. Throughout February 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “metaverse economy” + “deep learning computer vision algorithms,” “customer engagement tools,” and “virtual marketplace dynamics data.” As research published in 2022 was inspected, only 67 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 13 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality as- sessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

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