Metaverse Live Shopping Analytics: Retail Data Measurement Tools, Computer Vision and Deep Learning Algorithms, and Decision Intelligence and Modeling
Metaverse Live Shopping Analytics: Retail Data Measurement Tools, Computer Vision and Deep Learning Algorithms, and Decision Intelligence and Modeling

Summary/Abstract: I draw on a substantial body of theoretical and empirical research on metaverse live shopping analytics. With increasing evidence of retail data measurement tools, computer vision and deep learning algorithms, and decision intelligence and modeling, there is an essential demand for comprehending whether spatial analytics and computer vision algorithms can enhance immersive retail experiences in blockchain-based virtual worlds by leveraging business intelligence tools to determine consumer behavior and preferences. In this research, prior findings were cumulated indicating that data visualization tools can be decisive in retail analytics by optimizing experiential shopping as regards digital assets across shared virtual environments. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March 2022, with search terms including “metaverse” + “live shopping analytics,” “retail data measurement tools,” “computer vision and deep learning algorithms,” and “decision intelligence and modeling.” As I analyzed research published in 2021 and 2022, only 72 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 19, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR.

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