Customer Engagement and Data Visualization Tools, Ambient Sound Recognition Software, and Deep Learning-based Sensing Technologies in the Metaverse Economy.

• Author: Newell, Mark

1. Introduction

   Deep learning algorithms, augmented and virtual reality technologies, and data visualization tools can enhance customer experience, movement and behavior tracking, and brand loyalty across virtual environments. The purpose of my systematic review is to examine the recently published literature on the metaverse economy and integrate the insights it configures on customer engagement and data visualization tools, ambient sound recognition software, and deep learning-based sensing technologies. By analyzing the most recent (2021-2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that virtual navigation tools enable immersive 3D experiences in extended reality environments. The actuality and novelty of this study are articulated by addressing immersive virtual shopping and metaverse brand experiences, that is an emerging topic involving much interest. My research problem is whether customer predictive analytics leverages machine learning-based product recognition tools, picture-making neural networks, and cognitive artificial intelligence algorithms across interconnected digital realms and immersive virtual spaces.

   In this review, prior findings have been cumulated indicating that data tracking apps and deep neural network technology optimize customer engagement behaviors in virtual marketplaces and interconnected digital spaces. The identified gaps advance metaverse technologies integrating cognitive computing systems and virtual retail algorithms. My main objective is to indicate that business intelligence operations enable virtual retail experiences in the metaverse economy, attracting and retaining customers. This systematic review contributes to the literature on big data and customer experience analytics shaping live e-commerce shopping and metaverse engagement and experiences by clarifying that technology-enabled live shopping requires visual and customer behavior analytics, driving spending habits across virtual marketplaces and typifying customer engagement behaviors. This research endeavors to elucidate whether blockchain-based digital assets typify convenient consumerism through personalized digital shopping experiences, resulting in lifetime customer value. My contribution is by integrating research findings indicating that immersive shopping experiences can be achieved through sensory data mining techniques and machine vision algorithms in the virtual retail market.

2. Theoretical Overview of the Main Concepts

   Building brand image and customer relationships, configuring consumption patterns, and driving brand awareness through personalized services across interactive digital worlds can enhance virtual store experiences. Customer data analytics deploys business intelligence tools in immersive 3D experiences, shaping consumer purchase behaviors in the blockchain-based metaverse. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), live e-commerce shopping and metaverse engagement and experiences (section 4), immersive virtual shopping and metaverse brand experiences on augmented reality commerce platforms (section 5), metaverse purchase and digitally-enhanced personalized experiences in a blockchain-based virtual world (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including "metaverse economy" + "customer engagement and data visualization tools," "ambient sound recognition software," and "deep learning-based sensing technologies." The search terms were determined as being the most employed words or phrases across the analyzed literature. As I analyzed research published between 2021 and 2022, only 214 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, I decided on 51, chiefly empirical, sources (Tables 1 and 2). Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Distiller SR, ROBIS, and SRDR. Extracting and inspecting publicly accessible files (scholarly sources) as evidence, before the research began no institutional ethics approval was required (Figures 1-6).

4. Live E-Commerce Shopping and Metaverse Engagement and Experiences

   Big data and customer experience analytics shapes live e-commerce shopping and metaverse engagement and experiences (Akour et al., 2022; Crowell, 2022; Dwivedi et al, 2022; Grupac and Lazaroiu, 2022; Kozinets, 2022; Yeh et al, 2022) in terms of consumer digital engagement. Deep learning artificial intelligence tools shape consumer sentiment and behavior across immersive 3D worlds and the digital asset-based virtual economy. Virtual navigation tools enable immersive 3D experiences in extended reality environments.

   Immersive metaverse experiences can be attained (Akyildiz et al, 2022; Balica et al, 2022; Dawson, 2022; Hudson, 2022; Lukava et al, 2022; Wang et al., 2022) by integrating text mining and analytics, consumer location data, computer vision-based systems, and augmented reality shopping tools. Data tracking apps and deep neural network technology optimize customer engagement behaviors in virtual marketplaces and interconnected digital spaces. Building brand image and customer relationships, configuring consumption patterns, and driving brand awareness through personalized services across interactive digital worlds can enhance virtual store experiences.

   Immersive retail experiences can be achieved in Web3-powered metaverse worlds and across shared virtual environments (Bibri and Allam, 2022; Gills and Hosseini, 2022; Lyons, 2022; Popescu et al., 2022; Zvarikova et al., 2022a, b) through augmented reality shopping tools. Immersive shopping experiences can be achieved through sensory data mining techniques and machine vision algorithms in the virtual retail market. Natural language processing and virtual navigation tools further augmented shopping experiences in immersive digital worlds and extended reality environments. (Table 3)

5. Immersive Virtual Shopping and Metaverse Brand Experiences on Augmented Reality Commerce Platforms

   Metaverse technologies integrate cognitive computing systems and virtual retail algorithms (Adams, 2022; Deveci et al., 2022a, b; Gossling and Schweiggart, 2022; Park and Kim, 2022; Popescu Ljungholm, 2022), resulting in lasting competitive advantage in real-time immersive 3D worlds. Technology-enabled live shopping requires visual and customer behavior analytics, driving spending habits across virtual marketplaces and typifying customer engagement behaviors. Sentiment analytics and ambient scene detection tools articulate personalized customer...