Unlock the value of virtual data in the metaverse. Discover practical strategies, KPIs, and real-world lessons to help your organisation make the most of metaverse analytics for measurable business results. When thinking about your virtual data, it is important to remain focused on the business value for the end user or customer. Virtual data provides the voice of the users can be measured in terms of KPIs for the metaverse, as we find in business practices now. However, it is best to think about a data strategy and important concepts such as data governance for your virtual environments earlier in the process, rather than an optional add-on at the end of the project.
The metaverse is a rapidly growing digital ecosystem that’s reshaping how enterprises connect, collaborate, and create value. For forward-thinking organizations, the real opportunity lies in the data: every virtual interaction, transaction, and experience generates insights that can drive measurable business results. Unfortunately, you can’t be in Excel Hell and expect to use the Metaverse – so how do you move forward?
Statista Metaverse eCommerce projected around US$210 billion by 2030 (≈38% CAGR, 2025–2030). Statistica, 2025. But how can businesses turn this virtual data into tangible value?
Metaverse adoption is accelerating, which will be followed by the need for metaverse analytics. Figures vary by methodology and scope, so it is important to be clear on a definition (direct revenues vs. total impact) when using these projections. In terms of organizational impact, Accenture’s Technology Vision 2022 surveyed around 4,650 C‑level executives and directors across 35 countries and 23 industries. Accenture reports that 71% of executives believe the metaverse will have a positive impact on their organization, and 42% believe it will be “breakthrough” or “transformational.” (Accenture’s Technology Vision 2022 report.)
If your organisation is experimenting with virtual environments, then you are most likely already sitting on valuable virtual data that can inform better decisions and drive ROI. Virtual environments include items such as digital twins, immersive collaboration, or virtual events.
Key takeaway: If your organization is experimenting with virtual environments, you already have valuable data that can inform decisions, improve customer experiences, and boost operational efficiency.
Success with metaverse analytics is about applying proven data strategy principles to a new frontier. Here’s a step-by-step process I use with clients:
Start with a specific question or challenge. Although it’s very tempting to make a technology wishlist, it is best to pick a challenge that has real business value. As always, the virtual data tells a story. For example, one client wanted to know why virtual event engagement lagged behind attendance. We defined “engagement” with precise, measurable metrics before building any reports. A good Executive Board will help set direction by asking the right questions before signing off the project.
Metaverse data often lives in silos, like other types of data. Early on, map all relevant sources of the virtual data, such as user movement, transactions, chats. This approach will help you to prioritise and plan for integration with your existing Business Intelligence tools.
Small pilots build momentum and executive buy-in. Experimentation should be a step in the right direction. For a retail client, for example, the Metaverse project could focus on a single virtual product launch. It will also produce more virtual data for metaverse analytics, such as tracking dwell time, product interactions, and follow-up purchases.
For metaverse analytics, it is recommended to set Business Intelligence success metrics from the start, such as:
Your physical and online environments can inspire your virtual environments, and perhaps can even improve upon them. For example, PwC found VR-based training can be 4x faster than classroom learning. Learners trained with VR were up to 275% more confident to act on what they learned. Source: PWC, 2020 VR-based training offers significant advantages over traditional classroom learning. A 2020 PwC study demonstrated that VR training can be four times faster. Furthermore, learners using VR showed a remarkable increase in confidence, being up to 275% more confident in applying their new knowledge.
Build feedback loops with stakeholders and refine your metaverse analytics approach as you go. The metaverse is changing quickly, so adaptation and optimising are to be expected. Good data storytelling is important throughout, so that the voice of the user is at the front and centre of the Metaverse initiative.
I adapt my “7 C’s of Automation” framework for every metaverse analytics engagement:
For more, see Gartner’s overview: Metaverse: We’ll soon spend an hour of our day in virtual worlds.
Immersive data means greater responsibility. Transparent consent and ethical use are non-negotiable as regulations evolve. The Metaverse: searching for compliance with the General Data Protection Regulation (GDPR) (International Data Privacy Law, Oxford Academic, 2024). The legal analysis covers topics such as biometric/special-category data, explicit consent under Article 9(2), profiling and automated decisions (Article 22), transparency/fairness, DPIAs, and privacy-by-design/default in metaverse contexts.
Metaverse analytics is about extending your data strategy to a new, data-rich channel with a focus on delivering measurable value with a metaverse-friendly data strategy. It has to be more than hype, and it needs to be implemented responsibly and pragmatically. To summarise, start with clear goals measured by KPIs for metaverse success, leverage your BI strengths, and focus on incremental, business-driven wins.
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