VR Analytics - Crafting Data Driven Experiences in the Virtual World

The concept of virtual reality has been around for several decades. Over the past four years, VR tech has grown. This can be attributed to the dramatic advancements in technology, combined with public curiosity and investments from major companies, such as Facebook.

 With final production versions of VR hardware and software being readied for market launch, it is now clear that the market is rapidly approaching a cusp, a tipping-point of transition. VR technologies are on their way to reaching a critical mass of functionality, reliability, ease-of-use, affordability and availability.

When it comes to analytics, it’s fair to say that VR is still in its infancy. Currently, there are only a handful of companies—predominantly startups—that make VR analytics their main focus. The early stages of VR analytics are a mix of game and web analytics, yet there is much more possible.

When looking at the evolutionary steps necessary for VR analytics to evolve one could classify them into three phases: The most basic rests in performance-related analytics such as understanding CPU speed, framerate (frames per second, or FPS) target resolution etc., which mostly aims to optimize the user experience and the avoidance of causing nausea also known as “cybersickness”.

The second phase involves more in-game or in-video analytics – features such as multi-level heat maps can evaluate where users look and how long they engage with certain objects in the virtual world. Retinad, a San Francisco based VR startup is currently one company that’s producing VR heatmaps (Retinad Heatmap Demo).

The third phase of VR analytics has the greatest opportunity for content creators to design and optimize experiences in a way that has never been done before, by creating experiences that are optimized for the highest “emotional impact” that are customized based on someone’s individual emotional response. In this third stage, other sensorial inputs are required that could gather and evaluate biofeedback. From EEG-based sensors that evaluate brainwaves in order to measure engagement levels; Galvanic Skin Response (GSR) sensor that can evaluate stress level while being immersed in an experience or someone’s heart rate and heart rate variability, which could give insight into the impact the experience has on the nervous system and their stress level.

 All these bio feedback technologies could give more insight into how someone (consciously or unconsciously) perceives and responds to a virtual experience.  This data can be used for a multitude of things. Below are two examples:

Emotional Impact & Storytelling

With the rise of Artificial Intelligence, Deep Learning and Machine Learning more and more data a can be computed in real-time and generate emotionally impactful experiences on the fly. This would require the evaluation of a user’s biofeedback while reading their gaze via a heat map in the virtual world. This could result in producing an experience that is more meaningful, more touching, or even funnier.

 Emotional Impact & Products

Understanding emotional impact can help determine what someone might like or dislike. This is where brands, advertisers and e-commerce businesses can gain powerful insights. Capturing products that resonate on an emotional level the most with a user could be used to drive more sales, through precise targeting.

 Overall, information gathered from VR analytics can drive a deeper level of insight and open up a new way of creating experiences that touch us in a new way. This new era of crafting experiences in VR involves a multitude of expertise from fields that someone would not usually hear in the same sentence. To name a few, an ongoing collaboration between visual game designers, creative technologists, and data analysts is necessary; at the same time the involvement of non-traditional parties, such as neuroscientists and cardiologists is key to craft experiences that are highly engaging and emotionally impactful.