Skip to main content

    Case Studies

    Pattern-based XR analytics in practice

    Five examples of how spatial behavior signals — gaze, locomotion, hesitation, frame-time correlation — translate into decisions about UX, comfort, accessibility and stability. Patterns are illustrative and derived from common XR project profiles; no individual customer data is exposed.

    Five categories of XR behavior evidence

    Each case study belongs to one of five recurring problem families in immersive products. They map to the questions XR teams ask most often: can everyone use this, does it feel comfortable, where does attention actually go, where does the build break, and what can be anticipated before launch. Read them in any order — each one is self-contained and ends with the decision it enables.

    How to read a case study

    Every study follows the same four-step structure so you can compare them directly and reuse the reasoning inside your own team.

    1. 1. Signal type

      Which behavior signals were captured — gaze dwell, locomotion velocity, hesitation before an interaction, object focus, reach zones or frame-time correlation. Signals are always described before any conclusion is drawn.

    2. 2. Evidence

      What the aggregated data actually shows: distributions, spatial clusters, repeated sequences. Evidence is kept separate from interpretation so you can disagree with the reading without discarding the data.

    3. 3. Interpretation

      The most plausible explanation for the pattern, stated with its assumptions and its limits. Where more than one explanation fits, both are listed instead of picking the convenient one.

    4. 4. Action

      The concrete design, content or engineering decision the evidence supports — and the follow-up signal that would confirm the change worked.

    What we look for

    A pattern only becomes a case study when it clears four filters: it repeats across sessions rather than appearing once, it has a spatial or temporal location precise enough to act on, it survives a privacy review with no raw audio and no personally identifying data, and it points to a decision someone can actually make this sprint.

    How these studies are grounded

    Transparency about evidence basis matters more than dramatic numbers. Every study on this page is labelled so you know exactly what you are reading, and none of them names a customer or exposes individual session data.

    Pattern-based
    Composite profiles built from behavior patterns that recur across comparable XR projects. No single project is identifiable.
    Anonymized
    Derived from real work where all identifying details — client, title, environment art, dates — have been removed or generalized.
    Conceptual
    Illustrative scenarios used to demonstrate a method or a measurement model, clearly marked as such on the page itself.
    Demo-based
    Produced with internal demo environments and synthetic sessions built specifically to validate the analysis pipeline.

    Where a study is conceptual or demo-based, it says so in its own methodology block. We would rather publish an honest method than an unverifiable result.

    Run this analysis on your own build

    The methods described here are the same ones the Gossip Analytics SDK applies to your XR sessions. Talk to our team to test them on your project, or apply to the community program if you build for research, education or accessibility.

    Discuss your use caseCommunity program