Asynchronous video communication · PUBLIC RESEARCH BRIEF
LoomWhich sharing cue makes a video’s audience clear before the link leaves the workspace?
Atlassian currently documents Loom video privacy settings for public, workspace and restricted access, plus viewer and engagement insights with limits on viewer identification. This brief studies sharing and analytics comprehension; it does not claim that a particular setting prevents every disclosure or that a view represents comprehension.
Updated 2026-10-07 · Simulation results not yet generatedCHANGE ONE THING. LEARN WHAT MATTERS.
Three questions for the GTM team.
When viewer insights include anonymous activity, would a coverage explanation or a separate known-versus-unknown viewer display better prevent teams from treating the list as complete?
Set up this study →For a video moved from restricted to broader access, would a change confirmation or a before-and-after privacy diff better help an administrator catch unintended discoverability?
Set up this study →PROPOSED AUDIENCE
Who should weigh in?
North American product, design, marketing, sales and operations teams using or evaluating Loom, including creators who record and share videos, viewers who receive links and workspace administrators who set defaults. Recruit people with different sharing destinations and privacy responsibilities. Proposed audience; no private video, transcript or viewer identity is included.
TWO TIME HORIZONS
Trial today. A habit tomorrow?
Near term · 0–90 days
Over 0–90 days, test fictional Loom sharing flows with seeded workspace roles, video settings and viewer states. Measure correct audience prediction, overclaiming from analytics, unsafe link-copy attempts and recovery after a setting change. Record no real person or screen.
Longer term · 3–12 months
Over 3–12 months, follow consenting teams in test workspaces as defaults, membership and sharing destinations change. Examine mis-shares, repeated privacy corrections, viewer-insight interpretation and administrator support burden. Security or compliance claims require independent technical and policy review.
What would make the result actionable?
Use versioned Atlassian support pages, synthetic workspace fixtures with a hidden access matrix, logged task flows and role-based interviews. Verify each predicted viewer against the configured setting, test anonymous and signed-in cases, review accessibility and privacy, and keep all videos fictional.
A Gather simulation returns hypothetical customer reactions. Quantifying revenue, traffic or retention needs actual business inputs and validation against observed behavior.