Event Studio Best Practices
Design principles for Signal Events that produce reliable, actionable insights
A well-designed Signal Event is specific about what it measures, realistic about what a 15–30 minute conversation can cover, and built around facilitation guides that actually surface the evidence they're designed to collect. These practices help you get there.
Be Specific in the Intake Form
The AI generates better events when you give it a specific research question. ""I need to understand what's driving voluntary attrition among mid-level managers in our APAC operations"" produces a more targeted design than ""I want to understand employee turnover."" The more you narrow the frame, the more useful the dimensions will be.
If your research question is genuinely broad, consider whether you need multiple events — each focused on a specific dimension — rather than one event that tries to cover everything.
Upload Relevant Context
Domain-specific documents — frameworks, prior research, competency models — improve dimension derivation significantly. If you have a measurement model or conceptual framework you want the event to reflect, upload it. The Research Provenance panel will show you how the AI used it.
Limit Dimension Count
More dimensions means a longer session and shallower coverage per dimension. A typical 20-minute session can cover 4–6 dimensions meaningfully. If the AI generates 8–10, prioritize: which dimensions are most critical to your research question? Remove the ones that are ""nice to know"" rather than ""need to know."" Depth on fewer dimensions produces more reliable scores than shallow coverage of many.
Evaluate Research Provenance Before Publishing
Check the evidence basis flags on your dimensions before publishing. Research-backed dimensions (grounded in published frameworks) are the most reliable. Training-derived dimensions (based on the AI's domain knowledge) are often good but benefit from your review. Thin evidence flags are a signal to look carefully — consider whether that dimension is well-defined enough to score reliably.
Review Saturation Criteria for Realism
The saturation criteria define when Savo has ""enough"" on a given dimension. Criteria that are too demanding mean sessions run long and participants get fatigued. Criteria that are too easy mean you get thin coverage that doesn't support confident scoring. A good saturation criterion describes a concrete evidence signal — something specific a participant would say or reveal — not a vague sense of ""enough.""
Test with a Smaller Group Before Launching at Scale
Before running an event with a large participant pool, use a smaller pilot group first (10–20 participants). Review the resulting scores and evidence quality. Are the dimensions scoring across the full 1–5 range, or clustering at one end? Are there dimensions with high abstention rates? These patterns tell you whether the facilitation guides are doing their job.
High abstention on a dimension often means the guide isn't surfacing sufficient evidence — the probe directions may need to be more specific, or the saturation criteria may be too demanding.
Keep the Participant Introduction Clear and Warm
Participants read the event introduction before consenting. If it sounds like a research questionnaire or a performance evaluation, you'll get guarded responses. Frame it as a conversation — explain the purpose simply, set accurate time expectations, and remove jargon. The quality of participant engagement affects the quality of the data.