When we set out to build engagement scoring, we faced a fundamental question: how do you quantify something as nuanced as "engagement" in a meeting? Here's how our methodology works and why we built it this way.
The foundation of our engagement measurement is microphone state tracking. Our bot polls participant mute status every 5 seconds throughout the meeting and tracks total muted time, unmuted time, and the number of mute/unmute toggles.
From this data, we classify participants into three categories: Active Speaker (unmuted 50%+ of meeting duration), Mostly Listening (10-49%), and Silent (unmuted less than 10%). This classification is ~98% accurate for meetings lasting 30 minutes or more. The bot itself is excluded from all calculations.
Mute patterns tell you who talked, but not the quality of their contribution. Our AI analysis layer examines the transcription to evaluate speaking contribution context. We use Natural Language Processing (NLP) to detect: Questions asked, Decisions driven, Sentiment (positive/negative/neutral), and Information density.
Each participant receives a 1-10 engagement score that weighs both quantity (how much they participated) and quality (how meaningfully they contributed). A participant who speaks for 2 minutes but asks 3 critical questions will outscore someone who monologues for 10 minutes without substance.
A manager using Rolaa noticed that in her 1:1s, her engagement was consistently a 9/10, while her direct reports were at 3/10. She realized she was doing all the talking. By consciously muting herself and asking open-ended questions, she flipped the scores—leading to much more productive sessions where the employee felt heard.
Review your 'Engagement Matrix' after your next 5 meetings. Look for outliers. If your score is consistently low in strategy sessions, it might be time to prepare more talking points or ask for an agenda in advance to ensure you can contribute meaningfully.
No. Rolaa's data is intended for self-improvement and team alignment. We emphasize transparency—participants are notified when the bot joins.
Our AI distinguishes between speech and background noise, ensuring that a 'noisy mic' doesn't falsely inflate your engagement score.
Team leads can see aggregated team engagement, but individual scores are private to the user to encourage honest self-reflection.
Stop losing 20+ hours a month to manual follow-ups and unengaged calls. Put your meeting operations on autopilot.
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