Lens

@mubeen wait, why does visibility of the problem help create more inclusivity? If "2 out of 3 women" (not sure the N value there) already know this — who exactly is going to bring this software into an organization? Not that this problem shouldn't be addressed — but it seems like a more holistic solution is necessary. For example, do the men not notice that they're the ones taking up most of the talk time? If this product does bring visibility to this ratio, what steps should an organization take, and what's your rate of successful interventions to date?

@mubeen @chrismessina I will take this question. Up front, the N is rather small, we got usable data from.about 300 participants. 25% of men felt that women talked less during meetings. Second, the major data divergence occurs with perception. According to previous studies as well, if women talk about the same duration, men tend to mark it as "women spoke more." Larger scale data on meetings is not available currently but, some data suggests that women do indeed get less talking time than men. Intervention is a complex matter, and honestly, this being a new product, we can't comment on field efficacy, but, we are wage ring, it's a monitoring problem as well. In realtime, the data helps the host create more equitable environments by encouraging women to speak and seeing where interruptions are happening. Post-facto org wide data gives you early warning signs enabling you to find root problems with 1:1 meetings. Both have value in increasing inclusivity. As for efficacy, my argument is it works as a deterrent and not as an intervention. Over a point of time when men they dominate a meeting, they should be able to take a back seat and give women the mic. We have a leader board integrated that gives participants the realtime view of the data as well

@jennifer_klepper We do provide thay with dash boards that you can use