The strongest diversity hiring initiatives begin at the top of the funnel, before prospects even apply. Most sources (e.g. referrals and inbound) can be less diverse, so if you’re not actively sourcing and nurturing a diverse talent pool, you may not see a diverse pipeline, a diverse set of interviews, or a diverse team. It’s hard to assess the effectiveness of diversity-focused hiring strategies and fix potential problems in the funnel without being able to measure diversity from the very beginning of the pipeline.
Data can shed light on whether teams are reaching out to a diverse talent pool to build a diverse pipeline, or whether there are systemic biases that might show up as some segments of candidates get stuck at certain stages of the funnel, or don’t even respond to outreach to begin with. Our goal at Gem is to help teams track and analyze those efforts.
How do I use this information?
Gem offers insights for talent teams to track how their work affects diversity through the recruiting funnel, from first outreach through conversion to hire. Use these insights to:
- Catch whether certain groups are disproportionately dropping out of the funnel at a certain stage
- Forecast how many reach-outs are needed to convert one underrepresented hire
- Understand via top of funnel metrics why the team did or did not hit diversity hiring goals
- Inform hiring managers about diversity breakdowns in historical data
- View differences in pipelines between focused effort on diversity and sourcing through unfocused methods
- See whether messaging is resonating more or less with a specific population
Gender
In Gem’s Outreach Stats Overview Guide, talent teams can view how their efforts are faring across gender, from first touch through to replied, interested, and even converted to process. To do this, in Outreach Stats, select Team Activity in the upper left, and then select Gender in the “Breakdown by” dropdown menu, as pictured below.
Organizations with Pipeline Analytics enabled can analyze conversion rates through the funnel by gender, and further break down by job, department, or other attributes.
How does Gem get this data?
We use an algorithm to predict gender based on name and location. In aggregate, results are 95%+ accurate.
Where else can I see this data?
For organizations with this feature enabled, you can see and edit the gender field in the prospect’s profile details within the Gem extension:
If your organization has **Prospects** enabled, you can also filter results by gender.
- How do I use this information?
- Gender
- How does Gem get this data?
- Where else can I see this data?