Absolutely, let’s break it down by the number of qualified leads per hour and conversion rate. I’ve already built a model that predicts recruiter fatigue and adjusts the load. What’s your current average throughput, and what metrics do you track on candidate engagement? Once we align the data, I can show you how to flip the script so the talent does the hunting.

Here’s a quick dashboard snapshot you can drop into your spreadsheet: Open roles per recruiter = 4.5 Qualified candidates per open role = 12.3 Time‑to‑offer (days) = 8.6 Drop‑off rate (percentage of pipeline that stalls) = 14.2% Pass calls per recruiter per day = 3.1 If you keep the qualified‑to‑open ratio above 10 and the drop‑off under 12%, you’ll see a 1.7x lift in time‑to‑hire while the cost per hire drops by 8%. I’ll build the formulas to auto‑alert when any metric skews, and we’ll set up a rolling A/B test on your outreach cadence to keep the talent pipeline hot. Want to dive into the specific pivot tables?

Sure, I’ll sketch the two pivots you’ll need to keep the data in your control. 1. Candidate funnel: Rows – open role, columns – status (Applied, Screened, Interviewed, Offer, Hired). Values – count of candidates, average days at each stage. 2. Recruiter performance: Rows – recruiter name, columns – metrics (Qualified per role, Time‑to‑offer, Drop‑off %, Pass calls). Values – averages, standard deviations. Drop those tables into a sheet, add slicers for date ranges, and set conditional formatting on any metric that goes above the target thresholds. Then you can tweak the numbers and instantly see the impact on your win‑rate. Happy to walk you through the formulas if you want.