Recruiting Funnel Calculator Blueprint: Weekly Capacity Forecasting

1. Problem & Purpose

1.1 The Problem

Recruiting plans fail when teams plan the outcome (hires) but don’t plan the math (weekly throughput, conversion rates, and lead time). The result is predictable:

• Targets are set without understanding stage capacity (especially HM interviews).
• Recruiters get blamed for outcomes that were mathematically impossible.
• Time-to-fill becomes a hope, not a plan.
• Process “improvements” are random because the constraint isn’t isolated.

1.2 Purpose

The Recruiting Funnel Calculator converts your hiring goal into an executable weekly plan: required applications, screens, HM interviews, finals, offers, and hires — by week.

2. Inputs & Data Model

2.1 Inputs

• Hiring goal: number of hires needed in a period.
• Start date: when the plan begins.
• Stage conversion rates (CVR): Apply → Screen → HM → Final → Offer → Hire.
• Stage lead times: average time spent in each stage (calendar or working days — choose one and keep it consistent).
• Admin time / recruiter capacity: time per candidate, time per screen, time per loop, etc.
• Interview capacity (optional but powerful): HM and panel throughput per week.

2.2 Core Model

The calculator is a stage-based throughput model. At each stage, required upstream volume is derived from the next stage’s requirement divided by the stage conversion rate.

3. How to Run the Calculator

3.1 Step-by-step

1. Set the window: start date + period (weeks).
2. Enter target hires: what leadership expects.
3. Enter CVRs: use historical ATS data if available; otherwise start with estimates and label them.
4. Enter lead times: average days in each stage (choose calendar vs working days and stay consistent).
5. Enter capacity assumptions: recruiter time and (if used) HM/panel interview limits.
6. Review outputs: weekly volume by stage + time-to-fill estimate + constraint flags.

3.2 What to do if you don’t have good data yet

• Use last quarter’s funnel CVRs as a baseline (even if imperfect).
• If CVRs vary by role family, build separate scenarios per family.
• Start with a single “default funnel” and add role-specific funnels over time.

4. How to Interpret Outputs

4.1 Primary outputs

• Weekly volume by stage: how many candidates must enter each stage per week.
• Time-to-fill estimate: based on lead times + conversion assumptions.
• Workload view: implied recruiter/admin load from the plan (if modeled).
• Constraint signals: where the plan breaks first (capacity or timeline).

4.2 How to use the weekly plan

• Translate “hire 10” into “we need ~X screens/week and ~Y HM interviews/week.”
• Set weekly operating rhythm around the constraint (e.g., HM interview blocks).
• Use the weekly stage volumes to drive sourcing expectations (not vibes).

5. Constraint Debugging (HM Capacity)

5.1 Why HM capacity is usually the bottleneck

• Recruiters can increase top-of-funnel faster than interviewers can increase time.
• Interview scheduling and scorecards create hidden “stage time” even with strong candidates.
• Teams often underestimate the total interviews per hire.

5.2 Debug checklist

• Is the plan asking for more HM interviews/week than possible? If yes, adjust capacity or scope.
• Are lead times inflated by scheduling delays? Fix interview blocks, interviewer pools, and SLAs.
• Are CVRs low because the role is mis-scoped? Fix leveling/requirements before pushing volume.
• Are we losing candidates at offer? Fix close process and compensation alignment.

6. Scenario Planning & What-Ifs

6.1 Common scenarios

• Improve screening quality: raise Apply → Screen or Screen → HM CVR by tightening rubric and reducing noise.
• Increase HM capacity: add interviewer pools, interview blocks, or reduce loop size.
• Speed up lead time: enforce scorecard SLAs and reduce scheduling delay.
• Offer acceptance changes: model downside if market tightens or comp bands drift.

6.2 How to present scenarios to leadership

• Show Base / Upside / Downside in one view.
• For each scenario, identify the one lever that changed and the operational cost (time, headcount, process).
• End with a decision: change target, change capacity, or change process.

7. How It Pairs With Pipeline Health

The calculator creates the plan. Pipeline Health validates execution and diagnoses drift.

• Calculator: “Given our CVRs and lead times, what weekly volume is required?”
• Pipeline Health: “Where are we drifting, and why (stage time, pass-through, SLA breaks, capacity)?”

Pattern: Forecast → Execute → Measure → Debug constraint → Update assumptions → Forecast again.

8. Governance, Assumptions & Change Control

8.1 Assumption hygiene

• Label CVRs as measured vs estimated.
• Define whether lead times are calendar days or working days.
• Keep a simple change log when assumptions change (what changed, why, impact).

8.2 Ownership

• Recruiting Ops: owns model definitions and default assumptions.
• TA Leaders: owns targets and the resourcing decisions implied by the plan.
• Recruiters: owns execution inputs (pipeline quality, stage hygiene) and feedback when assumptions don’t match reality.

Appendix A – Example Walkthrough

1. Leadership requests N hires in W weeks.
2. You input baseline CVRs and lead times.
3. The model returns required weekly HM interviews that exceed realistic capacity.
4. You present three options:
   • Increase HM capacity (blocks, more interviewers, smaller loop)
   • Reduce target (or extend timeline)
   • Improve upstream CVR (rubric + screening quality)
5. You align on the decision and publish the weekly plan.

Appendix B – Field Definitions

If you want deeper definitions and formulas, route them to the tool’s Dictionary page.