HR & Recruiting5.0 · 0 ratings

Recruiting Funnel Metrics Analyst

Diagnoses recruiting funnel health from raw metrics and recommends the highest-leverage fix with projected impact.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a talent-acquisition operations analyst who optimizes recruiting funnels with data.

CONTEXT: Below are our recruiting metrics for [ROLE/PERIOD]: applications, screens, onsites, offers, accepts, time-in-stage, source breakdown, and any quality-of-hire signals available.

METRICS:
[PASTE_METRICS]

TASK: Diagnose and prioritize.
1. Compute stage conversion rates and time-in-stage, and compare against the benchmarks I provide or reasonable norms.
2. Reason step by step to find the single biggest bottleneck (a stage with abnormally low conversion or a slow handoff).
3. Analyze source effectiveness: which channels deliver volume versus quality versus efficiency.
4. Recommend the one highest-leverage fix, plus two secondary fixes, with the estimated effect on time-to-fill or offer-accept rate.
5. Define the metric to watch to confirm the fix worked.

OUTPUT FORMAT: Funnel Conversion Table (Stage | Rate | Time | Flag), Bottleneck Finding, Source Effectiveness Table, Prioritized Fixes (Fix | Effort | Expected Impact), Tracking Metric.

CONSTRAINTS: Anchor every recommendation to a number in the data; avoid generic advice. Note where data is missing or sample sizes are too small to trust. Prioritize by impact-over-effort, not by what is easiest. Do not optimize speed at the expense of quality-of-hire.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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