Audit intermediate Works with: Any AI

Hiring Pipeline Funnel and Time-to-Hire Review

Table output Has example

Find where candidates drop out or wait.

The Open buttons send the prompt text as currently shown below to that site only when you click. Fill in the placeholders first.

Analyse hiring data. Number of candidates at each stage and days between stages for the last roles: [PIPELINE_DATA].

Calculate stage conversion as candidates advancing divided by candidates entering, the average days at each stage and total time to hire. Identify delays and stages where good candidates may be lost.

Return a table, three improvements and a candidate communication standard. Use anonymised numbers only.

Add a worked funnel example: 200 applications, 60 screened in (30 percent), 24 first interviews (40 percent of screened), 8 second interviews (33 percent), 3 offers (37.5 percent), 2 accepted (67 percent). Average days: application to screen 6, to first interview 10, to second 12, to offer 7, total 35 days. Identify the longest delay, application to first interview, and test whether offers are lost to competitors who move faster. Provide communication standards: a reply within three working days, a decision within five after final interview.

Add a closing section recommending one experiment at a time, such as booking the first interview within five working days, and a measure of whether offers accepted rise afterwards, noting small samples.

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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

Invented funnel. 200 applications; 60 screened in (30 percent); 24 first interviews (40 percent of screened); 8 second interviews (33 percent); 3 offers (37.5 percent); 2 accepted (67 percent).
Days: application to screen 6; to first interview 10; to second interview 12; to offer 7. Total = 6 + 10 + 12 + 7 = 35 days.
Longest delay: screen to first interview (10 days).
Experiment: book first interviews within five working days; check whether offers accepted rise. Small numbers; do not over-read.

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