---
title: Six numbers that tell you the desk is healthy
description: Most agency dashboards report what exists. These six report what changed — and each one catches a specific failure before it reaches month end.
---

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 October 8, 2026

# Six numbers that tell you the desk is healthy

 By   Echo Recruit  ·   6 minute read

*A number is worth reporting if a bad value would make you do something differently on Monday. By that test, most recruitment dashboards report almost nothing.*

In short

- Counts of things that exist — open roles, candidates, activities logged — describe the size of the desk, not its health.
- Every metric below is paired with the decision it should trigger. If there is no decision, do not put it on the wall.
- Sort by the worst case, not the average. An average time-to-fill of 31 days is compatible with a role that has been open since March.
- Five of the six can be computed from data you already have; the sixth is the one most agencies are missing, and it is the one about cash.

Walk into most agencies and the screen on the wall shows activity: calls made, CVs sent, roles open, revenue against target. It is a picture of effort and a picture of the month so far, and it has one significant weakness. Every number on it can look fine on the morning of the day two placements fall through and a client goes sixty days past due.

The test for whether a number belongs on that screen is simple: *if this value were bad, what would somebody do differently before lunch?* If there is no answer, it is reporting, not management. Here are six that pass the test.

## 1. Slate coverage, per requisition

Not “candidates in the pipeline”. The number of candidates on this role who are at or past a stage the client actually sees — submitted, interviewing, offered — measured against how many that client needs to see before they hire.

A role with twenty candidates at “contacted” and none submitted is not 20% covered. It is 0% covered with a lot of activity attached, and it will still be 0% covered on Friday unless somebody notices today.

**The decision it triggers:** which roles get worked this afternoon. Sort ascending by coverage, not by how recently someone touched them.

**The wrong version:** total candidates linked to the job. That number goes up when somebody adds people and never goes down, so it rewards the wrong activity and never looks bad.

## 2. Time in current stage, oldest first

Time-to-fill is a post-mortem statistic. It tells you about roles that are already finished, and it arrives too late to change any of them. What you want is the live version: for every candidate in play, how many days since they last moved.

The specific thing this catches is the candidate sitting at “submitted to client” for eleven days. Nobody has dropped the ball exactly — the recruiter chased once, the hiring manager was travelling — but the candidate is now interviewing elsewhere and does not know they are still being considered here.

**The decision it triggers:** a specific chase, to a specific person, today.

**The wrong version:** average time in stage. Averages are built to hide exactly the case you are looking for.

A useful sharpening: set an expected duration per stage — two days at CV review, five at client submission, three between interview and feedback — and report the ones that have blown it, not the raw age. A candidate at day four of client submission is fine. A candidate at day four of “awaiting feedback” may not be.

## 3. Requisitions with no activity in seven days

A count, and the list behind it. Open roles where nothing has moved for a week: no new candidate, no stage change, no message to the client.

Every agency has these, and the reason is almost never that the role was abandoned on purpose. It is that the role belonged to somebody who left, or it was covered by two people who each thought it was the other’s, or the client went quiet and nobody decided whether it was dead.

**The decision it triggers:** work it, or close it. Both are fine. Leaving it open and untouched is the only bad option, because it inflates your open-role count, ruins every ratio calculated against it, and lets a client believe you are working on something you are not.

## 4. Pass rate against the bar, per requisition

Of the applications received for this role, what share cleared the shortlisting bar you wrote down? This one is diagnostic in both directions, which is what makes it unusual.

How to read a pass rate

| Above ~60% clearing the bar You are not screening, you are counting. The bar is decoration and the recruiter is still doing the whole job by hand. | Bar too low |
| --- | --- |
| Roughly 10–40% The bar is doing real work and there is still a slate to choose from. | Working |
| Under ~5% Either the brief describes a person who does not exist at that salary, or the advert is reaching the wrong people. Neither is fixed by reading more CVs. | Wrong upstream |

The bands are judgement, not law — a niche role with twelve applicants and one hire is a healthy 8%. The signal is a rate that is extreme and stays extreme across several roles.

**The decision it triggers:** a conversation with the hiring manager about the brief, or with whoever is placing the advert — before another three weeks of applications arrive against the same wrong bar.

## 5. Offer-to-start survival

The share of offers accepted that turn into someone actually starting, and the reasons for the ones that do not. Counter-offer, competing offer, background check, or simply changed their mind.

This is the number that converts directly into revenue you have already counted and will not receive. It is also the earliest warning you get about a hiring process that is too slow: candidates who drop between offer and start are usually candidates who were in another process that moved faster.

**The decision it triggers:** if counter-offers dominate, the conversation about money has to happen earlier in the process rather than at offer. If competing offers dominate, your client’s process is too slow and that is a conversation with the client, evidenced.

**Worth pairing it with:** guarantee-period survival — placements still in post at the end of the rebate window. Both of these are money you have recognised and might have to give back.

## 6. Days sales outstanding, by client, aged

The one most agencies do not have on the same screen as the rest, because it lives in the accounting system and the accounting system belongs to somebody else.

It belongs next to the pipeline for one reason: the client who is delightful to work with, gives you exclusivity and briefs you properly may also be the client who pays at 74 days. Those two facts are known by two different people in your business, and the person deciding where to spend Thursday is the one who does not know the second.

What a single slow client costs to carry

| Invoiced to this client in a year | $180,000 |
| --- | --- |
| Agreed payment terms | 30 days |
| Actual average days to pay | 74 days |
| Extra days of financing you are providing | 44 |
| Average balance outstanding because of the delay $180,000 ÷ 365 × 44 | ≈$21,700 |
| Cost at 10% cost of capital | ≈$2,170 a year |

Plus the part that is not in the arithmetic: that $21,700 is the money you were going to use to hire a resourcer.

**The decision it triggers:** where the next exclusive goes, whether this client’s next agreement gets different payment terms, and whether the reminders are going out on a schedule or when somebody remembers.

## What to leave off

Three popular numbers that fail the Monday test, and why:

- **Calls made and emails sent.** Effort, not outcome. They rise when people are being measured on them, which is the definition of a metric that stops meaning anything the moment you put it on a wall.
- **Total candidates in the database.** A number that only goes up, attached to a liability that also only goes up. If it is doing anything, it is encouraging you to keep data you should have deleted.
- **Revenue to date, on its own.** It is the score, not the game. By the time it is wrong, everything that made it wrong happened weeks ago and is visible in the five numbers above.

## The reporting rule underneath all six

Every one of these reports a change or an exception rather than a total. That is not a stylistic preference. A total is the sum of everything that has ever happened, which means it moves slowly, hides the individual case, and cannot tell you whether this week was good. An exception list is short, is different every morning, and each line has a name attached to it.

If you only take one thing: replace the largest number on your wall with the shortest list.

In Echo Recruit

Reporting what changed

The dashboard is built on this rule. Slate boards carry the real column totals rather than the cards on screen, so a column showing five of forty says so. Tasks group overdue first. Requisition health, pipeline conversion and sourcing performance sit in analytics; receivables ageing and days-to-pay sit in billing on the same data, so the slow-paying client is visible to the person deciding where the week goes.

## Common questions

Which metrics should a recruitment agency report on?

Six that trigger a decision: slate coverage per requisition; time in current stage, oldest first; requisitions with no activity in seven days; pass rate against the written bar; offer-to-start survival; and days sales outstanding by client, aged. The test for each is whether a bad value would make somebody do something differently before lunch.

Why is average time-to-fill a poor metric?

Because it is a post-mortem statistic about roles that are already finished, and averages are built to hide the case you are looking for. An average of 31 days is compatible with a role open since March. The live version — days since each candidate last moved, sorted oldest first — triggers a specific chase to a specific person today.

How do you read a shortlisting pass rate?

Above roughly 60% clearing the bar, the bar is decoration and the recruiter is still doing the whole job by hand. Roughly 10–40% means it is doing real work with a slate still to choose from. Under about 5%, either the brief describes someone who does not exist at that salary or the advert is reaching the wrong people.

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