Recruiting is the best-performing vertical on LinkedIn — which is exactly why it's the hardest to sound different in

The channel numbers in this vertical are the best published anywhere. Staffing and recruiting leads the LinkedIn table at 36.5% acceptance and 18.9% reply, roughly double the platform average on the second figure, and the headhunting and HR use case leads the cold-email table at 7.5%. That is not an advantage. It is the reason a recruiting buyer's inbox looks the way it does, and it means the channel is doing none of the differentiating. What is left to compete on is the offer, and the only credible material for the offer is the buyer's own hiring data.

The best reply rates in the published record belong to the most crowded message in B2B. Being good at the channel is table stakes here, not an edge.

1. 01

   The best channel numbers in the dataset belong to the least differentiated message

   36.5% acceptance and 18.9% reply describe what recruiting gets simply for being recruiting. A model that treats those rates as a result rather than as a starting condition will conclude that its copy is working when nothing has been tested.

2. 02

   The buyer's own hiring numbers are the argument, and they are published

   A median of 39 calendar days to fill a nonexecutive role, 97% of nonexecutive roles filled externally, and two to three of every five candidates rejecting offers. Against that, the published speed differential is stark: 56% of top-performing firms place in under ten days and 22% in three days or less. A message that quotes the prospect's own vacancy age against those figures is doing something the channel cannot do for you.

3. 03

   Deliverability sets the inbox count before the list size does

   Global inbox placement sits at 83.5%, and the provider split is what matters: Microsoft-hosted mail is spam-foldered at 14.6% against Gmail's 6.8%. In B2B, where Microsoft-hosted inboxes are heavily over-represented, that is the number that decides how many sending inboxes a programme needs and what the per-inbox cap has to be. The caveat travels with it: that data is full-year 2024 and all-mail, not B2B-segmented.

4. 04

   There is no published placement-fee benchmark, so price comparisons must be first-party

   The dataset that would settle agency fee percentages is behind a membership wall, and the range in circulation appears only in agency marketing copy. The model does not quote one. It builds the comparison from the prospect's own last three invoices, which is both defensible and more persuasive.

5. 05

   Fill rate is where the published technology argument actually lands

   Firms with AI embedded throughout the workflow have more than double the chance of holding fill rates above 75%. That is a delivery claim, not a marketing claim, which makes it the right thing to put in a follow-up and the wrong thing to put in a first message.

Staffing/recruiting connection acceptance **36.5%** · message reply **18.9%** (1.8× the platform average) · headhunting cold-email reply **7.5%**.

The model assumes the channel rates and spends its effort elsewhere. Lists are built on dated hiring signals rather than on job-board volume, because the published reply rates guarantee a response from an audience that is professionally obliged to answer hiring mail and guarantee nothing about whether that audience has a problem. Sending is spread across multiple inboxes with per-inbox caps sized against the Microsoft placement penalty rather than against list size. The first message references one observable fact about the prospect's hiring and asks one question. The speed and fill-rate arguments are held for the second conversation, where they can be attached to the prospect's own vacancy ages.

1. 1

   Weeks 1–2

   Offer and evidence audit

   What the firm can prove about speed and fill rate, written as claims with evidence attached

   Owner: OmniFlow + firm

2. 2

   Weeks 2–4

   List and signals

   Rows built on dated hiring signals with source links; job-board-only rows excluded

   Owner: OmniFlow

3. 3

   Weeks 2–4

   Sending infrastructure

   Inbox count and per-inbox caps sized against provider placement, warm-up before first send

   Owner: OmniFlow

4. 4

   Weeks 5–12

   Send and qualify

   One-observation first messages; qualification against the prospect's own vacancy ages

   Owner: OmniFlow + firm

5. 5

   Week 12

   Review

   Both denominators reported; cost per qualified conversation and per first search won

   Owner: OmniFlow

At the published rates, 1,000 connection requests in this vertical produce roughly 365 accepted connections and roughly 69 replies, and 1,000 cold emails produce roughly 75 replies on the headhunting benchmark. Those are the conditions, not the achievement. The model reports cost per qualified conversation and cost per first search won, and replaces every published rate with the firm's own from week four.

Modelled. Inputs: Expandi, LinkedIn Outreach Benchmarks 2026 (13,218,869 connection requests); Hunter.io, The State of Cold Email 2026 (31M emails sent in 2025); SHRM, 2026 Recruiting Executives Benchmarking (4,600+ organizations); Bullhorn, GRID 2026 Industry Trends Report (~2,300 recruitment professionals); Validity, Email Deliverability 2025 Benchmark Report (full-year 2024 seed data). Modelled outputs are not a forecast or a guarantee of results. Cited: Expandi *LinkedIn Outreach Benchmarks 2026*; Hunter *State of Cold Email 2026*.

Every figure below is published research, not a client result. They are the inputs to the arithmetic above, listed so it can be checked rather than taken on trust. The bracketed number points to the full citation at the end of this page.

These are the surfaces this engagement is run and measured from, shown with representative figures built around the benchmarks cited on this page. Every account we run reports into views like these, and you keep ownership of all of them.

Each figure on this page, the system it is read from, and the definition and window it is measured over.

Not applicable — this is a modelled engagement. Its weakest input is that the staffing and recruiting cut of the LinkedIn data pools recruiters messaging candidates with recruiters messaging employers. Those are two different messages to two different audiences inside one industry label, and candidate sourcing almost certainly carries the higher reply rate. A firm selling recruiting services to employers should expect to sit below the 18.9% figure and should measure its own before making any decision that depends on it.

A modelled capability, not a client account. Figures illustrate what the model produces and are labelled as modelled wherever they appear. See all our work

Full citations for everything cited on this page, with the sample and period each study covers, so you can go and read the original.