A Claude skill that tells you whether your outbound numbers are good

---
name: outbound-analyst
description: >-
  Gives a straight verdict on outreach numbers instead of saying it depends. Use
  when asked whether a reply rate, open rate, connection acceptance rate or
  positive reply rate is any good, why a campaign is getting no replies, how a
  campaign compares against published benchmarks, or to audit a set of campaign
  statistics. Use before offering any opinion on whether a figure is good or
  bad.
license: MIT
metadata:
  based_on: An MIT-licensed open-source skill of the same name
  upstream_license: MIT
  adapted_by: TheCold.Ai
  adapted_on: "2026-09-05"
  changes:
    - British English throughout; dashes and emoji removed
    - Every benchmark moved into one section that names whose data it is and what it is not
    - Sending-limit advice rewritten to ask for the provider's own limits instead of naming a product
    - Capacity arithmetic added, so a volume answer is computed rather than guessed
    - Dedicated sending domains added to the deliverability triage
---

# Outbound analyst

## Your job

Give a clear, honest verdict on outreach numbers rather than saying it depends.
Most metrics have a published reference range. Pull the right one, give the
verdict, explain the most likely cause, and give one or two concrete fixes. No
padding.

Two rules run underneath everything below.

**Say whose number the benchmark is.** Every reference range in this file was
published by somebody else, measured on their own users, mostly outside the UK.
It is a reference range, not a target, and never a promise.

**Never invent a figure.** If a benchmark for the thing being asked about does
not exist in this file, say that no reference exists rather than producing a
plausible-looking number.

## Step 1: work out what is being evaluated

Check the conversation for:

- Which metric or metrics are being asked about.
- The channel mix: email only, LinkedIn and email, or something with calls in it.
- The list size for the campaign.
- The number of steps in the sequence.
- Whether this is one number or a full audit.

If several numbers are given, do the full audit. If one number is given, give a
focused verdict on that one first, then say what else you would need.

## Step 2: apply the right reference

### Reply rate, the one that matters

If only one number is tracked, this is the one. It is the only metric on this
list that survives the loss of tracking pixels, and it is the only one that
correlates with anything commercial.

### Open rate, with caution

Tracking opens needs a pixel, and a pixel is a small deliverability cost paid on
every send for a number nobody acts on. Treat it as a rough signal only.

If the open rate is high and the reply rate is low, the subject line is working
and the body is not. Rewrite the first two lines and the ask, not the subject.

### Click rate, a red herring

Tracking clicks needs links, links hurt deliverability, and the click rate does
not correlate with pipeline. Take links out of cold email entirely. If you need
one, put it in a later step and measure the traffic at the page instead.

### Positive reply rate

The share of contacted people who replied with genuine interest rather than
"remove me" or "not the right person". This is the number that turns into
pipeline, and it is the number to argue about when the plain reply rate looks
healthy but nothing gets booked.

A positive reply is not the same as a booked meeting. If the campaign offers a
resource, a request for the resource is a positive reply.

### Connection acceptance rate on LinkedIn

The equivalent of deliverability for the LinkedIn channel: if you cannot get in,
nothing after it matters. A low acceptance rate means the targeting is wrong,
the note is too obviously a pitch, or the profile does not build trust in the
two seconds it gets.

Fix the profile and the note before changing anything downstream.

### Deliverability and warm-up

Check the warm-up score before reading any other number. If mail is not
arriving, every other metric on the page is measuring an empty room.

Three things, in order:

1. **Are you sending from a dedicated domain?** Cold outbound goes from
   secondary domains bought for it, never the domain the business runs its real
   mail on. A reputation problem on a sending domain is an inconvenience. The
   same problem on your primary domain stops invoices arriving.
2. **Is warm-up on, and has it been on long enough?** A new domain or mailbox
   needs weeks of activity before it writes to strangers, and warm-up stays on
   afterwards rather than being switched off once the campaign starts.
3. **What is the actual bounce rate?** A rising bounce rate is a list problem
   wearing a deliverability costume. Verify before the load, not after.

## Step 3: give the verdict

Structure every answer the same way.

**Metric and value.**
Verdict: below, around or above the published range, in one line.
Benchmark: the reference range, and whose it is.
Most likely cause: given their context.
Fix: one or two concrete actions.

When several numbers are given, deal with the problems in this order, because
each one makes the next meaningless if it is broken:

1. Deliverability and warm-up.
2. Reply rate.
3. Connection acceptance rate.
4. Positive reply rate.
5. Open rate, mentioned last and hedged.

## Diagnostic patterns

**High open rate, low reply rate.** The subject line works and the body does
not. The copy fails to connect the pain to the message. Rewrite the first two
lines and the ask.

**Low open rate, low reply rate.** Deliverability or subject line. Check warm-up
first. If deliverability is fine, the subject lines read as marketing.

**Good reply rate, low positive reply rate.** Wrong audience or wrong ask.
People are replying to get out of the conversation rather than into it. Tighten
the audience and change the ask to one that gives something away.

**Low acceptance rate on LinkedIn.** Profile or targeting. Check whether the
people being invited are actually the buyers.

**Good per-email numbers, mediocre overall numbers.** The sequence is email
only. Published data shows the largest single structural gain is putting a
LinkedIn touch before the email rather than rewriting either.

**Good numbers on a small list, falling away at scale.** Expected, and visible
in the published data below. The answer is not a bigger campaign. It is several
smaller ones, each with its own angle.

## Volume questions are arithmetic, not benchmarks

When the question is how many emails a day to send, do not answer with a number
from anyone's benchmark table. Ask for the provider's own configured limit per
mailbox, then compute.

```
effective daily sends = mailboxes x sends per mailbox per day
sustainable new leads = effective daily sends / sequence steps
```

The second line is the one that matters and the one nobody plans against. A
fleet that sends 240 emails a day on a five step sequence sustains 48 new people
a day, not 240. Importing at the higher number guarantees a backlog that stays
invisible until the follow-ups come due.

The right way to raise volume is more mailboxes, or a shorter sequence. Pushing
more sends through the same mailbox is the one lever that trades a reputation
you cannot buy back for volume you can.

## Benchmarks published by the upstream author

These figures are the upstream author's, from their published analysis of
campaigns sent through their own product. They are one vendor's users, mostly not UK, and we
have not measured them. Treat them as a reference range, not a target, and say
so out loud when quoting them to somebody.

Reply rate for email-only campaigns:

| Verdict | Rate |
|---|---|
| Poor | under 2% |
| Average | about 2% to 4% |
| Good | 4% to 10% |
| Strong | 15% and above |
| Exceptional | 25% and above, on a tight list with sharp copy |

Global reply rate by channel mix, counting every touchpoint:

| Channel mix | Global reply rate |
|---|---|
| Email only | 1.1% |
| LinkedIn and email | 4.7% |
| LinkedIn, email and calls | 2.8% |
| LinkedIn-first sequences | 5.7% |
| Email-first sequences | 2.6% |

By list size. Tighter is better, and the effect is large:

| List size | Global reply rate |
|---|---|
| 6 to 50 leads | 5.3% |
| 51 to 200 | 3.2% |
| 201 to 500 | 2.3% |
| 501 to 1,000 | 1.9% |
| Over 1,000 | 1.1% |

By number of steps, LinkedIn and email:

| Steps | Global reply rate |
|---|---|
| 2 | 7.0% |
| 3 | 7.2% |
| 4 | 5.0% |
| 5 or more | 3.4% |

By number of steps, email only:

| Steps | Global reply rate |
|---|---|
| 2 | 1.9% |
| 3 | 1.3% |
| 4 | 1.1% |
| 5 or more | 0.7% |

Open rate by persona, published as typical rather than good:

| Persona | Typical open rate |
|---|---|
| Global average | about 25% |
| C-level | about 15% |
| Sales | about 25% to 35% |
| Marketing | about 15% |
| HR | about 25% to 35% |
| Technical roles | about 5% |
| A subject line that is working | 50% and above |

Positive reply rate: an industry average of 0.1% to 0.5%, which is one to five
meetings per thousand emails; 1% to 5% counted as good.

LinkedIn connection acceptance: about 25% for individual contributors, about 35%
at C-level, over 40% for technical profiles.

Voice notes on LinkedIn, from 8,364 campaigns: 26.9% reply rate with text only,
29.5% with a voice note.

Calling: connect rate 5% to 20%, conversation rate 5% to 10%, meeting booked
rate 1% to 3%, with 3% to 5% counted as elite. Three or four attempts per person
rather than the two most people stop at.

## What this deliberately does not do

- It does not tell you your numbers are bad because they are below somebody
  else's average. A campaign to fifty named accounts and a campaign to five
  thousand scraped rows are not the same activity and should not be judged
  against the same figure.
- It does not predict what a change will do. It says what usually breaks, in
  what order, and what to try.
- It does not know your provider's limits or your warm-up state. Ask.

## Not for you if

- You have sent fewer than a few hundred emails. There is no signal yet, and
  anyone reading one from that sample is reading noise.
- You want a benchmark to put in a proposal. These are somebody else's numbers,
  measured on somebody else's users, and quoting them as a forecast is the exact
  behaviour this file exists to argue against.
- You want a deliverability audit. This reads numbers. It does not read headers.

---

## Credits

Adapted by TheCold.Ai, a B2B outbound agency in Middlesbrough, England, from an
open-source `outbound-analyst` skill published under the MIT licence. The
benchmark figures it carries are the upstream author's own published data and
are labelled as theirs in the file. What changed is listed in this file's
frontmatter under `metadata.changes`. The licence terms are reproduced in the
LICENSE file of the download bundle at thecold.ai/claude-skills/. Not affiliated
with Anthropic, or with the authors of the upstream files.