Benchmarks

B2B outbound benchmarks from real deployments.

Our own numbers, with the denominator attached. Last updated 2026-09-17. Free to cite with a link; the format is at the foot of the page.

These are the figures from our own deployments and from the book we wrote about them, gathered on one page so they can be checked and cited. Every number carries its denominator and its source. Two things they are not: they are not industry benchmarks, and they are not a promise. Reply rates depend on the market, conversion lift on the product, the pipeline multiple on where a team started. What travels is the mechanism: the same channel, aimed at accounts that already know you or are visibly in motion, returns several times what it returns cold.

Reply and acceptance rates by audience

Read the channels separately. A connection-acceptance rate and an email reply rate are different measurements, and mixing them is how a deck starts lying.

Audience	Channel	Result
Cold, no relevance behind the send	Email	1 to 3% reply
Had engaged with our content first (a like, a comment, a visit)	Email	Close to 20% reply, more than 9 in 10 positive
Cold, qualified leads	LinkedIn	About 21% connection acceptance, about 17% reply
Followers of an adjacent solution	LinkedIn	41% acceptance, 38% reply
Opener personalized to the problem behind a signal (the signal itself never named)	LinkedIn	Near 30% reply
Companies actively hiring for the problem we solve	LinkedIn	High-30s acceptance, about 20% reply
People who had just reacted to a competitor’s posts	LinkedIn	62% connection acceptance

Source: our own outreach, 2024 to 2026, as published in The Demand Compass, chapter 18.

Three deployments, with the cohorts that came back zero

Deployment	Figure	What it measured
Non-human identity platform	0 vs 8 positive replies	5,521 generic product-pitch emails against 2,907 event invitations to accounts the map said were moving
Non-human identity platform	0% vs 67% interested	11,514 contacts: generic outreach against contextual outreach on the same market, three offer families tested in parallel over 30 days
Non-human identity platform	14 buyer-grade conversations	Opened in the first 90 days with security leaders, a buyer generic outbound never reaches
AI trust and safety platform	56,000 of 59,000	CRM contacts with no recorded activity and no source-to-revenue path, found in the April audit
AI trust and safety platform	51 accounts, 45 on profile	The queue a two-axis score left for a small team, out of the whole list: 45 matching the exact enterprise buyer
AI trust and safety platform	6 hours to 5 minutes	Lead assignment time, while the team handled 200% more leads a week across 8 reps
Infrastructure software	829 to 150, 82% filtered	Signals captured across six sources against signals surfaced to a human; the rest discarded on purpose before anyone saw them

Source: the clients’ own operating systems, anonymized, as published in the three case studies and in The Demand Compass, chapter 18.

What the system produces once it runs

Composite ranges from our deployment patterns, not industry benchmarks, and every one of them moves with the market. Read them as the shape of what happens when the two halves of the system, creation and capture, are both turning.

Measure	Range	Against
Pipeline influence	About 2x conversion to qualified pipeline	Accounts that engaged with the brand before an opportunity opened, against cold accounts in the same segment
Sales acceptance rate	70 to 80%, against a third to a half	Share of routed accounts a rep actually works: an auditable, signal-led score against a bare MQL
Cycle speed	Roughly 25 to 30% faster	Deals where the brand did its work first and outreach landed inside the window, against cold-sourced deals in the same segment
Pipeline from the same headcount	Often 4 to 5x	Once the infrastructure does the production and the people move to judgment
Holdout	5 to 10% of high-scoring accounts	The randomized control group that keeps every other number on this page honest

Source: The Demand Compass, chapter 18, “Reply Rate Is the Doorway, Not the House”.

The market numbers we build on

Not ours. Third-party figures the model rests on, each with the study it comes from.

Figure	What it says	Source
5%	Share of a B2B category’s buyers in the market at any one time; the 95-5 rule	J. Dawes, Ehrenberg-Bass Institute, with the LinkedIn B2B Institute, 2021
272 days, 81%	Average B2B buying journey, and the share of it that happens before sales sees a name	Dreamdata, LinkedIn Ads Benchmarks Report, 2026 (66 million sessions, 3.5 million journeys)
121%, 67%, 51%	Return on ad spend for LinkedIn, Google Search and Meta in B2B	Dreamdata, LinkedIn Ads Benchmarks Report, 2026
17%, 5 to 6%, 61%	Share of a B2B purchase spent with all suppliers, with any single rep, and the share of buyers who prefer parts of the journey with no rep	Gartner, The B2B Buying Journey; Gartner Sales Survey, 2025

Method

Every figure is pulled from the client’s own operating systems, with its denominator attached, and anonymized.
We publish the cohorts that came back zero. A system that tells you what to stop doing is worth more than one that hides it.
Channels are reported separately. LinkedIn acceptance, LinkedIn reply and email reply are three different measurements.
Every deployment keeps a 5 to 10% randomized holdout of high-scoring accounts, so the score’s effect is measured against accounts it did not touch.
Ranges are composites across deployments; single figures are single deployments. The page says which is which.
The page is updated when a deployment adds a figure or a source revises one. The date at the top is the date of the last change.

Cite this page. Free to quote with a link.

automate rev.ops. (2026). B2B outbound benchmarks from real deployments. https://www.automaterevops.ai/benchmarks

The method behind the numbers: how it works. The vocabulary: the glossary. The book: The Demand Compass.