04The arithmetic
What outbound actually produces, with the denominators stated
Published outbound rates are useless without the denominator underneath them. A 10.4% reply rate measured against the accepted connections that received a message is a 3.0% reply rate measured against the requests sent to get them, because only 28.5% of requests are accepted in the first place. Same campaign, same replies, and it looks roughly three and a half times better on the narrower denominator. Most figures in circulation never say which was used. Every rate below carries its source, its denominator and its sample size, or says that the source does not publish one.
Outbound arithmetic from published benchmarks| Stage | Result | Rate, source, sample and denominator |
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| Connection requests sent | 1,000 | Your input, not a benchmark. One profile in a month, chosen here only to make the arithmetic readable. It is not a volume LinkedIn is known to permit: LinkedIn polices activity and does not publish a number, so treat 1,000 as a unit of arithmetic rather than a target. |
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| Accepted (Expandi reading) | 285 | 28.5% of requests sent (Expandi, 13,218,869 requests). Denominator: requests sent. |
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| Accepted (Belkins reading) | 260 | 26% of requests sent (Belkins, which does not publish a sample size for this figure). Denominator: requests sent. |
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| Variation inside acceptance | 27.6% vs 25.3% | Requests sent without a note vs with a note (Belkins, 11.5M requests). Separately, acceptance runs 17.5% to 40.1% across industries (Expandi, which does not publish a sample size for that breakdown). That is variation between industries, not between individual senders, so it tells you nothing about one campaign. |
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| Replies (Expandi reading) | 30 | 10.4% of messages sent (Expandi, 6,730,447 messages), applied to the 285 accepted connections from the Expandi row above, one message each. 285 x 10.4% = 29.64. |
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| Replies (Belkins reading) | 19 | 7.2% of messages sent (Belkins, 15.1M contacts), applied to the 260 accepted connections from the Belkins row above, one message each. 260 x 7.2% = 18.72. |
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| Positive replies | Not published | No vendor publishes a positive-reply rate. Anyone quoting one is estimating, and that includes anyone who calls all 19 to 30 replies conversations. |
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| Meetings booked | Not published | No vendor publishes a reply-to-meeting rate either. Instrument your own from the first month. |
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Check the arithmetic yourself, and keep each reading inside one source. Expandi: 1,000 × 28.5% = 285 accepted, then 285 × 10.4% = 29.64, which rounds to 30 replies. Belkins: 1,000 × 26% = 260 accepted, then 260 × 7.2% = 18.72, which rounds to 19 replies. So one profile sending 1,000 requests in a month is working towards roughly 19 to 30 replies, an unknown fraction of which are positive, and the first of them arrive in the same weeks the requests go out. The gap between 19 and 30 is the gap between two vendors reading the same channel, not a forecast range. The full stage-by-stage version, including where the published data runs out, is on our LinkedIn lead generation funnel page.
Two honest caveats. First, nobody publishes a positive-reply rate or a reply-to-meeting rate, so any page that walks you from requests to booked meetings is estimating the last two steps. Instrument those yourself from month one and replace the assumptions as soon as you have thirty of your own data points. Second, a month of published content, started the same week, has produced zero leads at this point in the comparison and will not produce its first for a long while yet. That is not an argument against publishing. It is an argument about which channel you can rely on for the number you owe someone in ninety days. This is how we run the channel mix in practice: LinkedIn lead generation opens the conversation and cold email outreach carries the follow up.