We read 59,259 posts about sales follow-up. Here is what people actually said.
Observational content analysis of what salespeople say goes wrong after a meeting, built from public posts and software reviews rather than a survey. That is a deliberate trade, explained below, not a shortcut. Every quote was machine-checked against the raw text before it was allowed in, and we publish what the evidence does not support alongside what it does, which is the part most studies leave out.
Method
No survey, no panel, no incentives. This is what people wrote when nobody was asking them a question.
What this can and cannot answer. This is observational content analysis. It is not a survey, not a controlled study, and not science in the hypothesis-testing sense. It cannot tell you how many. There is no random sample, so no number here describes the profession. It can tell you in what words. Nobody in this corpus was asked a question, paid for an answer, or aware a software company would read it. A survey buys representativeness at the cost of answers shaped by the question and the incentive to give one. This buys uncontaminated language at the cost of representativeness. Neither is better. They answer different questions, and this one only answers the second.
Where the 640 verified findings came from
What verification does and doesn't prove. Every quote was checked against the raw harvested text before it entered the study. That proves the quote is real and accurately transcribed. It doesn't prove the claim inside it is true. Those are different guarantees, and we grade the load-bearing numbers separately below. Why nothing was rejected here. Extraction required copying the text verbatim, so this step catches transcription error, not falsehood. A zero means the transcription held. It does not mean every claim survived scrutiny: five figures were thrown out at the grading stage, and you can see each one with its reason.
Read the grading rubric
The cost is measured in hours, and the evidence is strong
Six independent estimates of post-call admin time, graded by how much weight each one carries. See the grading rubric.
The most useful number here is the smallest one. 18 to 20 minutes per reply, spent not writing but remembering. That is the part people consistently underestimate when they describe follow-up as a writing problem.
The dollar figures are much weaker, and we're saying so
This is where most studies would quote a headline percentage. The evidence doesn't support one. See the grading rubric.
There is exactly one gold-standard dollar figure in 59,259 items. One non-promotional, specific, first-person account of a named loss. That's not enough to build a percentage on, so we haven't built one.
If you take one thing from this study, take the hours. The dollar case is real but thin, and anyone quoting a tidy market-wide percentage for this problem is almost certainly quoting a vendor.
The bad follow-up has a fixed, hated vocabulary
"Just checking in" is not a mild irritation. It appeared independently across at least eight separate sources, and it is a recognised joke on both sides of the table.
"But 'just bumping this to the top of your inbox' is not a follow-up. It's a reminder that you exist."r/SaaS, March 2026
"The standard bump email that says something like 'just wanted to circle back and see if anything has changed' feels like exactly the kind of thing that gets deleted before the second line."r/sales, May 2026
"Most cold emails end with 'would love to connect sometime' which is the softest possible ask. Sometime means never."r/sales, June 2026
The pattern underneath all three is the same. Each phrase is what someone writes when they have nothing new to say but still want something. Readers detect that instantly, which is why the phrases have become shorthand rather than merely unpopular.
Specificity beats effort, and it's measurable
The strongest signal in the corpus is not that personalisation works. It is that effort-shaped personalisation loses and relevance-shaped personalisation wins. A message that references what was actually discussed outperforms one that merely looks customised, and readers can tell the difference reliably.
What this study doesn't show
Published in full, unedited from our internal version. If you're going to cite the findings above, you should read this first.
Three quarters of this is Reddit
477 of the 640 verified findings come from Reddit, which the chart above shows rather than hides. If that disqualifies the whole thing for you, that is a reasonable position and we would rather you saw the number than found it. The counter-argument is deliberately narrow. We are quoting how people describe a problem to their peers, not measuring how many people have it. For the first question, a forum where nobody is being sold to beats a vendor's survey panel. For the second question this corpus is the wrong instrument, and we do not use it that way anywhere on this page.
We detected an attempted data-poisoning
An earlier analysis pass logged an attempt to inject fabricated, on-theme quotes into the evidence pool, and specifically named two figures to re-verify: "$7,500 worth of deals" and "$500 per month per user for HubSpot sequences". Both were present in the pool we were given. We could not independently verify either. Both are excluded from this study and should not be quoted from it.
Vendor contamination is heavy
Many of the most quotable "pain" posts turn out to be self-promotional. Their language is useful for understanding how the problem is described, but none should be cited as independent customer voice. Every dollar figure above is graded on this basis.
The dollar case is thin
One gold-standard non-promotional loss and one strong non-US one. That is the entire high-confidence dollar evidence in the corpus. Lead with hours, not with a percentage.
One area is genuinely empty
Nobody in this evidence base describes an AI sending an embarrassing email under their own name in a post-meeting context. The nearest analogue is a widely reported incident involving a meeting-notes tool sending invitations without permission. If you are looking for evidence that people fear AI sending on their behalf after a meeting, we did not find it here. That absence is itself a finding.
Quotes are attributed to source and month, not to a URL
Each quote was machine-verified against the raw harvested text. The published attribution gives the source community and the month rather than a permalink. If you're citing a specific quote and need the underlying item, contact us and we will provide what we hold.
This is not a representative sample
People who post publicly about a problem are not a random sample of people who have it. This corpus tells you how the problem is described by those motivated to describe it. It doesn't tell you what share of salespeople experience it.
Cite this study
Copy whichever format you need. If you quote a finding, please link to its section so readers can see the grade attached to it.
Scurry (2026). The Follow-Up Study: what 59,259 posts say about sales follow-up. goscurry.ai/research/the-follow-up-study/
Scurry (2026). The Follow-Up Study, Finding 01: post-call admin time. goscurry.ai/research/the-follow-up-study/#time-cost
Section anchors: #method · #grading · #time-cost · #dollar-cost · #vocabulary · #what-works · #caveats
Who ran this, and why
Scurry builds software that writes follow-up sequences from meeting transcripts, so we're not a neutral party and you should read the findings with that in mind. We commissioned this study to check whether the problem we had assumed was real, described the way we assumed it was described. Some of it was. The dollar case was not, and we have said so above rather than quietly dropping it.
The practical version of these findings is on our follow-up email templates page. What we build is on how it works.