u/cosankov

We recently did research on the ideal response time at Expandi, and these are some interesting metrics that we found:

Leads answered within five minutes convert around eight times better than the same leads answered a day later. Teams responding to intent signals within 2 hours consistently hit reply rates of 20%+ independent of the industry.

This might seem like a “cool, but what do I do with this?” kind of information, but it’s far from that, and could genuinely change a campaign’s success drastically. As the GTM director at Expandi, I deal with a lot of questions about why the campaigns aren’t performing ideally, and my first instinct is now to look into the average response time instead of messages/targeting. Because if the baseline response time is 8 hours, there is no point in looking any further. By the time a prospect receives your message, they are long gone from LinkedIn, and the message will probably get buried under a pile of others.

Now, being honest about the five minute number - on LinkedIn this mostly fiction; impossible to reach. Nobody sits in the inbox all day, replies can often arrive while you are on calls, sleeping, or outside your working hours. The realistic version is hours instead of minutes, and that alone is enough to begin with because, as mentioned above, consistently replying to prospects within the first 2 hours greatly increases the performance of the campaign. Teams acting on engagement within about two hours tend to hold reply rates above 20%, which is roughly double what the same teams see on cold outreach. But if you really, really want to pull the most out of your campaign and optimize for responses under 1 hour, this is how you do it:

• Move LinkedIn notifications into whatever you already have open all day, usually Slack or a shared inbox. The LinkedIn app itself is rarely open all day, but Slack probably is. If you want to take it a step further, use another platform specifically for this with a distinct sound that your SDRs don’t hear all day because they’re not used to it and will instantly know what’s up when the notification comes. For example, you can set up a Discord server and push only the notifications from your outreach campaign there. The sound of the pings is different than Slack, so the SDRs will know instantly to jump in and check the response.
• Assign one named owner per account and write it down. Two reps each assuming the other has the thread is the most common way a warm reply dies, and it costs nothing to fix. The alternative is to create an option for the SDRs to check the replies they are handling, so the other ones can know if it’s taken care of.
• Split the queue in two. Someone who replied to you needs an answer today, while someone who only viewed a profile or liked a post can wait until tomorrow without any damage. Your focus should be on the ones who replied, and always make that a priority.
• Set a floor rather than a target. Every open thread gets touched within one working day, even when the honest answer is just a note saying you will come back to it properly. Early on, this probably won’t even be an issue, but as you scale and more responses pile up, it’s smart to categorize them. If there’s an urgent/hot conversation or a prospect interested in your product/service, that’s where you want your SDRs to be, compared to a prospect asking for a deck to read when they have time. There is a way to automate this actually, and it’s relatively easy to set up. Just funnel the responses to an AI agent first - you can create one with Claude - teach the agent to categorize the responses into, let’s say, 3 tiers based on priority, and have the agent funnel the already evaluated messages to the channel. This way your SDRs can just open the channel and instantly see what to jump on right away, and what can be handled later.
• Keep drafted responses for the handful of replies you can predict - pricing, timing, "send me something". This is very important because people get tired, and speed should not depend on whether the rep still has writing energy late in the day. Templates help them deal with that, and the more you have, the easier it becomes. Ultimately, you can set up AI SDRs to dynamically draft the messages based on the prospect’s responses, and the human SDRs just review, improve, and send. But this takes a lot of time to do properly, so early on, it’s best to handle it all manually.
• Check the age of your oldest unanswered thread once a week. That one number will tell you more about the system than reply rate does.
• None of this makes the outreach itself better. You still need to set up your campaigns properly, target the right leads, craft the right messages. This “just” stops losing the leads from the outreach, which for most teams I've looked at is the larger of the two problems.

So, if we go by the Discord example, this is how the final setup could look:

Fresh Discord accounts (we don’t want any random pings) + a fresh Discord server to ensure these pings stay unique and distinctive for the SDRs. A channel (or channels) to which all the prospects’ responses are pushed. The responses first get funneled to an AI agent that’s trained to 1) evaluate them; 2) draft a templated response (for the pricing, timing, "send me something" and similar messages). The agent pushes the evaluated leads to these Discord channels with a templated message in some instances. The SDRs pick the leads, check them to signify they are taken care of, and use the templated messages when needed. The response time drops to under 2 hours, and you suddenly can’t deal with all these leads and money.

I wanted to share this breakdown because many companies still have a hard time figuring out which platform to go for. Luckily, as someone who has worked more than 10 years in this field - specifically LinkedIn and email sales - and who has access to tens of thousands of campaigns, I pulled the numbers and did the research so you don't have to.

Most channel comparisons still lean on open rate, which is totally inaccurate since Apple Mail grew to roughly 46% of email clients and started pre-fetching images by default. This means that a large share of the opens are now just a privacy proxy loading a pixel from the email rather than a person reading the subject line. So when someone tells you email got the opens and LinkedIn got the replies, that conclusion usually stems from a totally wrong assumption - that we can accurately measure email response rates.

Reply rate is the only number that survives the comparison because you can't fake these. However, it has to come from one dataset, or you are just stacking two vendors' marketing pages against each other, and essentially comparing apples to oranges. Across roughly 70,000 campaigns I've been able to look at here at Expandi, LinkedIn messages had a 10.3% reply rate against 5.1% for cold email over the same window. That gap is about double in LinkedIn favor, and this is actually something that's been consistent over the years. I can't recall the exact numbers because it was a long time ago, but before I was a GTM director at Expandi I worked as a director at an email sales agency, and at Skylead before that - and the difference in response rates was roughly the same over these 8 years.

Most B2B motions need something like eight touches before a first meeting, and one channel cannot carry eight touches without turning into virtual harassment. The LinkedIn sequence also often dies when someone ignores a connection request, because there is nowhere left to go. The solution is - multichannel approach. This is supported by numbers - Belkins published a figure of 63% more meetings booked by teams running two or three channels versus one, which also matches what I've seen in the accounts I've analyzed.

This, of course, doesn't mean that just going multichannel will solve all the issues. Whether the second channel helps or just doubles your noise depends on the alignment. Running both in parallel on a timer is the same message twice with a different envelope, and this quickly feels like spam, double as fast! So instead, the second channel should always react to the behavior on the first based on these rules:

• Trigger the LinkedIn message off an email click rather than a delay timer. A click is intent - mild, but intent, while day three is just an automated calendar entry.
• When a connection request stays unanswered for a week, move to email and reference the request directly. Pretending the first touch never happened is what makes the second one seem disingenuous, which turns down any interest instantly. Plus, many connection requests stay unanswered just because people forget to check them, and this way they get a reminder.
• Route email bounces to LinkedIn instead of dropping the lead. A bounce usually means your data is stale, not that the person stopped being a fit.
• Send the second follow-up, it's worth around 4% in additional replies. The third and fourth follow-up mostly just add noise and give people a reason to block you.
• This goes without saying, but just in case - pause every other sequence the moment someone replies anywhere. Talking over your own answer kills more deals than a weak subject line ever will.
• DON'T USE AUTOMATIONS TO HANDLE CONVERSATION POST-REPLY. We recently did a study on this, and replying to a prospect on LinkedIn 5-10 minutes after their reply boosts the chance of the conversation continuing through the roof. The timing is probably less important for email, but you should still have SDRs ready to pounce as soon as someone replies.

One last, very important note is that timing and proactivity matter more than channel count in your campaign. Outreach triggered off a real signal, usually a profile visit or someone engaging with a post, converts around 14.6% against 1.7% for cold lists.

To summarize: always use multiple channels when you can because there are only upsides of this approach. The extreme edge cases where you should stick to just one are:

1. If the market you're targeting is very narrow and you might burn quickly through it with mass-email outreach. Stick to only LinkedIn in this case and focus on quality instead of quantity.
2. If the market you're targeting is extremely huge and you might burn too much money adding LinkedIn to the mix because quantity matters more. Then stick only to email.

Automation earns its keep at the top of the funnel - finding the right people, sending the connection requests, running the first couple of follow-ups so you're not doing that by hand at 200+ a week. This is, generally, something most agencies and companies do because it’s almost not feasible or it’s too taxing to push a similar scale manually. However, there is a spot in that flow after which automating further has more downsides than benefits, and that spot is right after someone replies.

The main reason automating doesn’t work beyond a prospect’s reply is speed and judgment, and the numbers actually confirm how huge a difference even minutes can make in the sales process when it comes to speed. The odds of qualifying a lead drop by around 400% when your response time slips from five minutes to ten. That’s just 5 minutes difference, 5 damn minutes! A reply that sits overnight because a sequence was still "nurturing" the thread is, in a lot of cases, already gone: they booked with whoever answered first, or it cooled off, or it got buried under the next fifty notifications.

Can this be solved with an automation? Probably, you can set something up to reply moments after a prospect’s reply. But then we face a new problem, and that is:

Judgment. Reply rates tell us where the value actually lies among prospects: cold outreach to new connections performs at maybe 5-15%, prospects who are already engaged come in around 15-30%, and with someone who messages you first, it goes to 40-70%+. The further right you go on that scale, the more each conversation is worth and the less it tolerates a generic, canned answer. A person who asks a specific, half-committed question does not want a templated "great to connect" - they want the actual response, in the right tone, from someone who read what they wrote. Automations can’t do this, no matter how well you train them.

The automations do work very well for:

• Outreach - from personalized and signal-based to cold
• Reminders and follow-up tasks so nothing rots in the inbox
• Status and ownership tracking so two reps don't land in the same thread
• Routing, so a warm reply reaches your SDR fast instead of sitting in a shared queue

This is exactly why we’re keeping the automations at Expandi up to this level, and not having anything further. Believe me, as the GTM director, I get asked again and again why we’re not adding automated replies or even fully automated conversations as part of our features, and the reason is - they won’t bring anything good to your outreach. Everything up to the point when you get a reply can and should be automated with much precision and personalization because it saves time at no expense. The conversations with real prospects who replied stay manual because this requires a human to read the intent, tone, and the decision for the right next step.

At low volume, the difference won’t be that huge because you see every reply anyway. It only becomes the thing that makes or breaks your pipeline once you're running enough outbound that the good replies start slipping under the new ones, and "I'll get to it later" turns into "never replied" without anyone deciding it should. Plus, keeping that response time to under 10 minutes becomes harder, but it’s definitely worth investing in.

One note: This applies to every automated outreach, whether it’s LinkedIn, email, or any other platform. Automate until the reply, then take over the conversation manually, and do it quickly.

I’m the GTM director at Expandi, so full disclosure up front, this is our platform data from a recent study. Between May 2025 and April 2026 we looked at 13,218,869 connection requests, 6,730,447 follow-up messages, and 3,766,161 accepted connections across 13,302 active accounts, comparing the results between different seniority-level profiles, companies of different sizes, industries, and more. Below are some of the most important remarks we’ve taken from the study and the factors that have a major role in a LinkedIn outreach campaign’s performance:

• Sender seniority barely matters. A well-targeted message from a Manager performed almost identically to one from a C-level sender. C-levels sat at 29.4% acceptance, managers at 27.3%, juniors at 26.3%. Two to three points across the entire seniority ladder. The title on your profile is not the lever most people think it is, and this is one result I wasn’t any surprised about. When you think about it, most people active on LinkedIn receive dozens of messages daily - does it really make sense to check out each person, and on top of that, their seniority level? I don’t think so, the percentage of people who do that is quite low, hence the results above.
• Company size did not matter either. A 5-person startup and a 10,000-person enterprise both landed around 28% acceptance. And the strong majority of this volume came from companies with 200 employees or fewer, so this is not some small-sample effect from a few big accounts. People don’t care about company size, and they’ll care even less in the future with all the automations and agent employees making small companies run like mid-sized/large ones.
• The industry you are in plays a huge factor in the discrepancy between acceptance rates, and this is measured by the sender's own industry, not who they targeted. Recruiting firms running outreach hit roughly 2x the platform average on everything: 36.5% acceptance, 6.6% connection-note reply, 18.9% message reply. This is, of course, not because recruiters write cleverer notes than anyone else, but because reaching out to strangers is their core business, so the whole motion is tighter. Senders in Consumer Electronics sat at the bottom at 17.5% acceptance on the contrast. The full range ran from 17.5% to 40.1%. Of course, this should be taken with a grain of salt because some campaigns can show unrealistic numbers simply because of industry-only factors. I have a friend who does marketing for video games and his influencer email campaigns sometimes land a 50%+ response rate. That’s incomparable to B2B SaaS campaigns that often land between 2%-5%.
• Acceptance rate can hide a reply rate problem. Software companies are the biggest single group of senders here, 14% of all the outreach, and they show the trap clearly. They get connections accepted at a normal 27.5%, but only 8.8% of those ever turn into a reply, against 18.9% for recruiting firms. For them, the connection is the easy part and the conversation is where the entire thing dies, meaning that’s where the most customization should go to. Acceptance rate on its own is a vanity number if the reply rate under it is soft.
• The connection note is decaying in performance, the conversation after it is not. Note reply rate slid from 3.5% in May 2025 to 2.2% by April 2026, a 37% drop in twelve months. Post-connection message reply held flat at 10.4% the whole time. People have stopped answering the clever one-liner stapled to a request, but they still answer once an actual thread is open. This goes in line with the fact that people value genuine conversations more and more in the age of automation. Any LLM can write a clever one-liner, but it can’t hold a conversation.

The study had some interesting findings even for us, and it helped locate the exact pain points for different clients in different industries. For example, we now know that for software companies, the main focus should be on solving the “conversation dies after the connection”, while for recruiting firms the solution would be simply eliminating connection notes entirely and focusing on the conversations after the connection.

As the last note, this is a general benchmark we pulled from the study:

If you want two numbers to sanity-check your own outbound: message reply above 12% usually means your targeting is good, below 8% usually means it is not. Perfecting copy while you sit at 6% is treating a targeting problem as a writing problem, while it could often be easily solved by improving lead quality.

Most of what I do professionally is build and run automation, so I spent an inordinate amount of time thinking about its edges. The part that interests me now is not what automation can do, which is more every month (hell, even the basic GPT Codex can now do mad stuff) but the specifics that are always outside of the loop. The "edge cases" as it were. I have started calling them the black spots in the machine. The places where the process needs a piece of momentary judgment that the system, however clever, just cannot make from inside its own loop.

My own clearest example comes from outbound and sales, which is where I have spent most of the last decade. You can automate the list building, the data cleanup, the scheduling of first touches, and you should, because a person grinding through that manually is a waste of everyone's time. But the moment a conversation starts and a valuable lead does something off script, the automation becomes a liability since it can’t make high stakes decisions on any sort of level a human with actual sales experience can. I guess that's still the reason high ranking sales positions get their bread - it's not amount of work a person does, so much as the sum of their expertise. The ability to do something authentic and original, in other words, the spoke that drives the whole machinery... or so I believe.

The industry already ran this experiment at scale, and it failed. The autonomous AI SDR wave made this measurable. Meetings booked by the fully hands off tools showed a 52% in straight comparisons, against 71% when a human ran the outreach, and the pipeline underneath them was thinner than the booking volume suggested. The machine could not read the room. There WAS no room, just a loop.

These black spots are almost always about context that only exists for a moment. Every case I seen comes down to a tiny signal that this particular person, right now, needs a different response than the pattern would predict. Automation is extraordinary at the repeatable stuff and mostly blind to the one-offs, and a surprising number of decisions that matter are one-offs.

I am fairly sure this is not unique to sales, which is really why I am asking rather than telling. Every field that automates must have its own version, the task everyone silently agrees stays manual because the cost of getting it wrong by machine is too high. 

Which brings me back to the question. What have you deliberately kept manual, and what was the moment that convinced you it had to stay that way?

It sounds strange to say this, since it’s coming from someone who has spent almost a decade building and running outbound at scale. But the most useful change I have made lately is automating less of it - not more. And one thing especially

1.  Replies

Reply rates have absolutely PLUMMETED this year so far across the board, so it simply isn’t worth it anymore. When everything sounds AI generated, prospects have gotten very good at pattern matching it in a second. An slop AI reply to a warm lead just reads as someone who stopped paying attention to the person in front of them and stopped treating them like a real (potential) human client, even if it’s in the future tense. The tools got better at sounding human right as humans got better at spotting them, and that's not an arms race you win by automating harder when the pickings are slim already.

Don’t get me wrong, automation is still the right answer for the top of the outreach funnel: the sorting, the enrichment, the first touch grunt work that no human should be doing by hand, and the follow up logic. But once you get a reply, do it yourself and hook them hard and with all the human ingenuity you can muster to get them to a call.

I'll also give some evidence for what I’m saying based off my experience (being the chief of GTM at Expandi, an automation is basically my daily bread). And it's how I’ve seen a wave of fully-autonomous AI SDR tools that raised enormous rounds and then watched most of their customers leave inside a quarter, this goes cross tool and cross industry. When I dug into why that is - it’s because meetings got booked at pure volume, but they showed up at materially lower rates than human booked ones, roughly 52% against 71%. The savings from removing the human in the loop reappear as literal bad pipeline.

Where this matters most is the niche vertical, when your entire serviceable market is a few dozen named accounts, every one of them is precious, so automating the follow-up to a hot lead there is an unforced error. In other words, your prospects are worth a real, tailored, human breakdown if they reach back out to you. Something that shows you understand their specific situation and that you mean business. You wouldn't risk a relationship that took months to warm just to save four minutes with a template, right?

You automate the work that scales and buys back time, then spend that time being unmistakably human exactly where the deal is actually won or lost. That’s basically my working action plan now, for the reasons I mentioned above.  

But let me ask you, if you’ve had different experiences. Where do YOU draw the line between what you automate and what you don’t?

This is just a good practice I got in the habit of applying in every company where I had a leading sales role, and I developed it more or less naturally over the past ten years I’ve been working in B2B. I also have the chance to observe dozens of different companies run their LI outreach at varying volumes, as the current head of GTM at Expandi, and what I’m seeing now all corroborates this practice. One thing I noticed was how fussy some companies are about running A/B tests on their website landing pages and obsessing over things like button placement for days BUT then let their SDRs run their outbound with a LinkedIn profile that hasn't been updated since before they started in the role.

Now, to be fair, I understand that someone’s LinkedIn account is their account, after all. But we all know what LinkedIn is for (networking, sales… business, in a word) and it always seemed counterintuitive to me not to leverage it for your company. Either way, a well maintained and well built up profile reflects well on both the SDR and the company they work for. And a profile with a corporate headshot from years ago with a bland resume, and no activity at all in the feed, just won’t cut it… and it especially does not cut it when you want to optimize that account for serious outbound.

I’ve been on the platform almost since its inception, and the most important thing to know is that the connection/ acceptance happens from the prospect’s end before they read your message. Most people will just see your photo, headline, and (important!) mutual connections, and make a decision on the fly based on just that and that alone. 

In other words, your carefully written opener only matters if the prospect hasn’t been filtered by the profile itself, and it’s something I don’t hear or see many people optimizing: the profile itself, not just the contents of their copy plus targeting prioritization.

Anyway this is the regimen I have my SDRs on when it comes to how they ought to handle their LinkedIn profiles (or rather make them outreach ready)

• Have them check whether their headline communicates what your company helps with rather than their titles, e.g.  “Helping B2B teams book qualified demos” or the like instead of "X role at [insert Company Name]" because the first is more direct and tells prospects what you DO
• Have them optimize their About section with three short sentences about what problems you’re solving/what you’re providing, in somewhat greater detail. The hands on approach should be the focus - in the present - rather than a list of prior “accomplishments
• Have them pull up their recent activity and check with whom they had interacted with in the last 1-2 months  - and if the feed is empty, have them fill it up with some content. Zero social proof = much smaller chances of prospects taking you seriously
• Have them cross-reference their connections with your ICP. If you're targeting VP-level buyers and your network is entirely other SDRs, the prospect sees no social proof that you operate at their level

This is what’s helped me push acceptance rates consistently. Tried and true from my own experience, and though I learn something new every day, this baseline approach was always useful to have as something to fall back on.

I've created multiple Claude Code agents over the past 6 months and just a few weeks ago realized that I could just make them autonomous (huge time waste in the meantime clicking on all those "Allow" buttons). They are working autonomously now with their own dashboard through which I control them, and they are performing quite well with all the APIs and connections.

Today, I talked to a friend of mine who said that Open Claw agents are the real deal. That the autonomy, freedom, and access capabilities these agents have are on another level, and that if I really want agents who act like "real" employees, this is the way to go. I've never used Open Claw because I was scared of those "gone wrong" stories considering all the important company data is on my PC (I work as a GTM Director at Expandi and I'm building a few apps with Claude in my free time). The only similar tool I tried was MoClaw because a friend recommended it to me (he knows the developers and gave me some free credits to try) and I really liked what it could do, but never pushed it to its full potential (in contrast to how far I pushed my Claude agents). I have just one researcher agent and the rest are done through Claude.

I guess the main question is - will switching to Open Claw agents bring any measurable improvement to my structure and will they allow the agents to have more autonomy because even with all the APIs and accesses, my current Claude agents still have limits in browsing/researching. This is actually exactly why I use MoClaw for research.

If yes, I would be more than happy to buy a Mac Mini and fully move to this new structure. Tbh I've been eyeing this for some time, but never understood the benefit of the Open Claw agents enough to justify the switch (and the costs, of course).

Much thanks in advance!

I’ve been working almost 10 years in B2B, and I’m still learning every day, but this has to be one of the most important insights I have to offer to anyone in sales as of 2026. I noticed it observing my own team at work (as well as teams I’ve worked with/ and for), and I’d almost laugh now at how much time I used to set aside on on message generation and customization - overpersonalizing, using dynamic templates, all of it aimed at making the content of my initial pitch better. Almost none of that effort ever went into optimizing the timing of delivery based on behavioral signals from prospects.

Some studies I’ve conducted, tracking lead response rates, tell the following story. Teams that respond to a prospect signal within the first 2 hours see reply rates above 20%. Now consider that the average B2B team responds in about 42 hours. By the time your first 24 hours are up, the prospect will have already gotten messages from other vendors, who detected the same signal you did. And data on buyer behavior shows 1/4 of customers end up buying from whichever company responds first, not whichever company has the best pitch, because by the time a second vendor reaches out the prospect has already started a conversation and built enough momentum with the first vendor that switching feels like unnecessary effort on their part.

In other words, what most teams doing outreach on LinkedIn are missing isn't a better content strategy but a behavioral signal detector with a faster trigger, and faster response time in general. The signal sources themselves already exist for most teams, but they're siloed across platforms (LinkedIn vs email) and rarely unified into a single event stream that can fire an outbound action within minutes instead of hours, or days as it’s sometimes the case. 

At this point, and more importantly when doing this at scale, good outreach is more of a signal processing problem rather than a batch scheduling problem, and the performance difference is large enough that it makes most copy optimization questions look trivial. When compared to the factors that actually have a far bigger impact on reply & conversion rates.

The short version: timing matters more than the nitty gritty details of your copy, and if you let an opportunity pass you by due to inattention, you’ll never recapture with a slightly more polished pitch.

LinkedIn removed the fixed connection request cap sometime in the last couple of years. Well, it was more in general cuts, the latest of which happened this year, and replaced it with a dynamic per-account scoring model that most people building automation on the platform haven't fully mapped yet. 

The system weighs several behavioral inputs. Namely these: acceptance rate, reply rate, SSI (Social Selling Index), organic posting activity, and the number of pending unaccepted invitations sitting in your queue, which it uses to produce a trust score that directly controls how many outbound actions your account is allowed to take.

In practice, this means that accounts with high trust signals (SSI around 65 or above, acceptance rates above 40%) can push up to 200 connection requests per week without triggering restrictions. However, accounts with low trust signals get throttled to around 50 per week, sometimes significantly lower at 25-30. That's 4 times the capacity difference between two accounts on the same platform running the same automation tooling, based purely on how LinkedIn grades their reputation.

I think this is very relevant to anyone building or in any way using LinkedIn automations and as head of GTM at Expandi I’ve had the opportunity to see these patterns I’m talking about, in practice, over dozens of dozens of accounts running outreach at various volumes. 

But what makes this relevant to anyone building LinkedIn automation - is that the system creates a feedback loop that's really hard to reverse once it starts working against you. Low acceptance rates from poor targeting push your trust score down, which throttles your volume, which in turn pressures you to cast a wider net with less precise targeting, which drops your acceptance rate even further. And so on and so forth. I've watched accounts downgrade from 150 requests/week capacity down to 40 in under just a month because the initial list quality was bad and every subsequent adjustment made it worse.

The diagnostic is pretty straightforward, though, if you want to check where an account sits:

- Pull your SSI at linkedin.com/sales/ssi
- Check your acceptance rate for the last month from your sent invitations
- Withdraw pending invitations older than 2 weeks - each one is dragging your score
- Look at whether your sends are clustered since these burst patterns are a detection signal

TL;DR version - The acceptance rate on LinkedIn is the single highest weight input in the scoring model from what I've been able to observe and will impact your ability to automate profile actions more than anything. LinkedIn accounts that maintain 40% plus acceptance consistently get capacity that makes automation viable at scale, while accounts below ~25% acceptance hit flat walls the platform sets that no tool configuration can work around.

Most outreach automation still runs on cron logic - send message, wait 3 days, send follow-up, wait 5 days, send another. The intervals are arbitrary and the only variable being optimized is the gap between touches. I’ve done a research on this topic over the weekend based on our campaigns at Expandi, and looking at reply data across millions of outreach attempts, roughly 84% of positive replies come from the first message and the first follow-up combined. Everything after that is fighting over the remaining 16%, and the returns per additional touch drop significantly after touch three.

The interesting shift happening in outbound right now is teams moving from timer-based sequences to event-driven triggers. Instead of following the old logic "send follow-up in 72 hours regardless of what the prospect did", the trigger becomes a behavioral signal - the prospect viewed your profile, engaged with a piece of content, changed jobs, posted about a relevant problem, or anything that can be creatively used to break that first ice. Follow-ups become a response to something they actually did rather than something your calendar scheduled, and the conversion difference is understandably meaningful because the context is real instead of manufactured.

From an architecture standpoint this is just the same pattern that moved most backend systems away from polling and toward webhooks - you stop checking on a fixed interval and start reacting to events. Of course, the challenge in outbound is that the signals are scattered across platforms (LinkedIn activity, email opens, CRM triggers, intent data providers) and most teams don't have the plumbing to unify those signals into a single trigger layer, but it's possible to manage without a fully ironed out system. The ones that do have a full pipeline are seeing reply rates on event-triggered follow-ups that are 2-3x what the same message gets on a timer, because the timing itself carries information that the message content can't compensate for.

Another important practical implication of this is that optimizing message copy past a certain point has diminishing returns compared to optimizing when that message lands. A mediocre message sent within two hours of a signal consistently outperforms a polished one sent on a 72-hour timer, which is why you should always aim for being on time, rather than being technically perfect.

I recently became a dad for the first time (yaay), and after about 6 months of just getting used to this new life, free time as a concept came back to me - so now I want to hike again.

The problem is:

• I work for about 10 hours a day, not in one take but spread across several instances between my main job and some personal projects.
• I spend about 4 hours purely dedicated to my wife and daughter and help with household duties.
• I sleep for about 7 hours a night

When you take away all the physiological needs, some rest time between work/taking care of baby/house chores sessions, etc., I'm left with about 2-3 hours of "free time" a day. Not too much, but I currently used it to go for a walk around the neighborhood, walk a dog, or something like that. Just to get outside a bit because I work from home and do all my family duties at home.

Hiking used to be one of my favorite hobbies back in the day, but it's become quite hard to find time/energy for this as a dad. Some days, a problem emerges and I have to spend extra hours on that essentially deleting all my free time, and some days I'm just exhausted to the point where the only thing I wanna do is lay back and watch a movie. So my question is - how do you do it, how do you find time/energy to hike regularly? Do you do it on weekends, or off days? Do you go for shorter hikes because the dad life just doesn't have room for 6+ hour journeys?

I like to be hyper fastidious about every aspect of my outreach because I see thousands of messages run out daily (as head of sales at Expandi) and as the common wisdom goes, quantity is its own quality, or, rather, it becomes its own quality when enough volume is reached. And I wanted to know the exact factors influencing the baseline conversion rate of our campaigns.

What I did was pull the exact response times for every inbound lead from Q1 - both client leads and for our own campaigns, and compared them against each other en masse.

Results as follows:

The average time from someone getting a message to us actually reaching back was ordinarily between 42-47 hours, so let’s take ~44 as a mean. This was almost 2 full days days. And we thought we were fast because we had a round robin set up. However, the reps assigned to their individual leads, instead of responding in haste, decided to do extra qualification and draft something more polished, and loop an AE in for context (especially if they thought it was a particularly warm lead). By the time all that happened, the actual response would be sent only the next day... or the day after.

In general, this isn’t such a big timeframe, but here is where overqualification hurts more than it helps. Once a lead responds, it already means you have a part of their interest or they need something in a hurry and want it know, so it’s better to go with the flow and respond fast.

Of this sample from our Q1, as a rule - leads contacted within 5 minutes ultimately ended up converting at 8x times the rate of leads contacted after 1h, with an additional diminishing return after the first day. The handful of our reps who immediately sent a note within the first couple hours had MUCH better rates demo request + conversion rates, and not because their emails were better (in the abstract) - but just because they were the first!

I remember reading somewhere that somewhere around 1/4th of all customers end up buying from whoever responds first. I used to think that number was highly inflated for effect but after seeing the raw stats, you bet I can now believe that.

Once a prospect starts a conversation with the 1st vendor and gets the initial answers - and gets acquainted with their pipeline already - switching to vendor no. 2 and comparing the two just feels like extra work, especially if it’s a product, a tool, a service, whatever you’re offering, that they’re urgent to get be. At that point, after the initial outreach and response decay, it’s less your product that’s competing against a competitor’s, and more a competition against that initial inertia and that's a losing fight.

Long story short, the solution was obvious. We told all our reps to stop trying to over-perfect response messages and shift attention more to TIMING. By sheer statistics, a short relevant reply in that initial short window after a lead responds beats a carefully researched pitch that will fall on deaf ears a whole day after. That’s my takeaway from this case.

I’m seeing the posts about these overpowered AI agents doing all the work, and I’ve even met some people in my line of work who told me they do prefer working with agents instead of people, which I found quite weird, to say the least.

Is this all just talk because I’ve never been able to build an agent well enough that could replace a real human, nor the one I could give full ownership to? It looks like some stretch of imagination of what AI could become, or be, given it’s developed perfectly, and not something that’s doable (much less reliably doable) right now. As someone who works in sales, I’ve also seen posts on LinkedIn from companies burning through $100k+ in tokens monthly, and swear in the AI. For a casual Joe like me, this amount is almost inconceivable, but some people got there.

This is not to say I’m not using AI to the fullest extent of my knowledge and expertise. I have multiple Claude Code agents specialized in certain tasks, then a fully developed Codex dashboard with multiple mini agents for the more complex tasks. I have my researcher agent built in MoClaw that runs 24/7 lead generation, amongst other things, and wires all of its work into Claude Code and Codex agents, and I use SocialClaw for my social media. It’s not like I didn’t put 500+ hours into building my own AI agent system, but it’s still at around 50%-60% independence because I have to be there and check for quality, patch and update after every mistake, manually cover all the complex tasks, etc.

Regardless, I was wondering - are there any actually successful solopreneurs who automated their work entirely with an army of AI agents and actually lead their business on a larger scale without any employees? 

All I’m seeing are the posts of the systems, but never the actual achieved results or numbers. Is it still just a myth people are trying to sell, or am I just that much behind the curve?

LinkedIn's People You May Know tab is something a lot people simply scroll past to go build a filtered list from Sales Navigator. Which makes sense - it’s there for networking, not prospecting… or is it? That’s what I wanted to see, and as the chief person in charge of sales at Expandi, I thankfully had a lot of working room and big enough batches to confirm or disaffirm my suspicions with actual stats.

First thing first - the People You May Know About section on LI isn't just random suggestions, but a part of the platform’s overarching infrastructure (and algorithm) which ultimately also determines how many requests you can send. 

In other words, it is LinkedIn's internal recommendation engine running a proximity calculation on your connection graph in the background. 

These are the factors it weighs most heavily, roughly in order: 

• mutual connections (the big one - 5 mutuals beats 1 mutual every time)
• current or past company overlap especially with time overlap
• school alumni ties
• reciprocal profile views 
• synced email contacts
• shared groups
• geography plus industry match

You’ll also notice how every one of those is a prospect warmth signal. So we ran a straightforward comparison, with one batch pulled from PYMK suggestions and the other batch from a filtered Sales Nav export targeting comparable titles and industries. Same sender and same connection note, no difference other than the prospect batches themselves.

The results: the PYMK batch had a 73% acceptance rate within a week. The Sales Nav was less than half of that at 30%, which is roughly the standard for well-targeted cold outreach.

The mechanism behind this is also purely psychological, from what I can tell. When a PYMK request lands, the recipient sees how many connections you have with them right under the sender's name. That social proof on its own does the qualifying work. And the prospects themselves are less likely to wonder who the hell are you, and just naturally go with the flow and accept - simply because you (theoretically) know some of the same people.

However, there are caveats

• If your connections skew heavily toward your ICP, then the People You May Know section is a hot pipeline of more of them. 
• BUT if your network is a scattered accumulation of a bunch of randoms - old college contacts, recruiters and so on  - then the PYMK reflects that randomness back at you. And you can't filter it obviously - it just gives you what your graph gives you.

I still don't think this can replace a well targeted Sales Nav search for precision. But as a free supplement that runs alongside one, the raw accept rate difference might make it worth checking before each outreach cycle.

Everyone has an opinion on AI in sales now, but most of the takes I hear are still mostly just… vibes, without the data to back it up. I trust facts however and I wanted a data grounded answer to how much AI-personalization directly affects the reply rate, i.e. do people really really respond more often to personalized messages?

To clarify, my sample in this case were some 50 mil LinkedIn messages from various campaigns I ran as part of Expandi, both internally (as I am the lead salesperson and head of GTM in the company) as well as outreach sequences for our own clients (and business friends). 

And from what I tracked across campaigns

1. AI-personalized messages averaged a 4.19% reply rate. 
2. Whereas non AI messages averaged 2.60%.

 

That’s essentially a 61% relative increase on the whole, which is large enough that I don’t think it can be dismissed, but it also isn’t the spectacular fix-all some sales reps swear by. So in simple terms, AI personalization does the job, at least in the sense that a message tied to the prospect, their company, or something currently happening around them gets more replies than a generic template with a name…

But the incredibly funny part is that a 4.19% reply rate still means almost 96 out of 100 people ignored the messages completely. Which is less than 1 whole person (lol) per 10 people, when scaled down. And scaled up - that means if you send 1000 messages, you’re going from roughly 26 replies to 42. That’s extra 16 conversions… well, per 1000 people. It’s not the revolution of outbound I see some people make it out to be (especially since everyone is now doing it) but the difference is there, and it’s a measurable increase in pipeline.

However, the best use case I have for AI is not writing clever openers or dozens of sequences. It’s useful there but where it excels is in - research compression and signal surfacing, because your messages ultimately mean nothing if you’re not pushing someone’s pain point. What would take a rep half an hour, an hour, or more  to do -  checking someone’s LinkedIn, posts and manually qualifying leads - AI can do instantly, especially when it comes to surfacing enough context in seconds for the rep to decide whether a person is worth contacting at all. That part of the research matters more than the actual message wording.

Where AI use in sales goes wrong IMHO is when teams use AI as a permission to scale infinitely but still have atrociously bad targeting. If the prospect has no visible paint point you can leverage, and no reason to care, then AI just helps you get ignored in a slightly more elegant way. In the other words, the whole thing defaults to the hard lesson that is - your message may be personalized, but your outreach may still be irrelevant to your actual audience doesn’t give a jack.

Essentially - AI tailoring makes good outbound even better, but it does not rescue your campaign from bad targeting and missing out on actual interest signals from your ICPs. And that’s another thing where AI is again more useful (more than the one penny trick of personalizing messages) - doing the grunt-work part of qualifying leads for you and surfacing context, so your actual sales rep (or you if you are one) can decide if the person is worth contacting at all. That’s my take from this case.

I went through upwards of 300 recorded sales calls since 2026 started looking for a pattern in close rates, i.e. what factors ultimately contributed the most to conversions (and which were ultimately less relevant than I thought). Bear in mind these aren’t random stats - I work as head of GTM and sales at Expandi, so the prospect pool I had was as broad as it was enormous, and covered everything from enterprise to more midmarket deals.

The strongest variable I observed was this  - whether the prospect articulated the problem in their own words during the discovery call.

At first this seems almost counterintuitive, as the logical first instinct would be to assume that the quality of your pitch deck, or even your sales rep’s charisma, would play bigger roles. They do, to be sure, but not in a way that’s readily measurable.

However, the incidence of these instances where the prospect mentions their problems on their own volition and considers them IN FRONT of you, and explains them in unambiguous terms, was an observable and repeating signal that prospects gave when they were ready to convert. 

So, I compared two sample batches of calls based on a simple criteria - who did most of the talking, that is:

1. Calls where our sales reps did most of the talking
2. Those where the prospects themselves articulated their problems

In the first instance, the rep would go through the pitch deck, analyze a client’s pain points, connect the dots and provide solutions. But regardless of the quality of the pitch itself, a lot of the deals still stalled or died on the vine as it were. The exact close rate was 18%.

On the other hand, in cases where the client described the problem they were facing and used internal examples (and even when our sales rep was silent like 80% of the call) - the close rate jumped more than double to 44%

The gap was consistent across deal sizes and verticals. It held for $30K ACV enterprise deals and for $8K mid-market ones. And as for the sales reps who closed the calls - the best ones were those who asked one or two open ended questions early in the call and then stayed quiet long enough for the prospect to chime in with their own take of the exact issues they’re facing.

I might not be a psychologist, but here’s my take on the psychology behind this. I think a prospect who articulates the problem has already done the INTERNAL selling, as they've convinced themselves something needs to change internally for their business, which you can think of almost as an invitation to be that change. It’s a signal that there is a concrete gap and that they’re considering a switch, which just means you, as the salesperson, have the leverage to sort out the fine print of how you can be a part of that change (for their business)

TL;DR My main takeaway is: discovery calls should serve to extract the prospect's OWN needs in concrete terms, then respond to them; rather than a playing ground for a rep to demonstrate their understanding of the market.

LinkedIn stopped enforcing a universal connection request cap sometime during the previous year, and what replaced it is now closer to a behavioral credit system than most outreach teams realize. 

Based on what I've been tracking, each account now gets a dynamic Trust Score that adjusts your actual sending capacity continuously. This does not happen weekly or monthly but as an ongoing recalculation based on how your account  behaves on the platform in real time.

The inputs feeding the system are roughly what you'd expect if you think about it from LinkedIn's side

• Acceptance rate on sent requests 
• Reply rate on messages
• How many pending invitations you have sitting unanswered
• Whether you post or comment organically
• Finally - your Social Selling Index

Of all of these, acceptance rate carries the most weight by a lot. Accounts that are consistently above 40% acceptance tend to get access to around 200 connection requests per week. Accounts below 25% get squeezed down to 50 or fewer. Basically 4 times the outreach capacity gap between two accounts doing the same thing.

This is how it works, essentially: someone sending 50 well-targeted requests with an 80% acceptance rate will see their capacity grow over time, while someone blasting 100 requests at 20% acceptance will have theirs shrink. The platform is allocating reach based on whether your connections are actually wanted, which means the old playbook of maximizing volume first and optimizing later actively works against you now.

There are a few patterns that seem to trigger throttling faster than others. Let’s take the classic example here - sending all your requests in a single burst, or letting pending invitations pile up past the two-week mark without withdrawing them. Another factor that will throttle your account is having low organic activity while maintaining high outbound volume. LinkedIn’s algorithm interprets that, or any combination thereof, as a single purpose outreach account, and your Trust Score will drop accordingly.

If your account feels throttled and you're not sure what's happening, a quick diagnostic would be to check your SSI at linkedin.com/sales/ssi, pull your acceptance rate for the last 30 days, withdraw any pending invitations older than two weeks, and look at whether your sends are clustered or spread across the week. Acceptance rate below 25% is the clearest red flag. At that point, adding more volume just buries you further.

I should note that LinkedIn hasn't published the Trust Score as an official feature with documentation. But this is the operating model that best fits what we're seeing in the data. The capacity differences are measurable, even if the exact scoring formula is a black box on their end.

LinkedIn stopped enforcing a universal connection request cap sometime during the previous year, and what replaced it is now closer to a behavioral credit system than most outreach teams realize. 

Based on what I've been tracking across several thousand accounts over the past year, each account now gets a dynamic Trust Score that adjusts your actual sending capacity continuously. This does not happen weekly or monthly but as an ongoing recalculation based on how your account  behaves on the platform in real time.

The inputs feeding the system are roughly what you'd expect if you think about it from LinkedIn's side: 

• Acceptance rate on sent requests, 
• Reply rate on messages, 
• How many pending invitations you have sitting unanswered, 
• Whether you post or comment organically, 
• Lastly, your Social Selling Index.

 

Of all of these, acceptance rate carries the most weight by a lot. Accounts that are consistently above 40% acceptance tend to get access to around 200 connection requests per week. Accounts below 25% get squeezed down to 50 or fewer. Basically 4 times the outreach capacity gap between two accounts doing the same thing.

This is how it works, essentially: someone sending 50 well-targeted requests with an 80% acceptance rate will see their capacity grow over time, while someone blasting 100 requests at 20% acceptance will have theirs shrink. The platform is allocating reach based on whether your connections are actually wanted, which means the old playbook of maximizing volume first and optimizing later actively works against you now.

There are a few patterns that seem to trigger throttling faster than others. Let’s take the classic example here - sending all your requests in a single burst, or letting pending invitations pile up past the two-week mark without withdrawing them. Another factor that will throttle your account is having low organic activity while maintaining high outbound volume. LinkedIn’s algorithm interprets that, or any combination thereof, as a single purpose outreach account, and your Trust Score will drop accordingly.

If your account feels throttled and you're not sure what's happening, a quick diagnostic would be to check your SSI at linkedin.com/sales/ssi, pull your acceptance rate for the last 30 days, withdraw any pending invitations older than two weeks, and look at whether your sends are clustered or spread across the week. Acceptance rate below 25% is the clearest red flag. At that point, adding more volume just buries you further.

I should note that LinkedIn hasn't published the Trust Score as an official feature with documentation. But this is the operating model that best fits what we're seeing in the data. The capacity differences are measurable, even if the exact scoring formula is a black box on their end.