Charles Tenot

CEO @lemlist & lempire. I share what I learn as the CEO of a profitable SaaS growing to $40M to $100M+. Follow me to learn from the journey.

I love Tech, Sales, Marketing and Leadership. 

I share my learnings and thinking on LinkedIn, don't hesitate to follow & interact.

COO / General Manager

Growing lempire from 20M$ to 100M$ while generating 35%+ EBITDA

lempire products: lemlist, lemwarm, Taplio, Tweet Hunter, lemcal - more to come

I have no idea what my kids' life will look like with all this AI. 
But one thing I'm sure of: they need to understand that you can build things that other people use.

So I started vibe coding apps with them.

Last week, we built a cookie spot rating app to find the best spots in Paris.

This weekend, we made a Harry Potter sorting hat quiz (the "Sorting Hat" test).

Each one took about 30 minutes. 
And they're better than most apps you'd find online for the same thing.

I don't know what jobs will exist in 10 years. 
I don't know what skills will matter most.

But kids who grow up thinking "I can build this" will be in a better place than kids who just think "I can use this."

PS: sorry the test is in French

CEO @ lemlist & lempire

I just watched our PM build a complete feature in 2 hours.

No dev involved.

He used Claude Code to create a working mockup, test all the edge cases, then generated the PRD from the code.

This is how our entire Product team works now.

Here's the process:
→ They download the full codebase locally.
→ They use Claude Code with custom skills we built for coding best practices.
→ They vibe code their features.
→ Build the interface.
→ Test the flows.
→ Catch edge cases nobody thought of.
→ Then they use AI to generate the PRD from the working prototype.

What they deliver to Engineering:
→ A fully functional mockup that devs can test
→ A detailed PRD with user stories already mapped out

This reduces conception cycle by over 50%.

Better product decisions because they can interact with the actual feature.
Fewer bugs because edge cases are caught early.
Less confusion between Product and Tech.

Most Product teams are still writing specs and hoping devs understand.
We're shipping working prototypes before the first planning meeting.

This is what all Product teams will do in the future.

Curious to hear from other Product teams: how are you using AI in your process?

Help needed: if you are looking for a Senior Finance profile, my life partner, Violaine, is available now.

After spending 3 years building a network of kindergarten for children with neuro developmental disorders, she is now available for something new.

Why I recommend her, even though I'm clearly biased:

→ Worked with top Private Equity firms globally
Always recommended afterwards

→ Hybrid experience with Senior Finance roles (M&A, Reporting) and 3 years of entrepreneurship where she ran notably finance for their 50 people business.
She can be both strategic & hands-on, she's not the "ivory tower" CFO.

 → Amazing analytical skills, she notably worked on complex problems in manufacturing, much more complex than SaaS.

 → Great business sense, easy-going, high work ethics

What she's looking for:
→ CFO or Finance Director-level
→ Interim or part-time
→ Open to full time with flexible work location
→ Start date from March 9th

Don't hesitate to like or comment to help me get more reach, thanks a lot 🙏

→ Phone: +33 6 69 25 57 26
→ Email: vsaintromas@gmail.com

As a CEO, I'm almost never a decision maker, yet very few salespeople understand this.

I still get cold called, emailed, 10x per day.

But even when the approach is solid, the best I can do is forward it to someone on my team. 

And most of the time, I won't even do this. 
Just because I don't want to make my team think I have evaluated the solution and I recommend it, and ultimately I'm not the one who makes decisions for teams.

Teams make their own decisions.

The CEO role is about creating alignment, building the best team, and challenge them when needed. 

But I'm not the one who will say "we should buy this."

If your outbound strategy is targeting CEOs, it probably works up to a certain point. 
But past 100 employees, it might not be the best approach, unless you are actually selling to CEOs, but I can count on my hand the number of things that I decide to buy myself.

The person who feels the pain, evaluates the tools, and makes the call is almost never the CEO.  It's the C-Level, the VP or Head Of. 

My advice: don't waste your time on the wrong target.
Map your buying process, find the person who feels the pain, evaluates the options, and owns the budget.
Then spend your time preparing a great outreach for them.

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I read Cognism "Top 21 GTM Tools" blog post.

Clay wasn't in it. 
lemlist wasn't in it. 
Apollo wasn't in it. 
Salesloft wasn't in it. 
Outreach wasn't in it.
Gong wasn't in it.

The biggest names in GTM. 

All missing from a "top GTM tools" list.

That's not an accident.

When a company publishes a "best tools" list, they're not trying to help you. 

They're running a content play.

The rules are simple: 
→ Include tools that don't compete with you 
→ Include tools that make you look good by association 
→ Never mention the ones that could actually take your customers

So here's the irony.

If you're ON a competitor's "top tools" list, it means they don't see you as a threat.

If you're NOT on it, it means you're the one they're worried about.

Next time you read a "best tools" listicle, don't look at who's on it.
Look at who's missing. ✌️

Yesterday we ran a webinar about how ElevenLabs runs Go-To-Market at 330M$ ARR. 

Over 1,500 people registered. If you missed it and want the recording just drop a comment and I'll DM it.

Here's my human summary of what Jonathan Chemouny shared after 1 year there.

About ElevenLabs GTM:

→ 330M ARR, +300M in 2 years. Just valued at 11B.
→ Only 400 people. Close to 1M ARR per employee.
→ GTM team is about 100 people.
→ Org is SDR + AE but they call everyone "GTM." 
Limited titles, limited hierarchy.
→ They maximize distributed ownership per country and region. 
They hire founder-like profiles who run their region like CEOs.
→ Top performers come from Finance, even Product backgrounds (👋 Oscar Lanzendorf)
→ Their sales close an average of 20X their OTE 
(source: Carles Reina podcast at 20VC, link in comment)
→ They grew outbound from 5% to 46% of revenue in one year. 
Outbound lets them better target ideal customers and segments.
→ Average deal size ranges from 30K/year to millions for Enterprise.
→ Outbound sales cycle is slightly longer than inbound but average deal size is higher.
→ They receive thousands of inbound leads daily and built an AI agent that answers prospects 24/7.
→ Everyone does outbound, even if you're hired as a potential GM-level.

What enables their incredible success:
→ Very high hiring bar. 4 interviews, harsh panels.
→ They hire for smarts, not experience. 
They don't want people who come with playbooks. 
They want people who can build playbooks.
→ They use AI a lot but will never run things 100% AI. 
Humans think of a creative way to approach an account and craft the message. AI handles things like voice notes or AI-generated video.
→ They meet prospects and customers in-person a lot. 
Jon believes humans should do things AI can't, like building real relationships.
→ LinkedIn and WhatsApp are their primary outbound channels. Quality before quantity.
→ They don't monitor activity metrics. 
They track the number of meetings done with prospects.

And of course, they have an insanely strong product.

I hope you find this valuable. 

If you have a company or GTM person we should invite next, don't hesitate to share.

---

Bonus: we created an agent that was fed on Jon's knowledge and our webinar prep document, I'll share the link with the recording.

cc thanks Rémi Kokabi, Domitille de Saint-Exupéry Rafael Ornia Mitchell

You can beat 99% of companies by doing a few things well:

→ Set one clear vision and don't change it every 3 months 
→ Say "no" to everything that doesn't contribute to the strategy 
→ Remove low performers after weeks, not years 
→ Use your own product 
→ Talk to customers 
→ Keep reporting light 
→ Lead every meeting from the customer's standpoint

It looks simple written down like this.

But most companies don't have an execution problem. 
They just keep adding layers, processes, and distractions on top of it.

The companies that win aren't doing anything secret. 
They're just not getting in their own way.

I asked someone at ElevenLabs what makes their culture different. 
Their answer helped me understand how they went from 30M to 330M ARR in less than 2 years.

Yanek Brinkmann, one of their GTM team, told me:

"It's the closest you can get to doing your own business while still being in a job."

I've always believed that someone who works like they're running their own business has 10X more impact than someone who's just doing their job.

The difference is ownership. Real accountability. 

The kind you only get from people who actually care.

You'll get more from someone less experienced with insane grit than from someone senior who's just collecting a paycheck.

ElevenLabs understood this. 

And they still operate like that at 400 people.

Imagine 100 GTM people with a founder mindset.
→ Clear ownership on their region. 
→ Working directly with Product to adapt when needed. 
→ Flat org. No titles.

Their reps close 20X their OTE. The industry average struggles to hit 5X.

But this only works if you actually distribute ownership and responsibility. 
Not just talk about it.

I'm convinced this is how world-class companies are built now. 
And they will crush big groups with pyramidal organizations.

➡️ Later today, we're sharing more about ElevenLabs's playbook with Jonathan Chemouny. 

Already 1,350 people registered. You can still take your spot, it's free value.
No recording. We want people live and contributing.

Link in comment.

Really happy to see lemlist ranked #1 software in France on G2.

Not because of the ranking itself but because of what it took to get here.

The last few months have been the strongest innovation pace we've ever had. 

And all of it comes from listening to our 20,000+ customers every single day.

Sales is still one of the hardest jobs in most companies. 
No product has made it easy yet. Including ours.

But that's exactly why we keep building.
Let's keep going.

Big up to the team ❤️

I have data-backed proof that spray & pray doesn't work.

It comes from 350M+ emails and 4.5M LinkedIn messages sent with lemlist over the past months.

This chart represents our 20,000+ customers.

From pre-revenue founders to companies with 300M$+ in ARR and 100+ sales reps.

How to read it:
→ Horizontal axis: our 10 deciles of customers, from the 10% with the worst results to the 10% with the best
→ Each bar: the volume of messages sent
→ Vertical axis (line): the average reply rate

What we see:
There's a clear negative correlation between volume and reply rate.
The fewer messages teams send, the higher their reply rate.

In detail:
→ The first group shows that the bottom 10% aren't sending enough messages, or just lack adoption. They'll probably churn unless they use lemcoach.
→ Then across the other 9 deciles, each one has lower volume and a higher reply rate than the previous one.

If this doesn't show you that it's better to do less but better, I don't know what will.

Spoiler: it's not the spray & pray "AI SDR Agent" that will solve this.

Every Tech company board is the same: "We need to go Upmarket"

The reason is usually financial. 

Going upmarket increases "Enterprise Value", because is usually means higher retention.
And of course, investors want to increase enterprise value.

But going upmarket at the expense of losing your historical market isn't always the right call.

You might lower churn. 

But you can lose growth and end up struggling.

This is why at lemlist, we spent over 9 months debating if we should go upmarket or not. 

Because serving small customers was always important to us for many reasons:
→ They trusted us first
→ More customers = bigger brand
→ Small companies can become big ones with time
→ Serving emerging businesses drives real purpose 
→ Startups are usually the innovators, they push us to keep improving the product

We finally decided to build lemlist for sales teams and go upmarket, but we decided to do it slowly, not as a pivot, but as a continuous progress of the platform. 

And it paid out because today we have customers from 1 to 150 users.
We have grown by +70% in 2025 while being profitable, and across all segments.

After seeing from the inside 4 different companies go upmarket, I wanted to share my learnings:
Wednesday with Lucas Bédout from Hyperline, we are organising a meet-up in Paris.

Here's a preview of what we'll talk about:
→ Why it took us 9 months to decide between going upmarket or continuing to build a suite for founders 
→ The stages we went through, mistakes we made, and what we did right 
→ Key people we hired (hey Yann Guilleux) 
→ How we run inbound & outbound, and how we use our own product 
→ How we grew the sales team from 1 person (hey Tal Baker-Phillips) to 30+, across 3 areas and 2 roles (AE & Inside Sales) 
→ How we signed our first 100K+customers, coming from 1K$ARPA 3 years befre
→ How we monitor productivity & measure performance 
→ What we built in the product to maximize conversion & score leads 
→ How we organized RevOps & Tools 
→ Our Sales Quota, Payback and how we maintain <3 months payback while adding Sales headcount

Bonus: how we increased ARPA by +30% in one month by changing the wording of one question in our product onboarding 🔥 

Free event. Paris. March 11.
Link in comments.

cc: thanks Antoine Leprince for the organization 🙏

Slack, Figma, Notion, Dropbox, Datadog. They all started PLG. 
None of them stayed PLG. Here's why:

PLG is great for small customers. 

Low CAC, high scalability, self-serve magic. 

But as your product improves, you start attracting bigger companies. 
And enterprise buyers don't convert on PLG products. 
They need help. They need sales.

This is where PLG + Sales-Assisted comes in. 

And I'm convinced it's the ultimate state for SaaS:
→ PLG for small customers. Low CAC, fully self-serve, scales beautifully.
→ Sales-assisted for larger customers. You can segment by geo, industry, company size and organize your team around it.

This combination maximizes conversion across every customer segment.

Since we added a sales motion at lemlist, our growth has never been stronger. 

We grew +70% last year. PLG + Sales-Assisted together.

But here's the hard part:

In PLG, everything is streamlined: credit card payment, automated provisioning and payment retries.

But when sales gets involved, you start dealing with:
→ Custom quotes
→ Custom terms and legal discussions
→ Discounts and approval workflows
→ Custom billing terms and payment methods

You go from a scalable model to a non-scalable model overnight. 

And that's where most companies struggle.

The good news is that with the right tech, you can now build scalable systems even for sales-assisted motions.

On March 11, I'm co-hosting an event in Paris with Lucas Bédout (CEO at Hyperline) and the Business Operations Network to dig into exactly this. 

How to build a hybrid revenue engine that scales.

Free for BON members. 

Link to register in comments.

cc Victoria Dalleau 🙏

Nothing prevents you from sending 10 follow-ups. 
And yes, you'll get more replies.  But it's doing invisible harm to your outreach.

If you look at reply rates by number of follow-ups, the pattern is clear. 
Each follow-up increases your chance of getting a reply. Even a little.

But the incremental gain drops fast. 
After 4 follow-ups, you're barely moving the needle.

And here's what most people miss.

Every extra follow-up increases the chance your leads mark you as spam. 

Not by replying. 
By hitting that little spam button because they're annoyed.

When that happens, it hits your domain and email reputation. 
Your emails start landing in spam more often. 
Not just for that lead. For everyone.

This is the classic trap where short-term gains quietly destroy long-term results.

More follow-ups today = Fewer emails delivered tomorrow.

As a CEO, when people agree with me too easily, I don't take it as a compliment. I take it as something to question.

Things I don't expect from people in the team:

→ Telling I'm right
→ Liking my LinkedIn posts
→ Being overly positive on my ideas and initiatives
→ Nodding in meetings
→ Sugarcoating bad news
→ Using my notes / inputs as is without re-working them
... and certainly not telling me I'm smart, or giving any compliment

Here's what I actually value:
→ Saying what you think
→ Thinking on your own before defaulting to the group
→ Coming to me when you disagree or don't understand, especially when it's uncomfortable
→ Bringing problems early and as they are
→ Having a point of view and defending it

I recently listened to a podcast from an experienced CEO who said his biggest mistake was surrounding himself with people who gave him compliments but weren't truly competent at their jobs.

The best people I've worked with were never those who constantly agreed with me, they were the one who made me change my mind.

We’re organizing a webinar with of the brains behind Ramp’s GTM systems, now building Perplexity’s Enterprise growth engine. 

Nathan spent 3.5 years at Ramp building the business systems behind one of the most successful sales-led companies in the world.

Now he's Head of Enterprise Ops at Perplexity, building the GTM machine of an AI company. From the inside.

I'm really excited about this one. Nate is an absolute GTM master, and we're very lucky to have him.

There are hundreds of LinkedIn posts telling you to comment "Claude" to get an AI config that runs your GTM on autopilot. 
We've tried many of them and the results are always disappointing.

Over the past months, I've interviewed 50+ companies to understand how they actually use AI for sales. 
The reality is far from what you see on LinkedIn, most sales teams are still doing things manually, with a ChatGPT tab open on the side.

Nate is different: he actually built AI-powered GTM systems that people use every day, and that deliver real ROI. 

Here's a preview of what he shared with us:
→ Reps used to spend 30 min prepping each account. 
Now they trigger deep research in natural language and get a full brief in minutes.

→ Outreach used to be {first_name} + generic template. 
Now it references real strategic context. 
Earnings calls, hiring signals, tech stack changes.

→ RevOps used to own every playbook. 
Now the rep is the operator. No bottlenecks. No stale processes.

→ And some things they tried but decided not to release, usually because AI can prep you, but AI can't be you 👀 

He's going live with us to walk through the exact workflows. 
Not theory, but how things actually runs in production.

If you lead Sales, RevOps, or GTM, this one's worth your time.

Link in comments.

VIP hosts: Pierre Touzeau and Rémi Kokabi  🔥

We have 2 data analysts at lemlist, but one is paid in tokens and never sleeps. 
It's's called Squilly. 

Squilly is our AI data analyst. 

It runs queries, builds dashboards, and answers data questions from anyone on the team. 24/7.

You don't have to wait that someone has time to handle your query.

What's good with Squilly is that he qualifies your need before answering.
As you can see in the video, it always asks what exactly I'm looking to know.

You can see that it shows you the query & the cost too, pretty useful.

Squilly now handles a significant share of our data requests.
It's pretty cool because it manages simple, low value requests and our Data team can actually focus on more complex tasks. 

Employees have answer faster as it removes the ticketing logic.

It's the same pattern I already shared with AI managing 56% of our support tickets, AI does a great job to manage most simple tasks, leaving human with more value-adding things to do. 

Big up to the Data team 👏

This week, a bot fixed its first production bug at lemlist. 

Here's what happened:

A user double-clicked a delete button. That sent two requests at the same time.  The API returned a 409 conflict error.

Our bot detected it in the logs, investigated the root cause, traced the code path, wrote the fix, and submitted a pull request with the right reviewers assigned.

Total human involvement: reviewing and merging the PR.

In the meantime, we also had 3 features shipped to production by... Product Managers.

A year ago, we never thought this would happen so fast.

I was talking to a friend, a developer with 20+ years of experience.
He told me his workflow has completely changed.

Before, he spent most of his time in "flow." Going from one coding step to the next.
Now, he spends more time thinking about the overall architecture. 
Then he works with AI to develop the different parts. Then he reviews the code. Makes sure the logic is right.


And yet, we're still hiring developers.

Because our bet is that the job isn't disappearing. It's shifting. 
And good developers will always be a scarce resource required to build great products.

You could argue AI will improve so fast it won't need developers at all.

My gut says you'll always need people who understand systems deeply enough to work with AI.

But we'll see. What's your take?

Sending a LinkedIn message in a sequence increases email reply rate by 38%. 
And no, that's not a typo. I'm talking about email replies. 

LinkedIn reply rates are averaging 15%, vs. 1-3% for emails (up to 15X higher)

But here's what we found across 35M leads:

When you add LinkedIn steps to an email sequence, your emails start getting more replies too.

The prospect sees your name on LinkedIn. 
Maybe they check your profile.
Maybe they read your message.

Then your email lands, and you're not a stranger anymore.

That familiarity is worth +38% on your email reply rate.
This data is coming from 35M leads that we analyzed at lemlist.

This is pure facts that multi-channel is the right way to do outbound today, and why we build lemlist to include as many channels as possible.

---

6 examples why short-term growth almost always comes at the expense of long-term growth:

→ Hiring the least worst candidate because you need someone now vs. waiting for the best even if it takes 6 more months. 
Impact compounds. An A-player generates 100X more value than a B-player.

→ Sending 10 follow-ups to squeeze out more replies vs. stopping at 4. 
Each extra follow-up barely moves the needle but increases spam reports. 
Your domain reputation takes the hit and deliverability drops for everyone.

→ Blasting your entire TAM in one month to hit pipeline targets vs. working accounts in waves. 
You get a short spike, then you've burned your brand with every account.

→ Cutting product investment to boost short-term margins or hire more Sales. Customers don't leave immediately. 
They leave 12 months later when a competitor ships what you didn't.

→ Discounting to close a deal before quarter end vs. holding your price. 
You hit this quarter's number, but you've trained the buyer to expect discounts. Next renewal, they push harder. 
They talk to their friends. 
And your average deal size erodes across the whole pipeline.

→ Not promoting a great rep with manager potential because they're bringing in short-term revenue. 
They'll leave 6 months later. And you'll end up hiring someone less good.

Every one of these looks like the right call in the moment. The cost just shows up later.

That said, there are moments where you need to move fast and accept some debt. 

The interesting part is knowing when you're making a conscious trade-off vs. when you're just not seeing the long-term cost.

Most outbound teams find out about deliverability issues weeks too late. 

By then, the damage is done, your pipeline is already melting.

Then what I call the "circle of short pipeline" starts:
→ Reps have fewer deals
→ They desperately want to close the ones that are open
→ They start looking desperate
→ Prospects feel it
→ They ghost you
→ You lose deals you could have won
→ CRM reason: "no decision"

This is why fixing deliverability issues fast is critical.

But the problem is getting visibility on what's actually happening. 

Deliverability depends on multiple factors: technical setup, domain and email reputation, copy, data quality, bounce rates, and more.

This is why we just shipped the Deliverability Hub in lemlist.

One place to see everything:
→ Delivered vs. spam for warm-up emails
→ Delivered vs. bounced for outreach emails
→ Deliverability scores by mailbox and domain
→ Reputation issues spotted early, before they tank your results
→ Filters by timeframe, mailbox, and provider

No more guessing. No more juggling. 
Everything is in one place.

We already have great feedbacks about it, kudos to the team 🔥

Scale revenue with AI & tech

I decided to skip the traditional corporate path & start building in the AI x Sales space at 27. 
Here's the journey so far:

Age 18-21: Business school in Brussels, realized I needed practical experience
Age 22: Coding bootcamp + lucky Bitcoin exit gave me 50K to fund a world trip
Age 23: 12-country world tour learning new skills every month in a new country.
Age 24-26: Growth hacker roles at German startups, learned systems but felt unfulfilled 
Age 27: Co-founded ColdIQ with Mich
Age 28: Hit our first major milestones learning everything the hard way 
Age 29: Scaled to $6M ARR and became Clay's #1 global partner. Running a business that does $477K/month with 30+ team members across 4 continents.

It's been wild. Today through ColdIQ, we help B2B companies build AI-powered sales systems that work. 

We've run outbound for 300+ organizations and generated millions in pipeline.

The best part? We built this entirely remote with people I genuinely enjoy working with. 

Turns out you can have rapid growth AND life quality - you just need the right systems.

If you want to build your own AI sales agency, I teach everything in our ColdIQ Agency Accelerator, the exact playbook we used to hit $6M ARR in 2 years. 

From Clay workflows to hiring A-players to scaling without burning out.

If you're already doing $1M+ ARR and want help scaling your existing business, let's talk about our done-for-you services.

https://coldiq.com/

If you're not there yet, I share everything for free on LinkedIn and in our 7-day email course (in the featured section).

The AI x Sales opportunity is the biggest wealth creation event of our generation. The market is so massive that there's room for everyone to win.

Ready to start your agency? Join 130+ founders in our accelerator.

https://coldiq.com/accelerator-program

Alex Vacca 🧠🛠️

I built Claude Skills that create a full GTM team, generate campaign ideas, and write cold email sequences for you.

All inside Clay. Without switching tools.

Instead of sharing "AI prompts."
I’ll share the skill files that actually work inside Clay in our $7M Agency.

Generic prompts give you generic outputs.
These are different.

Each skill file is built for a specific Clay workflow:

→ Company Research Agent
→ Personalization Writer (that doesn't sound like AI)
→ ICP Scorer
→ LinkedIn Profile Analyzer
→ Data Cleaner & Normalizer
→ Objection Handler
→ Email Sequence Writer
→ Competitor Analyzer
→ Job Posting Analyzer
→ Technographic Qualifier
→ News & Signal Synthesizer
→ Account Brief Generator

Here's how it works:

Drop it into Clay → Map your columns → Run.

Built from hundreds of Clay tables that got us to $7M ARR.

And today I'm giving the full pack away for free.

Want them?
Comment "SKILLS" and I'll DM it over.

Co-Founder @ ColdIQ ($6M ARR) | Helped 300+ companies scale revenue with AI & Tech | #1 AI Sales Agency

We built an ABM system that generated $2M in pipeline in 90 days.

Here's the 4-step playbook behind it.

Step 1: Build a high-potential account list

We looked at our closed/won deals and reverse-engineered the ICP from there.

Then we found lookalike companies, qualified them, and enriched the contacts.

1,250 accounts made the cut and were added directly to our LinkedIn Ads campaign manager.

Step 2: Create thought leadership content worth promoting

We went through our last 100 qualified sales calls.

Pulled out the exact pain points, objections, and language prospects were using.

Then studied what was already performing well in the niche.

That became the content engine, published across every active leadership profile in the company. 

Step 3: Turn organic wins into paid distribution

Every post that performed well organically became an ad.

Promoted directly in front of those 1,250 target accounts.

We tracked the pipeline through UTMs and monitored the full journey from impression to engagement to pipeline.

After about a month of warming up the audience, we layered in mid-funnel and bottom-funnel retargeting.

Step 4: Warm outbound on engaged accounts

We monitored every like, comment, and website visit from target accounts.

De-anonymized traffic. Identified qualified leads.

Then routed them into automated outbound sequences.

The result:

- $2M in pipeline.
- Under 90 days.
- Each channel feeds the others.

Ads warm up outbound. Content fuels ads. Outbound re-engages the hand-raisers.

It's not about picking one channel.

It's about making them work together.

What's your biggest challenge with ABM right now?

Most people think that impressions = revenue.

Here is what to do instead: 

Most people see content funnels and think they just need to follow the formula.

40% awareness, 30% consideration, 30% conversion.

Looks great in a strategy doc. 
But here's what actually happens:

You're so busy optimizing percentages that you forget to make content people actually want to read.

I learned this the expensive way at $2M ARR.

Posts hitting 200k impressions with zero pipeline.

Because the content was checking boxes instead of starting conversations in my client's mind.

There’s one shift that took us from $2M to $7M ARR in under 2 years:

We stopped asking "what funnel stage is this?" and started asking "would I actually stop scrolling for this?"

Here's the structure that worked, but not how you think:

→ Top of Funnel (40% of your content)

Yes, get attention. Get people to know you exist.

But if your viral content isn't tied to your expertise, you're just collecting the wrong audience.

Generic thought leadership that 500 other people posted this week it’s not awareness. It’s noise.

→ Middle of Funnel (30% of your content)

Show your actual work. 

Walk people through your process, your thinking, the results you've driven.

This isn't about proving you're smart. It's about showing them you get it.

Most "consideration" content fails because it reads like a sales pitch disguised as education.

→ Bottom of Funnel (30% of your content)

Be direct about who you help and how you help them.

Most people skip this entirely because they think it's too "salesy."

That's exactly why their content never converts.

But here's the thing:

The content that drove the most revenue for us didn't fit neatly into these buckets.

Some posts drove awareness and conversion at the same time.

Others built trust by being brutally honest about what didn't work.

The posts that made people say "these guys actually get it" did more for our pipeline than any perfectly orchestrated funnel.

Stop optimizing the structure and start making each post worth reading.

What's your technique to transform impressions into revenue?

I'm doing something stupid.

Sharing my entire AI GTM resource library.
In a Notion file. For free.

What's Inside:

→ Best AI Sales Tools, B2B Data Providers & Tech Stack for 2026 
→ 37 Best Performing Sales Triggers (the ones that actually book meetings)
→ Cold Email Infrastructure Setup Guide (deliverability frameworks we run for clients)
→ Campaign Strategy Templates (HOT Copy Framework, lead magnets, trigger-based outreach)
→ Clay templates, GPT recommendations, and content ideation systems

It's a Notion doc. With sections. And a lot of tactical information.
But it's what we actually use at ColdIQ to run GTM for 80+ clients.

When I need to:

→ Build a campaign from scratch for a new client
→ Fix someone's cold email deliverability in under 24 hours
→ Remember which sales trigger converts best for SaaS vs agencies
→ Compare data providers without wasting 3 hours on research

I open this library. That's it.

So if you want it, comment "𝐋𝐈𝐁𝐑𝐀𝐑𝐘" below.
I'll DM you the link.

Save this post if you think you'll need it later. Share it with someone scaling GTM.

If I wanted to turn my entire company into LinkedIn marketers tomorrow, here's the exact playbook I'd follow.

Not hire a ghostwriter. Not ask the marketing team to "post more."

You have to implement a real system.

Step 1: Get every customer-facing team member posting.

Not just the founder. SDRs, AEs, CSMs. Anyone who talks to prospects or customers has stories worth sharing.

The goal isn't polished thought leadership. It's genuine insights from people doing the work every day.

Step 2: Centralize the scheduling.

You can't manage 5-10 people posting across LinkedIn natively.

I'd move everyone into Taplio so there's one place to schedule, review drafts, and track what's performing.

The analytics layer matters here. You need to see what's working across the whole team, not just individual accounts.

Step 3: Build shared engagement lists.

This is where most companies stop and it's where the real leverage starts.

I'd create lists of target accounts, partners, and industry voices inside Taplio.

Then every team member spends 15 minutes a day engaging with posts from those lists. Meaningful comments. Real takes. Not "love this."

When a prospect sees 3 different people from your company showing up in their feed with valuable insights, something shifts. You're not a cold outreach anymore. You're a brand they already recognize.

Step 4: Run a weekly content review.

Every Friday, pull analytics for the team. Which hooks worked. Which formats drove comments. Which posts generated inbound DMs. Double down on what's working. Kill what isn't.

At ColdIQ, our team publishes 200+ content pieces a month. That contributed directly to scaling to $7M ARR.

But it didn't start at 200. It started with a system.

The volume came after.

How many people on your team are posting on LinkedIn?

I just crossed 60,000 followers on LinkedIn with this exact tech stack

1. Taplio (Post ideation)

This is where I discover high-performing posts in my niche to draw inspiration from.

I break down what made them successful and adapt those insights to my own content.

2. Scripe (Team analytics)

I analyze viral content from top creators in my space.

It also lets us monitor our entire team's posting performance so we can replicate what's actually working.

3. Grammarly (Writing assistant)

Fixes my typos and grammar errors while drafting posts.

4. Canva / Figma / Adobe (Visual design)

Every post I publish includes a visual element.

We've developed a distinct visual style that audiences now recognize and connect with my brand.

5. Claude (Content brainstorming)

Helps me generate fresh hooks, rework successful content, and discover different angles for posts.

It's also great for understanding why certain posts went viral on LinkedIn.

6. Screen Studio (Screen recordings)

Creates professional-looking screen captures.

It automatically focuses on whatever part of the screen you're working with.

7. Ezgif (GIF creation)

Converts my screen recordings into GIFs.

I typically accelerate videos to stay under 10 seconds. This makes people replay the GIF multiple times to catch all the details.

8. Notion (Content planning)

My hub for organizing my content calendar, storing ideas, and writing drafts.

It's where all my content strategy lives.

What tool am I missing?

tools to 10x on LinkedIn 

We've built GTM systems for 267+ B2B companies. Most were running 20+ tools. They didn't need half of them.

The teams that close the most deals don't have the biggest stack. They have the leanest one.

Here's what actually works in 2026 across every layer of the revenue stack:

1) CRM 

We started on HubSpot. Data lived in 3 places. Nothing talked to each other. Now we run everything on Attio. If your team is under 100 people, you don't need a $50K/year CRM. You need one that plugs into your stack without a 6-month implementation.

2) Data Enrichment 

In 2021, you'd pay ZoomInfo $30K/year and still manually verify half the data. We replaced all of that with Clay. One workflow chains 50+ data sources. FullEnrich for phone numbers. What used to take a team of VAs a full week now runs in minutes.

We literally used to hire virtual assistants to write the first line of every cold email by hand. Clay killed that workflow overnight.

3) Outreach

This is where most teams still burn money. In 2021, sending 500+ emails a day was standard. 

The numbers tell the story: the bottom 10% of outbound teams send the most volume for the fewest replies. The top 10% send micro-campaigns and achieve the highest positive reply rates. Same tools. Opposite strategy. Completely different results.

We use Instantly.ai for cold email with warmup and inbox rotation built in. 50 targeted emails beat 500 spray-and-pray sends every time.

4) Intent Signals 

This category barely existed 3 years ago. Now it's the backbone of what we do at ColdIQ

We don't prospect cold anymore. We stack signals: did the company hire recently? Are they hiring a GTM engineer? Did headcount grow 20% in 6 months? Did they raise a round?

We use RB2B to identify anonymous website visitors at the person level, then push that into Clay and trigger outreach automatically. Cold outbound is dead. Warm outbound is all we run.

5) CPQ

Salesforce CPQ entered end-of-sale in March 2025. Most of our clients were quoting with spreadsheets and PDFs anyway. Hyperline now connects CRM to billing natively.

6) Billing 

67% of SaaS companies now use usage-based pricing. Legacy billing tools weren't built for hybrid pricing, subscriptions plus usage in one system. Hyperline handles this natively. 

7) AI & Automation 

This layer changed everything for us. In 2021, "AI in sales" meant lead scoring models nobody trusted.

Now AI runs the infrastructure. At ColdIQ, one GTM engineer can manage 10 clients. Three years ago that number was 3. We use Claude Code to build internal tools, research accounts, and write personalized copy at scale. Clay orchestrates the data layer. n8n connects every workflow in between.

—

The 2021 approach: bolt together 20 tools and hope the data syncs.
The 2026 approach: build a connected revenue system where every layer feeds the next.

What's the most outdated tool still in your stack?

This AI onboarding system has saved us $29,013 → off a single deployment.

I wanted to simplify the onboarding of new clients without relying on 10 different tools or thousands of manual tasks.

When ColdIQ passed 30 active clients, onboarding started breaking.

4 hours per client. Contracts going out late. Payments chased manually. Folders set up wrong. Team finding out last minute.

I didn’t want the team to lose time on tasks that could be automated.

So I built an AI agent that runs the whole thing:

→ Contracts sent and signatures tracked automatically
→ Invoices are fired the second a deal hits "Closed Won"
→ Drive folders and CRM records created instantly
→ Slack pings sent to the right people
→ Kickoff call booked
→ Welcome sequence goes out to the client

It runs 24/7 without missing a step.

The result?

→ 12 minutes instead of 4 hours
→ 0 errors across 47 onboardings
→ Team doing actual work, not admin
→ Every new client gets a clean experience from day one

Another agency saw it in action and wanted the whole thing.

The agents. The workflows. The logic.

Why am I sharing this?

Because this system can transform how other companies manage their onboarding, and I also benefit from every implementation.

You win. I win.

I'll send you the full workflow:
→ Same tools
→ Build order
→ Logic

Want to duplicate it in your business?

You can get access to the whole agent here: https://lnkd.in/dKAyEgGM

Most AI explanations overcomplicate things. Here are the 7 layers of Agentic AI explained so simply that your entire team will get it:

Think of AI capabilities like building blocks.

Each layer adds capabilities that the one below it can't provide on its own.

Here's the stack:

Layer 1: Machine Learning (The Foundation)

This is where AI learns patterns from data.

→ Supervised learning (teaching with examples)
→ Unsupervised learning (finding hidden patterns)
→ Reinforcement learning (trial and error optimization)

Think: Apollo's lead scoring, Instantly.ai's send time optimization

Layer 2: Large Language Models (The Processor)

Now AI understands and generates human language.

→ Processing massive text datasets
→ Understanding context across conversations
→ Handling multiple input types (text, images, audio)

Think: ChatGPT, Claude, Gemini

Layer 3: Generative AI (The Creator)

AI creates new content, not just analyzes it.

→ Writing code and content
→ Generating images and videos
→ Converting speech to text and back

Think: Midjourney, ElevenLabs.

Layer 4: AI Agents

AI that can use tools and execute tasks.
→ Breaking down complex requests into steps
→ Calling APIs and using functions
→ Remembering context between actions

Think: Claude with computer use, GPT with function calling

Layer 5: Agentic Systems (The Team)

Multiple specialized AI agents working together.

→ Each agent has a specific role
→ They coordinate and share information
→ Parallel execution of different tasks

Think: Lindy, Relevance AI

Layer 6: Agentic AI (The Strategist)

Agents that run autonomously for extended periods.

→ Long-running operations without human input
→ Self-correction based on results
→ Adjusting approach when something fails

Think: AI SDRs running full outbound sequences Artisan, 11x

Layer 7: Governance & Safety (The Guardrails)

What keeps everything from going off the rails.

→ Human oversight at key checkpoints
→ Cost limits
→ Kill switches

Think: Enterprise AI deployment infrastructure

Most companies are stuck at Layer 2-3.

The ones moving fastest are building at Layer 5-6.

Where's your team right now?

—
Repost to share it with your network
Follow Alex Vacca for more insights on how to scale revenue with AI and tech

The agency model as you know it is about to become obsolete.

And Y Combinator just confirmed it.

In their Spring 2026 Request for Startups, they officially listed AI-native agencies as one of the biggest opportunities they want to fund.

Their words: "Agencies have always been crazy hard to scale. Low margins, slow manual work, and the only way to grow is to add more people. But AI changes this."

This isn't some random prediction. 

This is the fund behind Stripe, Airbnb, and OpenAI telling the world: the agency game is about to change forever.

Here's what we figured out early:

The agency world is splitting into two completely different businesses.

Camp A: Agencies that sell hours.
Camp B: Agencies that sell systems.

We chose Camp B.

We don't scale by hiring more SDRs, researchers, or copywriters. We scale by building AI workflows that do in minutes what used to take a team days.

Lead research. List building. Personalization at scale. Signal tracking. Follow-up sequences.

All running on AI-powered systems now.

And YC is saying exactly what we've been experiencing, these agencies will start looking more like software companies. With software margins.

The model works like this:

→ Services fund the development
→ Repetitive client work becomes automated IP
→ The IP becomes the real asset

Most agency owners will read this and go back to hiring their next account manager.

But a few will realize the window to make this shift is right now.

YC doesn't add something to their wishlist unless they see a massive market opportunity forming.

If you run an agency and you're still scaling with headcount instead of systems, this is your wake-up call.

Repost with your network so they can take advantage
Follow Alex Vacca 🧠🛠️ for more about how to scale revenue with AI and Tech

Our business hit $7M ARR in 3 years.

No VC money. No outbound team when we started.

All from one thing: content.

One post changed everything.

23 inbound meetings. From a single LinkedIn post.

That was the moment I realized:

Content IS the pipeline.

ㅤ
But most people treat LinkedIn like a job board.

They post what they want to talk about.

Not what their buyer needs to hear.

So we built a system around it.

ㅤ
1/ We stopped posting about ourselves.

Started answering the exact questions our ICP was already asking in Slack channels, Reddit threads, and sales calls.

If they were struggling with list building, we broke down our entire process.

Step by step.

ㅤ
2/ We picked 4 content pillars and never deviated.

→ GTM systems and strategy
→ Tech stack breakdowns (tools we actually use)
→ Workflows and automations (screen recordings, walkthroughs)

Every post had to fit one of these four.

ㅤ
3/ We gave away everything.

Templates. Frameworks. Playbooks.

The stuff agencies charge $5K+ for.

People said we were insane. "You're giving away your IP."

But here's what happened:

→ 100+ inbound meetings every month from LinkedIn alone
→ 267+ B2B clients served without sending a single cold DM
→ Pipeline that compounds every month because old posts still drive traffic

When you teach someone something valuable, they don't want to figure out the rest alone.

They want YOU to do it for them.

ㅤ
4/ We turned content into a flywheel.

Content → Engagement → Warm signals → Outbound → Retargeting → Conversion.

All of that becomes signal.
Signal becomes outbound.
Outbound becomes pipeline.

That's how you build a $7M ARR business from content.

ㅤ
If you want to see how our full GTM Flywheel system works and how content fits into it.

Click here: https://lnkd.in/dy8dMZ8e

Lessons from scaling to 7M ARR

We've built 3200+ outbound campaigns at ColdIQ.

Here's how we ideate every single one:

𝗦𝘁𝗲𝗽 𝟭: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗜𝗖𝗣 𝗠𝗮𝘁𝗿𝗶𝘅

Every client gets one in before we touch anything.

We look at:

→ Industries where they already have traction
→ Company sizes they typically win
→ Titles that actually buy (not just influence)

If there are multiple segments, we pick the one with the most clarity first.

𝗦𝘁𝗲𝗽 𝟮: 𝗙𝗶𝗻𝗱 𝗹𝗼𝗼𝗸𝗮𝗹𝗶𝗸𝗲𝘀 𝗼𝗳 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

Simple question: "Who else looks like the people already paying you?"

We run Clay enrichments to find patterns.

Industry, headcount, region, tech stack.

Then we use Ocean.io to build a match list.

We're looking for repeatable wins. Not unicorn edge cases.

𝗦𝘁𝗲𝗽 𝟯: 𝗟𝗮𝘆𝗲𝗿 𝗶𝗻 𝘁𝗿𝗶𝗴𝗴𝗲𝗿𝘀

The best campaigns don't just target the right person.

They target the right person at the right moment.

Triggers we look for:

→ Fundraising rounds
→ Headcount growth
→ New tech adoption (Salesforce, HubSpot, Shopify)
→ Job openings in key roles
→ Leadership changes (new VP of Sales, new CRO)

Combine triggers with firmographics and the campaign gets tight.

𝗦𝘁𝗲𝗽 𝟰: 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗹𝗮𝘂𝗻𝗰𝗵

Before we build anything, we ask 3 things:

→ Is there enough volume? (at least 500–1,000 accounts)
→ Does it align with what the client actually wants to sell?
→ Have we confirmed the idea with the client?

Alignment > Assumptions. 

𝗦𝘁𝗲𝗽 𝟱: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗱𝗼𝘁𝘀 𝗶𝗻𝘁𝗼 𝗰𝗼𝗽𝘆

Now you have the persona, the trigger, and the data.

The last step is writing copy that speaks directly to their situation.

Reference the trigger
Address the pain it implies. 
Then offer something that solves that exact problem.

Generic copy talks to everyone.
Trigger-based copy talks to one person at the right time.

That's the difference between a 2% and a 10% reply rate.

Most teams skip ideation and jump straight to copy.
That's why most campaigns underperform.

The best outbound starts before the first email is written.

What else would you add?

30M impressions. 100 inbound meetings/month. It wasn't enough. Here's what we were missing.

We just added $506K in contract value from SEO and AI search in 4 months.

Here's how it happened.

Buyer research has changed fast. More people are using ChatGPT, Perplexity, and Claude to solve problems, compare vendors, and build shortlists.

All before they ever talk to sales.

For us, that meant one thing: If someone is searching for the problems we solve or evaluating agencies like ours, we need to show up in those decision moments.

4 months ago we started working with Reach AI to turn SEO and AI search into a real growth channel. Not top-of-funnel traffic. High-intent searches where people are actively researching solutions and shortlisting partners.

Here's what changed:

→ Organic + LLM search combined generated $506K in contract value
→ LLM search traffic grew 7.6x in 3 months
→ Organic search traffic grew 7.3x in 4 months
→ Combined traffic share went from 16.5% to 33.3%, doubling in 3 months
→ SEO and AI search is now our #1 traffic source
→ Ranking keywords increased by +41,470 (+983%)

And this doesn't even fully capture the AI search impact on direct traffic.

Here's the truth about attribution: It's messy. Anyone claiming perfect tracking is either naive or trying to sell you something.

In practice, we track first click and last click in UserMaven, look at assisted conversions, and ask prospects on sales calls where they first heard about us. Then we log it in the CRM. That's enough to guide decisions without pretending the model is perfect.

But it still undercounts AI search. A prospect discovers ColdIQ through an AI answer, then later Googles "ColdIQ" or types coldiq.com directly. The original AI touchpoint disappears.

Shoutout to José Velez, Bernardo Ferreira, and the Reach team. 

They pull real questions and intent signals from where buyers actually learn and decide. Then they narrow it down to the keywords and prompts that really matter for pipeline.

From there, they monitor how different AI models respond, reverse-engineer what patterns win, and adapt as models evolve.

That's what makes SEO and AI search feel less like guesswork and more like a repeatable system.

If you're a B2B company wondering whether SEO and AI search optimization can actually generate pipeline: This is the most concrete proof point I've seen.

Let me know if you want an intro.

Agency owners doing $5k to $50K a month, this will sound familiar.

You can sign clients. That’s not the problem.

The problem is every time you push past a revenue ceiling, fulfillment cracks.

New clients come in, quality drops and the team starts to struggle.

Aaron Shepherd at GrowthFlare Inc. went through this.

- His team now handles 2x the clients per person.
- Delivery works across multiple industries, not just one niche.
- Issues get caught before they cost revenue.

$30K to $50K MRR. On track for $100K.

The constraint was never talent or demand.

It was how operations are run inside the business.

I sat down with Aaron to walk through the full story. You can watch it on YouTube.

Do you want to add $20K–$50K in MRR over the next 90 days?

Click here to see the 3 GTM systems we help agency owners implement: https://lnkd.in/dUecHPeG

How do you 10x your pipeline with outbound + ads running together?

We're showing you on Thursday.

At ColdIQ, we run outbound and paid media as one motion. Not two separate teams hoping for the best.

Same accounts. Same messaging. Same timing.

We've done this across 70+ B2B clients.

On March 12th, we're breaking down the exact playbook with AirOps. Live.

What we'll cover:

Account-Based Outbound
→ Building and tiering target company lists with Clay
→ Syncing outbound and LinkedIn Ads to hit the same accounts
→ Signal-triggered plays that actually convert

Thought Leader Ads
→ Why we put 80% of ad budget on founder content, not brand ads
→ Creating webinar-based outbound plays
→ Cross-channel feedback loops between outbound and paid

The Results
→ 10x ARR growth
→ $10M+ qualified pipeline
→ 15x pipeline ROI
→ 164 deals, 30 closed-won
→ 1,058 outbound replies

We won't make it high-level, we will show the entire playbook we run every day.

Hosted by Ivan Falco (Head of Growth at ColdIQ) and Eoin Clancy (VP of Growth at AirOps).

March 12th. 1:30 PM EST.

Link to register below 👇

https://lnkd.in/dVSm55ki

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