Chez ESSI Marketing, j’aide les entrepreneurs, dirigeants et experts à structurer leur vision et à transformer leurs ambitions en stratégies marketing concrètes. Mon approche s’adapte à vos besoins, à travers 3 offres modulables : 
🌱 SEED - fondations stratégiques
🌸 BLOOM - déploiement opérationnel
🚀 FIELD - accélération de la visibilité

Sophie Martres

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

Last week, I asked everyone at lemlist to always write a human-generated 1-pager with every internal note they share.

We've seen huge AI adoption at lemlist in the past months, and I'm genuinely proud of how fast the team embraced it. 

I'm still bullish on AI and how it can impact productivity. 

But it's a threat too: the threat is that people stop thinking on their own, stop challenging what AI produces, and that their job becomes just orchestrating AI-generated intelligence.

I've started to see internal notes that are obviously 100% AI-generated, with not enough actual thinking behind them, and sometimes even mistakes.

AI has made it possible to produce 20 pages in 10 seconds, and you can literally bury anyone under content that says nothing.

More output doesn't mean more clarity. It often means the opposite.

So we're going back to basics: every internal note now needs a human-written 1-pager. Not AI-assisted, human-written.

We want everyone to keep thinking and make sure they distill what actually matters.

I'm beyond excited to see lemlist ranked #1 Sales Engagement Platform worldwide by G2, based on user satisfaction.

2 years ago we decided to change what lemlist should be, after hesitating for almost a year.  Here's how that strategic decision was made and what we learned along the way:

At that time, lemlist was a "Cold Outreach" tool, mostly used by single users: founders, growth marketers, or small agencies.

I remember a workshop we did with Elena Verna.

She told us that having only 1 user per account is a big problem, because you can't be sticky.  
She gave great examples of solutions like Miro where team mate invite is pushed during onboarding.

The more users you have on a product, the more people need to align when they want to churn. It's a natural retention layer.

We realized that our target persona by nature would never be sticky, and we had to look somewhere else.

What lemlist was already enabling, multi-channel outbound, was a capability useful to sales teams too. 

Tools in this category are called "Sales Engagement" platforms, like Outreach or Salesloft.

We started talking to their users and they shared real frustrations: lack of innovation, poor adoption due to complex UX, and the feeling that nobody was listening.

We saw the opportunity and started to build.

For the past 24 months, we built with customers. 

Not for them, with them.

We started with very small sales teams, like 2-5 reps. Then 5-10, then 10-25, then 25-50, and now 50-100 or even more. 

Always the same approach: we created a shared Slack channel where our customers (RevOps, Sales Managers) worked directly with us (Product Managers, Sales, Account Managers).

2 years later we have amazing customers like ElevenLabs or Pennylane with now up to 100 sales people using lemlist daily.

Switching from Cold Outreach to Sales Engagement was not obvious and the devil is clearly in the details. 

It required recoding our entire CRM integrations and changing the product in many aspects.

We are far from having a perfect product. 
But being ranked #1 Sales Engagement platform in the world, ahead of amazing products like Apollo, is huge.

It really shows that you can build whatever you want as long as you stay close to your customers and stay obsessed with building and shipping every day.

Big up to the team ❤️

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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ceo @lemlist @lempire

Tracey-lee  C.

We ran an AI-analysis on hundreds of our sales calls to understand why we win and lose deals, and this tells a lot about what matter in Sales.

We used Claap to analyze conversations and score each one on 5 qualification dimensions from the SPICED methodology we use: Situation, Pain, Impact, Critical Event, and Decision.

Here's my analysis:

→ Decision is the #1 differentiator between won and lost deals. 
Deals we win almost always have a strong decision-making process identified early. 
Deals we lose have a lower score.

→ Critical Event is a close second. 
Knowing why a prospect needs to act now makes a massive difference.

→ Situation scores high for both won and lost deals. 
Most reps already qualify it well, so it doesn't separate winners from losers.

→ Pain matters less than expected. Won deals do not necessarily always surface pain very well, but this might be linked to what we sell, because the pain point is already well known and not knew.

→ Impact is the surprise. 
Won and lost deals score almost the same. 
At first glance it looks not important to Sales, but it's not what it means. 
For me the real reading is that the team never qualifies impact well, period. 
So we have no idea how much better our win rate could be if we nailed it.

This is an important nuance. 
The chart shows where won and lost deals differ, not what would happen if we improved on a specific dimension. 

A dimension that scores low everywhere isn't necessarily unimportant, it might be a big untapped opportunity.

This scoring will allow us to better score deals earlier in the process, and potentially fix poorly qualified deals to improve chances of closing. 

Data doesn't give all the answers, but it definitely helps.

Every day another CEO announces they're cutting headcount while claiming "we're doing great, AI just allows us to do more with less"

I think this is a convenient lie most of the time.

The real reason is that too many businesses, especially in SaaS, grew exclusively by raising more and more money. 

In 2021, the bubble burst. In 2025, the SaaSpocalypse made investors turn away from most SaaS businesses out of fear that AI will replace them.

So now these companies need to cut costs and AI is the perfect excuse.

But my thinking is that when you have an amazing team with great talent density, when you hired carefully and deliberately, you don't want people to leave. 
And if your team members are smart, AI makes them much better. 

AI just allows you to be even more successful, and generate more growth, so why would you cut headcount if you are doing great? 

My personal thinking for speaking with dozens of CEOs and COOs is that, empirically, enterprise AI adoption has produced little visible productivity gains so far. 

At least not to the extent of justifying letting go of 30% of a company when you're a SaaS business with Tech, Product, Sales, and CX teams. 

We're not there yet, at least from what I'm seeing.

AI has became the perfect excuse. Instead of admitting you forecasted badly or that you just can't raise money like before, it's easier to blame something else.

That being said, layoffs can be part of any company's life and I'm not criticizing that. 
I'm just saying it's usually better to take accountability when you go through this kind of crisis.

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 🙏

When Sales were saying "the product is the problem", I used to think they were making excuses. But now I think about this very differently.

Because so many companies have forgotten something fundamental.

They build a product, find PMF, grow the sales team, and expect Sales to fuel growth forever. 

Very often, the product stops shipping new things, because very few Product & Tech orgs are actually well managed and because pressure is always only on revenue-generating teams.

Meanwhile Sales feels the pressure to always do more: higher quotas, smaller territories, new verticals and geos to address. 

And barely anything improves on the product side. Feedback piles up, never gets addressed, and no one even tells the GTM teams what's happening with it.

But in tech, you are selling a product in a competitive world. 
Customers always expect more, because competitors don't stop.

This is the main reason why I think we keep growing at 70% at lemlist. 

I think we've found a real way to make Sales and Product work together. 

It's not perfect, but:
→ Sales gives feedback and is actually listened to
→ Roadmap is built using input from Sales, CX, and Account Managers
→ Things actually get shipped. The product today looks nothing like it did a year ago
→ We create Slack conversations with big prospects and build during POCs to show them we listen

Just last week, one of our biggest customers sent us a thank you for a product improvement we shipped in a few days.

My advice to CEOs and CxOs in tech: most of the time, your revenue engine's problem lives in your Product & Tech organization. 

And you're refusing to see it because it's easier to blame Sales for not hitting quotas you arbitrarily set.

Last week we added SMS as a new channel after adding WhatsApp a few months ago.

As for WhatsApp, we do not recommend the use of SMS for cold outreach but rather for relationship management, nurturing, or any use case where you had previous discussion with someone.

Few use cases:

→ Reply to an Inbound lead to get a faster answer, eg. "Are you free now to jump on a call?". 
You can do website form > lemlist via API > automated SMS step.

→ Prevent no-shows to meetings across all channels: Inbound or Outbound

→ Nurture past leads: update them about new features/services or just get some news

→ Re-build connection after some time

Generally speaking, any use case where you or someone in your company had a previous convo with a prospect or customer.

The feature is available once you purchased a phone number with lemlist. 

We want to keep adding new channels, and build the platform where you can activate all relevant channels for sales to maximize reply rates.

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  🔥

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When someone tells you "we have an agent checking inbound leads and helping us decide if we should engage or not," you need to understand that he means the following:

There is a workflow based on criteria like company size, job title, and geography that routes leads properly. 

This has nothing to do with AI. 

Companies have been doing this since 2020, it's just that now more companies do it.

This is not an agent, because it doesn't involve AI in the LLM sense. 
There's no reasoning, no judgment. 
It's a scoring rule with a new name.

I have hundreds of other examples of misuse of the word "agent."

Today, everyone wants to be « AI » but let’s keep in mind AI is a mean to deliver more value to customers, not a goal in itself. 

You can optimize for the hype, or you can build products that actually deliver impact, whether they leverage AI or not.

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 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?

The SaaS companies most at risk from AI are not the small ones, but the ones whose entire business model depends on vendor lock-in.

It's not me saying this, it's a16z, in one of the best pieces of content I've read on AI this year.

Their core thinking is that there are 7 competitive advantages: (1) Scale, (2) Network effects, (3) Counterpositioning, (4) Switching costs, (5) Brand, (6) Cornered resources, and (7) Process power.

Most of them will hold in the AI world, but the first one to change is switching costs.
Agents can now handle migrations that used to take months, and companies that kept "hostages, not customers" are about to feel real pressure.

This is where it gets interesting for PLG companies like lemlist.

We never relied on lock-in. 80% of our customers are on monthly plans. Switching costs have always been low for us, by design.

So when AI makes switching easier for everyone, it doesn't change our game. We already play it.

And it arrives at the same time as the EU Data Act, enforceable since September 2025. Europe is now forcing SaaS providers to let customers switch at any time, export their data within 30 days, and by 2027, all switching fees must be eliminated.

Lock-in is being attacked from both sides: technology makes it easier to leave, and regulation makes it illegal to prevent it.

Here's how I appreciate the AI opportunity for lemlist on the different competitive advantages:

→ Brand. 
When anyone can vibe-code a sales tool overnight, trust becomes the real differentiator. Every "comment Claude to get my config" post on LinkedIn gives trash results, and people will get tired of it fast, just like they got tired of AI SDRs. 
Once people are done getting scammed by magic solutions, they'll go back to brands they actually trust.

→ Innovation speed. 
PLG companies ship fast because their users are innovators who expect it. We've been compounding product improvements for years with that feedback loop.

→ Process power. 
We've been studying what works and what doesn't in outbound for years. We interview customers daily and constantly identify the most successful tactics. That knowledge doesn't get replicated by cheaper code.

→ Cornered resources. 
We have billions of outbound messages, leads, and conversations stored in lemlist. That data makes our product smarter every day, and no new entrant can just recreate it.

→ Network effects. 
lemwarm works better the more people use it. Every user strengthens the warm-up network for everyone else. 

---

AI is a huge opportunity for software companies that understand it and are ready to embrace this change across their whole business.

My goal for lemlist: in 3 years, same team size, 3X the revenue, 3X the average salary. 
AI makes that possible when your product actually earns its customers every month.

Everyone commenting "Claude" to get a magic config that runs your outbound on autopilot but we have only 1000 registrants to hear Nathan break down how he built GTM systems at Ramp and now Perplexity.

We should have at least 5x more. 

The guy spent 3.5 years building the business systems behind one of the most successful sales-led companies in the world, and is now building the growth engine at an AI company from the inside.

You can still register and help save good content. 

It's free, but it shouldn't.

Link in comments.

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The Battle

Sophie Martres

Charles Tenot

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The Battle

Sophie Martres

Charles Tenot

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Charles Tenot's LinkedIn Performance

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