S4E4 – The story behind Ocean.io: AI first & 10M in funding With Michael Heiberg

The story behind Ocean.io: AI first & 10M in funding

In today’s episode of the “Grow Your B2B SaaS” podcast, we dive into the journey of Michael Heiberg, CEO and founder of Ocean.io, a cutting-edge account-based data platform powered by AI. Join us as we uncover Michael’s path from sales at SAP to launching his own venture, exploring the challenges and triumphs he faced along the way. Discover how Ocean.io is reshaping the sales and marketing landscape through precision targeting and innovative AI solutions. 

Ocean was officially incorporated in 2017, they bootstrapped their initial years before securing investments from angel and institutional investors in subsequent rounds. In total Ocean raised over $10 million in funding to develop its AI-powered data models, essential for precise targeting and reaching a wider audience efficiently. With a team of around 29 to 30 employees, Ocean is focused on continuous growth, with plans to expand further.

Michael shares insights into his transition from working in large corporations to embracing entrepreneurship, emphasizing the shift in mindset and the allure of the startup environment.

Market Redefinition

Ocean aims to revolutionize the data market by offering a unique AI-driven engine capable of identifying similar companies worldwide, catering to both enterprises and startups.

Challenges Faced

Michael discusses setbacks faced during the COVID-19 pandemic, highlighting the resilience needed to overcome personal and business struggles.

Collaborating with a supportive team helped Ocean overcome challenges, emphasizing the importance of teamwork during tough times.

The Key to Success

Ocean’s success was attributed to being ahead of the curve in AI technology, with the market recognizing the value of precise targeting and efficiency in sales and marketing efforts.

Go-to-Market Strategy

Ocean’s success was fueled by a strategic go-to-market approach, focusing on niche industries and specific customer needs to drive growth.

Marketing Strategies

Initially using a mix of outbound and inbound strategies, Ocean eventually pivoted to a product-led growth model, reducing Customer Acquisition Cost (CAC) and enhancing efficiency.

Lessons Learned

Michael reflects on the importance of trusting one’s vision, avoiding premature scaling, and the significance of entering the US market earlier for accelerated growth.

The Importance of Automation and AI

Ocean’s success is attributed to leveraging automation and AI technology to streamline operations, optimize the customer journey, and enhance overall efficiency.

Advice for Founders

Michael emphasizes the importance of focusing on customer quality over revenue, trusting one’s instincts, and utilizing automation and AI to scale effectively.

Key Timecodes

  • (00:00) Show Intro
  • (01:45) Ocean’s Funding Rounds and Employee Count
  • (02:40) Michael’s Entrepreneurial Journey
  • (05:01) Market Redefinition and End Goal
  • (08:20) Overcoming Rock Bottom Moment
  • (09:55) Incremental Success Factors
  • (14:00) Go-to-Market Strategy
  • (16:20) Targeting Specific Industries
  • (18:17) Marketing Approach and Challenges
  • (26:40) Advice for Founders Starting Out


[00:00:00.000] – Show Intro

In today’s episode, my guest is Michael Heiberg. Michael is the CEO and founder of Ocean. Io. Ocean is an account-based data platform that harnesses AI to help sales and marketing teams. Before starting Ocean, Michael started off his career in sales at SAP, worked at Oracle, i2, and helped companies to grow via the incubator De Zema. So he’s seen the inside of big corporations and many startups before starting his own. So love to dive in. Welcome to the show, Michael.

[00:00:26.510] – Michael

Thank you, Joran. I love to be here.

[00:00:30.150] – Joran

Nice. To get to know you, OCEAN, and set the scene a bit, let’s start with some quick fire questions. When did you start at Oceane?

[00:00:38.840] – Michael

2017. We did some research early on in ’16, but incorporated the company in ’17.

[00:00:47.450] – Joran

And are you bootstrap-funded?

[00:00:50.270] – Michael

Yeah. The first year was pretty much bootstrap. They had some angel investors in. Institutional investors didn’t come in until 2019. In 2020. So the first couple of years were really bootstrapping.

[00:01:05.990] – Joran

Yeah, you experienced both sides.

[00:01:07.790] – Michael

And then pre-seed in ’19 and seed in ’21.

[00:01:11.200] – Joran

Two rounds in total?

[00:01:13.430] – Michael

Two rounds in total, yes.

[00:01:15.100] – Joran

And how much funding did you raise?

[00:01:17.210] – Michael

Quite a bit. If we include what we have put in ourselves and all kinds of things, we are north of 10 million.

[00:01:24.320] – Joran

Got you.

[00:01:25.680] – Michael

It’s an AI tool. It costs money to build the data models. Very large-scale data models.

[00:01:32.100] – Joran

We’re going to dive into that. Currently, how many employees do you guys have?

[00:01:36.530] – Michael

We’re not that many. I think we’re 29, 30. I think we just offer an offer letter for number 30. It’s just going out. Okay.

[00:01:46.190] – Joran

In one sentence, what does OCEAN do?

[00:01:52.290] – Michael

We allow you to target in on your ICP by using AI to clearly define your ICP and map that out in the market as clearly target audience. And we do mean ICPs.

[00:02:06.990] – Joran

Yeah, that’s going to be really important. If we go to the personal side, have you always wanted to be an entrepreneur because you work first at bigger corporations than in startups?

[00:02:18.360] – Michael

It’s actually a funny story. When you work in large corporations, I had no idea how to be an entrepreneur. It was always you get your payslip. You work in this large corporation Going out on your own is not really on your vision. But then gradually, I started to move into the US startup environment, and there you got the flavor of it. And once it’s in your veins, it’s there. I could never imagine. Going back 20 years, I couldn’t imagine going back to corporate life. I couldn’t imagine that. Interesting.

[00:02:54.510] – Joran

You took to Funding route, so you took the VC route. Do you already have an end goal Is that a market defined for Ocean or is that something undefined?

[00:03:04.070] – Michael

We have come to a market with a complete redefinition of data. We have built a ground-up AI engine, which had its first release in 2019. And when you do that work and you build the ground up, we have 59 million companies worldwide with an AI marker on each of these companies. And that means I can key in any company and I can find the identical lookalike to a company anywhere in the world on any language represented or anything. Once you’ve built that and once you have built the ability for people to use AI in their daily targeting, you’re also aware that you have redefined the way you go to market. You have redefined the way people are efficient in targeting and everything else. And And we think we have an opportunity to redefine the entire market, whether it’s enterprises like Sony is a customer of ours, or whether it’s a small startup somewhere in the US, Europe, or New Zealand, for that matter. It doesn’t really matter. It’s all about the more precise you can target, the more efficient you can go to market. So the end goal is really to roll this out to the world.

[00:04:28.100] – Michael

Yeah. So I Yes, but that’s what we do.

[00:04:33.380] – Joran

Exactly. I love it that you don’t put a number on it. You actually want to redefine the market. So you want to build a good product and take it over. You mentioned you started in 2017. 2017, the eye from the beginning, which wasn’t back then such a hype as right now. How did you guys came up with the idea of basically implementing it from the ground up?

[00:04:56.520] – Michael

It came in two iterations. The first version of the system The actual first version was a very simplistic keyword embedded system that allowed us to pull certain keywords from companies and then allow us to compare companies based on keywords. That first version was what we can call a minimal MVP, but we also realized the limitations on the keywords. Orange is a fruit or a color. We needed to get into more sophisticated stuff. We had a very skilled data scientists in our team. We started to explore the whole contextual vectoring aspect of… That means, and this will sound familiar because this is what may I did at the same time. We’re looking at a contextual understanding of an entire text body. Orange is the new black. The moment you hear that sentence, you understand orange is the color. Because it’s in a context of another color. We started to explore that, and we realized that was the way forward. This is 2019. We were, unfortunately, on the bleeding edge of technology because we needed Elastic Search and we needed version 7.2. We needed the ability to have a database that allowed us to store vectors. And that came out in, I think, late 19 or 20.

[00:06:31.360] – Michael

And I’ll tell you a funny story. We had a grant from the Innovation Fund in Denmark, and it was all fine, and we were happy, and we explained to them how advanced we were, and that grant would help us tremendously. But we couldn’t release our product from prototype to release it until we had the availability of that Elastic Search 7.2. And that pushed our release date beyond the release date we have communicated to the Innovation Fund. Due to that, they pulled the grant. Okay, that’s to give you the constraint a founder has. And we tried to convince them that it was outside influence how our technology works. It doesn’t help. Innovation Fund, here you go. We have to stick to the rules. That’s just some of the today funny, but in those days, it was not funny at That’s working with the government. It’s following the rules. Yeah, exactly. It’s working with the government.

[00:07:35.620] – Joran

I don’t know if this is going to be your moment already, but every founder has it. They hit rock bottom, either financially, where you didn’t get the fund or maybe even personal. What was a moment for you?

[00:07:49.690] – Michael

That was probably 2020, around New Year’s, 2020. I got COVID. I had to go to a hospital. It was really bad. And the whole company was not doing well. And we were hit very hard by COVID, not just from a health perspective, but also from a business perspective. And that was probably a rock bottom. We crawled out of that hole. It took me a couple of months, personally, to get back on my feet and really start to think again. And my cognitive skills, I had a clouded brain for at least two months, but got back on my feet.

[00:08:27.690] – Joran

How did you get out of it? Because it sounds now, I guess, simple, but it took two months to get your cognitive brain back, or at least your brain power back. Any advice, for example, how other founders could get out of a hole like that and pick themselves up?

[00:08:43.620] – Michael

This was circumstantially, obviously. Yeah, it’s rely on good colleagues. I have our CTO, our head of products. We have been together right from the beginning, 2017 and 2018, and we rely on each other. It’s a teamwork, and I could rely on them to help pull us out.

[00:09:05.490] – Joran

When we go to the more positive side, you guys started in 2017, had 2024, now 30 employees. Is there one thing which has been incremental to the success you guys are having now?

[00:09:18.460] – Michael

I would say for quite some time, we are now at version 4 of the system. For quite some time, we were too far ahead of everyone else. What we had was That’s not really what people were looking for. They were looking for telephone numbers and contact data. They were looking for all kinds of things that we did not have because we have spent so much resources in building this AI engine. It’s not because we grew like rocket. It was a very cumbersome growth because we had to find the needle in the haystack. That particular company that had that particular need that we actually solved. Then came Two things. Openai released to the public their ChatGPT. Overnight, people realized what contextual understanding of text can mean and what AI NLP models can actually do to you. Over the night, we started to get attention. Secondly, the whole downturn in the SaaS industry helped us tremendously because people were starting to focus on efficiency. There They were not putting an army of SDRs throwing mud at the walls to see what start. It really started to look at efficiency, and that’s where we come, because this is all about efficiency.

[00:10:42.200] – Michael

It’s all about using AI to obtain an optimal target audience. That helped us. Since then, we have really started to take off, and that’s been beneficial for us. The moment we had was actually external forces starting to validate the technology we have spent her blood, sweat, and tears to develop ahead of the curve. But on the other hand, we have a very fortunate situation. We’re the only game in town. We’re the only one who can do this. Here’s the funny story. When we started When I started out with this, we created a term called Lookalikes. When I founded this, my inspiration came from the B2C. It came from the digital marketing where you cluster people into Lookalike audiences. I wanted to do that to B2B. With that mindset, I reused the lookalike audience from the B2C, but nobody realized that. Nobody resonated with that message. We started to use similar companies or things like that. Then two years back, everyone was starting to talk about lookalike, and now, okay, Hoptium became the one that actually defined lookalike in B2B. Sometimes you’re lucky, okay?

[00:12:02.560] – Joran

Yeah. In the end, you sometimes might think that OpenAI would be a competitor, but in your case, it actually did a really good thing because they educated the market on what you’ve already been building.

[00:12:13.650] – Michael

We have it dedicated in. Openai is a very general purpose engine, and it’s great for what it does. But when you look at companies, there’s specific elements of how a company describes itself and on where it describes itself that goes in to actually representing what it does. It takes a lot of machine learning to understand that. Openai is not geared for that. That means if you run a lookalike in OpenAI, the first three, four, five results will be okay, but it doesn’t scale. It doesn’t scale down to the 500, and 800, or 10,000 companies that are actually lookalikes to that relatively. That’s where I open my AI, open the eyes for everyone. But when it’s all about scaling to an entire tam, that’s where we come in. We’re the only game in terms.

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[00:13:22.900] – Joran

Let’s talk about before that time because you guys already launched. You mentioned you were looking for a specific company with a particular need. A lot of companies, SaaS founders, struggle with go-to-market. You had a really specific niche, as you mentioned.

[00:13:37.430] – Michael

Very niche.

[00:13:38.400] – Joran

What was your go-to-market strategy?

[00:13:42.150] – Michael

It’s really targeting, since we could build a very focused or using OSHA building a very focused ICP. It was within IoT. Iots are looking for very specific applications. To give you an example, one of our very good customers are building pretty much the inner of drones. They’re building the inner of drones. That means, Ocean, find me drone manufacturers. Ocean, find me drone manufacturers for this particular application, which is inside mapping of whatever, and/or outside mapping or specific drones, we can do that. We can to give you the minute detail of an ICP. That was one area that we were very successful in. Then we had a German customer that built an architectural internal mapping of buildings. That means they were looking for applications that did that, and we could solve that problem. One of the more particular situation we have had was somebody selling spinal decompression. Spinal decompression is a very specific pain relief. They were targeting pain clinics in the US, and we could actually, and there’s around 39,000 pain clinics in the US, and we can find all of them, but we will also find the 3,750 who actually do spinal decompression. That’s how detailed we do this.

[00:15:23.280] – Michael

It was those niches in the beginning. Now it’s mainstream, but it’s also PLG, so it’s It’s a whole different game now. Everyone is looking for lookalikes because they realize the more targeted you can create your audience, the more specific you can be in communicating with them.

[00:15:45.570] – Joran

When we then talk about these companies, you found them different industries, different geos. How did you start getting those clients in?

[00:15:55.400] – Michael

Basically, from the day one, it was showing the product. We still, to this day, have not found a way to articulate what we do on our landing page to the degree where people say, Okay, that’s unique. They have to see it. They have to see it. That means it was a question of getting people in front of the platform once they keyed in one of their customers. We show them exactly anywhere in the world or anywhere in that country, the exact lookalikes to that customer. Then the dropped. You got the, whoa, I haven’t seen that before. For our sake, it was getting them in front of the system as quickly as possible. We have taken that the whole way now to PLG that we self-onboard people into the platform. They can go ahead. It’s even on our landing page, go ahead, key in your customer. Here’s the lookalikes to it because it’s that aha moment. It’s the same thing that happened when you used ChatGPT for the first time. It’s the same thing. You got something, you got a result you didn’t expect that it could be that accurate. We had that ChatGPT moment each time a customer goes in front of OSHA.

[00:17:15.460] – Joran

Yeah, it makes sense. You really try to get them to that wow moment quickly.

[00:17:18.520] – Michael

Try to describe ChatGPT on a landing page. It’s very hard.

[00:17:26.940] – Joran

If we go even one step further, because you mentioned you try to get them on a demo, right? But then how would you even market yourself? Did you do cold outreach at the beginning, paid ads, organic?

[00:17:39.290] – Michael

We did everything. We did LinkedIn ads and everything else. The funny thing about LinkedIn ads is that with Ocean, you can bypass the entire industry segmentation in LinkedIn, and we use our own. That means we created our own target audience of IoT targets, and then we upload it. Because In LinkedIn, you can’t target that precisely. It’s a software company or it’s an IT company, whatever it is. And that’s why they recommend that you run audiences of less minimum 50,000. Why? Because you’re literally throwing mud at the wall. But we bypassed that and really, really used our own tool within LinkedIn. The most curious thing is that LinkedIn called us and said, Why is it that you have a significantly, and we’re talking orders of magnitude, higher conversion rate than anyone we see out there. That’s because we use ocean targeting. But we use everything, cold calling, everything else. But in the end of the day, a couple of years ago, we all had to be efficient, and the CAC was too high. The CAC is simply too high in an outbound engine. That’s why we converted the whole thing to inbound, converted it to PLG, reduced our Cog to nothing, and in a very good space these days.

[00:19:10.000] – Joran

Yeah. It’s a funny thing you mentioned already before, you used your own tool to find the companies with a particular need or find companies here. Yeah, exactly. Within LinkedIn, you went outbound first, then PLG after, just to reduce your customers’ acquisition cost.

[00:19:26.520] – Michael

Exactly. And also get it out there in front of as many as possible.

[00:19:31.230] – Joran

Yeah. This sounds really good, right? Let’s talk about the other side again. Any big failures you made along the way, which you can share?

[00:19:41.360] – Michael

Yeah. We tried to scale too fast, too soon. That cost us quite a bit of money. I would say that was one of the bigger mistakes. The consequence of that was that when you look at Oceans, it’s mind boggling how it works and simple and everything else. But there’s a lot of algorithms and all kinds of things behind it to make it look that is efficient. So we needed a very high cognitive skills from our sales to understand how to convey Ocean to a customer. And I think that was probably the biggest problem for us, in a market where everyone could go into sales to attract the best ones. Today, the story is very different. We probably have one of the best sales teams in the market. But in the early days, that was the tough part. That was really the tough part, getting someone who really understood what we did into a ocean. Yeah. And get the understanding of the math and everything else into it. Because, sir, unless you do that, you don’t know how to articulate. It’s not because you’re articulating math, but you’re articulating the subtle understanding of how the the rhythm works when you’re starting to compare things.

[00:21:03.810] – Joran

When we look at really practical, you mentioned scaling too fast, too soon, hiring sales. Did it mean you already had one or two salespeople which really worked out, who had the knowledge, and then you hired people who just weren’t able to pick it up as fast?

[00:21:17.350] – Michael

They couldn’t pick it up, and some of them would never do it. I would say, and that means we went through a lot of staff churn in terms in sales. That’s probably the unfortunate situation, lessons learned.

[00:21:31.310] – Joran

When we- Painfully. Sometimes they are painful.

[00:21:35.790] – Michael


[00:21:37.340] – Joran

If we talk about a thing you didn’t do, is there something you regret you didn’t do when you now look back in your skinning?

[00:21:45.740] – Michael

We should have gone to US earlier. We should have gone US much earlier. There’s two aspects to US. First of all, US is literally minimum five years ahead of Europe. From our technology side and everything else, we built as we would be in the US from the birth. We were struggling around in Europe. If we had gone US earlier, much earlier, we would have generated traction much earlier because the target market was actually US. We should potentially have gone, what I mean when I’m in US, not just, but also operational and development-wise, some of it in the US, and somehow split it up because we didn’t have the ear to the ground in the US. We of what the customers were requiring. We were listening to European customers, and that was a big mistake. That cost us at least a year.

[00:22:41.360] – Joran

How did you now fix it? Do you guys have now two entities, one in Europe One in US? And one in US?

[00:22:45.720] – Michael

One in US, and 60% of our business, 80% of our business is out of the US.

[00:22:50.490] – Joran

Got you.

[00:22:52.500] – Michael

We caught on to it, but we should have done it much earlier. If I can redo this, I would do the same thing. I have Copenhagen, I will do… We should have been in US in 2020.

[00:23:03.940] – Joran

How does it look practical? Then would you also really live in the US or would you just travel a lot?

[00:23:10.180] – Michael

No, we don’t. Everything is PLG. For me, it’s talking to customers, and I do that on Zoom. But it’s also talking to the market, on the standard market. So that requires that I travel in the US, obviously. But it has that daily contact with the US market is really important for a company like ours.

[00:23:29.990] – Joran

Yeah, makes sense. I always wanted to ask, how do you leverage technologies like AI machine learning? But obviously you do, right? I’m not going to ask you that question.

[00:23:40.800] – Michael

How are we born that way? Yeah.

[00:23:43.520] – Joran

But I am going to phrase it a little bit different because you now have a lot of AI tools which are built on top of OpenAI or any other them. But what if you build it yourself? You’ve been AI first. What is going to be the impact of that and the difference between all the AI tools tools you see right now?

[00:24:01.170] – Michael

When you build things from ground up, you make it very dedicated to the problem you want to solve. When you do that, you can achieve a depth in the AI that you cannot achieve by taking a legacy platform and just putting it ChatGPT on top of it. Let me give you an example. For customers, in general purpose, we’re building the ability that when they close one customer, this is automatic within their CM system. This is what’s happening within their CM system. They close one customer. Automatically, Oceans generates the lookalikes to that customer. That can be 250 companies. Peel the target people from those 250. Use ChatGPT to write a specific mess email to those 500 people or 800 people because there’s more than one in each company. Relative to the company and context of the company they just closed and automatically send those emails out saying, Here is what we do for that particular company. And this is happening automatically. That means the moment a new customer move in to close one, this goes away. This is automatic. This is what AI can do based based on a really focused. You cannot do that in ChatGPT alone.

[00:25:35.420] – Michael

You need a platform like Ocean to automate that process to the degree that we use ChatGPT for the text. It’s because that’s what ChatGPT is really good at. But the contextual understanding of the company, that’s what we know. You see the difference?

[00:25:51.760] – Joran

You trained it specifically for something.

[00:25:54.280] – Michael

We trained it specifically for this.

[00:25:57.730] – Joran

Nice. If we zoom out, is there Any advice, founder to founder, that you could give people listening right now? What should they do? Maybe avoid.

[00:26:07.100] – Michael

Listening to their own compass and trust their own compass. It’s hard. It’s a hard subject because I’ve had so many wanting us to change direction. I’ve had so many, including investors and auction, because we were struggling in the beginning, that had all kinds of advice for us. But in the end of the day, what makes the difference your tenacity and the trust in what you believe in is right. That will make you successful. There’s a reason you got the idea, and there’s a reason that that idea is going to come out successful. It’s not because you’re not going to pivot or it’s not because you’re optimized, but it’s because the core function that will make this successful is your sense of direction from the idea. Don’t ever lose that. Love it.

[00:27:03.890] – Joran

This is your first SaaS, right? We talked a little bit about mistakes already, scaling too fast, too soon, hiring sales too quick. If you could do it all again from the start, are there any other things you would do differently?

[00:27:16.520] – Michael

No, not really. I would probably have done it in the US. Access to market, access to capital. We talked to some of the fancy, some of the tier one VCs in the US, and they really like what we did. But had we been in the US, they would probably have invested. But we were sitting in Copenhagen, and we were so far ahead of everyone. But on the other hand, because building an AI engine that basically processes the amount of data we do cost a lot of money. It cost a fortune to build. I would say, yeah, We were three years ahead of the curve, and you can say that’s three years wasted. But on the other hand, when the market actually hit us, we were at version four and the whole thing is taking off. You can argue both ways. Yeah.

[00:28:18.400] – Joran

Then you came to version four, you’re now able to build a product-led growth.

[00:28:22.060] – Michael


[00:28:22.760] – Joran

Cool. We’re going to dive into the two final questions. These are more practical for SaaS founders who are on a specific trajectory of their MRR. What advice would you give a SaaS founder who is just starting out and growing to 10K MRR?

[00:28:37.730] – Michael

Revenue is less important. The quality of the customer is much more important. The quality of the first 10 customers And make sure you can learn from it and they can validate or change your original concept is far more important than reaching 10. Because it’s all about building that thing that can scale and has the stickiness with the customer. And you need the first 10, 15, 20 customers to validate that. That you’re not a nice to have, but you’re a need to have. Revenue will come as a function of that. Once you are a need to have because you hit it and you got it validated by your first set of customers, revenue is a function of that. Nice.

[00:29:26.880] – Joran

Let’s assume now we passed 10K monthly recurring to you, and we’re going to make a huge step. I know we’re going to go towards 10 million ARR. What advice would you give SaaS founders here?

[00:29:37.430] – Michael

Automation. Use AI to automation. I just gave you an example a couple of minutes ago about automation. Automate. There’s so much magic in automation. If you throw people at it, we are only 30 people. From a revenue standpoint, we probably represent 90, 100 people. But that is because we are so highly automated.

[00:30:04.560] – Joran

Can you give some examples of where a lot of where normal startups maybe spend a lot of time where you have automated things?

[00:30:12.440] – Michael

We have not a single SDR. We have very few AEs. Our revenue is generated entirely inbound, entirely inbound. We do not develop. Everything is automated in terms of we measure Here to the nth degree the customer journeys we have, and we optimize our product constantly. We have far more engineers than we will ever have in sales. The product and optimizing the product and the journey for the customer is far more efficient for us than actually having 10 times the sales number in AEs we don’t. Aes can be extremely efficient if you channel quality to them.

[00:31:02.390] – Joran

Makes sense. The PLG motion is definitely in place of it. Cool. If people want to get in contact with you, Michael, how can they do something?

[00:31:11.760] – Michael

Linkedin, obviously. I’m a strong believer in network. I’m a strong believer in we can help each other. I think I have at least three to five meetings a week with their founders or peers to exchange ideas and GTMs and all kinds of things. And I’m a strong believer in that. Cool.

[00:31:32.780] – Joran

We’re going to make a note of that. For people listening, leave us a review for the show so we can help other SaaS founders as well. We’re going to add a poll to this show as well. So let us know what you thought about this episode. We’re going to add his LinkedIn profile. So if you do need GTM advice, reach out to him or for anything else. Thank you for being on the show, Michael.

[00:31:52.510] – Michael

Thank you for having me.

[00:31:53.590] – Joran

Thank you for watching this show of the Grow Your B2B SaaS podcast. You made it till the end, so I think we can assume you like this content. If you did, give us a thumbs up, subscribe to the channel. If you like this content, feel free to reach out if you want to sponsor the show, if you have a specific guest in mind, if you have a specific topic you want us to cover, reach out to me on LinkedIn take a look at it. If you want to know more about Reddit, feel free to reach out as well. But for now, have a great day and good luck growing your B2B SaaS.

Joran Hofman
Meet the author
Joran Hofman
Back in 2020 I was an affiliate for 80+ SaaS tools and I was generating an average of 30k in organic visits each month with my site. Due to the issues I experienced with the current affiliate management software tools, it never resulted in the passive income I was hoping for. Many clunky affiliate management tools lost me probably more than $20,000+ in affiliate revenue. So I decided to build my own software with a high focus on the affiliates, as in the end, they generate more money for SaaS companies.
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