Business Breakdowns
· Matt Reustle, Brett Larson
[00:00:00] S0: I did read quite a bit about Keyence and their sales process and how effective of a sales organization they built. But in the sale for someone like Cognex, where it sounds like it's much more technical, built to spec, perhaps advanced and not so much off the shelf, are they selling to an Amazon who then works to integrate it into the other hardware and equipment that they're using, or are they selling into the equipment manufacturers, whoever builds the conveyor belt? Who is the customer for Cognex? [00:00:00] S1: The answer is yes. If you think about how they're going to market, 70% of sales are direct. And that would be direct either to the factory floor where Cognex and the automation engineers at the customer are working to implement the system. But direct also includes selling to a machine builder or an OEM who would then integrate it into the machine and then they take that machine to the end factory floor. And then the remaining 30%, about half of that is going to be through systems integrators and that's primarily logistics at this point. And then the other half of that 30% would be distribution. And that's primarily just for end markets like Cambodia or something, where they just don't have any presence. That's kind of how I would separate the customer.
· Brett Larson, Matt Reustle
[00:00:00] S1: The answer is yes. If you think about how they're going to market, 70% of sales are direct. And that would be direct either to the factory floor where Cognex and the automation engineers at the customer are working to implement the system. But direct also includes selling to a machine builder or an OEM who would then integrate it into the machine and then they take that machine to the end factory floor. And then the remaining 30%, about half of that is going to be through systems integrators and that's primarily logistics at this point. And then the other half of that 30% would be distribution. And that's primarily just for end markets like Cambodia or something, where they just don't have any presence. That's kind of how I would separate the customer. [00:00:00] S0: That makes sense. It's an interesting dynamic within the overall industry structure conversation. Before we move on too far, I do want to get into a bit of the history and what came across through the work that I was doing is that there is technical expertise here and there's a focus on that technical expertise. So can you bring us back to the beginning and the origin story here, when they got into the market and some of the evolution over time?
· Brett Larson
[00:00:00] S1: It's really important to understand for Cognex the DNA of them stacking S curves essentially over 40 to 50 years. The company was founded in 1981 and they came to market with what was the world's first industrial optical character recognition system. So again, reading numbers and letters. And at the time the system looked more like a big video camera with external processing. And the first application was actually to read Serial numbers on semiconductor wafers for IBM. That was the DataMan product family that still exists. From there they got a call from Johnson & Johnson to do all optical character recognition for labels. They did that application. But then J&J asked them, hey, can you do some of these other at the time, what would have been novel applications like verifying the caps are on the bottles and the labels were present and the like. So that's when Cognex added more of these inspection type of applications. The first 20 years or so of the business was pioneering machine vision for all these different applications. But it was very much for semiconductors and the electronics capital equipment industries primarily. It was as high as 80% of sales going into the dot com bubble were to those industries. And that went to 54% following the hard way. Around 2000 was when we saw smart cameras really come onto the scene. So where you embedded the processing directly into the camera. Smaller footprint, more ruggedized now that enabled the product to be ready for the factory floor as we more often think about it. So we saw machine vision get adopted in more heavy manufacturing industries like automotive, consumer electronics, food and beverage packaging. So by the end of 2010 that semiconductor and electronics capital equipment was down to 15% of sales as they really brought in the end markets that they could serve all those new s curves. Around 2010 was when Cognex came out with their first ID product for reading barcodes. It was primarily aimed at displacing laser based scanners in logistics facilities because it could do it faster and then with much higher read rates. If you go back in 2010, they were talking about they'd be happy if one day, long term target $75 million of sales from this line of business. And then they worked very closely with Amazon to develop the technology.
· Brett Larson, Matt Reustle
And in 2021, the peak, it was about $300 million in sales and 30% of Cognex's overall business. So it turned out to be just an enormous new S curve for them. At present we're really in the early innings of the next tech evolution for the industry. And it began in 2017 when Cognex acquired ViDi. And then in 2019 they acquired a company called SUALAB. And those were both essentially IP and acqui-hires in the field of deep learning or the application of AI into machine vision. ViDi's co founder and CTO is Cognex's current VP of AI technology. So after those acquisitions there was a big development effort and they came to market with these new Deep learning and Edge learning products. And the way I conceptualize this is moving from rules based programming of vision systems to teaching by example, which has large implications. And I think we're probably in the first inning of this next chapter behind the scenes of a long cyclical down cycle. [00:00:00] S0: Could you paint that picture in terms of an example rules based? Let's say a package is over 100 pounds with these dimensions, send it in the left lane. If it's under that, send it through the middle. If it's super small, send it to the right. And it's a pretty standard tree of logic and decisioning. Where would this new example or this new training come into play? Do you have any real world applications that would paint the picture?
· Brett Larson
[00:00:00] S1: There's deep learning and edge learning, as I mentioned, deep learning first. So traditional rules based vision is great for a lot of things, but not for things very subtle or nuanced tasks with a great deal of variation. Those sorts of tasks, you still usually have a human performing them because frankly, it's just hard to quantify them to then program them. Maybe an example of that would be inspecting a phone case for very subtle defects. There's obviously a wide variety of colors and there's just an endless list of potential defects. So scratches, dents, blemishes in the paint, and they're very small, very nuanced. And then there's also usually a scale of what's acceptable and what's not, where a human would be able to look at it and pretty quickly discern that. But it's very hard to program a machine to be able to do that, especially at obviously high speeds. But with deep learning, instead of programming it, you teach it with very large data sets and very large image sets. These are good phone images. These are defects. These are acceptable defects. And the machine learns what is acceptable and what is not and is able to accomplish that task, just to quantify this a little bit. So there's currently still 30 million people in the world doing visual inspection. And that's something that humans actually aren't great at. They can do it quickly, but they get fatigued and they miss things. That's a big opportunity. Another example would be something like deboning chickens, where every chicken's different, it comes down the line a different orientation, the wings are somewhere different. It'd be impossible to quantify and program a robot to be able to grab the right places. But with deep learning, that's an application that they can now do deep learning. The implication would be potentially new applications for machine vision. Edge learning is the other end of the spectrum and it's probably more financially tangible for the investor community right now. These products, they come pre-programmed for specific applications but then they're trained with just as few as five to 10 images. So it's essentially very easy to deploy relative to traditional rules-based vision. You can usually get an application up and running in a few hours. So the major implication here is that Cognex can now sell to less sophisticated customers.
· Brett Larson
Again thinking about where they've historically focused on the top of the pyramid. And they can do it with a much less technical sales force. So broadening their customer base below where they've typically served. So now they have the product and Cognex is currently making a Salesforce investment called the Emerging Customer Initiative. And it's really taking the Keyence playbook. These emerging customers, salesnoids, they call their employees cognoids, their salespeople, salesnoids, they're hiring young salespeople instead of technical applications engineers. They're arming them with these new edge learning products that are really easy to implement and, and they're going after these less sophisticated customers, more SMB types and even less sophisticated tasks within existing customers where those exist. And then it's a much more formalized selling playbook that tracks the KPIs. Just like Keyence, they're getting compensated on both their selling activity as well as their commission. Last year Cohort One of these salesnoids hit the field and they did 80,000 customer visits, added 3,000 new customers to a base of what was previously 30,000, had accretive gross margins and they exited the year at about a million dollars a week in sales from that first cohort. The second cohort entered this year in 2025 and hopefully we'll do a bit better just from lessons learned. Obviously they're now competing in Keyence's bread and butter target customer set. So they are running into them sometimes with these customers being vended. But the majority of the time these customers have never used machine vision at all. I think it's more like 60 to 70% of their sales have been people that have never had a camera in their factory. Obviously the financial implications here are they're trying to grow their customer base from 30,000 to hundreds of thousands over time, which could be obviously revenue opportunity and then at accretive gross margins and reduce the customer concentration as well over time.