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The Margin is a podcast from MGI Research that explores the evolving world of business monetization. Hosted by MGI Managing Directors Andrew Dailey and Igor Stenmark, the show features candid conversations with founders, CEOs, product leaders, and industry experts at the forefront of pricing, billing, and revenue operations. Each episode dives deep into the strategies, technologies, and trends shaping how companies generate, capture, and grow revenue—from subscription and usage-based models to AI-driven monetization. Whether you're in finance, product, or IT, The Margin offers practical insights to help you navigate complexity and drive growth in the digital economy.
Andrew Dailey: Welcome to The Margin, a podcast exploring the forces shaping business monetization. I'm Andrew Dailey, Managing Director and Analyst at MGI Research. In today's episode, we're talking about the challenges facing companies when it comes to selling and optimizing pricing. Companies spend millions on innovation and launching new products. Yet when it comes time to price, those new features, those new products, they spend a few weeks or months at most. Setting the price. And beyond price discovery companies are challenged to manage pricing and product catalogs. In today's unpredictable business climate, there's being buffeted by the threat of tariffs. Joining our conversation is Eric Carrasquilla, CEO of Vendavo. Erik brings decades of experience in the B2B software and manufacturing industries. And in his current role at Vendavo, he meets with their 200 plus customers on a regular basis. Erik has a unique perspective on the challenges and the opportunities facing companies as they manage revenue and margin in these volatile times. He'll share his insights into how companies can improve pricing and sales efficiency. The role of AI in shaping the future of price optimization, and his vision for the future of Vendavo. Eric, welcome to The Margin.
Eric Carrasquilla: Thanks, Andrew and Igor, I’m really happy to be here.
AD: So, Eric, you have decades of experience in the enterprise software enterprise application space, bringing new product and innovation to market. Going back to the days of ERP, CRM, more recently in the CPQ market, prior to Vendavo, you were running a significant division, at a very large publicly held company. Presumably lots of opportunity in front of you. And, what about six months ago, you opted to take the role at that Vendavo. Why Vendavo? What do you see as the opportunity there that was so particularly attractive?
EC: Yeah, I net it down to three things, Andrew: the space, the challenge and the investors. So on the space side of things, it's really important for me to be able to explain to one of my friends or one of my kids, like, what problem it is that we're solving. And, in the pricing space, in the monetization space in general, people get that and the kind of things that we do in the problems that we solve, it's not a nice-to-have, it's a need-to-have for people running companies and having them grow and be profitable. So this was the first piece and a check on Vendavo for that. The second piece is related to the challenge. And, I would describe myself as a recovering product guy. So for product guys in general, they love to build stuff. So for me, the challenge of building, the 2.0 version of Vendavo, I love it. one of the conversations I had with the board recently is, I was like, God, I feel like you guys are paying me to play with bubble wrap. Like, I love this stuff. Figuring out the puzzle. How do you go through it and grow? And then the last piece was on the investor front. Wanted folks that had skin in the game, understood the space, pushed hard but super supportive, and I couldn't be more happy with the folks at AKKR and Francisco. So for those three reasons, I was like, “hey, love where I'm at. CSGo is a great company, but this is just too good of an opportunity to pass up.”
AD: What's the ideal customer for Vendavo? What's your ICP? Who's the ideal fit?
EC: Yeah. So if if you look at it, it's typically a larger organization. It's probably a billion up on and up on the revenue side of things. It's something that's business to business. It's something that's in the manufacturing and distribution space. And when you start—and there's a lot more granularity around it, but that's, that's the starting kind of area code where we can really drive value, with our, with our platform. When you start to get outside of that, it's either in the overkill lane or, it's something that there's other vendors that serve that particular vertical industry better. And especially being in the SaaS business, at least my personal philosophy on this, it does you no good to sell something to somebody that's just not going to use it and not going to get value out of it. Like, we are in the rentals business and for the rentals business, happy, smiley winning customers. That's how you're going to grow your business versus just kind of stuffing stuff into lanes that it doesn't fit.
AD: Well, the other way we like to ask this question is “where is Vendavo not a fit?” When should a customer shy away?
EC: So, I would say again less revenue like, more of a mom-and-pop shop or something that that might be, $100 million a year in revenue. It's overkill. It's not even a vendor. Although, thing or not, a vendor thing, but like a price optimization tool, if you really got to ask yourself, hey, are spreadsheets good enough? Or is this something that, we can use this, this kind of a tool for the other thing and other industries like, if you look in the travel industry, honestly, someone like PROS does a really good job with, a lot more of the like the B2C commerce related items kind of offer management, search optimization. So in that case, again, outside of that manufacturing and distribution line, it's a, “hey, there's a competitor actually has really good in that space. Probably want to have a conversation with them.” And and just the thing that I'd say again, I think a lot of times software vendors fall in the, the spray and pray trap. there's never a customer that they would walk away from one way or the other. I've just seen over and over again, getting back to the “hey, what's the win for the customer?” Let's work back from that. And if there's not such a good fit, like really, it's best for everybody involved. Be transparent and go on to the next thing.
AD: We talk about the configure price quote or CPQ market and then price optimization pricing being the P within that, at MGI we've long held this view that CPQ is not a single market. It's three distinct markets: people in the market for a product configurator who have complex products and configuration needs, people that want to do price optimization, and then the sales quoting tool space. That's our view. How how do you think about CPQ? Is that a single market? Is it three? What's your angle of attack?
EC: Yeah, I agree with you 100%, Andrew. And it took me working in three other competitors to kind of go through and understand when you really look at and lens this through, what is the problem that they're looking to solve? It is three, as much as the vendors want to, like mash it up and make it one. So if you look at someone that's doing engineering to order for them, it's a super capital C in bold and underlined. It's, visual configuration that they're looking at doing like zillions of configuration rules. That's a C for them. Not that the P and the Q is irrelevant but it's less relevant, for customers that will go into particularly in the distribution space or make to stock, manufacturing. The P component of it for them is huge or specialty chemicals. It's huge. And it's more about, kind of the, the framework and the agreement related to the pricing and how you go through and set. And then there's other ones that it's just a quote volume component. So I'm with you. Really, it is three. There's some overlap but I think maybe the way to say it is there is a primary problem the customer is looking to solve and then the other two are adjacencies. Yeah.
AD: I want to go back to one of the things that I touched on in the opening, which is companies expand, invest so much in their product and services. And yet they spend on sales tools, on marketing tools. When it comes to pricing, which is the single lever any company can pull, quickly and relatively easily to improve the top and bottom line, are companies are less willing to invest in price optimization tooling and products like Vendavo? What's holding companies back? What do you think people aren't getting that they should?
EC: Yeah, I've seen a few things. Resistance to change, perceived ROI, and then on a data front. So on the resistance to change kind of a thing. It's just we've always done it this way. And what I'm mentioning isn't just related to CPQ and pricing. It's in a company in general. you have lots of manual effort. You have big teams of people that have kind of built their career around and like, “why would we put in something that's going to obsolete a team of 100 and 150 people?” So there's a piece of that. On the perceived ROI, there's two ways to to look at this. And one is the “oh, why are we spending X amount of dollars with this particular vendor to solve the pricing problem” versus, “hey, if you're a company that's a billion and up on the revenue side of things, for you to be able to get a 200 basis point lift in your profitability by putting some of these things in place, like the ROI is insane on that,” but it requires kind of going to the hey, this is the the outcome that we're going to get. And then the last piece I'd say is on the data side of things. And one of the things, a former colleague of, of mine who's a Brit, had this phrase he was talking about “grasp the nettle.” And I was like, “What? What is a nettle?” But it's basically going through and saying, hey, look, your data is never going to be 100% clean. Get it to 80 or 90 and start to go and roll and iterate from there. And then you can course correct. So for the companies that we've seen that have been very successful at this, they've been able to navigate those three areas. So on the resistance to change, there's a champion or a sponsor in there that that has the vision and understands the end state can help folks to get there. They're able to communicate that ROI internally and help people understand how this is an easy lift. We don't need to build something else. We don't need a factory. It's not a capital expenditure like can help there. And then on the data side of things, just how they roll and iterate.
AD: Igor, do you want to jump in here?
Igor Stenmark: What do you see as kind of the innovation horizon in your areas of pricing optimization, specifically? We've been watching this market for 25 years, and I've seen so many companies come and go and so many investors kind of investing and losing their shirts on some of this price optimization. Going back 15, 20 years ago, big data was all over rage. Lots of companies got funded, lots of PhDs were developed alongside that and written pieces, however, about optimizing pricing for consumer goods. But given what we see right now in the technology space, but generative AI, we are about to see kind of the next big leg up in innovation in this space. What are your thoughts about that?
EC: So on the AI front, there's a piece of it where I roll my eyes and go, look, most people that are talking about AI have no idea what it is. If they know what it is, they have no idea how to apply it. They know how to apply it. They're doing science projects, things versus everything else. So before I say what I'm going to say, realize I come from a place of going through so many of these trends, being skeptical, being a product guy with that said, something got let out of the box that is never going to go back in. I see a tectonic shift related to AI and specifically generative AI. The impact on the pricing space for this, and I think what you're going to see come out over the next 2 to 3 years in the space will define it for the next 20 years. And the reason I go through and say that and back to the hey, what's been part of the break around the data side of things, the kinds of access segmentation, putting this in the hands, more of business analysts to go through and sniff and do discover and then kind of iteratively roll from there, they'll be able to do that with the speed and the precision. That just has not been possible over the last 20 to 25 years. And what I've seen going through and having talked to like, more companies than I can count, one of the kind of anecdotes from one of our user groups sticks out to me where in the pricing space, you have kind of this dichotomy of two personas. You have the people that are the OGs that have been doing it forever, 20, 30 plus years and that are kind of not stuck in, in the traditional way, but are in the traditional way. And then you have, more of a transitory persona who might be in that space for 3 to 5 years. And then they're moving in different areas or these teams are getting mashed up in a revenue operations group or a growth group. And for them, the most important thing: help me do my job more easily. Help me look smart and help me do my job more easily. And generative AI on that front can give them such a massive lift. Back to your original question on the where do I see the innovation horizon? Absolutely. It's around generative AI, and you're going to see big things over the next 2 to 3 years.
IS: Do you think that the pricing discipline market might split even into more component parts? Because there's really a couple of different needs here. There's one customer that says, help me plan my pricing given my supply and demand picture, given competitive pricing, given my historical sales prices, help me design new alternative pricing methods and simulate this stuff for me and help me, like you said, make me look smart in front of my my peers and my management, my board versus someone who says I need to get a price—a correct price right now, the optimized price right now. Say I'm an Uber, or I'm an airline or logistics provider, I'm selling capacity on a ship in a container. I just need to do a price right now. I don't need to do any kind of design exercise. What do you what are your thoughts about something like that?
EC: Yeah, I think it kind of goes across three. So one is the set piece. And do that more intelligently, more quickly and more precision. Then the second piece around the get piece of somebody who's actually going to be using it within a particular deal. And then the governance piece on the whether you call it deal desk or the, that component of it. Three different personas but those three personas are absolutely connected, whether they're part of the same organization or they're adjacent and they work with each other and being able to have, a streamlined flow across those three areas and transparency and optimization, that's where you'll really see separation of the companies that are doing it well, and more successful in getting a better financial uplift versus kind of silo, silo, silo, which is basically what it is now. That's the that's the big challenge in the space for, and the downside of using spreadsheets. Great if it's on someone's laptop or on a SharePoint file. But then the how that gets into the hands of the rep and how that gets into the deal desk and the flow and tracking and optimization, that's what really falls down.
IS: Yeah. Integration, I agree with you. It's right now it's a bunch of stovepipes basically where people are doing, planning, are now talking to the deal desk, not talking to, the guy who needs immediate pricing right then and there.
AD: Eric, when you look at your customers that are most successful in how they're using AI today, whether it's in POCs or otherwise, what are the things, what are the characteristics of those companies that are making them successful or giving them better outcomes than others?
EC: So, the way I look at this is it's kind of Maslow's hierarchy of needs. And the AI-related use cases are higher up on the, on the hierarchy there. What I'm still seeing now with customers is so many of them struggle with the data layer and then the workflow layer of just getting the basic plumbing governance, the rest of those components in, once you have that stuff in, it is way easier to then apply AI across those three different pieces that I talked about. So most of them now are using it on the more of the discovery and insight side of things, more of the setting versus when you get into the rep and going into the deal desk. But that's a fast follow.
AD: We talked about CPQ being three distinct markets. If you look at Vendavo, in the past it was primarily been in the pricing space but has also made an acquisition in the other areas of CPQ–the other letters, if you will. Where as you look forward, where do you see the company investing? Where do you want to invest? Where do you see the most opportunity? Is it in bringing more of these things together or is it going deeper in pricing?
EC: Yeah. So so a couple of things I'd call out the first piece. And one of the things I didn't talk about was the role of incentives or rebates, particularly for those that go through the distribution channel. So if you look at that kind of set, get, and then the deal desk component of it, making sure that there's a clean flow across those items, because even though, companies will go through and start with digitizing one piece of that, the rest of them are fast follow. So this this is one piece. And then the second piece is—and that back to what I mentioned earlier, the generative AI use cases related to how somebody interacts with the application. So I have a friend of mine that's, in tech pubs and one of the things he had posted on his LinkedIn profile two years ago was “books are dead.” And for people that do tech pubs, that's basically what they call a user guide or an API manual or whatever else they call them books. And I was like, “John, what do you mean books are dead?” He's like, “no one wants to read the friggin manual, and no one's going to read the manual.” It's just I have a question. I need a particular answer. Go back to to what I mentioned earlier about the person or user group saying, help me do my job better, make me look smarter and faster and quicker, and everything else. It's not flipping through 20 screens to go through and hitting all these, dropdown boxes is, I need a deal that hits these particular price points. I need to solve these particular problems. And that's how they go through and interact with the application a lot more on making it easier, for folks to get value out of the app. That's the other piece that's a big, investment area for us as well. So you'll you'll see more about this in the tail end of the year and it's not just simply throwing a GPT interface on something. It's much deeper than that.
AD: Speaking of that, within Vendavo, a couple questions: one, what are you guys using in terms of, different models and what kind of data? Where are you getting the data to train the models?
EC: Yeah. so the last company that I was in, what one of the, the product sets was around it, it's like, journey orchestration and analytics, very much like B2C customer kinds of thing. In those areas there's a lot on the compliance side of things that you need to be sure that what you're doing is ethical, that you don't have bias, if you're giving medical information or something like that, data privacy and security, if it's anything related to PII or you're touching customer information, there's tons of guardrails that you need to do around that. And then monitoring and accountability, especially if you're doing something that's going straight out to the customer. A lot of companies that we've worked with said, hey, there has to be a human in the loop 100% of the time because of those sensitivities. On the pricing side of things, it's a bit different in that, what you're doing to construct and build models, it's anonymized data. You don't have PII, there's not customer information in there. You're not giving a cancer diagnosis to somebody. So, you worry about, hallucinations. There's always a human in the loop as you're going through and constructing and iterating. So most of the data, you're pulling from either some sort of data warehouse or straight from ERP or from CRM. I think the common denominator there is you're pulling it from multiple areas and the kind of techniques that you can do for machine learning and AI, these things have been, these things have been out for decades to go through and Use. I think the real trick is, especially with things like generative AI, how can you get smarter and more intelligent around the prompts that you're using? How can you get smarter and more intelligent around specific industry vernacular? And nuances within, again, the industries that that we focus on. That's, that's more of the, the trick that's there. And I think another thing is people maybe not as familiar with the space think of this as more of a newer phenomenon but vendors have been using machine learning and those techniques and constructing models, again, for decades, this pop recently that's come from folks like OpenAI and Anthropic and the other folks. But it's this is something that's been cooking for a while.
AD: As a CEO, presumably, your board is saying, hey, how are we using AI internally? Where do you see the biggest opportunity? Where are you investing across the company, not just in the product, but across the company? Where do you see it having the biggest impact?
EC: Yeah. So, product stuff aside, if I look across kind of horribly oversimplifying in a company, there's folks that build stuff, they sell stuff, or they deliver stuff. On the build side of things, there's a lot of very interesting use cases, like the documentation thing that I talked about earlier. That's an obvious one of how you go through it and you have all that stuff in like JIRA anyways, and just going through and doing GPT interfaces off of that for user manuals, implementation guides, API documentation, even writing code kind of a thing. On the sell side of things, leveraging generative AI. We've got some folks internally with our within our content team, that start with super talented, creative folks, paired together with generative AI and different ways of cutting together clips and different things for our go-to-market kit. And then on the deliver side of things, there's lots that we can do within projects and how we go through and scope things and how we articulate them in different views. So the thing that I found was even outside of the—I think where this really got traction was less of a corporate mandate of you should go through and do X, Y, and Z with AI or generative AI. It's more of a grassroots thing where if you stop and ask, ten people across those different areas of how are you using it? It's stuff they were coming up with on their own. I think that's to me one of the most interesting things about this is like the great democratization of the data and of the technology and with some great use cases that you couldn't have come up with yourself.
IS: Let's go back for a second to a product side and how you guys are planning to use AI for that. And, and this is, I think, a challenge with many vendors across many markets have a basic right now. So your customers expecting you eventually to optimize whatever AI interface you're going to give them to their data and keep it only to their data and make sure their data doesn't flow, say, from General Motors to Honeywell and from Honeywell to ABB or someone like that. But that puts a lot of pressure on someone like you as a vendor to really isolate the data and really feel that. Is that realistic? Let's say you have a thousand customers. That's a significant challenge. If you have, 20,000 customers, that's a whole different ball of wax. Have you guys thought about that? And are you countering that in the field already with AI? How do you guys think about it if you have a point of view on that?
EC: Yeah, I think, in other industries, that's a bigger deal when you start dealing with PII or you're dealing with something that a company feels is their secret sauce. I really feel the biggest impact AI can have is less on the “hey, we pulled pricing science and models from all these different components,” but more on the front end of it of how can we help folks involved in that commercial chain do their job more effectively and get the time-to-value and better business results and how they go through and interact? If you even look at Apple's products, you don't get trained on any of that, you pick it up and do. And so I think that's the bigger that's the bigger opportunity in the space. And look, I totally get going across different customers to a lot of extent. The models that they end up using, they've put a ton of time into their they have big data science teams, big pricing teams. It's their secret sauce. They don't want to share that with other people. There's a bit of territorial-ness of what do they know? We've got this team. This is what we have. So on that front, I think it's more about the ability to ingest their models and what they've put together. There's stuff that we have within Vendavo anyways, but what we found more often than not is like, “hey, we've we've done this over the last 20 years within this particular lane in a distribution channel. Use our model. You can augment what with different scoring that you have as well. But we want you to use ours. I don't care about the other stuff.”
IS: I agree with you. I think the we use cases for use of product documentation. Just give me a basic answer. I don't want to be left scrolling for pages of information. Just tell me how to use something. What's new, what's inside of my system? That's huge and that's often overlooked. And AI can play a huge role here.
EC: Yeah. Time. Time-to-use in time and time-to-value.
IS: Implementation.
EC: Yeah. And the other thing that I look at too is there's a company that a number of my colleagues from my Conga days
went to called Eightfold. And the idea around Eightfold is not just a little bit better, but this much better kind of a thing. And I think, part of the danger or, back in the Gartner days, the what is it, the trough of disillusionment, all the hype around the science project and it's like, okay, explain to me like I'm a five-year-old kid, this is how much better? This is better. And it’s like this? And we're paying this much for, like, the law of diminishing returns. So, I think that's something that I always lens this through for AI, where is the eightfold. And I strongly believe the Eightfold is on the front end of that versus the back end.
AD: So it sounds like you're a kind of what you're saying is that, just as the internet kind of had this mass democratization of access or as an entry point into applications that kind of transformed the UX of enterprise software. It sounds like what you're saying, in part, is also that generative AI is going to transform how people think about how they interact and get benefit from systems. And it may not necessarily be this giant, unique, a-ha but it's going to be a whole bunch of little things where people it's it's far easier to use, analytics and reporting gets transformed, becomes that it's just a query now. Is that it?
EC: Completely. Now, there's a little asterisk at the end of that, which is to say, okay, now that you have this insight, what do you do with it? So then it starts to get tricky. How do you handle workflows across those different personas areas? How do you handle security? How do you handle transparency? How do you handle interfaces into other systems? And that's where platforms like Vendavo really shine. So I think the combination of those two things, the help me do my job better, quicker, help me win and look smarter with also the how do I connect across the rest of the commercial chain to get that margin and revenue uplift? That's really the trick. Because right now, I mean, you can throw a bunch of spreadsheets into ChatGPT and get some interesting insights, and that's like, okay, now what do you do when you do with that?
AD: Yeah. How much? That's one question we didn't, I want to dig into, which is how important is domain expertise? I mean, in software we talk a lot about feature function capabilities. But in these enterprise spaces, in particular areas like CPQ and specifically pricing, understanding the vertical industry, the nomenclature of that industry, the idiosyncrasies and seasonality of it. How critical do you think that is and how much do you enforce or not enforce that when you're talking in a sales situation, the importance or value of bringing domain expertise to the table?
EC: Extremely critical. I've been part of vendors that were horizontal and kind of had to spray and pray and we'll throw it over the wall, have a partner deal, and go ahead and take it. And, in 1.0 versions of a particular space or maybe, back however many years ago and it's kind of a perpetual model and who cares if they're successful? That worked. Doesn't work now. So many customers that we have are they did that, didn't work, threw a bunch of money down a hole. It's like, hey, tell me something I don't know. Like you guys do this every day for customers that look like me, tell me something I don't know and help me get a better result and a quicker time to value with lower risk. I look at what companies like Veeva had been able to do, Vlocity before they came into Salesforce. Even Model N that I was at on the pharma side of things, that it it really, really matters if what you're trying to do is get happy. Smiley customer that's winning and not just, hey, we got to deal through. It’s critical. And that's one of the reasons why we focus on the industries that we do. I mean, we have—if you'll go through with our business consulting team, these guys are gangsters. They've been customers, they've been competitors. They've been in pretty much every role across the company. Every major role customer facing, they're just fountains of industry-specific information. So if you threw them in a telecom environment, they're great people, it's not the same value add. So, anyways, going back to the original thing, yeah, I think domain expertise is critical. And for any vendor that you're dealing with, I'd go through show me five customers that look and sound like me that are using your solution and getting value out of it and I want to talk to them. I want to talk to the people that are going to be on this project. I want to talk to—for the rep, are you just a general sales person or do you know how to solve problems in my lane?
AD: Excellent. Eric, it's been really fun. In closing, what's one thing every CEO or CFO should know about Vendavo but doesn’t?
EC: I would say that Vendavo is not a pricing company. Vendavo is a, “Hey, you want to bump up 200 basis points on your EBITDA?” It's that company and we can walk you through how we go through and do that. But, I look at for myself as a CEO and coming into the role, I'm not looking at at buying any software asset for the sake of software asset. I'm thinking constantly for my board, for my investors. What can I do to profitably grow the business, and what can I do to get my margins bumped up? And someone coming to me and saying, hey, I can, get you 2 or 3 points on that. I want to have that conversation all day, every day. Tell me how you do specifically for my industry.
AD: Terrific, Eric, thanks so much.
EC: Thank you again, Andrew. Great seeing you again. Igor, really enjoyed the time.
AD: Excellent. Thanks. Thank you for listening to the margin. If you have questions about today's episode or if you'd like to schedule a call with an MGI analyst, reach out to us at insights@mgiresearch.com. You can also reach us on LinkedIn, Facebook and X. You can find more information about our research and advisory work at mgiresearch.com. Until next time.