The Future of GenAI Pricing: Monetization Strategies & SaaS Trends with James Wilton

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Mark Evans: It's Mark Evans, and you're listening to Marketing Smart. One of the biggest debates in the world of Gen AI isn't about what it can do, it's about how to monetize it. Despite the rapid growth of AI powered tools, there's still no definitive answer on the best pricing model. Should it be a flat subscription, purely usage based, or something in between? Today, I'm joined by James Woven, one of the world's leading experts on SaaS pricing and monetization, monetization, a former McKinsey pricing expert. That's on his LinkedIn profile. James has spent over a decade helping startups and fast growing companies capture more of the value they create through smarter pricing strategies. He's also the founder of Motivate, the go to consulting firm for SaaS and tech companies looking to optimize their pricing. In his upcoming book, Capturing Value, James explores the emerging trends in Gen AI pricing, including his take on the hybrid usage tiered model, one that could shape the way AI powered software is sold in the future. In this episode of Marketing Spark, we'll discuss why pricing GenAI is such a challenge, what companies are getting wrong, and how SaaS founders can future proof their pricing models. Welcome to Marketing Spark.

James Wilton: Thanks for having me, Mark. Great to be here.

Mark Evans: Before we dive into the Gen AI pricing landscape, I'm interested in getting your take on the BDB SaaS landscape. Wasn't that long ago that SaaS pricing was pretty straightforward. Companies sold their subscription on a monthly or annual basis and offered different tiers of service, mostly around features or the number of users. So that model seems, I don't know, antiquated. Price has become more complex, and subscriptions are just one way to purchase services. What's happening, and what are the biggest challenges facing SaaS companies when it comes to pricing, meeting customer demands, and, of course, the rise of Gen AI pricing?

James Wilton: That's a great question. I think probably the first important thing to think about is it's true that generally SaaS pricing has been done a certain way. People have got quite comfortable with these kind of user based subs subscriptions tiering by different features and so forth. But just because something is done that way all the time doesn't mean that's the way that it necessarily should be done. You know? And I would honestly always say, like, the right way to price something, you've really gotta think about several things. You have to think about how the product adds value to users. You have to think about your company's own internal objectives. What are you trying to achieve? You need to think about your market, their needs, their wants, their their buying preferences. All those factors are really going to come together to decide what is the right way for you to be pricing. Within SaaS, you'd see a lot of concentration of certain metrics and certain styles. But actually, if you think about the whole set, there's a lot of variety because people who are doing it well are thinking about all these different things and are coming up with quite different models. It usually means that there's some form of charging a reoccurring or recurring payment month month over month, but the way that they scale the value and the options that they that they give you are quite different. And so when when I think about newer technologies and and gen AI, all the principles are the same. You still have to think about that in in in the same way. It's just that the type of products we're dealing with now are slightly different, and the knee jerk responses are gonna be slightly different as well.

Mark Evans: If I say this, you'll probably push back, but I always think of pricing as a combination of art and science. Mhmm. In some respects, you do an analysis, you quantify the value of your product or you try to quantify it, and you establish a price that you think people will pay. Sometimes it's an easy proposition. Sometimes it's gut feel, especially for early stage companies that really don't have a track record and don't really know how much value they're delivering to companies. Has the emergence of these different models made pricing that much more complex? Because you have so many different options, so many different paths that you could follow, so many different models that you could adopt. Or now there's you're adopting a combination of models at the same time. If you're a SaaS company or a SaaS founder, how how do you approach pricing these days? In some ways, can be fairly intimidating.

James Wilton: There are a lot of options. If you go back ten to twenty years or so, I think at that time, the amount of complexity you started seeing really increased. Right? Because you previously went from a system where software was still fundamentally installed by by CDs or DVDs on people's desktops. Right? The pricing models for those became relatively simple because you're almost always gonna be charging based on how many seats you have because that's how many that's how many installations you you need. But now since everything's moved to the cloud, it gives companies so many more options for, like, the metrics that they can pick for how do they how do they scale the pricing for their different customers. It's a massive opportunity because you have so much more control and freedom to be creative now, but it also makes it more difficult because you have so many more degrees of freedom and things that you have to consider. It can become overwhelming for companies.

Mark Evans: The reality for many SaaS companies is Product comes first, then they do sales. Mhmm. Then sometimes marketing comes as a necessary evil. Pricing is one of those things for many companies that when it gets thrown into the mix, they probably don't spend as much time focused on it as they should. They probably don't do as much research as they should. Don't They talk to customers about pricing as much as they should. So what is the better approach now? Given the complexities of the of the pricing landscape, given the fact that there's the different models, do companies need to build pricing departments? Do they need to work with companies like Motivate to make sure they're doing pricing right? It strikes me that they probably have to make a much bigger investment in terms of time, energy, and resources than than they've done historically. Is that an accurate description these days?

James Wilton: Pricing is an opportunity that, a lot of people don't take advantage of, honestly. What you just said at the beginning of that was exactly right, Mark. People tend to think of pricing as an afterthought. Right? They build that product. They get themselves set up to sell it, and then maybe I'm exaggerating, but a week before, we have to make our first sale. Oh, we need to decide on a price point. How should we price it? The usual thing to do there is just to think, well, you know, what's the right way to price something in this market? So they go and they look at their competitors and see what's done and just kinda copy those. Right? That would probably be a fine way of doing it if there was a right and a wrong way to price something in a particular industry segment. But I would suggest that's just not true. Right? Like, so much of it is gonna be based on what you are trying to achieve. And I always use this example, but if you imagine that you have two companies, right, and they serve the same customers, they have exactly the same product, same executive team. Everything is exactly the same for these two companies apart from what they're trying to achieve. Right? And company a is really focused on just trying to get a lot of customers quite early, and they're gonna worry about really monetizing later. You have the other company, company b, who wants to maximize profitability right out out of the gate. Mhmm. If you think about the decisions that those two might make to to match that that that set of objectives, company a, the one who's focused on volume, is probably gonna price really low because they're gonna maximize the amount of companies that they can that they can sell to then. They're probably gonna give very few options, make a couple of simple choices because that's gonna make it easier for people to buy. They're gonna make their their choices quicker and speed up that whole process. They're probably gonna be very transparent because they want to maximize the chance that people will consider them. If people can see their pricing, they're gonna be more likely to do that. And they're probably gonna come in with a a guarantee that we will never raise price on you so that people don't get worried that they're going to suddenly find out that they're doubling, tripling their price. But company b, when they're trying to maximize profitability, by contrast, they're probably gonna price really high because they wanna make sure that they get the maximum margin they can on every single deal. They're probably gonna have an incredibly complex and granular pricing strategy because it's in their interest to monetize every distinct part of piece of value that they can give through their product. They're probably going to not be transparent with their pricing because it helps them to just get to a customer, feel them out, figure out what their willingness to pay is, and then put the price on the table that matches their their their willingness to pay. They're probably gonna want to ramp up price quite aggressively once they get a customer on board on the assumption that the customer's gonna see more and more value over time as they start to use it. We just talked about the fact that these companies are exactly the same in every in every respect apart from the fact that their objectives are different, and we just defined two completely different pricing strategies for these two. Right? If companies, when they're building their their pricing strategy were clearer about what are we trying to achieve, given that we're trying to achieve multiple things which may pull us in different directions, what is the the relative priority of these things? That alone would be a massive shift in how likely you are to build a pricing strategy that is actually gonna help you and actually be a strategic lever for you as opposed to just a tactical decision.

Mark Evans: It is interesting that you talk about pricing being strategic lever as opposed to an afterthought. You you take into account the creativity of the options for different models and the competitive landscape. Curious about your take on how does a company, given the the strategic importance of pricing, how do they properly do I I don't know whether properly is the right word here. How do they do competitive research? Every company has different levers that they're pulling Mhmm. Yep. Different models that they're embracing. How do you effectively do competitive research so that you can go to market knowing that we have context here. We've benchmarked different types of companies, different types of models, so that you can make a smarter decision.

James Wilton: Yep. I'd say in general, companies when they're building their pricing strategies, they need to do they need to get a really good fact base to do it. And they think you need you need different types of fact base. You do need a competitor fact base and understanding what your competitors are doing, how they're pricing, what their relative price levels are. Doesn't mean you need to copy them as we just talked about, but just knowing what they're doing and inform probably how familiar and how palatable certain pricing strategies are going to be to to the customer. And then also, of course, when you build your your offerings, it's gonna dictate how favorable they look in in in comparison to those. And, of course, you also need to do customer research as well, is all about going out and understanding their needs, their buying preferences, their willingness to pay, and so forth. You're gonna see differences across different segments. But, yeah, with with with competitors, this can be it can be tricky. It can be well, it can be easier or trickier depending on the kind of market that you're in. If you're if you're a consumer product or you're selling to SMBs, it's probably relatively easy to do competitor research because a lot of those kind of companies are gonna publish their pricing strategies openly.

Mark Evans: Right.

James Wilton: On their their websites, you can go in and you can look and see this is what their pricing and these are the features that they have. You may not be able to get, you know, great granular data on exactly when they're saying they have a feature. Is that the same level as your comparable one? Is it better or worse? You may not get to that, but you get you pretty good pretty good data. In situations where you don't have that, maybe it's more you're you're serving the enterprise space and everything is, you know, behind the contact us area. It's a lot more difficult. I would say what's proven to be quite helpful for for us, and I know a lot of other people who are building out pricing strategies, is to go and speak to people who who have formally worked in these in these companies or customers who have formally bought from these from these customers and speak to them and try to understand what they what they were quoted at, what they paid, like, directionally. You really shouldn't go in and ask them and say, like, hey. How much does this company charge for for this? You start getting into sort of competitive stuff. But, generally, how much would you expect this kind of a company to charge for this is generally certainly anything from a pricing strategy point of view. Like, generally, how do they think about their offerings and what's their price metric and stuff that is follow So that information is out there. You just need to find the right people to talk to to go and get it.

Mark Evans: Let's turn our our sights on the Gen AI pricing landscape. The obvious question is, why is monetizing Gen AI still an open debate? And what makes pricing in this space uniquely challenging compared to traditional SaaS models?

James Wilton: Yeah. I don't wanna say that we're overcomplicating it in general. That's probably slightly wrong, but it's it's also not entirely wrong. Right? What is really different about GenAI is you have a bunch of these functionalities or products which create value in quite a different way than SaaS products typically have. It feels very different from that point of view. So we talked about earlier, there's these kinda knee jerk pricing strategies that everybody goes to for SaaS doesn't necessarily make sense in an AI world. Like, knocks everybody out of their comfort zone. It forces you to go back to those principles that I talked about. How do we create value? Right? What do the customers want? What do they how how do they want to buy? How are we thinking about that? There's a little bit of that, I think, which makes it difficult. The other real complexifying factor of Genii is that right now we do have this cost challenge, which is not something that SaaS companies have typically had to to to deal with. Mhmm. And that GenAI products, they typically have a huge amount of compute needs. At the moment, that compute is quite expensive. And also, people are just starting to adopt gen AI products, and I feel like we don't really have a great understanding of just how much usage is expected or typical yet. It's gonna be changing all of the time. If you're gonna set a pricing strategy and you're trying to think about how much cost am I going to incur through the usage, it's just very difficult to to do. Of course, companies on one hand, they want to get adoption of the product so that people see the value and they're more likely to scale with them, but they don't wanna put themselves in a position when they're underpricing it and reduce their profitability or put them in the red. That's a big concern in the back of everybody's mind.

Mark Evans: You add into the mix the emergence of DeepSeek, claiming they have a model that's just as good as ChatGBT, claiming that they develop it for $6,000,000, which threw Wall Street and Silicon Valley into mayhem because of the models and the the valuation models were thrown out of whack. It's left to be seen whether what DeepSeek actually has and the fallout of that, but it's very volatile right now. A lot of AI companies are experimenting. They're dabbling. They're seeing what models work and what don't. What are the biggest mistakes that you've seen AI companies make when it comes to pricing so far?

James Wilton: I've got two sort of categories of mistakes. I think one is how do people price when they start adding GenAI functionalities in, and second one is, like, mistakes they make in terms of the actual pricing pricing structures. I think the first one, just in terms of price levels, I have seen so many examples of companies who are building or have built GenAI powered features or capabilities or whatever it is, And then it just gone to customers and said, oh, we've built this. Here it is. We're now gonna increase the price of this package by twenty thirty, sometimes in Canvas case, 300%. There's these huge price rises that come from this because we're trying to cover the cost of of doing this.

Mark Evans: Mhmm.

James Wilton: But we're still in a world right now where most of this technology is niche. Right? Which means that some customers are gonna really value it and be willing to pay quite a high price for it, but a lot of customers won't. So you can't just force this functionality in a standard to a base package and expect that everybody's gonna pay it automatically. You've got a lot of these stories now of people pricing these things quite high and having real pushback from customers. And as I say, I think Canva is probably the biggest, most noteworthy example of that because they increased the price of one of their packages by 300% to a level, by the way, that actually is quite a reasonable price level even taking out GenAI. Like, it's just more competitive now. But they justified it through the fact that they had this new GenAI features and customers were not having that justification. There's been some good research recently, and Kyle Poyar called out on his on his newsletter growth on on Hinge. He actually cited some research from a company, the name escapes me, but talks about how customers actually are not willing to pay more for features just because you say that there's GenAI tech in there. Right? Like, if you can convince them that there is extra value that comes from that, and this is how it creates value that they gain, but just saying that it's GenAI powered is not enough. Makes sense, but that's practically what a lot of companies, I think, are doing and falling into that trap.

Mark Evans: I spent a lot of time focused on brand positioning and messaging. Companies like Intercom, for example, their home pages say AI powered. They've abandoned their brand positioning and focus and jump on the AI bandwagon. Some of them try to torque their pricing by saying, it's AI. It's gotta be worth more right now. It's an interesting marketing strategy, let alone a pricing strategy. Wanted to ask you about the fact that major players like Google have set early pricing benchmarks for AI usage. How should startups and midsize companies position their pricing strategy room to remain competitive while while capturing value?

James Wilton: Is it it's probably one of the most difficult areas to get into. Right? Because everything's changing all the time, and you do wanna make sure you are competitive. More than saying there is any one particular thing that you have to do in terms of your actual price setting, it's just making sure that you have a really good understanding of a, what your costs look like and how they scale, and then b, what your customers look like, what their usage patterns are starting to look like, although you're gonna have to keep analyzing this on a on a long term basis, and what their willingness to pay is across these different segments. If you can know all of that in addition to keeping tabs on what your competitor's doing and what those reference price points are for what they're doing, then you'll at least have the full set of information that you need to be able to make good balanced decisions. It's it's not gonna be a set it and forget it type exercise and landscapes constantly moving, and you're gonna have to keep refreshing. You advocate for a hybrid model that blends subscription and usage based pricing. Can you break down why this approach works best for Geni applications? I'm actually surprised we haven't seen more of these models yet. There's a lot to be gained from that approach. When you started to see these user facing GenAI products like ChatGPT and Copilot and so forth, that's right. The initial move was to price it unlimited use on a per seat basis. That's generally the model that came out, which of course is about as traditional SaaS as you can think. We've got this incredibly new tech. We're saying everything's different, and we go right back to what the sort of probably the most comfort comfortable SaaS pricing model is. I don't think it's a terrible model in itself. For some of these assistant type platforms, there is unique value that keeps coming for every single user who uses it provided they're gonna use it well. Right? It's not that it doesn't make sense and the value doesn't scale with users. But I think because we have the cost issue and we don't know exactly how much people are going to use it, then the price level that you end up having to set for that per user license ends up having to be really high in order for you to hedge. Like, Copilot was priced at $30 per per user, which is more than you'd pay for the entirety of of the office suite. Addition to that. Or, you know, so you it's it's a really high price to get people in at. If you want to build comfort and get people adopting it, that's a barrier to that approach. That's tough to get around. You could also argue that value does scale with usage to a certain extent here. You could have one user paying $30 and getting an insane amount of value given the amount that they're they're using it and somebody who's not getting really much. You're not price differentiating particularly well with that offer. Then if you go to pure usage, that's sort of the next sort of wave of price models you see when we're pricing very granularly by usage. You're now far more able to scale with costs so you can cover that far more accurately. You can get down to lower price levels, which also makes sense. But you get to problems there as well, I think, because although value probably does directionally scale with usage and that people will probably not argue that the more I use this, the more value I I get. I don't think people will necessarily always argue that if I use it three times as much, I get three times as much value. I think when anybody who's interacted with these tools like like ChatGPT, they incur costs every single time you type a query and the machine runs. You don't necessarily get value every time you ask it a question. You usually have to ask it several questions to get the thing that you are looking to get to. If you're charging customers for every single, for want of a better word, ping of of the system, they're gonna start questioning whether this is really valuable or not because I'm getting I'm having to pay extra for every single one of these. Plus, you get to the situation where as a buyer, I don't know exactly how much all my users are going to use this, so I don't know how much I'm gonna end up paying. It's the very, like, typical pains of usage based pricing that you see back in the the SaaS world. So I think that's that model has some issues as well. The hybrid model that we got to where I think you're charging by users, you're giving user licenses, but you're scaling the price of those user license by tiers of usage marries the best of both of those worlds. In that situation, you can create a low usage, low price version, could even be free, and you can make sure that people then can dip their toe in the water and experience it and try to understand the value and get somebody who's maybe not using GenAI to start using it. But then you have a mechanism within your licenses to bring people up to higher price levels as their as their prices increase. I might not wanna pay for 300 additional transactions, but I might be okay saying if my usage level doubles, I'll pay an extra, whatever it is, 40%. That might make sense to me. You marry the models. You still have a way of scaling with value and with cost, but you keep the nice simple user based based model, which keeps everything easy to to engage with. You are starting to see some players getting towards that point. I think ChatTP team have a couple of different tiers. I don't necessarily think they've thought about the tiers in exactly the right way, but it's moving into that general structure. We're gonna see more gravitation towards that kind of an approach going forward. Yeah.

Mark Evans: I use a lot of AI gen tools. I love the technology. I feel like I'm at the early days of the web when all these tools are starting to emerge, you could do just about anything. I have a pro account for ChatGPT, $20 US a month. I use it a lot for different reasons and different ways, and it's tremendous value. Like, the ROI on that is unbelievable. For companies like Chatuchi BT, there's an opportunity and a challenge there. There's an opportunity for them to embrace people like me and upsell me and offer even more value. There's also a challenge trying to get people like me to pay more. You mix into that the competitive landscape and all the pricing strategies, it's huge challenges, huge opportunities at the same time.

James Wilton: I'm certainly not gonna argue that it's an easy thing to design because there are a lot of considerations, right, for how you design the tiers and the thresholds and so forth. I'm very similar to you, it sounds like, with the chat GPT usage. I have pro. I use it all all the time. I feel like I get a huge amount of value from it now in the level that I'm using it. As far as I'm aware, the only tier above actually, plus, isn't it? It's like plus is the version that we have and pro is the one that they just Pro the

Mark Evans: big one.

James Wilton: Yeah. $100 per month and it's basically unlimited users, which actually it sounds like they're still they're losing money on that from what what Sam Altman said recently. If ChatGPT turned around to me and said, hey, you're using plus too much. We need you to buy pro now. I'd be like, well, I'll just scale back my usage because I don't wanna pay $200 per month. But if they turn around to me tomorrow, tap GPT, by way, if you're listening, please don't do this. If they were to turn around tomorrow and say, James, your usage just kinda crushed over a threshold, you now need to be in the $30 a month, $40 a month tier based on that. I'd probably be like, okay. I'm still getting a reasonable amount amount of value. As long as I'm in that tier for a while, I'm not gonna spiral out of it. That probably is an amount that I would pay extra. Likewise, there might be people who aren't willing to buy plus for $20, but might be willing to pay $10 a month for lower usage restriction. There just needs to be some more thought on the right level of granularity to be able to effectively price differentiate across the base. Yeah.

Mark Evans: The other dynamic is we're in the midst of an arms race. We're at a time when building these tools, relatively speaking, is easier than ever. There are no lack of competitive options, whether you're in the chat space or any other business application. If something gets too expensive or you don't like the features or simply have app fatigue, There are other applications. Some of them are free. A lot of them are low cost. That adds to the pricing dynamic. You have to think about how do we manage pricing? How do we manage churn at the same time when there's lots

James Wilton: of options? I think the the vendors need to think about features which are gonna help them defend price point even in a world when there's a lot of cheaper options around it. It's probably an obvious one to everyone, but I've been thinking recently about data safety. I've been using ChatGPT as I know a lot of people have, but I'm still somewhat nervous about what I put in there because I've played around with the privacy features and tried to make sure that the information is not going anywhere, but I don't know for sure that it's not ever gonna end up anywhere that I wouldn't want it to. I probably feel a lot better with ChatGPT's level of privacy than I might do with some other providers in this early days that providing that level of security is probably something that people would pay extra for even if the the output of the program is similar.

Mark Evans: We started this conversation with a loaded question, multifaceted question. I'm gonna end the conversation with a loaded question. What trends do you see shaping the future of SaaS and Gen AI pricing, if you can delineate between the two these days? And could we see pricing shift towards models like revenue sharing, paper performance, or flat rate pricing as the market matures?

James Wilton: The short answer, I do see that there's gonna be those kind of models. With GenAI, we're going to end up completely converging towards any particular way of doing this. Honestly, I don't think that's the answer. We might end up with one model that is slightly more prevalent than other ones, especially when we get into the world of agentic AI. You've got these things that are actually doing stuff in addition to just giving you information or giving you written things. You're gonna end up with the value being provided by all of these different agents as being very different. It may make sense for some of those to be priced on a per user model. Right? You have an agentic AI who basically becomes your personal assistant. You're probably gonna think about paying that in the same sense that you would be for paying for an an EA or somebody who would be handling those kind of tasks. You wouldn't necessarily want to think about something that scales with a level of value because it's more of an administrative thing. You're probably gonna think about the value of that way versus if you were using it as like a a marketing use case and you're doing it to to generate campaigns and things, there's probably a certain amount of value that comes with how much work and how many campaigns it's able to do. Then you might end up with more of a workload based or a usage based way. That's another example, an an agentic AI that works in a manufacturing shop and constantly analyzes the processes to make sure that there aren't sources of inefficiency and corrects them and saves you money. That seems like a perfect case for an output based based model where, you know, you're gonna pay it based on how much that it it saves you. So you do get it not revenue sharing. It's more of a sort of a a cost saving sharing type approach. But I think all of those models are very different pricing models, but they all make complete sense given the use case of those different things. Now that we have this world where you're gonna be able to see that value alignment far more clearly and the attribution is gonna be so much easier because there's gonna be less of a step between where the AI ends and where the the value ends up happening. You have the opportunity to be able to price in all

Mark Evans: those different ways. Final, final question. I've written three books. I understand the pain that you go through when you write a book and the fact that it's a labor of love more than a financial exercise. I'd love to talk to you about your book, Capturing Value. What prompted you to write it when, you know, what kind of madness did you suffer from that said I need to write a book? Why would someone wanna read it? What's the value that you're hoping to deliver with the book?

James Wilton: Well, thanks for asking. I'd say with the initial idea for the book, it it was to answer a pain point that I was seeing all of the time. You've brought up earlier how sort of people stay in start up start with a great product, and then they think about sales, and then they think about marketing. It's very rare for a start up to bring in pricing expert person who knows how to build a pricing strategy. What tends to happen is they get to a certain point and somebody on their leadership team is told, by the way, we'd love you to build us a new pricing strategy now. This person invariably doesn't have any experience in doing that before, so doesn't know how to do it. And, obviously, changing your pricing is not like a casual thing. And there's a lot of opportunity, but there's a lot of risk there as well. Your customers can leave. You can end up putting yourself in a worse position. It's really difficult. These people have this hospital pass given to them. And if they don't have budget for consultants or people who are used to doing this for them, all they can do is start looking for information out on the the Internet and books and things. I I would say there's a lot of information out there around SaaS pricing, which is at best misleading and probably at worst wrong. What I wanted to do with capturing value was to be able to give these people something that can talk them through the entire process of building a pricing strategy for a SaaS product or really any kind of tech product that you might be wanting to monetize. It's structured so that you go through all the different things that you need to think about. I think you're given enough theory so that you could understand how to think about the area. You've got enough to be dangerous. Talks through the different options that you have at your at your disposable and what are the what are the what are pros and cons there. And then also gives you some practical steps for saying, if you now need to go and build this out, these are the things that you should do. These are the things that you should get, and this is how how you do it. It should be a really good one stop shop for being able to talk somebody who maybe doesn't know exactly how to do this to be able to make a pretty good attempt at it. That's how I hope people will use the book.

Mark Evans: The book is available on Amazon and all the leading bookstores?

James Wilton: Absolutely. Yes. It's really available on Amazon right now.

Mark Evans: Awesome. Tell me where people can learn more about you and Monovate.

James Wilton: You can learn more about me on my LinkedIn profile. The you can learn more about motivate at our website. It's motivate.com. And capturing value has a website as well. Actually, the book, it's it's capturingvalue.com.

Mark Evans: Thanks, James, and thanks to everyone for listening to another episode of marketing spark. If you enjoyed the conversation, rate it, follow it, of course, subscribe via Apple Podcasts, Spotify, or your favorite podcast app, and share via social media. If you're a b to b or SaaS company with a million to $10,000,000 in revenue and you're looking to jump start your marketing, we should definitely talk about how I can help you as a fractional CMO and strategic adviser. You can reach out to me by email, mark@mark.ca. Connect with me on LinkedIn or visit marketingspark.co. I'll talk to you soon.