Ep. 161 - Is User-Based Pricing Dead? How AI Is Reshaping SaaS Monetization

Guest: James D. Wilton, Founder & Managing Partner of Monevate

The advent of AI is forcing a complete rethink of traditional SaaS pricing—particularly the long-standing dominance of user-based pricing. 

In this episode, pricing strategist James Wilton explains why AI-native products are undermining the foundations of per-seat pricing models. 

Unlike traditional SaaS, where users are the proxy for value, AI tools often perform tasks autonomously, disconnecting cost from usage in unpredictable ways. 

As a result, the most value-aligned and sustainable pricing approach for AI-powered platforms is shifting toward usage-based models. 

Wilton argues: "If you’re still defaulting to user-based pricing, you’d better be ready to defend it."

📌 Other Takeaways:

• Misaligned Incentives in AI Pricing
  With AI, the metric that drives cost (compute) is often not the same as the metric that reflects customer value—creating tension between profitability and value-based pricing.
• The Rise of Usage-Based Pricing
  As automation and AI reduce the need for additional users, user-based pricing loses its relevance. Usage-based models offer better alignment with customer value and revenue scalability.
• Customer Expectations Are Changing
  Enterprises prioritize cost predictability. Even in AI-native environments, buyers want fixed pricing or capped usage tiers to maintain budget certainty.
• Real-World Example: Help Scout’s Pivot
  Wilton shares a case study on Help Scout’s move from per-user to “contacts helped” pricing. The shift improved customer satisfaction, better aligned with value delivered, and even offered greater revenue predictability.

📚 James Wilton’s Book:
 Capturing Value: The Definitive Guide to Transforming SaaS Pricing
Get the blueprint for designing pricing that drives both growth and profitability without shortchanging strategy.

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Transcript

Welcome to SaaS Backwards, the podcast that delves into the strategies and stories behind successful SaaS companies. Today we're returning to one of my favorite topics, the critical role of pricing in driving SaaS growth and profitability. 

Ken Lempit: Joining us today is James D Wilton, an expert in SaaS pricing with more than 12 years experience helping startups and fast-growing companies maximize value through strategic pricing. James is the founder and managing partner of Monevate, a boutique consulting firm specializing in monetization and pricing strategies for SaaS and Tech companies and was formally at McKinsey where he led the pricing practice for tech startups and scale ups. In addition to his consulting work, James is the author of Capturing Value, the Definitive Guide to Transforming SaaS Pricing and Unshackling Growth, A comprehensive resource offering Actionable Insights into developing effective pricing strategies.

Welcome to the podcast James.

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

Ken Lempit: Happy to have you here. This really is one of my favorite topics. before we dig into the questions I prepared for you today, could you please tell our listeners just a little bit more about yourself and your company, Monevate.

James D Wilton: Yeah, absolutely. I guess my background is I've been in consulting for pretty much my entire career. Goes back, about 20 years. Started off in sales and marketing and strategy consulting. I happened across pricing strategy largely by, by chance I had the opportunity to go join an internal consulting team that was focused on that.

And I kind of, I rolled the dice on it, mainly from a lifestyle perspective at the time. But as it turned out, like a light bulb, you know, flashing, I just found that I, I love pricing. I found that it was, it was way more strategic than I was expecting, and there was a lot more room for creativity than I was expecting as well.

So I was, I was hooked and I decided that I wanted to spend the rest of my consulting career really focused on pricing. and I, I built a real specialty in pricing for, for SaaS and for tech, for other, innovative products. As you mentioned, Ken, I then I went to McKinsey. I joined McKinsey in about 2018.

I was brought on to lead the pricing practice for Fuel, which was the part of McKinsey that was focused on startups and scale ups and other fast-growing tech companies. And then I decided to go off for my own in 2021. I really liked the work we were doing at McKinsey, but I found it, you know, quite difficult to serve the kind of companies that I was interested in working with because of, McKinsey provides a lot of value, but the fees are, the fees are, uh, commensurate with that.

So I really wanted to create a firm that could keep that level of, of value and keep that level of pricing expertise, but be able to work with a wider range of growing companies. So really that's what, that's what Monevates vision was in 2021. And we are, we're going strong.

We've, we've grown quite a lot since that time. And yeah, I guess personally, I live in Saratoga Springs, with my wife Lee, and I have three boys age, 13, 11, and 3.

Ken Lempit: Well, as you know, I love Saratoga. It's a, an amazing little town. A lot happening for a smaller, smaller kind of exurban city. It's a beautiful place. So if our listeners haven't been, they should get there consulting with you in person and enjoy and enjoy everything that Saratoga Springs has to offer.

James D Wilton: Exactly. Enjoy the springs. Enjoy the race track. There you go. That's my, it's my free advert for, for Saratoga.

Ken Lempit: Yeah, like Cafe Lena, I mean, there's just so many great restaurants. I mean, it's just like really fun place.

So, but let's get to the matter at hand. When, when we talked before the episode, you mentioned that AI is really changing the landscape for SaaS pricing, and I'd love you to elaborate on how AI functionalities are impacting, you know, the way companies have thought about traditional pricing models and

what you see as the challenges coming when, we're trying to price and, and launch AI enhanced products. Seems like a whole new world for, software entrepreneurs to be considering. 

James D Wilton: Definitely, yeah. I think it's thrown everybody in the SaaS world for a bit of a loop, honestly, Ken, this AI thing, and it's, it's interesting because honestly, I would argue that monetizing AI is at least in theory, exactly the same as monetizing anything else, which we can, we can talk about a little bit later if it makes sense.

But you know, I think it has, it's thrown everybody for a loop because it is different, right? Like the technology is different. I think everybody's gotten very, very comfortable with SaaS over the past know 10, 20 years. So I think it was quite easy for everybody to knee jerk to the usual way that we think about SaaS and the models that we use to monetize it easily.

But there are just a few things that are quite different about these AI functionalities, which mean that you, you get knocked off your rhythm a little bit. I think. I mean there's, firstly, I think it just fundamentally. The way that AI creates value is different from the way that traditional SaaS products create values.

And I'd say very simply, right? You think about it, it's usually SaaS tools that you, that you buy are things that you use in order to help you do something. Whereas all these AI tools and AI functionalities are usually doing something for you to an extent, right? There's something that, so I think just the way you think about value there is,

is different and the way value scales is different. So it makes those pricing models that people default to a little bit different. So that, that's different. I think secondly there is the cost component as well. I mean, obviously one of the reasons why SaaS businesses have been so attractive in the last few years is that the margins are typically very, very high.

But at least at the moment, you know, AI uses a huge amount of compute. And that compute at the moment is very expensive. So the margins technically are a lot lower, at least if you're not monetizing usage specifically, or, or controlling or monetizing on the same metric in which you incur costs really.

So I think those. Those costs force up the price levels and mean that people are kinda, are, are a little bit more cautious when it comes to setting price. And I think the added wrinkle onto that is that we all know from thinking about SaaS pricing, that you really should be thinking about setting your pricing strategy, aligning the price levels to the value that, the customer gets.

So you'd usually pick some kind of a metric to be able to do that. I think the issue with AI is that the metric which aligns with value is not always the same as the metric that aligns with cost, right? So if you're sort of pricing in a way that is going to cover your cost very well, you're not necessarily pricing in a way that's gonna scale with value, which is gonna create friction for that buying process, or vice versa, right?

If you're setting price on something that you've just chosen, something that aligns with value. Then you may not have picked something that's gonna cover your costs. And then you have these kind of issues with profitability. So there are already these just complexities, which mean I think that people can't just make those pricing decisions so quickly.

And it's making people, you know, kind of, I guess a mixture of anxious and excited about what the most likely pricing strategies that are gonna come forward are gonna be.

Ken Lempit: Yeah, that's a really interesting one, the value and cost thing. And it, it's interesting in speaking with, AI entrepreneurs, these AI native young companies. They don't talk a lot about their costs and I wonder if there's, you know, are there providers insulating them from their true costs?

James D Wilton: You know, like, why is that? Because I haven't heard that and I think it's sort of interesting. 

I've only got my sort of anecdotal pieces of feedback from speaking to a couple of people here. I don't think I can speak to a general rule, but what I hear is a mixture of some companies that are really concerned about costs and wanna make absolutely sure that they're covered because they don't want their margins to go crazy.

James D Wilton: And there are other ones who I guess are taking more of a forward looking view, rightly or or wrongly, and saying, I believe that the costs are gonna go down eventually, which I think is probably true. We just dunno how quickly right? But I think with models like deep seek coming in, you know that there's, there's at least perhaps some precedent for those costs going, going down.

But I, I believe that the costs are going to go down eventually. And therefore this is something that I'm just not gonna worry about right.? For now, I'm gonna take the hit to my profitability for the moment, build preference, and then, you know, bet on the fact that eventually as these costs come down, I'll be well set up and I'll be, I'll be profitable in the end state.

Yeah.

Ken Lempit: So this definitely for companies that are reimagining their business, right? Moving from a more traditional SaaS to an AI native. I don't think the copilot thing is, compute intensive as an AI native environment. But if you're gonna completely reimagine your business around an AI native capability in whatever application area. 

Maybe that's something you have to really understand is what is your cost gonna be? 'cause your old pricing might not actually work anymore. That's one of the

James D Wilton: No. Exactly. Exactly. You know, and I think even though, and I think you're exactly right, Ken, I definitely think there are some which are more, compute intensive than others. But I think even the ones which are lighter, if you just compare it to the old SaaS models, again, it's massively different. I mean, you think about the way that you would, interact with a copilot or a chat GPT or one of these kind of, these AI assistants, which we're, I think, I think a lot of people have adopted and started using all of the time.

You know, I think like if I go and work with Chat GPT, I'm working on a certain task, then you know, I'll start kind of quote unquote talking to it and asking it to do stuff. But, obviously, you know, you don't get what you want in the, in the first go, right? You ask for something, ask a question, it gives you an answer back.

You refine, you iterate. You kind of mold it round to something that kind of produces value for you at, at the end. So for me as the user, the value for me comes at the end of that process when I've created or got what I want out of it, right? But the GPT is incurring costs every single time I ask it a question, right?

So it's just so like all of those steps in there are running up costs in the back end. So, you know, if I think about the unit which I get value out of, it's really the, did I get my product at, at the end of it, it's like one unit at the end of that. Whereas the cost for that could be what? 10, 20, 300?

It's like, it's kind of difficult to check.

Ken Lempit: Yeah. And it, the dialogue encourages you to try again, right? It's like, Hey, is that what you wanted? If not, you know, let's, let's go at it again. So they're, they're almost hurting themselves in that encouragement, right?

James D Wilton: Exactly. Yeah. It keeps it, it keeps going. And I think that's the, that's the thing In the old sort of SaaS models, you wouldn't have that disconnect so much. You wouldn't expect that the cost is gonna go up massively every time you're doing that. But it is the case here. So you know those, those copilot and ChatGPT models at the moment, they all basically charge per user.

And they might have some kind of a usage threshold, but they don't really talk about usage too much. I mean, they're really kind of betting on how much people are going to to use it. And some users may be incredibly profitable and some users may be not profitable at all. 

Ken Lempit: Yeah. And, and I think the traditional SaaS stuff, you're used to compute and storage becoming almost free, right?

James D Wilton: exactly.  

Ken Lempit: you're gonna face a very different proposition on a cost side. I wanna kind of shift the conversation a little bit. I mean, we're, we're, obviously it's all gonna be about pricing, and maybe we've just sort of gotten here naturally, but the traditional SaaS pricing or the most common is user based, right? It's how many seats do you have? How many sites do you have, how many data sets, right? I mean, those, it's usually a quantitative kind of thing. But it's counting. And now, there's a lot happening in SaaS AI and not

on usage based and, and I think this sort of, maybe this shift to usage based could help with a transition to AI native applications. But what's, what's driving the change, even for more traditional SaaS firms to be going to sort of usage based pricing, you know, sort of this metering notion.

James D Wilton: Yep. Yeah, it has, it's been, it's been really interesting to watch this over the past 10 years or so actually. 'cause you have seen a, a gradual progression towards usage-based pricing, but also a few swings backwards and forwards as you get reactions to it. Honestly, I think we have, finally arrived at the moment in time when you can really say that if somebody was to have a new product and say, I want to price it per user, you've gotta really defend that at this point.

Right? 'cause it feels like it's probably not the right answer, at the moment. Doesn't mean it definitely isn't. There are definitely products where per user pricing is the right answer, but I think whereas before you could say that per user pricing probably works for a large portion of companies.

Right now, I think per user being the right answer, the amount of companies it would apply to is a lot smaller. And the reason for that I think is that, you know, if I just, you can just bear with me as I go through a couple of pricing principles for you. I think if you go back to thinking about how to pick a good price metric for you, there are several criteria that we would think about and I think the ones that are probably most noteworthy here are value alignment.

So the idea is you want to pick a metric that as the number of units of the metric increases, the customer gets more and more value out of the product. You also want it to be growth oriented. So you want to have a metric which naturally is gonna increase in the number of units over, over time.

And this is just a land and expand thing really, right? We want our customer revenues to go up over time as opposed to staying flat or God forbid going down. And you also want it to be acceptable. You want it to be something which seems to be fair and reasonable to set price, based on,

predictability is a big part of that, right? Can I predict what my number of units is going to be on an ongoing basis? So kind of with those criteria in your head, if you think about user-based pricing, I would argue that user-based pricing for a lot of companies has never been that value aligned.

It's never been one that was most value aligned. 'cause it's not always the case that as you add users, you get significantly more value. It might be that larger companies who've got higher willingness to pay, need more users, and so they'll be willing to pay for more users. But it's often not the case that you start adding a ton of value for, for every user that that you add.

Sometimes it is, but I think what user-based pricing had going for it was that it generally was growth oriented because as companies grow, they tended to need more users and therefore the number of users would go up. And it was very acceptable because it was everywhere,

right? User-based pricing is everywhere. We know that's how SaaS products are typically priced. It's seems easy and logical to set price based on that. And then you look at the last few years, right? We've seen an increase in user-based pricing. I think mainly because, we're in an age now where there's a lot more automation, and so a smaller number of users can get more work done than perhaps they could previously because of all these automations.

So users becomes even less value aligned, right? Because there's like, there's less of a need to add more and more users in order to unlock the same value. 'cause I can do more with less. And at the same time you have AI coming in, in some industries at least is actually coming in and disrupting things and allowing people to decrease their head counts in their companies as a whole or their teams, right?

So actually sometimes, literally the number of people who could be users are decreasing. So users is not as growth oriented as it used to be either. There's often a case when people won't add more users, and in some cases they may need to, to decrease it. So the value alignment and the growth orientation relative to usage-based is really compromised for user-based pricing.

And I think at the same time. The big barrier to usage-based pricing has been this predictability angle, right? And this, and this acceptability angle, people were wary of usage-based pricing. You know, you don't know how much you're gonna use something and therefore you are, you're kind of scared of the prices that you might end up paying if you were just pricing by this.

But there's been pricing models that have come out, like, you know, various forms of hybrid and creating bandage systems and charging for capacity of usage rather than just usage itself, which means that actually that predictability issue is not quite the same as it was. And of course there's just more usage based pricing now as well, so people are more familiar with it.

And so it's become more, more acceptable as well. So I guess for me, as I look through these things, the scales have just really tipped over the last couple of years. And now I think all the sort of reasons why people might have been clinging to user-based pricing have now tipped the other way. And it means that the only time that you really want to be pricing per user now is if

your value really, truly does scale very well with a number of users, which, you know, I guess businesses like Zoom and Slack where there's a lot of collaboration between people. I mean, those are the kind of examples I think where that really does make sense. There's a lot of other ones where it doesn't anymore.

I.

Ken Lempit: So how do you communicate that? That alternative pricing to people in a way that they're gonna be like, you have to educate people almost right.

James D Wilton: Yeah, exactly. You have to educate people, I think, and make them. It's almost this difficult thing with pricing, right? Because from a vendor point of view, you wanna pick your pricing strategy in part because it's going to make you more money in the long run, right? You're gonna better monetize the value that you create, which obviously is perhaps counter to the interest of the customers who would rather, you know, you make it as, as cheap as it possibly can be.

But I think it's not always distributive, right? There are times when actually pricing in a way that works better for you, the vendor also works better for the customers. And I think the most obvious example of that is, you know, you can have whoever you want using this, and it doesn't cost you anything extra, ideally, right?

Because you think, I mean, that's what I was talking about earlier. imagine you've got an accounting product, right? I'm a small business and I have an accounting product, and in my finance department who are gonna be using this, I have a CFO and I have a finance associate from doing this.

If I was to pay by seats, the first seat is gonna be the CFO. Once the CFO gets access, then I can really unlock the value of this, right? How much more value do I get from having another seat hoop and then another finance associate is there. Probably not a ton of extra value, right? Because I could do everything that I needed to do when the CFO was able to use it.

I might get a little bit more convenience from having the second person in there, because it's just helpful for that person to be there. But if I was literally gonna be charging, being charged twice as much to add the finance associate as I was for the CFO, if this was a high price thing anyway, I've got a real incentive to just deal with the inconvenience and not let that person in. 

It's frustrating, you know, you'll.

Ken Lempit: A disincentive to use the product

James D Wilton: Exactly. You know, you'll take the hit, but, it's not convenient. Whereas in a world where switch to some form of usage based pricing, you can say you can let anybody who you want have access to this and get in. So all of that kind of friction is taken away.

I'm just gonna charge you by how much as a group you end up using it. And obviously there are usage disincentives then, and I think the big part of the way that you design the model should be to not have people second guessing whether they should be, you know, doing a session or running a transaction or whatever it is.

So you don't have that thing you can manage through that. But I think the idea of now I can let whoever I want in this system is quite often attractive to customers.

Ken Lempit: It makes a lot of sense. And it's something we, unfortunately we don't have enough time to really dig deeper on that. 'cause I think that's a really interesting topic all by itself. I want to talk about the sort of the change in pricing, whether it's from AI native AI product, or traditional SaaS.

Like what are the changes in pricing doing to the investability of these SaaS companies? You know, there's been a lot of hand wring about, VC and PE money getting, you know, harder to come by. But it seems like pricing has a role to play in making a company more or less investible. What's your perspective on that?

James D Wilton: I mean, it's, honestly, it's, I think we're. Really early in this journey here, and it'll be kind of, it's still, I think time needs to elapse before we see exactly how it's going to, how it's gonna play out. But, you know, you're hearing a lot of people now talking about the way that we think about ARR or annual recurring revenue.

In case anyone's unfamiliar with that term it, it might change, right? I think traditional SaaS models. ARR has been king, right? It's been the metric that everybody really tries to solve for because it's this nice, predictable, revenue stream that tends to grow and consequently, investors really like it.

And so you get really good valuation multiples on ARR far more than you do on transactional revenue. I think the interesting thing with these. AI products, at least in a way that a lot of them are being priced now, is you see a lot of these AI native products which are being priced on some form of workload, right?

So, you know, you see a lot of these credit based systems when a user will buy credits and then they can spend them on having the AI run tasks in the way that I was talking about right? Every time it, it does something it runs up, expenses. So it's a very much a. Cost coverage play for these ai native platforms.

Not necessarily like a value linked play. So it keeps them so that their unit economics work. But it does push you into this transactional revenue stream now, right? 'cause I'm not, I'm. Unless the AI company designs it in this way, it's not necessarily I'm committing to a certain number of tokens or a certain amount of usage each year.

I'm just buying a block of tokens and I can spend them as I want to. So a lot of people now are saying, either you're gonna have a situation when a lot of these companies are not seen as very attractive because they have these transactional revenues, which, you know, is relatively unlikely, I would say.

Or you get to a situation where, the market stops demanding recurring revenue in the way that it has previously, because we're more gonna be heading towards these models, or as I think the answer may end up being, is that actually I wouldn't be at all surprised if AI we end up in some other form of ARR.

Well, it's still ARR but it's like it's, we're buying usage capacities on this kind of recurring basis as opposed to the sort of traditional models, because this is just a hypothesis, right? But I do not think the desire for predictability both on the vendor side and on the customer side is going to go away.

Right? There is a reason why SaaS models have become so, so dominant in the last couple of years, and it's because fundamentally I think customers like to know what they're paying each month. And in fact, actually I've seen, we've done research within enterprise companies specifically who obviously have quite, you know, specific budgeting needs, but we found that these companies will be happy to pay,

10 to 15% more on average, just to have certainty on what their payment is gonna be each month, rather than letting it oscillate around. I think from a vendor perspective as well, right? I mean, I. You would generally rather, have this kind of committed revenue coming in than sort of have to have it based on something which you have no control over?

I mean, I would say, you know, as a provider of consulting services, I would love to have recurring revenue. My business model doesn't work that way, so it's not something that we can do, but I would, all else being equal. I would much rather price that way than price on a kind of a project base if I could.

So I think the desire for those two things are still gonna be there, which means I expect that once these, once this need to cover costs gets decreased because the cost naturally go down. I imagine the system will sort of end up in this kind of hybrid usage type models where you end up still having some form of ARR.

It just looks different from what we've seen, previously.

Ken Lempit: Yeah, I mean, especially, I just wanna underscore this thing about the enterprise predictability of cost. I mean, I think that's driving a lot of decision making regardless of what kind of platform it is. At that level, they have budgets, they, these systems are pretty big usually.

So they're multi-year lifespans, if not, you know, decades long lifespans of these systems. They wanna know what's it gonna cost to keep the lights on in our factory, or whatever the application is. So, yeah, I think that's very true. I want to ask you to talk about the book Capturing Value and some key insights that folks can expect if they go and buy that on Amazon.

 what should they expect to get in the book and how can companies ensure they're maximizing value capture? I think that's the one of the big ideas in the book.

James D Wilton: I hope that, people who look at or buy the book are gonna find it quite useful, especially today with all these changes going on, Ken, because I think one of the big principles from capturing value is that you shouldn't shortcut the design of your pricing strategy, right?

You see so many of these SaaS and Tech companies who design their product and then they're about to release it and think, oh, you know, we need to figure out a way of pricing it. Let's copy what our competitors are doing. Or, you know, it's just like, make it up and do it. And that makes the whole exercise very tactical, right?

Which means that maybe that will be helpful for you and it'll work, but maybe it won't. I mean, really. Your pricing strategy should be a strategic lever for you, and it should help you grow or help you achieve whatever it is that you want to achieve. So you should really be starting from base principles and designing it with a view to do what you want to do.

And that's what the book does. I mean, it kind of takes you through every single step that you need to go through for that. Understand the principles, and then look at your options and then decide what you what you need to do. So it should be a guide for people who wanna do that. And I think a few of the things that are really important with that is like, is firstly just clarifying what it is that you want to achieve.

And this is something that sounds so trivial, but it's, it's something that not a lot of companies do. And I would say, I there is a very big difference between a pricing strategy that is gonna help you maximize your sales volume and you know, get loads of customers really quickly, versus one that is going to help you

get really high profit margins out, out of the gate. Like those two things just look completely different in terms of the price level and the way that you structure it and the amount of transparency that you have. I mean, all those things are completely different. And obviously it's not usually the case that people are completely binary and say like, I either want to.

To drive volume or I want to focus on profitability. There's usually some kind of a combination of these things, or I'm gonna mainly focus on one thing subject to constraints around revenue growth or profitability. But just the exercise of getting a prioritized list of what those things are mean that then when you start to build your pricing strategy, you can weigh up the trade-offs between achieving those different objectives and find something that's gonna achieve the balance that, that you are right.

And that's it. You know, when we talk about Capturing Value within the title it's not necessarily about capturing all the value, right? It's about capturing the amount of value that you think is fair given what you are trying to achieve. And if you're going for a volume-based play, you're gonna be capturing less of the value by definition than you will be if you're gonna go for a profitability based play.

So I think that's very critical, thinking about the objectives, we spend a lot of time on price structure. So you know, what is that system that you have to be able to price differentiate across your customer base, basically, you know, which is how do you charge different amounts for different customers who get different amounts of value from your product and have different willingness to pay?

And there's packaging is a component of that. You know, we think of good, better, best models for SaaS a lot, but there are actually lots of different motifs that you can use even if you are picking, a good, better, best. There's a real art to thinking about how you build each one of those tiers and what features and capacity thresholds you put in each.

And then there's the price architecture side, which is, we were talking a little bit about price metrics earlier. It's about what is the right price, metric or price metrics for you that is going to align with the value that you create and is gonna be growth aligned, and so on, and so forth.

How do you scale the price with that metric? Do you literally just charge the same amount for every single unit of this metric that you add, or do you ramp it down as you're going up so you kind of apply this volume discounting approach? Or do you have bands, right? Like I have a flat price between 1 seat and 10 seats and have a flat price between 11 and 20.

So you structure it that way. They sound trivial, but they're very important decisions that might materially impact how these customers are going to engage with your product and purchase it. And I think the last thing there as well, is the willingness to pay itself.

You know, in terms of figuring out what is the right price level that you should be setting. And I think the big principle there is I get asked all the time as a pricing expert, you know, James. What is the right way for me to get willingness to pay? And my answer is usually like, that is the wrong question, because there's no one right way.

You should be getting willingness to pay data in a few different ways and triangulating across them to be able to figure out what your right price levels are. Because you know, getting willingness to pay data is tough. It's tough to ever get anything that is completely reliable, all on its own.

So it's usually best to get a few different measurements of it. Look across all of them. Then make a balanced decision based on everything that came that came out.

Ken Lempit: I mean, the willingness to pay to me is the. It's the mystery in this because, you know, if you ask people directly, they're unlikely to tell you the maximum amount 

James D Wilton: They will lowball you so hard if you do that. Yeah, exactly.

Ken Lempit: Yeah. You just can't ask that, right, flat out. That's just not possible. Hey, one last thing I wanna cover. I think, it'd be good to make this a little real for our listeners. You had a client that you worked with where you helped them transition from user base to usage based pricing. One of the things we talked about before, can you, uh, walk us through that case study and, you know, what the considerations were as they went into it and how the change actually worked out?

How did their customers respond?

James D Wilton: Yep. Absolutely. Yeah, so the company in question here, it's a customer satisfaction platform, customer experience platform called Help Scout. Really interesting company. It's one of my, favorite companies that I've worked with actually. I think out of all the clients I've worked with in the past, you know, five, six years, I can't think of another, company that is as customer-centric as Help Scout were.

I mean, they were very dead center on making sure that their customers have a great buying experience, have a great usage experience, and it was just customer all the way, you know? That said, obviously I think as we, kind of aligns with our mission at Monevate and helping companies do this.

You know, you kind of realize if you don't monetize your product effectively and set yourself up for success going forward, then you can't help any customers, I think looking at the way that they were pricing now, knowing that it wasn't working for them and knowing that it needed to change without getting to, um, incredible amounts of detail.

They had kind of a PLG motion. Good, better, best style packaging. And they were pricing, per user on this. And as I say, this is a, it's a customer support platform type play. So, you know, generally users you'd be charging for with these customer support agents who would be using this platform to help deal with their customer's.

concerns. I think the issue that they found was, again, we talked earlier about the challenges with user based pricing. They were seeing them, right? They were finding that firstly, their customers were sometimes reacting negatively to the idea of having to pay for more and more users.

You know, if they were larger and had more people they needed on the platform, it didn't necessarily always correspond to more value for them. So they had some friction there. It wasn't helping their customers. And of course they were looking forward in seeing AI and automation and thinking, I think customer, customer support in general is one of these areas where there really is potential for disruption through AI so are the users that people need gonna decrease over time and are our revenues at risk for that? Because, you know, we're gonna end up having smaller numbers of users. So they were really going in this dual avenue of like, what is right for us and what is right for our customers going forward.

They knew they needed to change it. And I'm gonna, you know, distill about 10 weeks of work into a couple of minutes here, Ken, but I mean, ultimately worked with the Help Scout team and decided that the right price metric for them going forward was contacts helped. So if you have your customer support function, it's how many of the client's customers did you engage with?

Did you help? Did you help them, resolve, their, concerns? And that's obviously usage based, right? It's tracking how much is actually used through this. But it's very value aligned, right? I mean, it aligns exactly with how this platform is set up to help customers so far more aligned than just the number of people who are using it.

It's actually based on the amount of output that you would get through using this tool. So worked really well from a value proposition point of view, and actually also worked really well from a customer point of view as well, right? It enabled them to be able to get down to lower price points for smaller customers who didn't have a lot of their customers who they needed to serve.

And it was also actually, one of the more surprising things that I found out through doing this kind of work, it was actually turned out to be more predictable than users was for them. I mean like, because they were charging previously on active users. So how many customer support agents are using the platform, those numbers tended to oscillate around a fair bit per month.

Whereas the number of customers that you are helping is far more stable. So actually it worked really well from them in terms of a value alignment perspective and being able to protect their revenues going forward. And it actually also works really well from a customer point of view as well.

You know, helping them make sure that they're not gonna have, have to change the amount they're paying month to month a ton.

Ken Lempit: Yeah. And it just makes sense intuitively, right? As a user of the software, you know, user organization the management thereof. I mean, they can see pretty specifically, what does it cost us to serve our customer base so they can build that cost into their pricing? So it just seems like it's, maybe more healthy for clients. 

James D Wilton: Absolutely, no, exactly. It's one of those, rare occasions where it really, truly is a win-win. So it's, it's very early days. They only launched it a couple of months ago, but it'll be, interesting to see how that plays out.

Ken Lempit: Maybe we'll have to check in six or nine months and see how that worked out. This is a great place to land our episode. Great conversation about, you know, one of my favorite topics, pricing and packaging. James, if people wanna reach you, learn more about your firm, how can they do that?

James D Wilton: Absolutely, probably the best place to go Ken would be either our firm's website, which is Monevate.com or my LinkedIn profile, which, my, my handle is James D Wilton. Or we also have a website for the book, which is Capturingvalue all one word .com.

Ken Lempit: Fabulous. And, folks wanna reach me. I'm on LinkedIn/in/kenlempit, my Demand Generation agency for SaaS and AI native firms is austinlawrence.com. That's Lawrence with a w. And if you haven't subscribed to the podcast, please do so. SaaS backwards is available wherever podcasts are distributed, as well as full length episodes and some great, YouTube shorts on YouTube itself.

Hey, James Wilton, thanks so much for being a guest here on SaaS backwards.

James D Wilton: Thanks for having me, Ken.