Beyond Tiers: Redefining SaaS Pricing with Gary Bailey of FinTech Strategic Advisors

Speaker A: Google and Meta price everything uniquely for individual users at different point in time. In a sense, it seemed kind of one size fits all in SaaS. In reality, sometimes four or five fits all, but not really unique as I'd seen in banking to be. You're now listening to Monetize the Art of Pricing.

Speaker B: Hi everybody. Today we have Gary Bailey with us, a renowned expert in monetization and pricing strategy. He comes from a background of banking and fintech and brings a slightly unique perspective to how pricing is done today in the world of SaaS and AI. So very excited to speak with you, Gary. Maybe we can start off a little bit about hearing introductions from the horse's mouth. Cool.

Speaker A: Hey Abhishek, nice to meet you and thanks for the invite. Well, I'm an accountant, so really that turns people off your podcast. So I've been an accountant for 30, uh, odd years and started with Deloitte Standard Big Four, and then after four years went into banking probably I think end of 94. And that was at the time where banking in the uk, I think in the US to a certain degree was expanded massively. We had a thing in the UK called Big Bang. And I think in the US they call it the glass steel. And basically a lack of regulation or a loosening of regulation and that enabled all sorts of transactions to take place that couldn't take place in the past. And so I got in at the early doors of the new product era of derivative bond options and all that kind of financial math that didn't really exist up until the early 90s. And so I was in finance, but in a function that was essentially tasked with valuing derivatives, let's be fair, derivatives and bonds. But bonds are either derivative bonds, swaps, et cetera. And so the trader would mark heed or her book. So, and then we would be the second line of control having to work out what have they done where the market pricing, Bloomberg, et cetera, et cetera. And so that got me into the fact where it's relevant to the day that we would have a book. We only had like a book of a hundred thousand trade, but every single one of those trade was valued differently. And so it kind of in my mind talks about the future of SAF price, it may be, or maybe more relevantly talks to what Google and Meta do. Google and Meta price everything uniquely for individual users at different point in time. And so I think for a while, I'll uh, be honest, after banking floundered with some of the pricing that we spoke about earlier in A sense, it seemed, uh, kind of one size fits all in SaaS. In reality, sometimes four or five fits all, but not really unique as I'd seen in banking derivative. So last few years I basically went back to the drawing board and said, okay, how can I translate some of the kind of detailed mathematics that we used in banking to actually create value and price SaaS products and maybe AI in a different way?

Speaker B: Getting. Thanks for that introduction. And that is actually a pretty unique background to come into SaaS pricing. So maybe my first question to you would be, what are the similarities and differences you see with respect to pricing in the world of finance and the world of software that you are now pretty much ignoring?

Speaker A: Yeah, I think the segmentation in finance is per user at point in time where the segmentation in SaaS generically is four or five buckets, sometimes three, sometimes four, sometimes five. And whereas say in SaaS you'll have the three plans, you say three or four, and then you'll have features loaded on and on and on to the product. Normally in finance you would basically have to retrade to get a new feature. And it could be you and I doing, uh, a loan deal. If you want a new feature, yeah, we could change a deal, but actually you'd probably cancel the old one and reissue a new one. And so in many ways, finance in the kind of 90s now was set up to manage a massive portfolio of massively individual trades. And in a similar way, if we look at any SaaS plan, you might start here for small, for basically the entry level, then it goes to this month feature, then you have like an insane number of featured for the last plan. In reality, without going into math of a massive good, but not bullying, if someone's charging $100 for the entry and say $900 for the premium, the top tier, those features must add up to £900 or dollars. But no one ever does that calculation. Say the sum of the parks must add up to 900, otherwise you either giving away features or the features have negative values, which I assume they don't. And so just by doing that, use Salesforce for example, if they charge a 900 for the top tier, then if they've got 40 features in there, there'd probably some sort of power law. But those features must add up to 900. So in banking the thesis was they probably don't. They either add up to 1200 and you're probably not valuing some of them properly if they were broken out, or you've got some features that are Trashing basically that no one values or almost detract from the value.

Speaker B: I'm actually quite intrigued that banking thought about pricing this deeply. Actually that part of time. I always assumed the banking to be very sales led, keep it parallel to what everybody else is doing kind of pricing. So before we get into SaaS, could we actually talk a little bit more about that? So if you had to take like maybe a couple of banking products that you priced, like anything, that's maybe simple for the audience to understand you. How did he go about pricing that? That would be fascinating to understand.

Speaker A: Okay, so say actually yeah, say you get a mortgage, just check it easy. Say you're a company but you could have a mortgage and the mortgage is 20 years. And so at that point in time and it's much more popular in the US than here, you could fix it or you could say, you know, uh, what I think interest rates are going down. I want to pay a floating rent. A lot of people in the US actually fix it but in the UK we actually mostly go floating or a very short fix, 2 year, 3 year. Whereas I know in the US it's like 20 year fixed, don't really have that product over here as much. So Abhishek, you choose to go fixed and that's cool. And then two years later you think I'd like to swap this into floating because I think rates are going to nosedive, uh, but before they nose dive. So essentially you would need a transaction to do that and in the old way you could just cancel the loan and reassure a new loan or you could just basically buy a uh, derivative on top of your actual existing loan. So you have the loan with your bank and then you have a derivative say with my bank and that derivative will have a price every day based on the market expectation, the yield curve. But similarly if in that contract we add certain things, we would add a feature which is we are going to look at this rate every three months and we're going to look at this rate every three months at 11am UK time and it's going to have a actual 360 rate similar to that. I'm also going to give you a discount based on your credit, which is another feature because I'm going to value your credit different from mine and anybody else. That's a different feature. Also maybe give you another kicker, uh, based on the fact that we've got a long standing relationship. I'm also going to basically throw in a closeout uh, clause which actually has a value, an option value Which I don't throw into everybody else's contract. Your value client. All of these features have a value individually. The option has a feature because we could just price it on the options market, which is a closeout. The length has a price, the interest rate you took out the price. The credit rating, I could trade out, uh, of your credit because I found it on your credit risk and I would trade that separate. So if you break it out, it's very similar to SaaS. Every single one of those elements essentially is separately tradable. So if you take it in that way, any SAS product with fixed Salesforce, it has a database and it has some automation, then it has some ability to basically fill in the information from a tool like Zoom Info. And all of these have an individual value. And what they tend to assume is that one person valued, let's just use a random example. One person value the Zoom Info integration as much of the other. So ADI value the Zoom integration as much as I do. As much as you do. That's not necessarily the case. It could be the case, but over a million customers, if I'm likely to be the case that uh, we both value the Zoom Info integration as much as they Addie and the mere fact that we value it differently means, well, actually I should be charging either Addie more or less more, whoever values it more because that is really valuable to him. Uh, well actually we're like, it's just trash. We just don't need it because we don't even use him info or whatever. And so each of those features within that bundle adds a unique value that a banker would actually break out and try to trade it individually and then see where he can or she can hedge it where they can hedge it where they trade taking a margin. And all of those are tradable and hedgeable, you could argue. But what we tend to do is bundle in every single feature we can get our hand on and then there's a continuous list of features added because we're spending 25% on R& D that would never happen in the bank environment.

Speaker B: In a way it feels like what's happening to SaaS very recently banking has been doing for a long way back. I don't know if it's as systemic, but thinking about pricing and hedging at a feature level based on usage and user, um, that's what he's saying essentially like how you wanted to call it at that point of time. So very, very fascinating, but seems like a long way away from where you are now doing pricing consulting for SaaS. Right. So couple of things I want to touch upon. One is we will get into specific examples of how to price and what goes into pricing, how does the exercise work, but also about a journey of being a pricing consultant. So we do speak to people in that space and there seems to be a little bit of trepidation across customers, our class companies that hey, how can somebody externally come in and kind of help something? That's very key to it. Right. Like key to what we do. So how did the journey come about for you and how did you realize this was something that, that could be really helpful to the companies that you're going to be working with?

Speaker A: Yes. Thanks.

Speaker B: A question.

Speaker A: It's a good point. I basically had to tried to put into a framework because essentially I struggled for about two years and I read all the kind of psychology books, et cetera. And then for actually pricing is really a mixture of a few things. It's economics, which I do like. Then it's maths, a little bit of maths, not too much and psychology. And if you add that to a little bit of marketing, you have SaaS pricing bundled in. So I was looking for areas where the mathematics came in just in logic, the way things look and then saying okay, where does psychology come in? And all sorts of tricks and 99 and less than 100 pound, all sorts of shenanigans you can do psychology wise. And then you got the marketing part and then it was like, okay, if you added the economics in supply, demand and all the elasticity, you richly have the four disciplines that make up pricing. And so if you know that then you start to look for trends and kind of think where that actually makes sense based on that framework. And one of the big best cases I can see is HubSpot. And there was a thing, if you look across the successful prices, uh, or six full SaaS company, they had this kind of parallel pricing thing going on. So they would have like a graduation of pricing. So the entry price might be, it's just say one could be a hundred. The next price would be 3x300. The next price would be 3XL. And that's based on Pareto. And you think okay, who's using that? And then you look at HubSpot and similarly Salesforce for a while and HubSpot was pricing like the entry price was quite cheap. And then it went up to 12x and it went 64x. So it still followed a power law which you could say actually wait a minute, they've done a Pareto there to try and maximize demand all the way up the terms. Now obviously we were saying in banking you wouldn't do that because you just had six, you'd have hundreds of thousands of trades. But for sas, if Abercrombie looked at it, they would say well if HubSpot's doing it, why am I not? But they're not. So you'll find some people who do. Entry price, let's just make it up $19, next price $39 and the next price 99. That in economic terms is leaving some money on the table because there should be a graduation of the price all the way up to the top. So at the top really you should only have 1% of people willing to pay that price and then it goes down, et cetera, 3.2%, 16% down to 84%. So just following the principles and you look at which sasses are doing it and you're like well hotspot's doing this and they've been doing it after year two for um, 14, 15 years. You're like why are you not just copying Salesforce? But people aren't necessarily looking at, there's a mathematical relationship, why they price each individual plan from one to four in that way. And so you look at those correlations and relationship, you say well without doing anything fancy, there are simple economic relationship, mathematical Pareto stuff which you should be doing.

Speaker B: So why do companies settle for the good, better, best, right? I mean for a good part of like decade and a half since SAS has really been a thing or even slightly longer, that's been acceptedly the standard way to price. I mean it's pretty clear it leaves money on the table. It's not how general supply demand should work like you said. But that's how companies have done it, right? Most majority of the companies. Why do you think that happened?

Speaker A: Yeah, the good pair best is perfect. It's just that uh, the points that they put on it tend not to be. And so I think HubSpot is a perfect example because you start 100 pound, then you go to like 800, then you go to an eye watering amount like 2,400. Now that is good graduation because only very few people are going to pay the 2400 but you've know you've exhausted the full demand at the price they're willing to pay. Similarly you would say okay, the reason why they've done that is uh, they know that at the top end they can do a different sales motion, etc. And also the minute someone looks if you and I look at HubSpot, we don't know anything about it. We're going to say someone in this world is willing to pay 10%, two and a half thousand pounds ish for this product. I am getting a bargain at a hundred. So all the elements that HubSpot already were doing for 10 years, the anchoring up top with the crazy price 2,400, the graduation good, better, best is standard like day one that you should be

Speaker B: doing that it just seems just a little easy. Like in the sense the process of doing that is just more easy than actually when do companies start thinking about this the way you do? I think, okay, so you start a company, you kind of like price it in the easiest way, good, better, best. But then when do people start thinking along the lines of what your talks about at the hotspot? Like when is it a good time to start thinking more deeply and say okay wait, we're leaving money at the table. We should be doing this slightly more scientifically or uh, mathematically like typically when do you see the inflection point happening in companies?

Speaker A: Not for the first three, four years actually because essentially everybody's head down trying to scale volume and so in many way that scaling volume and they're seeing the volume scaling evolates to the product. So they're putting more money in the product and more money in Facebook, meta or whatever go to Market Motion. And in reality Abhishek is if you have an accounting person in a firm, they're going to want to produce accounts, they're going to do bookkeeping. If you have a compliance person in the firm, they're going to probably overdo the compliance. So if you had a pricing person in the firm, they're going to do pricing, they're going to do a pretty damn good job. If you don't have a pricing person in the job, it's similar to trying to get me to do some SQL queries. I'm okay, but um, I'm not a programmer or Python. I'm okay. I'm going to do a uh, kind of not very good job because it's not my day job and I'm not actually that interested in it. So I think the core problem I'd be shaking is that there is no nominated person to go down a rabbit hole and say well I'll just go best practice, look at Salesforce, look at HubSpot, look at Zoom or whatever and just work out. And so I think it's not the skill set because they could learn the skill Set just by looking. I think it's the lack of a person who dedicated to doing it even part time in the main issue.

Speaker B: Yes, I do agree with that point. I think even the current change in respect to how companies are looking at pricing is coming from a place where more and more product folks have gotten into pricing. Like as against originally there's probably a finance problem to solve. And um, as for products folks come in, they're looking at it by building the product that are now talks about hey, we should be thinking about pricing while we think about product as well. So this is a good question for me to ask you in that case. So who is the person? What does he or she need to be? I mean how do you become a pricing person within a company or even externally?

Speaker A: Yeah, that's a good question because I asked someone after that I'm saying that in reality that category of person doesn't exist. And so we were just arguing or conversation um, about okay, look at all the new things that have um, come about. And so Adi's are uh, now a uh, podcast producer now. No way he could have said that 10 years ago. Well maybe 10 years ago he could have done. But if you send it to a career's personal university lecture and the like more secret, well this guy talking about. So in many ways if you look at in our world in tech and stuff, obviously I'm older than you so I've come from when we would call them the IT guy all the way to now. You have to really segment. No, he's full stack, he's DevOps et cetera. But the best example you can say is a DevOps. And so DevOps is a made up name. It kind of sounds what it is but it had to be made up by someone. So I'm going to have to say we're moving from on prem to cloud and hence we need a kind of more dynamic person. And you couldn't say you were undynamic in your way, but you need a person that's much more dynamic, much more kind of tools, focused, integrated and things are moving faster than we used to. And in a very similar way that when I first was growing up in banking we had project managers. And even now if you say you want to be a product manager in a bank, they don't really like it that much, a little bit more. But it's not like well, the standard. Well no, they still want project managers managing discrete projects where the product manager is continuous. And so I think in a very similar way that someone stood up and said don't call me a project manager, whatever you do or for whatever reason many years ago Guy Kawasaki, whoever someone has to stand up and say yes, I'm a product, uh, guy or girl but actually I'm a pricing product person. And I think if they did that it would move from a generally company don't change price in more than once every six months. This year lacking could be a year, six, uh, months, occasionally three months. But freelance is actually a massive amount of time compared to the big company that changed it, big tech company changing it hundreds of times a week typically. And so that journey needs someone who can work with tooling, who thinks dynamically, who wants to ab test quite in which he hadn't really done that much really and wants to constantly be repackaging stuff based on new segmentation that comes up. And so I give you an example of things that I knew nothing about or every time someone would say it I was like what the hell is that? For someone like it could be segment I listened to the story years ago and the guy who left YC&M then they didn't know what they were doing and stuff and then he created this thing called a CDP role. You're like okay, why do you need a cdp? Well, you need a CDP because you want to know that the CRM's okay, but actually didn't show you what people are doing. So then you need a CDP and you're like well someone invented that term. I don't know if it was him or his company. And then like everybody's got one now or everybody thinks they need one, they can't afford it. So in a similar way, if I've got a CRM, which is good, so someone's done some segmentation marketing or whatever and I've got a cdp so someone now can tell me insane amounts of data about how Abhishek is actually using the product. Okay, so what do I do? I just throw it off a cliff? I can fit it back into CRM, but even if I put it back into cn, I've only got three clans. Where am I going to go? I can sell him more product and stuff, but actually you and Addie could have wildly different use in the same top tier. But I've got nothing to do. What can I do with that information? So now there's another piece necessary which I'm messing about with Name value capture management systems or something which is like your system, like many others, which is actually I want to experiment now with the usage data that the CDP gives me and I want to see where the value capture, value creation is coming in and that allows me to resegment further. And so in many ways to answer your question, it's like the person that can look at that journey. So yeah, CRM is marketing salespeople. Then you've got cdp which is the kind of product analytics. Okay so between that and the actual price and the actual billing, there's a missing piece which is yeah who's going to actually work out the usage, not just calculate it, the value of the usage in terms of who's getting value and actually change some of the monetization around.

Speaker B: Um, a lot of teams actually a lot of companies are ah, opening now monetization teams over the last five, six years and that's been a phenomenon that we're seeing a lot of companies coming up with. And that's great. I think this could be an onset of pricing manager being an uh, ubiquitous job like how product manager has become in the last two decades and hopefully the next decade. I definitely see a world where that's going to be. Each company is going to have maybe if not as many, but definitely certain roles that are specific to pricing. So let's talk about that more specific. So let's say there are companies today that has a pricing manager or the company wants to think about pricing, they want to hire a pricing concentrate. How would the process, what would happen? So I will start a founder, I start a company, I scale it to some level and I start thinking, okay, now I need to think about pricing. I hire somebody or hire a person, what is their day job like, how would they go about what are the tools that they use? Could you throw us some specific light into a life at work or a day of how a pricing manager works?

Speaker A: Okay, so let's use a real example. I use Leonardo say like one of the AI tools. Big company bought by Canva. It's perfect. It's really, really good. It's a different version of compared to midjourney Australian, really good design, looks beautiful. So their pricing goes from about 20 pound up to about 60, 70 like that. And so before we do anything say that product is semi developed. When you say okay, we have an AI image lnm stroke uh model who find value from that. So currently they pushed it out and it's beautiful. They did a freemium, then they did a price it paid etc. So the first question we have to answer me shek is we need to before we do anything else in price in try and work out Mathematically how much value it creates. And that's where it gets a little bit hand wavy in price. And people say it's a conversation now. But this is not a conversational tool. There is no sales team getting on the phone trying to sell a 20 pound package a month. So let's use you as an example. We're saying we have now this AI image tool. I somehow need to work out how much value it can give you and then maybe four or five other segments. And so I did an example the other day saying, so imagine an AI image tool, I'm an accountant, I use it. An architect can use it. Slightly more going to get more value than me because I'm just going to post LinkedIn. And then you could say a graphic artist use it even more money. And then you could say okay, maybe a graphic artist who's running an agency and gave them more value because now he or she can run an agency on acid, basically just pumping out pictures and images. And so as you got that, we work out each individual for an accountant, they for a founder, how much it's worth. Then you say for an architect, all the way up to say a graphic designer. So okay, these have different value points for me, yeah, it's worth like £10amonth, 20 pound a month. For the person running the agency it could be worth thousands because they can now take on contracts at 2,000amonth and just chuck out 500 images to that individual payer every month. So the first thing before we do anything is calculate the value they could deliver for different segments, actually put it on paper and then put it out to them and they can write back and say no, that's just ridiculous. Uh, no way you create $500 or 600 or thousand dollars, but you prove it mathematically and say I believe it can create this much value to you. And then the next question you ask is what is the risk of them getting that value? So just like when you're using Chat GPT, the answers are wild. And there is a skill to prompting. I don't fully have a perfect skill, so therefore my chance of getting a really good image is less than a person who's very skilled. So my chance of actually getting that output, that value of say 10 is might be, let's just say 20%. So my value is really 2. But as you go up you might have, okay, the value for the person paying $1,000 is 50%. And so the question is, you've got now a real estimate of the value that you can create for Them and you've done some survey and stuff with them, they look and you get back and forth but you've mathematically proven it. Then you've said okay, what's the risk of delivery of that value?

Speaker B: Before you get into that, like also go about the process of getting this information. So you briefly test upon a survey and stuff. But that's a little more DPS to how do you processify this entire means through which you get information on which you can make the decisions?

Speaker A: Yes. So say like the AI image is a good one because it's easy and everybody's using it now. So someone in marketing would have said we think we have five different ICP types from an accountant just posting on LinkedIn to an architect to a graphic designer, uh, individually on fiverr to an agency all the way up. So someone in marketing and it's not the person in pricing, can you say well I see the ICP could be this. So then they would say okay, how do we estimate the value for that ICP35 ICP@ uh, the top end you would say well okay, what is an agency going to do? An agency is going to take this product, use an API, get some clients on board which you already had and those clients basically want graphic design. So then we have to work out what is the list for them to have to use at all. That's the onboard and stuff. And then how accurate is this tool, the risk in delivery of what they've been delivering manually for a long time. So taking that concept, you've got the ICP which normally every single startup has at least a guesstimate of the top three ICPs you can add to it. But what they don't always do is what is the value of the ICP in terms of what it creates for Abhishek. Yes, Abhishek is a free user on ChatGPT, but actually what's the likelihood of getting really really good results at uh, 20%. So therefore he's an ICP, will pay 10, 20 pound a month with a 20% risk which means he's worth 20 times 0.24. So that's not good. So as you move up uh, that you're able to show people and then argue with the founder, the et cetera and the market team and say I think Abhishit's only going to get $4 worth of value out of GPT and they go what are you talking about? Well you charge in 20 and he's got a 20% likelihood of value extraction from it without Wasting your time. And so as you move that then you cut a chart of how many basically ICP times the value creation, times the risk of the creating value and then you come up with a number which said okay, this is the risk adjusted value creation. And then you can go out and test that with people and say okay, with survey 5, 10, 15. This is what we believe, your value creation. And we're going to prove why you can create that much value. Because you agency are going to generate this many queries and be able to onboard this many clients and like anything they can push back. But you already have a very solid two tier calculation that shows that you've created value.

Speaker B: Then brings me to AI like you spoke about, how to break down pricing for a company like Dall E or Midjourney or an imaging tool of the AI. You think with the advent of AI coming in, the way companies are looking at pricing has actually dramatically changed. I ask this because I see a lot of conversation about it, a lot of founders talking about hey in this world how are we going to price? What are costs are fairly expensive and how are we going to price? Is this going to go back to traditional cost plus plus models or this value per user, what is the value that we are creating? How do you price that? So how do you see pricing being different than the AI world? And do you think there are companies who figure it out yet or because it's still very much in the face of experimentation with respect to pricing.

Speaker A: See, I think they still experiment but say the AI thing kind of breaks it open back to the banking day because essentially it could just be made up. But even yesterday I heard on on TechCrunch where Salesforce think they're going to launch a million agents or something in the next year. It's probably insane, but let's say they launched a thousand, that means you have a thousand agents which could all be priced differently. So where we've moved from, they have five price plans and loads of products and stuff in a platform. They're going to need someone to basically price a thousand different AR agents. They can't just stuck $2 on it. That's fine, okay, that they're big, they can afford that. But you, the startup can't afford to just copy behemoths that do. You have to think of something a bit more original because otherwise they'll just marvel those Salesforce. And so if you look at the agent world, which is it does come in agents have really just jumped up. Features that talk, continuous talking features. So that Means we come back to someone needs to price every single feature because an agent in the feature and then you bundle them together and into a product if you want to. And so everyone is going to have to move from I had four pricing plans with packaging and all sorts and now the product managers want to release a hundred agents a month. Someone's going to have to come up with a price for those agents, someone with a better testing guesswork. But they can't all be $2. So whether people like it or not things are going to change.

Speaker B: That's also a reason I kind of think this pricing is not something that's. You talk about how pricing is not that in every company. So Salesforce a great example, right. They're thinking of moving pricing from just user to in this space about resolution or per conversation. So now that that's a big thing and on the earnings call they made a statement like this. Now it looks like a uh pricing for this product has been solved or for a matter that then now they know what the pricing is then what does the job of the pricing manager again become? So is the problem that people think of this as a one time problem, okay, somebody comes and solves this and then cool, they can go do something else. Or do you think this is something more continuous because you keep saying the rate could be priced all of these ages differently as against $2. So is this a continuous job that and is there a place where you see companies evolving to where they are getting super gander at pricing at a level of, at a customer level or that by making it a continuous job today do you see that happening in any companies or do you still think this is something that we do once a year, once in six months?

Speaker A: No, I think it basically I think if we look at the companies that don't say they're a pricing company which they pretty much are and they are the Googles and the Facebooks and the Amazon and we kind of ignore what they do because they don't start telling people that they're all about monetization. But there is no better value capture machines than Amazon Meta and Google. So then we say okay, why are they so good at value capture? And one because they put a lot of engineering time into it. They're constantly analyzing and changing prices. 2 They're much more dynamic. 3 they basically have units that people combine to where it be ad units or see on um Amazon it's a bit different Amazon willing to change their prices like two and a half million times a day or whatever prices so they have basically a setup which is like an airline. How can I maximize this seat? Nothing has changed. I don't think there's anything new in any of this pricing stuff. Airlines extract maximum pounds or dollars from everybody. They've been doing it for 30 years. Google copied what airline did and added a little bit of economics to it and price auction stuff, but it did exactly what airline can be doing for 30 years. So there's nothing new that the pricing people in SAS need to do. Just look at the airline and look at Google and Meta and if you can go anywhere along that journey towards them and Amazon, you're doing okay. And so I think AI agents are going to be the personification of the Google per click or the meta per click model or the airline seat per tick model, because they've been doing it for a long time. So I think we've got away with three or four plans. Well, that's lovely, but not that profitable. Let's be fair. Not many SaaS companies can go public because because they're not that profitable. And there's a reason. It's not because the product's not good. The product's fantastic, the people are smart, the product's fantastic, the go to market is good. There's only one thing left. The price, it must be wrong.

Speaker B: Goes back to the four P's and where the B you're missing. Fair enough, right? But is there a slight difference in the way like B2B and B2C works? Because when it comes to SaaS, then again it also becomes how the buyer thinks of it. You're typically selling to a more rational buyer than somebody buying on Amazon. So can you afford to be in a place where you are changing pricing that often or trying to go super granular? It's not like you're hunting for the best price somewhere. Like it's a whole different kind of buying Persona. So do you think the same rules apply in both these places?

Speaker A: Well, I suppose if you take cloud of the example, there is no person on earth that can work out their cloud bill at the end of the month. So I don't know Plato's property. There's no one that can predict that I've used aws. There is no one on earth that can predict that because each instance, does the instance run too much, did the runtime. There is no one that can work it out. And yet everybody uses a cloud. So these companies, the cloud AWS is yours. These companies are killer value extraction, value capture machine. And so it's the same example of the cloud. Amazon, like I, uh, don't seem to produce like a product every week that they chuck into AWS. So they're doing exactly what we're saying for SaaS. AWS has been doing it. They don't deprecate anything. Everything's new. So I think there's nothing new that we need to learn. We just. Okay, I wanted to look at AWS. Every single one of those, DynamoDB, this, EC2, elastics, they're all separate features and you can do what you want. You can add them in, take them out. Here's a console that shows you how much you're paying, the instances that are overrunning. This is your cost paid. There's whole consultancies that just do optimization of cloud costs. Literally Big four firms like make lots of money out of it. So we've seen already the examples of exactly what good looks like from a value capture perspective. I think SAS just has to just look at AWS and endure no more.

Speaker B: Is there a late stage problem in the case? Can an observation be made that the product has to be so key to what you do before you can actually think of pricing in such a sophisticated manner? So thus by nature pricing become a late stage problem beyond, let's say once you're beyond a 50 million of revenue. I'm using the revenue as a metaphor for the importance of the product for the customer. Like so whatever metric doesn't by nature become a late stage problem. Because general advice is start think about pricing on day one. But is that practical? I mean clearly it's great advice, but is that even practical? Do you even have the power you do?

Speaker A: Yeah, because essentially a. I've been kind of thinking of the idea of if you assume a product is really a platform, a product with more than five or six features of the platform. So if you basically up your. Whatever the product is, it could be, and you say it's actually a platform with a bunch of products underneath it. And then you were saying, okay, well I didn't need to price all of these. No need to enable Abhishek to be able to bundle what he wants. Now I'm doing the bundling for you. But AWS doesn't do the bundling for you. It basically says, yeah, we'll do consultancy, we'll have a chat and discuss whatever and we can keep in line and up to date. But AWS doesn't say, here I take all these things at one go. It says, have you got a problem with databases? Take Dynamics. Have you got a problem with storage? Take this. And so does Azure. And so in many ways they started with just one product in AWS and then they kept on adding to them. Some people didn't take the second product or the third or the fifth. Some people took six. And so what they enabled people to do, which I think is the future with agents, is even though we're working for the Fame firm, we're both using CRM, we're both kind of premium users of, say, Stealth Source. Uniqued your profile in a similar way that you would use Notion in a different way than I would use Notion. You've got certain templates, you've used the community from Salesforce, you put together your package and I've been transparent in showing you the price of those. And you can check it out in the user group stuff, just like you can with AWS for different databases, instance, etc. And storage. But you have personalized your bundle. And so the beauty of that is like SAP or any other industrial military system that once you put in, you never want to jump out. You now have created a perfect profile for you as a salesman. Similarly, if you take on someone well, you're going to literally copy and paste the profile. That's your logical access. Similarly, if you're one of the best sales leaders in the firm, they're going to say, wait a minute, why are we messing about? Abhishek's got the perfect profile. That is the template for the profile, that is the feature list that we're going to all put together for this group. And then someone usurps you when they say, no, actually it's adi. ADI got their profile because he's doing better himself. Look how fast he is. He's efficient. And so you create this kind of notion concept. But in the same way, aws, there is very few firms that can say my AWS instance, even if they're in the same field, selling the same product is the same as someone else's. Whereas in crn, yeah, they can. My instance is pretty much the same as everybody else's. So I've really nullified that benefit I could have got from using a system. Whereas AWS doesn't do that. No, you perfectly select your AWS package and then someone else's may be using in similar tools that won't be the same as yours.

Speaker B: So as a, uh, last concluding question, so I know you have pretty strong frameworks that you work on, especially when it comes to pricing like that, a bit of it. So maybe we can leave the audience with a little bit in depth Breakdown of the frameworks around which you go about pricing and maybe that'll be a good way to continue this conversation as well.

Speaker A: Cool. Okay, so say if we try and someone, which I uh, did last week, basically say there's the five steps. The first step is okay, right? Uh, the icp, work out the value creation for each one, that one. And the second step is okay, work out the risk of value, kind of receipt add in. Yes, there's value creation right? There is with LLNs because it's unfortunate but the actual risk of that person getting it given their ICP, given their knowledge, given their is 10, 20, 30, 40%. So you times those two first step, calculate validation. Next step, the risk of actually them getting that value. Then you have a number which is okay, here's a risk adjusted value creation. Then you say okay, what share can I capture that? What is fair? We know that Facebook captured like 40% basically. Could they 2.5, uh, 2.5 low ass. We know Google captures about 40% of the value they create because most agencies say well actually put $100 on and you're going to get 250 back. So we know how much they capture and say okay, I'm not going to take as much as 40. We know Amazon captured like 15, 20% on um, platform. We can see that doordash. So we can see what platforms capture. You say what percent am I able to capture? Based on economics, competition, uniqueness, relationship etc. Say on average it should be between like 5 maximum. So for a normal SaaS, but it can be up to 10. So I capture 5%. So now you've got the framework of created value, risk adjusted value creation. What percent can I capture on that that we've been agreed with the client and the market. Then you say okay, now I need to put it into a monetization framework which is how do I actually collect that agreed 5%? What system do I put in place? And this way you come in, how do I calculate it, how do I respond, how do I put a console, how do I show the client I'm capturing that much, how do I show my team, feed into finance, et cetera. And then the last stage, stage five is that needs to be represented in a price point that is coherent. Yes, I know we've agreed 5% between you and I as a client, but actually I need to put it into a price point that you think yeah, I get that. And it could be 20 cents per call, $2 per call, 20 pound per month blanket two grand. So it's in a framework which you can coherently understand, but really, the pricing, which something will start here, is really only the last coherent phase of actually, you've done all the work before, and the client can see you've done all the work because you've proven to say, yeah, okay, we like that reference point. We like that prohibitive money that you've stated. Pricing. Now I can sell it to my boss. Fair enough.

Speaker B: Thank you. Thanks, Gary, again for that summary and lovely conversation. Thank you again for your time.

Speaker A: Thank you, thank you.