The Critical Requirements of a Subscription Management Process

Speaker A: Foreign.

Speaker B: Welcome to the Data for Subscriptions podcast where we learn and explore how to run better subscription businesses. I'm your host Berat Bonyan and uh, today we have the pleasure of welcoming Stephen Hurl, Director of Revenue of Office of the Revenue from ISG Research to the show. Stephen, welcome.

Speaker A: Great to be here. Thank you.

Speaker B: Today we have a really befitting topic as uh, one of the final episodes of the year as well Stephen, because we've been diving into several pieces of the subscription management process with various speakers over the uh, episodes that we've had during the last weeks uh, and months of the year. I'm looking forward to discussing the full end to end process with you. And you've done a lot of research uh, looking into the subscription management process with the research report that we are going to lean towards as well. But before we get into both talking about what you see from overall trends and issues as well as some of the details that we want to discuss, why don't we share with the audience a little bit more about yourself, uh, what you're doing at ISD Research and what led you to become so uh, passionate about subscription management.

Speaker A: Great. Yes, thank you. Um, yes, my name is Stephen Harrell, I've been at ISG just. Well I was part of a company called Ventana Research who was a boutique company started about 20 years ago focused on the application of technology for business and we were acquired by ISG just over a year ago. Now um, ISG has been around for 20 years. Um, many of the audience might know them, they have focused predominantly on research and benchmarking around service providers for enterprises and um, but what they didn't have was any in depth research into software. So they made a strategic acquisition of isg. I'm sorry, of Ventana Research. So we're now part of the ISG software research department. Myself personally, uh, I've been an analyst for just over four years now. I spent majority of my career um, on your side of the fence as it were. But on the software provider side, uh, I've had jobs at companies like Oracle. I've also worked a number of Silicon Valley startups. But the previous or the company previous to joining Ventana which is, was a, ah, company involved in subscription management um, and billing software. So I led product there and I led the introduction of AI to that product. Um, but one of the things that I found interesting about becoming an analyst was being able to take a 60,000 foot view of the entire software industry and that has led us to um, what we call the Subscription Management Buyers Guide. So as part of Offers of Revenue, I cover subscription management, but I also cover digital commerce and technology that supports the sales process. So for us and for me, um, it's a continuation of technology that supports revenue generation. And as we'll sure go on to discuss, I'm sure your audience appreciates subscription management is now a fundamental part of what it means for business models of how they generate revenue. So that caused us to focus heavily on subscription management and billing. And as you alluded to, um, last year or this year, sorry, in the summer we published a comprehensive survey of most of the major players in this space.

Speaker B: Yeah, so let's dive right in. I mean whether we call it subscription as an offer or as a service or a digital service, I mean they come in many different ways and forms and shapes. But essentially what we're talking about a type of service that rather than a one time sales on a product we're speaking about in this case we're referring with the nomenclature of subscriptions. I mean it's been debated for a long while, it's been around for, depending on who you ask, at least for uh, the last 20, 25 years with the telcos offering us subscriptions for our cell phones and furthermore the classic examples of the Netflix. But you made a point in the research that you've done that it's a uh, specific point in time where companies need to invest in their subscription management processes. And why is that, Stephen?

Speaker A: Yeah, I think if you, you know, you're right to raise the history because subscriptions are not new. Newspapers, uh, have been having subscriptions for 100 years, magazines, etc. But I think what, you know, and then, you know, 20 years ago with the launch of digital digital entertainment and digital products, SaaS products especially, um, the notion of, of perpetual licensing and software went away to be replaced by subscriptions. And I think there's a very good reason for that, not least led by the consumer, this notion that um, why should I bear all the risk as a buyer and the seller have no risk. Um, I worked for Oracle back in the day where it was on premise and you made a sale and the customer was then committed in a perpetual license. I think the SaaS model lends itself better to a shift in that risk between the seller and the buyer. And I think what has been interesting over the last 10 years is that it's moved from being predominantly companies like we mentioned digital entertainment, uh, we mentioned uh, sas, and it's now been recognized across many different industries. And I like to thank BMW for coming up with the example, even though it wasn't a great example of offering heated seats in their cars on a subscription basis. But I think it illustrates that many more industries, especially as they introduce um, digital products which accompany a physical product, maybe it's a vending machine vendor who has a IoT sensor that allows you to have a subscription, to have a maintenance or to have um, uh, uh, notification when stocks are running low. This is now mainstream. This is now part of almost all industries vernacular how they deal business. Now that proportion between what is one time and what is subscription may vary but I think the trend is there. I think customers are used to it. Customers want to it because of back to the idea of this shifting of the risk from the buyer back towards the seller. And as we'll go on to talk about perhaps with usage, maybe that risk goes even further back towards the seller.

Speaker B: Yeah and I would add to it that there is a significant correlation towards technology availability and maturity. Because you mentioned for example IoT sensors from um, my background having been at Ericsson, we've been working with IoT since at least 15 years back in time where there's tons of examples, there's plenty of them in different industries. But it seems like just only in the last couple of years where it's not so much about the IoT sensors, it's more about what you learn from the data and what you can do with the data. And you also made a great point in terms of therefore there is an expectation and anticipation from the buyer whether being on the business side which is actually um, really, really interesting because largely the examples historically that we look at come from the consumer side. Today we see much more pull from the business side. And as we will get to discuss as well when it comes to AI and some of those areas, we just going to see a rampant increase across multiple industries versus the few that were the front runners. But let me just immediately go ahead and ask you where do you believe if you look ahead three, four years, where subscription businesses. And let's also immediately define that when we speak about subscription they can come in many different forms. Uh, you can speak basic flat fee subscriptions but uh, we might include usage or hybrid where you have bundles and so on. So just to kind of frame that. But in light of that Steven, where do you see subscription models um, go in the next three to four years?

Speaker A: Well I just see a continuing trend and I think what is interesting is that you know we talk about subscription but it's a shorthand. I think you mentioned Hybrid subscription really means it's a bit like you know, people read NoSQL. Well NoSQL doesn't mean not SQL. It means not only SQL. That's what the no means. And I think really what we're talking about is not so much subscription is not only one time. It's uh, not as catchy as subscription so it won't catch on. But that's really what we're saying and I think what we're saying, what we're seeing, what I'm seeing in my research is just a broadening and a deepening that more organizations, more enterprises, more sellers are saying, well I have a variety of different pricing models I can use, I can use one time, I can use subscription. As you mentioned it could be flat fee, it can be triggered based on um, you know, hitting certain volumes and then on to usage. But we've also got milestone. You know, if you're in the construction business or your professional services or you're project related, then you have milestones in there as well or when people get paid. So I think what's happening is that more and more organizations is that it's not only one time, it's actually now we have a plethora. There's a multitude of different ways for different styles of products. You know, if it's a one time physical good, um, and you want to deliver it then maybe that lends itself to a one time pricing. But even there you know we're seeing in the medical profession medical hardware which is extremely expensive to buy you, we're seeing um, early offerings. I think Philips is offering um, some of its medical imaging on a, on a subscription basis so a smaller practice can avail themselves of the latest technology. And you know the final point, I'll say, you know anecdotally I remember talking to a, a German manufacturer who was saying that they see this as a way of competing with perhaps lower priced suppliers from the far east where they can change their business model to be subscription such that it's back to this idea of uh, why do you insist the buyer has all the risk in the sale? Moving to a subscription model makes that a much more uh, digestible from a buyer point of view. So I think there's many motivations as to why one would want to move away from not only um, or move away from one time but I think it also adds to uh, the competitiveness or adds to the competitive of organizations, enterprises by being able to choose an appropriate pricing. And you know, more interestingly you said how is it going? I Think there's going to be uh, technology improvements and what it means to test different pricing scenarios and then operationalize them quickly rather than the today you come up with a new pricing, you go to it, they say 6m months. That's not going to work. In a more agile digital economy.

Speaker B: Do you see any fundamental differences between B2B and B2C when it comes to subscription offers? And by that I mean uh, uh, for example demand and requirements from customers as well as capabilities that businesses need to have.

Speaker A: Well I think, I think the fundamental difference is how in a way how you pay. So typically a B2C consumer is that the um, you pay at the point of transaction. Whereas we're in, in the B2B world there is the concept of negotiated pricing, longer term contracts and the concept of an invoice. So though fundamentally the concept of a subscription between a B2C and M B2B on its face is not different. I think there are implementation and the process implications of why it's slightly different between B2B and B2C. You also get much more complexity in pricing on um, B2B. Um, you know, B2C, if you think of a Netflix, you know it's a single price or 2 prices or 3 prices typically in B2B because of the be able to discount and offer promotions uh, off list. Each contract then becomes personally negotiated as opposed to standard list price.

Speaker B: Let's talk a bit more about trending topics when it comes to subscription uh management. So if we go back about two to three years, all the rage was about usage based pricing. Today is largely around AI monetization. Uh, my question to you is what, what would you say are the predominant, underlying let's say reasons for these trending topics? What is actually driving usage based pricing and AI monetization?

Speaker A: Yeah, I think we have to take a step back and talk about why usage has not um, been such a, why it hasn't taken off. Because if we go through our, our um, you know, the discussion points we had about you know, who, who bears the risk, um, you know anecdotally I can remember 30 years ago um, working with a large CPG company and the person I was talking to said but why can't I only use, why can't I only pay for what I use? So I think this concept has been there all the time. I think there have been some fundamental technology issues as to why, why it hasn't really taken off. But I think, you know, I think and um, some of those obstacles have been around predictability you know, from both the buyer and seller. So I think what is interesting about this move in AI is, you know, is I, is AI going to be the driver that really makes usage more prevalent across the industry? Um, and I think, you know, one of the reasons for that is basically a cost basis that AI transactions are not free. Um, they're very resource intensive and I think a lot of providers of AI are in a sense trying to ensure that their business models cover their costs. And if they were to offer a one time or a flat fee, there is no real way of uh, of assuring that they'll cover costs because the costs are very much dependent upon how much a particular consumer or business buyer is using. So I think there's a real economic driver here that's now starting to parallel the consumers desire to only pay for what they use with the supplier or the vendor's side of saying, you know, to make sure to ensure that I have a viable business, I need to ensure I'm covering my costs. So I think this is driving and it's, and it's very interesting as you say that is, is usage, AI usage pricing going to be the real driver that breaks out or that causes usage pricing to break out into wider audiences?

Speaker B: I second your thought there because I do believe that usage, uh, based pricing, initially when it became a hot topic, it sounded almost like it would be the medicine to fight what we also saw about the same timeframe two to three years back, something referred to as subscription fatigue. Basically mostly on the consumer side, whoever was paying was feeling that. We're sitting here with maybe 10, 12 different subscriptions, I'm not even using half of them. Why am I paying for this? And what happened, what I refer to is that we got a imbalanced value equation and it really doesn't matter if you're in a classic one time purchase model or if you're a subscription model. If you feel that uh, what you pay for in the value balance is off, then you will be unhappy. Usage based pricing came along as a means to kind of fight that off. I believe that unless there is a strong value proposition which makes sense and is understandable for why you should have usage as a pricing model, it will not work. I'll give you one good example of if you roll back the time five to ten years versus today, uh, it's energy consumption because of energy and the price of energy has become a hot topic and a big topic for most countries around the world today. Everybody is fairly accustomed to you pay for the kilowatts that you use and we have the right type of support technology applications to kind of see that that's a good case where in many cases there's been less good cases. My view, and maybe a question back to on AI monetization is we have seen some good examples of where pay for what you consume on AI applications seems to work. Can you share with us which ones you see that you feel are good? And have you also seen some examples where you see that it doesn't make complete sense again, from a value standpoint?

Speaker A: Yeah, I mean, I think, you know, to, again to step back a little bit. You know, we have to think about how it's, it's, you know, in the technology industry and in the analyst industry, sometimes you fact you forget some fundamental basic organizational principles. And you know, businesses work on fixed accounting periods, typically 12, you know, monthly periods. And they have the concept of budgeting, which is some pre estimation of how much you're going to spend on a particular activity. It could be salaries, it could be buying desks, it could be buying technology, and of course it includes buying AI. Likewise, as a consumer, we get paid weekly, bi weekly, monthly. There is a real economic psychology here is that we're used to having fixed sums of income which we then spend. And one of the interesting aspects of usage is a priori, you don't know how much either you're going to be spending or if you're on the selling side, how much you're going to be earning. So I think there is some quite complex thinking that has to go into, you know, I can't offer AI at a price point that if somebody really enthusiastically uses it, they're going to be spending 10 times what they anticipated. You know, you can't, you can't meter things in the same way. That said, oh, you, you know, if you have a purchase order for 100 units and you run out of 100 units halfway through the month, um, what do you do? Do you stop offering the service? Does the customer or does the customer get overages? You know, there are, it, it's not quite though fundamentally it's a simple concept in the actual implementation of it. Because of the nature of how businesses operate and how consumers think about spending. You do need to think more closely about when I'm pricing, is it going to be such that anybody who enthusiastically uses it is going to blow through their anticipated spend very early on in the accounting period? So those are some of the thinking, um, characteristics that people need to consider. It's not just I'm going to charge on a usage basis. What is the propensity of my buyers? What is their propensity to buy? What are their constraints, organizational constraints in terms of budgets? You know, and we've seen this with, you know, some vendors are now offering the, you know, digital wallets where you can draw down on a digital wallet. And we've seen this in consumers as well. You can top up, you know, as, as you're progressing through the period if it looks like your usage, um, is going to be more than you can top it up. But that again, you know, most organizations, most business organizations are not used to this concept of topping up. It's, it's there in B2C less so in B2B. So I think early days, lots of things to work out. But I, you know, I think they can be worked out and I think technology can really help here as well.

Speaker B: Stephen, going back to uh, the question about AI and if you'd seen any examples, I'll just toss in two of the ones that I see that I think make sense so far. And still we, we are extremely early on when it comes to the whole AI monetization question. But some of the cases that um, I see is just that you get a number of credits for uh, the number of queries or searches or the specific tool that that application allows you to do. So suppose you pay a fee and you get one, uh, thousand credits, then you use that application and you eat into your application. So it's very intuitive. You can basically see as a metered service how much you're uh, consuming. And when you're reaching an empty uh, bag basically for yourself, you can choose to top it up. So it's very simple, very rudimentary, but works some other m. AI monetization cases, in the case of for example chatbot services, or let's say customer service that are AI driven, where you pay for a number of customer questions or queries that are being solved. So you're not paying for anything more or less than that. So here, here we're flirting a little bit with more of an, let's say outcome based pricing. Ah,

Speaker A: yes, and I apologize, didn't directly answer your question, but you're absolutely right. Um, you know, a number of vendors, uh, have launched the concept of, as you say, you get a set number of queries, AI based queries for a fixed price, and then anything over, you start paying overages on a sliding scale. Obviously agents are a big topic at the moment and there are a number of monetization methods put forward for Agents. One of them is on a per transaction basis, which interestingly enough is the way that a lot of call centers work. Um, you know, call center providers, a lot of it is outsourced and it's outsourced on a cost per call center or a price per query answered. Um, so, you know, I think there are, there are good analogies that people can pull from existing, you know, you mentioned telcos, um, you know, utilities. There are a lot of these things have been come across before and solved in different ways. So it'll be interesting to see how much organizations look to perhaps previous examples and then use those as methods of monetizing. Um, but yes, I mean fundamentally today there seems to be two or three emerging. There's the, what I'll call the digital wallet where you basically get a preset amount and you prepay and then you draw down and then you have overages. Or there is a cost per transaction or a price per transaction. Price per query answered. Uh, price per call answered. You know, those seem to be the main methods and we'll see over the years whether those actually work. Because again, you know, to reference what I was saying, it isn't just that the how am I pricing? It is how does that fit in with the way that organizations typically work. And maybe there is a point at which, you know, agents really do take over the world and organizations have to rethink what it means to have budgeted finances for the following year. Um, you know, maybe we'll see some changes there. Um, you know, it's early days yet. Uh, I've not had any conversations with, on the procurement side or the financial side, but that would be an interesting conversation with them to understand. Are, um, they aware of how their uh, vendors that they use may be shifting their pricing? I think a lot of things to work out here, but it's very interesting always. And as a technology analyst, I'm always thinking, how can technology help us?

Speaker B: Yeah, I'm really excited personally about AI monetization for the simple reason that I think it could be, uh, the door opener for the question, um, of outcome based pricing. I mean for many of us have been working with pricing for many years, outcome based pricing is nothing new like many of the other topics we spoke about today, Stephen. But the fact that with, with the advent of the various services that we anticipate is going to come fueled by AI, it could be, um, the road we're going. And that to me is very exciting. But as a bit of a bridge towards another topic that you raised in your Buyer's Guide Data mediation. Again, just before I, before I let the word over to you to kind of go through some of the key points here is for a very basic subscription, of course, you might not need to be thinking so much about how do you manage the data behind the usage data, so to say, regardless of how you price. But for all of the exciting topics we've discussed here and now today, it's largely dependent on, let's say, meticulous and well handled process for the usage data. So by that data mediation, before we jump into the topics, why don't you just help us define what you mean with data mediation when you refer to it.

Speaker A: Right, So I think you actually have given the answer, um, data for the purpose of rating. And I would also add in there, it's not just for rating. So you use the Netflix example. Netflix is very simple. You're a user, you pay whatever it is, $19.95 a month. Um, but they actually use data mediation not for pricing per se, but for actually understanding how people use what they're watching. So the transaction there has two purposes. Yes, in a sense for pricing, but not really for pricing. But there it's much more people are using it for, well, what are people watching? Um, so when we're thinking of data mediation, I think there's a danger in thinking of it purely in the application of deriving a price. I think it also has other purposes which I think lends itself to, you know, you, you must treat data mediation as the serious topic that it is. And I think, you know, the more, the more complex your pricing gets. Um, you know, when you're into tiers, maybe your price arrangement says that if you hit a certain volume target in a particular period, the per transaction price goes down. Well, you can only do that if you're accumulating the data to be able to understand, to do it. And now you introduce, well, when do you actually rate it? You do you wait till the end of the period and do it on mass, or do you do it incrementally for performance purposes? Then that relates back to, well, how are we charging? Are we charging monthly or weekly or daily? All these things, um, are characteristics of data mediation being not just here's a transaction, let me price it. You may have to normalize it, you may have to aggregate it, you may have to aggregate it for purpose one, but not aggregate it for purpose two. Um, you know, we look at data for usage, usage patterns and as we'll come on to talk about usage patterns are going to become Increasingly important. Um, um, you know, so, so data mediation is not a one to one mapping. It is a very complex mapping between the source data and now as we get into. You alluded to, you know, are we talking about outcome pricing when it comes to usage pricing? Well, outcome now is, I'm measuring, not the initial activity I'm now measuring. Did the activity achieve the outcome for which I'm pricing for. So now you have the need to pull data from different sources because the initial activity data, I'm sure, is captured in one system, but the outcome is captured in another. So now we're pulling in data from different sources. We've got to match them, we've got to match them at the right level, the right time period, the right person, the right, you know, so it now becomes a very, very complex, but potentially a complex way more than just single data data mapping.

Speaker B: From your vantage point, Stephen, how are you seeing businesses today manage usage data or data mediation today?

Speaker A: Well, yeah, that's a, that's an interesting topic because I think there's two sides to it. So as part of my buyer's guide, I was looking at a lot of vendors who say we handle usage. And I was very careful when I was looking at how they, how they position things, what do they do about data mediation. And a number of them said, you know, I'm paraphrasing, but you know, we assume you have the data organized for us to be able to rate it. Well, I think that's a very big assumption. So that led me to think about, well, how are other organizations doing it? And I see movements around using a data warehouse or a Snowflake to do this, um, which, you know, to me, maybe I'm, you know, maybe it's because I'm an ex product guy. I feel like that's off, off offloading that, that resolution to somewhere not necessarily in the right place to resolve. I think there is real value in resolving that at the same time as you're thinking about rating. So, you know, I see people doing spreadsheets, I see people doing in data warehouses, I see people doing it in commercial, um, denormalized databases like Snowflake. But that's putting a lot of work onto teams of people who are not actually involved in the pricing and rating side of things. So now I think, you know, I started off my career in BI analytics. It wasn't called that then. That tells you how old I am. But um, you know, there's an inherent history of people putting together data into denormalized warehouses and Then saying okay, what do you want to do with it? And I think that that route will miss the point of. In fact it should be inverted the other way. This is my business model. What data do I need to be collecting? So it's driven by the use, not this idea of we'll put all data together and then say have it, you know, go use it. I think that that will be, that will lead people down the wrong path when it comes to this through process of what it means to monetize your, your information.

Speaker B: So if you would take um, you know, two minutes and give us, and everybody was listening a bit of advice of what should one think about when one wants to set up data mediation in a good way? So we're not after about pointing to specific software tool but if you would say think about these five or six or seven factors and capabilities that you need to ace, you need to do really well.

Speaker A: Well, I think it starts with. It's a data pull model, not a data push model. So you have to start with the business concepts. What business models do you need? What pricing models do you need? And not just don't just set it today if you're going down usage. Well think about usage in all its permutations. If you're in subscriptions, subscriptions in all their permutations. Because it should be the pricing data model determines what data you need to collect and the velocity of the periodicity of the data and the level you need data. So that's the requirements driving the capture. And it's interesting that um, some vendors are now thinking, well could I use Genai to basically determine what data I require to populate to make this business model work. That to me is the fundamental thing that I'd recommend everybody start from the business model. Don't start from data sources. Don't start from the typical um, data management. It let's get all the data and work out how that's the wrong way to do it. You'll probably fail that route. If you start from the business model that satisfied not just today but also a degree of future proofing as business models change, it'll then be much easier to have that I'm changing the business model. Oh my gosh, six months we have to wait to get new data. No, no, no, it can't, it can't work like that. It has to be much more agile and quicker than that.

Speaker B: Yeah, I second that. I think it's um, I would definitely say I think the typically the examples that I've seen where companies start from The IT side you often end up with not um, an ideal solution if I keep it constructive. In fact, uh, more often than not quite poor solutions that end up being quite costly, uh, never ending, let's say IT project systems where you have to constantly make amendments and changes. You mentioned something really important as we were speaking early on. You said it's really important for businesses and systems to be agile now with the risk of you know, being a, uh, let's say a buzzword in terms of things being agile. But I think it's really important because what you need from your, let's say your subscription management process or your quote to cash environment is that it is able to quickly adapt to your business needs and your customers needs. And if that is that you need to do bespoke pricing as you mentioned, for business to business environment, specifically for every customer having their own specific pricing or if you need to change and have different bundles, all of that speaks flexibility, that you're adaptable and the fact that it shouldn't slow down your business processes, right?

Speaker A: Oh absolutely. I have a maxim that if your technology is preventing you to run the business the way you want to, you have the wrong technology. It's very simple. Um, and I think I've shared with you an anecdote that I worked on a project for a property and casualty insurance company in South Africa and I think it's illustrative. It's an illustrative anecdote that in African countries insurance works slightly differently from perhaps in the west where you're insuring a house and a swimming pool and a car. Um, that's not how things work. In other countries insurance, um, companies are much more innovative and come up with new products all the time and they test market it and perhaps it's running on a spreadsheet and then they want to operationalize that quickly. In this particular instance as a real life example, they said to it, we need to operationalize this and it said six months. I'm not trying to knock it, I'm just saying that if that's your process, that's going to defeat your ability to innovate. You know, you mentioned bundles. The whole point of bundles is you can combine almost, uh, not at whim, but you can combine very quickly different bundles for different audiences or different markets. And you need to be able to do this quickly because if you're not doing it quickly, maybe your competitor is. And you know this ability to go from innovation to operationalization is going to be the difference with companies that succeed and fail in the Future, uh, if you can't react to market conditions, to customer demands quickly enough, you're going to be, uh, left in the dust, basically.

Speaker B: Stephen, as a bit of a final segment of our dialogue here, I'd like to move back to some extent to the forecast and discussion, but more the topic is from a financing perspective or financial process. Forecasting is obviously a concern when we speak about usage based on. But do you have an idea, uh, perhaps that with better usage data management or data mediation capabilities, that one can mitigate some of the forecasting concerns that companies have?

Speaker A: Yes. You know, let's deconstruct that a little bit because, you know, you talk about forecasting for finance purposes, and that is true. But I think also, and I, excuse me, I alluded to in my preamble around the history of usage, why hasn't usage taken off? You know, intrinsically, it makes sense. I'm only paying for what I use. Why would that not be attractive? And, you know, and it goes back to what I was talking about. Well, yes, but the way that organizations and people think about money is on a periodic basis. So, you know, I also, I talk about forecasting not just in. I'm a cfo. I need to know where the revenue is coming from this month, next month and next quarter. But also for the fundamental. How do we get acceptance of usage? And forecasting is going to be absolutely fundamental to understand what it is that the customers are likely to be using in any particular period, both for the company that's selling, but also the company that's buying because they need to have a handle on. Uh, you know, I worked in usage situations where the company I'm selling, it, uh, wasn't me, it was a customer had a purchase order. Well, a purchase order is a fixed amount of money and it's allocated by finance. And it's much easier to have that PO adjusted prior to breaching it than it is after the event. So here is where forecasting enables both the buyer and the seller to understand from a organizational constraint point of view, how to make that work. So that's one facet very important and then obviously the other facet, as you said. And for. If you look at revenue recognition rules, revenue recognition rules now require you to have some concept of an expected average usage to be able to measure against. Well, how do you know what that is? Um, you know, you don't know until you actually do some analysis. So that's where, you know, if you remember, we talked about patterns of data in data mediation, it's not just for pricing that pattern of usage. It also is going to be an extremely valuable resource for organizations to be able to forecast both to share with their user so the users can understand what usage patterns are and when they're likely to breach, uh, limits. But also, as you say, for the cfo, who, if you've ever met a cfo, uh, the one thing that keeps them up at night is, you know, where are we going to be next month, next quarter, next year? You know, doesn't matter whether you're a public or a private company. That's something that the CFO is really, really focused on. Um, so this is going to be a very, very crucial step that makes usage work across the board. And I think again, it leads you to think, well, you know, data mediation is not just for pricing, that data mediation can also have dual, if not triple use to understand pattern usage, to be able to forecast, well, what is the likelihood in the next period or the next quarter. Maybe it's seasonal, you know, maybe there's seasonality wrapped in there. Uh, maybe it's driven by events, you know. So again, very interesting. Well, interesting to me and probably hopefully to you too, vidad about how technology can help move this business process forward.

Speaker B: That's quite clear. I think neither the paying customers or the solution providers are comfortable and willing to operate, uh, in blind. Meaning that you don't want to, you don't want, you. It's uncomfortable to not know what you're paying for how much you're going to consume. Again, I'll bring this fairly complicated discussion into something easy to understand with the reference I made to the energy consumption. And I do think that what you're pointing to is absolutely correct in terms of handling usage or data mediation is actually the common denominator for all of the findings that you've shared with us today. Meaning access to structured quality usage data, uh, can unlock multiple things. And to bridge now to what you mentioned is with the energy companies today, at least in Sweden, based on your usage data, M, I as a consumer can see what I am forecasted to consume for the month as well as for the day that I'm in. So it's broken down, down to the day 24 hours. So I can see that literally. But that's because the energy providers here in Sweden, Steven, they've been collecting my usage electricity usage for years before stepping into pay for usage. Therefore, with that structured data, they're able to forecast my consumption. So I'm happy. To your point about transparency, I can see where I'm heading I can make my own calculations. They are probably happy from their side because they can do their forecasting based on how much or how little. And while this might sound simple, I don't think it was so simple seven, eight years ago. But today with everything being in hand, we look at it as, yeah, that makes quite a lot of sense. But I take with me from our discussion that companies need to really be on top of data mediation or usage data mediation to uh, unlock basically their business models regardless of what variation that is.

Speaker A: Absolutely. I mean and this is, and this is, you know, again, you know, if your technology is preventing you from exercising a business model that you think is appropriate for your business, you know, you either don't do it or you try and do it in a spreadsheet or some hack together, um, you know, ad hoc solution which then prevents you from doing some of these very value added things we talked about. You know, you mentioned the ah, electricity usage in Sweden. I'm also sure it's correlated with weather forecasting. So they will know that given a cold spell or a warm spell, what demand is going to be down to your personal preferences. You know, some people be happy at um, and I'll uh, use Fahrenheit, you know, we're happy at 64 in their room. Some people need 72. So it's not just a factor of what is the general temperature, it's also how do you personally react to it. So there's, you know, there's a lot of really interesting personalized information we can get out of there. And then if you extend it further into, into payment models, this is how you can get to balance billing. If you understand what your usage across a year is, then a provider uh, is going to be much more comfortable saying well don't pay us, you know, on this volatile basis, pay us 120 bucks a month. You know. So again, a lot of things are driven, you know, as a business model that helps the utility because they know, independent of usage, this is the money they'll be getting each period. But you can only do that by having this advanced understanding of patterns of usage, how that correlates with externalities like, like you know, uh, weather forecast, weather temperatures and weather forecasts. So again, really, really interesting applications of data to help drive for business driven by the business model. Um, as opposed to how I think some people think of things as just, oh, you get a piece of data, you price it and move on.

Speaker B: Yeah, just want to confirm that you um, read the situation absolutely correctly. First and foremost they correlate bunch of different data points based on weather and other factors. Two, it's fascinating, they move from once upon a time fixed yearly annual subscriptions to usage based. Now because of all of the data that they have, they do exactly what you said, meaning they can reverse it back to. But if you want to have a special deal, if you can wanna kind of contain your cost at a certain level, you can tie it up to this and that model. It's fantastic. Now I'm also again just had to put a bit of a cherry on the top here. As we spoke so much about outcome based pricing, but we're seeing the advent of some of the utility companies are saying what temperature would you like to have within your facility or in your house or apartment pay for that temperature, Forget electricity whatsoever or the hardware that's needed. So it's interesting as we were discussing outcome based pricing with AI monetization, but again here in this example, it's a very good one in terms of coming from basic subscriptions, manage that data, uh, going to much more sophisticated, let's say offering models and now flirting with outcome based even.

Speaker A: That's really fascinating. And as we know, uh, outcome basing again is not new. But one of the struggles that people have had trying to implement outcome is attribution. What do you attribute that outcome to? I think the more defined data you have, the clearer the ability to delineate that attribution becomes. And I think it makes outcome again. Outcome is on the face of it a very desirable situation to be in. I only pay for what I achieve. But the impediment to that has not been the concept. It's been do we have the data and technology to support that? I think we're now getting to the stage where we do have that data. We do have the technology to be able to support and have that clear delineated attribution of what achieved that outcome. And if it's your product then you can charge for it accordingly.

Speaker B: Stephen, as we're going to wrap up now, today's discussion, for anybody who's listening to us and to you and they want to really level up their subscription management process, what is your advice that we should do tomorrow? What are the two to three things that we need to grab a hold of and do right?

Speaker A: I think, you know, central theme of what I said, I think two major themes here is one is always be thinking of the business model. Don't be thinking of technology and data and infrastructure. What is the business model that your business requires not just today, but potentially in the future and have that as the driver of what now is the technology I use. Uh, I think too many times we invert the process. Let's go buy a subscription product and then we'll try and implement it. No, no, no. Think about what it is you're really trying to achieve for today and that will determine. Um, it's a bit of a chicken and egg situation. I know because I talk to companies that they don't do things they'd like to do because the technology doesn't support it. Well, I mean, that almost brings a tear to my eye. You know, you're a business, you're supposed to be operating the best way that you can to achieve returns for your employees, for your shareholders, your stakeholders. You know, why are you letting technology limit yourself that way? So I, um, really would encourage people to really think about the business and then work backwards. Um, and then the other thing I encourage people to do is, um, you know, don't take at face value necessarily what providers say. You know, you should really use your research to understand. I want to do usage, but as we hopefully we've touched on topics today, but that, uh, that cause people to think, ah, well, maybe I need to think about usage. Not that I don't want to do it, but there are characteristics about this that I really need to make sure that I fully understand before I go down the path of, you know, expensive, you know, data mediation, data integration. Buy a new product to do it, you know, really, really do and, and seek advice. I mean, you know, none of us know everything. You know, always, always look to seek advice from other, from other, other sources, whether it's research analysts like myself or it's published information about, you know, what are some of the things you don't know? It's so the things that catches out are not things that we don't, that we knew, that ignored. It's things that we didn't know and we didn't know that we didn't know. So that's, that's what I hopefully would leave the audience with.

Speaker B: Thank you very much and also thank you to everybody tuning in today and listening to us. If you're interested to learn more about subscription management, um, my simple advice is just drop a comment subscription management into the comments field for this event or you can reach out to us in any other channels that you prefer and we'll make sure to connect back with you. There's tons more we can share and we'll for sure share the buyer's guide as well and information about Stephen so that you can read the full report, um, and also get in touch with Stephen, if you would, so like. Again, thank you so much for your time, Stephen. I've really appreciated this conversation.

Speaker A: Yeah, it's been great. I've really enjoyed it. Thanks, Buddha.