Mastering usage-based models across order-to-cash

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that exact same link, and that will take them to the recording. All right, today, um, quick introductions. Uh, my name’s Catherine. I am a product marketing manager here at Zuora. I specifically look after all of our solutions, particularly for SaaS finance teams. It’s really timely I’m here today, and I’m really excited that we’re joined by David Crowell. David, do you wanna give us a quick intro on your end as well? Yeah. Absolutely. Thank you for having me, and it’s great to be on the call today. So, my name is David Crowell. I’m a partner at PwC. Uh, I sit inside of our lead to revenue capability, and I’ve had the opportunity to, uh, specialize myself in that value stream for over 20 years now. Uh, I’ve

had the privilege of working with Zuora over, uh, at least over a decade now. And, uh, specifically, uh, I’ve also, at PwC, had the opportunity to engage in, um, a practice that we call our business model reinvention practice. And through that practice, I get to help clients reinvent who they are in the marketplace, adapt business models, adjust, uh, ultimately their, their, their pricing strategies, their go-to-market models, their, uh, their engagement with partners, and the like, and, uh, take that all the way from the front office through to the back office and the finance teams. So, great to be on the call. Looking forward to the discussion today. Yeah. With that, a little preview of our agenda. So, um, again, all of this is about how usage monetization impacts

finance teams. So, excited to get Da- David’s take on, you know, things like visibility in a usage-based world, and why it’s so important, eh, whenever you’re leveraging usage models to have, um, clear, consistent data visibility, um, not just for internal teams, but also for customers. There’s also the notion of being able to scale your monetization. So there’s a host of different usage models that we’ll chat through, and how you can scale that from an operational perspective, and some of the downstream impacts that finance teams are having to look after whenever they introduce new, different types of usage models. And the last topic we’ll touch here is reconciliation across functions. Usage data can bring in, you know, one extra source of, of data that you have to reconcile and, um, create accurate, auditable,

uh, uh, processes across order to cash, and we’ll dive into that. And then, like I mentioned, um, we will, will have some time at the end of this presentation for a bit of Q&A. Again, throw those questions in that Q&A tab in the meantime. All right, the first topic that we wanted to chat through is all around, um, visibility, the visibility of usage data. And it is arguably, especially for finance teams, probably one of, you know, the biggest challenges that we see. Um, in traditional pricing, you know, it’s kind of easy to understand exactly what your customer has used, and you don’t have this nuance of something constantly changing, constantly evolving. And whenever you introduce something like usage,

uh, there, there’s this notion of discrepancy between what might actually come out of the product service, what your customer sees, and then what finance teams are actually seeing on their end whenever they’re going to, to create bills and revenue recognition schedules for their customers. David, I wanna get your, your first reactions to this, and see sort of where, where you’re h- hearing some challenges lie in this visibility area. Yeah. I mean, especially in today’s world, where data is such an under, you know, an underpinning of the entire lead-to-revenue process, and it’s exploding in volume, it’s especially challenging, right? Many of a client’s product platforms are generating huge amounts of data. Um, many organizations that are also

looking at moving into usage-based models are dealing with, um, very nascent data generation, uh, elements inside their product or inside other transactional components across the end-to-end lifecycle. So, challenges of having so much data that it’s hard to give real-time visibility to it, or give visibility to it to finance teams in the back office in ways that, um, uh, they can actually, you know, navigate that, that volume of data, uh, dealing with actually high-quality data in some cases, or accurate data in terms of how it ties back to what usage the customer is actually paying for, uh, can also be challenging. And one of the other things, too, that we’re seeing a lot right now as well is, um, just there’s so much change in the security landscape, and so data privacy regulations

changing very much. There’s lots of things that are considered sensitive data. So how can you, how can you actually surface that data in ways and move it across your end-to-end ecosystem where you’re not actually breaking, you know, uh, elements of compliance or putting data at risk through data security and concerns? So, lots of challenges with data. Uh, absolutely.Yeah. And I, and I kind of see this coming to, to a head in maybe two primary ways. And the example that we have kind of represented on this slide is, is all about the customer experience. And while that might not be the, you know, primary thing that finance teams are, are sitting here thinking about, is, is customer experience as they’re trying to accurately bill and recognize revenue for usage, um, som- uh, something I’ve heard from, you know, a customer that we’ve worked with is because the usage

data itself was being processed by a separate team, typically that’s, you know, product teams, engineering teams that are close to the product platform, because the data lived there, it was really challenging for any time a customer had any sort of dispute on their bill for them to track back that to any capacity. Uh, what, what that ended up resulting in for this particular customer was they had to, you know, call up their engineering team. Their engineering team had to dig through, you know, log files, go back and debug any code that might have had errors in it. And the dotted line for that is that finance teams and even this company’s, like, s- office of the CFO, they were having to go and justify these charges to their customers. Um- Wow… furthermore, if the customer, you know, did not agree with how they arrived at some of these calculations just because there was such a discrepancy,

they were having to r- actually write off some of these charges, therefore impacting, you know, things like revenue recognition, impacting the finance teams and taking, you know, time out of their day that they were spending justifying exactly how they arrived at these billable amounts to individual customers. So having sort of a consistent flow that comes, you know, from the product platform, how you’re metering that usage, how you’re rating that usage, how the pricing logic is applied all the way through to revenue accounting teams can become increasingly critical as, as you’re, as you’re thinking about usage monetization Yeah. I mean, I can resonate with that, uh, actually a lot. And what, one of the things that I see, actually the example you gave sounds like it’s actually a little bit up on the maturity curve.

And what I say that, what I mean is, some of the other challenges I hear even start before that, where customers themselves don’t have timely access to the usage data when they’re trying to process bills more in an enterprise context, but actually customers trying to navigate what usage actually happened in the first place so that I can, uh, I can understand the charges that are being billed to me. Sometimes organizations struggle with that as well. To your point, um, having that available oftentimes engaging more of the product organization, making it, uh, visible, visible to customers through the product platform, uh, and then having discrepancies when the back office is, is absolutely a, a, a key consideration, uh, that sits in there as well. And one of the things that I think is, i- is, um, evolving in the space, and it’s been great to see Zuora make headway

in this as well, there’s not many applications on the market that can actually process really large volumes of data, which makes this really challenging. A lot of, a lot of historical context has been that if you did have large volumes of data, the only way to really make it available was through custom build. And now with evolutions in technology, we’re starting to see transactional systems like Zuora being able to handle usage volume, uh, much more, m- much more robustly than, than in the past. So, um, definitely one of the key challenges in, in, in the whole process. Yeah. And that, that sort of brings us to the, the next piece. And, you know, Zuora has supported recurring revenue almost since its inception, and what we’re seeing with usage models is that there are a host of different,

um, either recurring revenue or different types of monetization models that crop up whenever it comes to usage that, um, existing systems just were never built with the fundamental concept of understanding those models. And the, the reality is, is these turn into manual contracts that, you know, billing operations teams have to create billing schedules for, and, of course, you know, that trickles all the way down the line to, to revenue accounting and the whole order to cash cycle. Um, these, these are models like, you know, applying a top up, going back and actually saying, you know, mid-cycle a customer needs to add, you know, another bucket of usage on top of what they’ve already paid for. Maybe it’s bundling usage in with some sort of existing, uh, e- recurring model. Maybe the tiers of their usage are actually calculated, you know, in arrears after

they figured out actually how much they’ve used over a customer’s period. And all this can become incredibly manual work for, um, billing operations teams and accounting teams further down the line. Um, d- David, I’ll open it up for you on, on your take on this, ’cause this is just, you know, a huge, you know, order to cash problem that really affects sort of the entire, um, the entire monetization process. So, I love the slide, first and foremost. It’s, um, it, it’s a really good representation of the fact that usage is not the model itself. Usage represents a variety of ways to monetize a different grain of your transactional events. And when you talk with customers and e- uh, I talk with clients, uh, you, th- the idea that you’re gonna invest

in a solution that can support usage in and of itself is way too high of a perspective, right? You really have to get into the details of what I like to describe, as you see here on this, this slide, is the innovation that will happen as you mature in your usage monetization strategies themselves. Many organizations will need to engage their customers through a variety of, a variety of measures to really understand which usage models really drive uptick across which products, and what that means then across the entirety of the end-to-end process, from upstream forecasting, pricing and discounting, but especially all the way down into the back office when you start thinking about the finance and revenue implications

of some of these models. Specifically, it’s very complicated when you have elements of commitments embedded in with your contractor, you’re rolling over from period to period, or you have prepayments that you need to account for over time.Um, there’s a- so many different variations in usage models themselves. And many organizations, they’ll start at a simplistic model, but they’ll quickly find that there is a lot of innovation that they’ll ultimately go through and it’ll be by product, it’ll be by geo, it’ll be by customer- customer segment. Um, so, uh, you know, really looking more holistically at usage monetization and what it means for an organization. Again, I- I- also, you know, kind of specific focus in this particular context

on back office and what it means to actually support these in terms, again, of the accounting, the rev rec, and- and- and the implications in terms of, you know, management reporting and other considerations for the back office is really important. Yeah. And as we think about companies that are even adopting new technology, like, this is an area where things like agentic AI and d- different sort of AI technologies are really impacting this. Because it makes- it makes a lot of sense to monetize AI- AI b- off of usage, whether it’s outcome-based, whether it’s token-based. So, if- if your company’s adopting something like AI or new technology solutions that you’re- that you’re monetizing, this is gonna very quickly start to surface across the order-to-cash. Are- are- are you seeing that in- in your space, David, as companies are sort

of evolving, not only, you know, what they’re selling, but Absolutely…. how they’re selling it Absolutely. Yeah. And- and, um, I mean, the trend has been, um, it’s not new, it’s not novel, right? The idea that usage exists and is prominent has been around for a while. But again, as the industries, uh, start to evolve with the technology evolution that’s happening in AI, with, uh, you know, what’s happening from more of a, you know, the different routes to market and product-led growth and- and- and a lot of the self-service motions, there’s just so much more, uh, data and there’s so much more experimentation that’s happening, and usage models are a great way to capture that. But to your point, there are a lot of other considerations. Cost of goods

is a key consideration, and trial models don’t work all the time, and you know, there’s, uh, key elements inside these usage-based models that have heavy implications in the back office. But making sure that it’s enabled from an end-to-end perspective is- is, um, got many nuances to it. And some organizations, um, you know, don’t, I think, fully- fully, uh, appreciate all the considerations that go into properly assessing usage-based models and deploying them in their organization. Yeah. The- that- that’s- that’s a great point. And I know, speaking with a lot of the order-to-cash leaders, accounting leaders, you know, CAOs in our space, I- I’ve heard this notion of- of those leaders wanting to b- be in more strategic functions. And like, this is one of those areas that, you know, finance teams, finance leadership can really inject

themselves into more of an advisory strategic level as it goes to partnering with go-to-market. Although, you know, it’s- it’s a catch-22, right? You have to have the, you know, systems set up so that you can actually handle some of this in an automated fashion, or at least a low-touch fashion so that you actually have the bandwidth between your teams to be that strategic partner and provide, you know, d- actual deal advice on a deal-by-deal level, rather than being sort of constrained by, um, by system challenges or constrained by manual processes. Yeah. That’s absolutely right. Um, that partnership is so important. And, you know, I- I will expand that even more broadly. So, I work with organizations, uh, through what we call at PwC business model reinvention,

and that has so many implications across end-to-end lead to revenue. Because it’s not just about enabling the front office to do things, it’s about ensuring that the back office is enabled to support what needs to happen in the front office. And that partnership from finance to the front office and the operations team and with the product organization is so important, but it is something that in some organizations has not traditionally existed. So, breaking down some of those, you know, traditional silos and- and helping organizations to connect better, you know, around those initiatives and what reinvention and shifts in business models actually mean is so important. Yeah. And I think that’s a- a good transition into- into this last piece around, you know, just efficiency of- of the evolving role of finance

teams and how getting back some of that time that you would spend- be spend- spending on manual processes, manual data reconciliation could ultimately lead to a more strategic function. Um, but again, you know, with usage, there is this whole, you know, other set of data that you have to reconcile. Again, this kind of ties back to the two other things that we were discussing, but a lot of times, the systems that we see, you know, bookings might be in one system coming out of your CRM or CPQ system, your billings might be hosted in a separate billing system, and your revenue recognition could be, you know, a combination of spreadsheets, journal entries, your accounting system of record. And adding usage into this equation is- is one more set of data that, again, thinking

towards the back office here, thinking about revenue accounting teams across quote-to-cash, this is one other, you know, system of record that they could ultimately have to swivel chair between, um, if things are- and if- if systems, technology, and processes aren’t really set up to- to handle that seamless flow of data. Yeah. It- that’s absolutely right. And not to get too technical on the conversation, but some key considerations that sit in here, right? Um, you do have, um, uh, processes that are intended to be more automated and lower touch in the back office. That’s the goal, that’s the hope. Um, automation can’t happen without the right level of data to fuel that automation, because automation really uses data as a basis

to drive business rules and other things of that nature. Having platforms that can support data ingestion at high volumes with security from a large number of sources, uh, do it in a timely fashion, and get it processing through the back office in ways that the automation rules can run is a critical factor in determining w-How those back-office processes operate with efficiency, where you may have risk and exposure through things like revenue leakage, and just really how much, you know, how many, you know, adjusting entries and other things need to be accounted for on the backend. Um, there is also some consideration here about the connected platform. As you think about processing that kind of data into the back office, um, there is something to be said

about having a variety of different data models that exist, and having to integrate and keep those in sync as your go-to-market is evolving. So, when you think about supporting finance automation in the back office, you, we look at a lot of times this whole concept of order to revenue in a platform. And the idea that you would have, um, really a, more of a unified data model across that span of the, the, the value stream. It really does help to drive that automation in the back office and give you better data to work from, right? And that has implications, I’d say, also not only to the back office, but again, the flow back into the front office. I know some of the, you know, some of the conversations around how, eh, you know, how these different functions are connected is, is, is so important as well. But, you know, a lot of this data feeds back into sales forecasting. It

feeds back into sales compensation. You know, finance becomes a pretty, you know, pretty involved partner in that because of the data that ultimately finance owns when it gets to the back office. So, um, lots of considerations to be mindful of here as well. Yeah. And, and David, I particular use cases, like, um, like, like rolling over usage credits or, you know, prepaid models where the, wh- the revenue timing is, is really dependent on when the- Yeah…. actual usage event occurred, when it, when the customer actually consumed the token, when the, when the usage actually happened on the product side. Yep. And there’s a, there’s a lot of true-up that would have to happen for revenue teams on the backend. Now, now these models are really flexible for businesses, right? Like, most B2B businesses kind of want to provide their

customers, or at least evolve into a model where- Hmm…. they have, you know, a predictable prepaid model. But, but the, the revenue implications of that can be, can be quite challenging if those teams don’t have, you know, a red thread of data all the way through end-to-end. I would say the revenue implications, absolutely, when was my performance obligation actually satisfied in relationship to my agreement with the client, is sometimes not immediately clear. Especially when you’re having, you know, commitments that roll over usage from period to period or, um, you know, you’ve got obligations to maintain commitments over periods of time. Even some of those commitments might be prepaid. Um, you know, the other thing, again, I would go back and say, sometimes, you know, putting finance in, in partnership with, you know, with

the teams that come up with those models. Uh, we were with a client recently, and we were discussing the idea of rollover as part of some elements of what front office was looking to structure. And there was a huge risk because of increasing cost of, you know, cost of, uh, delivery, cost of goods for that usage. So, ha- having, you know, multi-period rollovers, that actually created a, a lot of implications to discounting structures and the idea that usage rolled over from period to period in the first place. So, really enabling finance to be a team, uh, and partner with, uh, you know, with the, the front office in developing those models can be critical. But to your point, understanding where your performance obligations are at, timing, and all of that can become, you know, more complicated. But having good data and having systems that can

help you, uh, process, um, you know, according to those models and for those rules will, will, will ease a lot of pain. Yeah. And I know we have about five minutes left here, so I do wanna make sure that we reserve some time for Q&A. And I also, I also want to chat about, you know, the, the, the, the partnership and the work that Zuora and PwC does together. And, you know, really quickly as an overview, Zuora can become sort of that single source of truth for usage at scale. We have an incredibly robust mediation platform that lets you ingest all of your usage data, turn it into something that you can actually meter and rate and bill on. Um, we do have our full suite of billing solutions around order management, how, uh, contract changes happen over the course of time, um, a fully

fledged usage-aware CPQ so that the, you know, the bookings aspects of this also align into that. And as well as automated revenue rec- recognition with full support for the usage models we mentioned around, you know, prepaid drawdown, minimum commitment models. Um, and, and David, I’d love to hear some of, you know, your take on how, uh, Zuora and PwC have partnered on some of these aspects on the past, as you’re thinking about, you know, companies transforming their business model, and where Zuora fits into that. Yeah. Um, uh, we, we’ve had a great partnership with Zuora, and it’s been, um, it’s been, uh, exciting to partner with you again as we help clients through what we refer to as business model reinvention. Um, obviously, the, the technology enablement is a critical

pro- uh, portion of being able to scale, um, business models. And so, um, we, uh, have seen, uh, in a, in, in, in, in a number of scenarios where, um, you know, Zuora has been able to enable a variety of shifts in organizations transforming from one type of a company to another type of a company. Um, and I think, you know, as you, as you really look to it, that, that some of the investments Zuora’s made into the end-to-end platform have really helped. Some things that are, you know, on this that, that really stand out, the mediation that sits on the front end, again, having the full connected ecosystem from, you know, from the full, the full life cycle is, is another. And, and really being able to handle the scale and volume, of course, is, is critical as, uh, we mentioned before. But, um, yeah, it’s been great. And,

um, uh, you know, I, I think as the, the industries continue to evolve and we see more of it, um, you know, there’ll be, uh, you know, a lot more discussion on how organizations are gonna change. But a lot of the needs that we’re talking about today are gonna be foundational in making sure that those are successful transformations, so.Awesome. Thanks, thanks, thanks a lot, David. And for folks who are interested in potentially engaging in one of these assessments, potentially engaging in some conversation around this, you know, we would love to follow up with you after the fact. Again, there’s that Speak to an Expert button. We’d be more than happy to engage with you in conversations around this. But with the few minutes we have left, I do see a couple questions in the Q&A, um, that we wanna get to. Uh, so it says, “One of the biggest challenges with usage that most of us might have come across is how

do you recognize revenue on a m- on a monthly basis if your customer is billed on a quarterly basis? Two questions. Does ZORA support true-ups for revenue recognition? Is there a way in which revenue can be picked off of a monthly meter reading instead of the need to bill the customer?” Uh, first part of that, on the true-ups, I believe that is supported. Would love to put you into contact with some folks from our product team, though, on the second part of that question, in terms of p- uh, “Is there a way in which revenue can be picked off of a monthly meter reading instead of the need to bill the customer?” G- great question, great question, ’cause I can see how that’s incredibly important for you to have the revenue timing ahead of particularly bill- billing the customer.

ZORA does have a concept of, we call it unbilled usage. So, the usage record is already rated, and you can actually see what was consumed by the customer ahead of actually billing. Um, I do believe that informs revenue timing, but would love to follow up with you after the fact. Um, David, maybe this is, uh, a great question for you to take as well. “Do you see different usage requirements in telco?” Yeah. Um, short answer, yes, absolutely. Um, so, regulated industry has always got unique requirements. Um, telco is regulated industry. Um, those considerations do tie into, uh, elements of the data itself. Some

of it’s sensitive, especially with lots of considerations around PII these days. Um, call data records are, uh, you know, generally considered sensitive data in that regard, and, uh, especially if you go in, uh, international scenarios, it becomes, uh, much more sensitive. Uh, telco has lots of considerations for tax. Um, taxes, um, i- is one of the considerations that, um, you know, does complicate, uh, telecom. Uh, there’s also other considerations as well. There was like truth in billing, and- and- and- and some other regulatory considerations that tied into the need to provide customers, uh, better visibility, better grain, uh, you know, o- of insights. And, and a lot of that, you know, again, just, I think, as we were talking about before, this isn’t, this isn’t necessarily

a unique requirement, but telco is one of those industries where you do have high volumes of usage data. So, I would say from a telco perspective, it’s less about the, um, innovation and pivoting to usage models, and it’s much more about the complexity of managing regulated industry. Um, and then there is also actually the interesting elements of some telco, how it’s evolved into more, you know, UCaaS models, but then also there are some considerations for people that have experimented with, uh, you know, more subscription-based models, more, you know, unlimited plans, things of that nature. Um, those have different implications as well. But, um, but yeah. Short of it is, definitely different, uh, different requirements for telecom as a regulated industry. Yeah, and I think even on the- Really-… sales side, we, we even see in,

in our own customer base, you know, they’re, uh, sort of evolving telco landscape around sort of these next generation telcos that look and feel a lot like SaaS companies of, of, you know, of- of high tech, in the sense of like, you know, complex bundling, the different ways that they’re actually creating offers to go to market. They, they sometimes look and feel a lot like SaaS companies, so I think it’s- it’s kind of a merging of this, you know, legacy, super high volume regulated telco industry with, um, some of these more, uh, more tr- I’m gonna call it traditional, like SaaS models and- and marrying those two together, is I g- at least what we’ve seen in our customer base. Um, I realize we are two minutes over, so I really appreciate everyone hanging on with us. David,

thank you so much for the conversation- Thank you…. today.