Every time I have the opportunity to introduce a speaker, I think a little bit about our history. And when I first started Benchmark and had this audacious idea of how we better share benchmarks across the industry, David was one of the biggest supporters and said, how can I help? So with that, I'm going to give the stage to David Appel, the Global Head of SaaS Vertical at Sage. Thank you, David. Thank you, Ray. It's good to meet all of you. Thank you, everybody, for being here. As Ray and I talked about what role I wanted to play with this session, it's to complement all the amazing insights you're going to hear from the investors and to give the operational feel. How do you act upon all this? What are five visceral tactics you can take in order to actually deliver these metrics that you need in order to run the business? And they're an amazing set of speakers. Byron and the rest of our team were some of our investors through our acquisition by Sage for 9.8 times revenues. Never get sick of saying that. And it's been a great run for all of this. Please, as we go through this, drop questions into the chat that I or my proxies can answer as we're going through this. Because what I really want to do is pay this forward to all of you and make yourselves successful. I just want to start with where we're living. Before everything was about growth, how do you differentiate with billing? Make sure you've got good reporting and manage the risk that you've got to handle. But now it's about profitability and underneath that, the tech consolidation, make an even more efficient tech stack that you've got. Still forecast, still manage risk. But all the more as we're doing this, where the puck is going. So the underlying theme of what it is you want the output to be of your tech stack to produce the metrics is, where you're on cash, where you're on compliance, but still handle a great forecast and still manage the risk. One of our CSR roles as finance leaders is to complement the CEO and CRO peers with how we're actually going to fund making that successful and then being aware of the risks of what goes underneath all of that. Because when you go through this, we're in budget season right now, you're doing everything in spreadsheets, but then are you absolutely sure with all the versions that you've gone through that the data is actually accurate? And then how do you really ascribe where there's big swings in the data? Is that a version issue? Is that a system of record issue? Is that a calculation issue? And that can cause so much stress that you're going through. And then are your numbers really good? And then what's the impact? Because if you get a metric wrong in your due diligence as you're trying to raise money, which many of us are trying to do as cash goes through right now, like for instance, if you get 1% NRR or a 10% rule of 40 overstatement, you can get a huge reduction in your enterprise valuing. So 1% error getting discovered in due diligence, this happened to a firm that we were earning their business at an $8 million swing to post money valuation, right? And the thing is, it's an avoidable problem for everybody. So here's the five insights, try to structure, there's a takeaway for each of these that you can apply to yourself. Where are you at in the process maturity lifecycle? Where are you at in building your tech stack checklist based upon where you are in your lifecycle? What billing use cases have you put in? How are you, once you get those three foundational elements in place, where can you automate all of that, particularly with AI and pattern recognition in your data? And then we have a joke we say internally, every FinOps tech stack project is ultimately a reporting project. So based upon all that infrastructure you put in place, how are you going to do in your planning and your forecast? So this starts on the left-hand side at the bottom seed and works its way up. At each stage of gated capital, there's a main goal that the investors want to see from you. You've heard in these other presentations about what they expect to see in order to justify getting more money comes out of it. This puts some drivers on what you need and finance need to accomplish in the processes and oversimplification, the metrics are more than this, but these are certainly central. So product market fit, get 10 happy customers, CEO sole receipt in a shoebox. Series A, prove you can sell it. Prove the revenue model with a fast triple, triple, double, double, double growth rate and start to automate cashflow and billing. And then series B, prove you're an expansion model that you can get people to buy from you a second and third time with more money. And there's a lot that happens here with amendments, the impact of RevRec, complexity of volume, and perhaps multiple billing models and the impact of closing. And then what's the data set for the forecast? Later stage growth as you start to merge winning your market and forum shivering, the competitors that you have, getting to growth profitability and making it predictable and repeatable. And you've done budget versus actual, but FP&A customer success really need to be hyper efficient here. And then a big outcome. You take a capital efficient model, move to adjacent markets and geographies where all the compliance and reporting work you've had, but it has to be precise in order to have public markets surpass public market scrutiny, pardon me, and your acquisitions compliance. And so what I'm going to ask each of you is take a look, pretty straightforward. Where are you at? What stage are you at? And again, jump in with chat, in the chat if you have questions on all this stuff. And then what are you asking your teams to most focus on against all this? And I built this conjunction with an amazing leader, Jeff Epstein from Bessemer Venture Partners. He was our board member. And Jeff was the CFO of Oracle and is a speaker at the Graduate School of Business at Stanford University. And this is curated from what he developed. So this is very efficient work about what's going on. So where are you at in things? So then let's move to step two, the Finance Tech Stack Checklist. And you can see it goes through six boxes. We've done, I've been very fortunate, grateful that my organizations have learned the business of over 2,000 of your peers. And we constantly learn what we do right and what to improve upon for next time. This checklist has come out of that, about how to really build great statements of work and your business requirement documentation. Just be thoughtful about this, because like building your home, if you've ever done that, the more you're clear what you want in the architecture, building it, then the builder can be really clear on building it. And then for the future, how you want the house to be used, same thing with your tech stack. So first one is set up your foundation, build your GL, what's your chart of accounts, what's the dimensions that you truly want to report and understand the business on, both today but into the future. And then second, know your billing use case. Nailing down what quote to cash looks like today, in a way that leaves you with the least amount of technical debt, that layer on more billing models as you perhaps do acquisitions or get a more sophisticated and move to adjacent markets is key. You got to solve for today, but keep one eye on tomorrow. Then underneath that, what's your revenue recognition scenario, gosh, I'm jumping on the chin on this, on how much it takes to manage deferred revenue well, particularly with contract amendments, and particularly, and if you've got to adding or building around a consumption and usage-based model. And we can get into the details of how we want to do this, send me a note, david.apple at sage.com, that we talk about this, I talk about this incessantly with your peers and chair people of the audit committees that are clients, but you really want to be aware and thoughtful on all that. And then where's your data coming from and reporting, because there's questions and answers that you want to get out of the information, but tying back into it, so what's the dimensional tags from the billing use case? And then the number one place where I get pulled in when a project's going askew and got a fire jump in to help, is when there's exceptions. Exceptions in the billing process, elsewhere too, but primarily the billing process, where procurement teams wrap your sales leaders around like a pretzel on, we need certain deal terms in order to do business with you, and you need the win, but then you've modeled the system and all these flows we just talked about, your billing use case, your rev-rep model to handle one thing, and all of a sudden you're managing exceptions. So where can you anticipate those by talking to your investors and peers in the market or people who have similar billing models, try to head those off of what your policies are to allow you to win the business, but to reduce these, because these are the killers. And then underneath all this is the compliance, because you have to pass an audit, you have to pass due diligence, not when you get to the point, you have to be SaaS compliant. But you do things in this order, do things in this order and bring, you as the finance leaders, bring the leaders along with you. Have the talk with your chief product officer, your chief revenue officer, your customer success leader, and others that depend on your org structure, and talk through each of these, the implications of it, not getting mad and yelling at each other because an ounce of prevention is worth a pound of cure, but talking through the implications of it and do the research, because one of my favorite quotes from Harry Truman, the only new thing is the history you don't know. So you can avoid a lot of problems in this, but in the right tech stack checklist. I want to say one thing about all this stuff is, the second place I could pull in is when systems don't reconcile with one another, doing manual integrations, because a lot of companies build up and just solve process problem after process problem, but don't think about it across departmentally, across what the reporting flow needs to be in order to be able to report on the buyer's journey and customer journey with you. When you do this, the more integrations you got, the more manual you are with reconciliations, the less you got one system of record, the less you got good accuracy, and that goes back to that spreadsheet issue we showed before where numbers are inconsistent because they're getting pulled from different places. These are things you want to try to avoid and be really thoughtful on how you handle them. So that's the to-do off that one. Think about getting together as a team and walking through these questions. I'm very happy to send the deck out afterwards, thinking through your tech stack and how it looks like. Well, let's move into billing use cases. There are so many amazing billing cases that are out there where people are trying to differentiate not only with taking friction out of the product process, the implementation process, but trying to take friction out of the purchase process. That's a lot of the value of the product-led growth model. They could really use the adoption that comes on board and then scale it up over time. And these are just eight simple examples of different usage and consumption. There's a myriad of options that are out there in our product. We've developed it to support over 500 different billing use cases to help people get in there, and I'm amazed at the creativity that gets thrown at us. To all this, and we could have a long discussion on pricing, and I'd be very happy, and there's some great resources and firms out there that are specialists and consultants on this. I certainly would love to help our clients with it. Just picking where are you delivering value to your customer, there's this old adage, the best salespeople don't sell, instead they help people buy. What are people trying to buy of the return that they want from using your product? And what's the best way for you to give them that value that allows you to best monetize the model that you have? And you don't want to make these decisions in isolation. You want to make them in conjunction as a leadership team with discussing this. So product might think they can do it on an island, but really you've got to set up with how sales is going to try to do it and how customer success is going to do the renewals. You might be early stage, so you might be seed in Series A and you just don't know yet. I get it. You can still get some counsel to do this as you pick it. And then in some respects, just go and try to experiment and do it. Again, being mindful because you, last point, you set yourself up well to avoid having technical debt that you don't paint yourself into a corner. And we could go deep on each of these and the strengths and weaknesses of each of them as you can apply them as somewhat unique to your model and tracking the value you can come in. And this is paramount because then your revenue recognition follows your billing use case, particularly around the complications of usage and consumption. And we could get into the whole thing there, but we oversimplify it. And the more you can model out what the historical precedent is and what your median customer looks like, that's what allows you to then have some baseline to be able to communicate to your auditors what you anticipate the future revenues to be and how you're creating revenue recognition scenarios. That's a simplistic answer, but I'm very happy to get into it. And then once you have established these foundational elements, what you have to do based upon the stage at which you're at that Gated Capital wants to see from you, going through those six steps and being really mindful of what your billing use case is, then the whole point of AI is to understand where there's patterns in your data that you can automate and have it self-learn about how to execute the patterns within you. Because we're all finance leaders, although some revenue and product leaders, thank you, I see some of you in the box, thank you for being here also, but leaders in the companies that you got, finance has got to be right. If you get, if you put it, if the AI puts a lead in incorrectly, there's some implications to that, but there's not the same implication as if you got the journal entry wrong. And so it's how you put in every transaction, like this is capabilities we do in our product, is every journal entry comes in, we match it against the pattern of how other journal entries have come in, look to see if there's things that looks like it's an exception and flag it, but we learn, system learns in the early days, there's more false positives that get ironed out as you go about it, which we learn what a good transaction looks like. We've also got a capability where you can automate accounts, payable invoices coming in or to learn the pattern of what the event, who the vendor is, what the items are, and then self be able to be intelligent to assign the dimensions to those journal entry based upon the pattern of how it's seen things do afterwards with the ability to have trust and human intervention of all that and automate the workflow that comes in from all that. And so when you've got every journal entry coming in accurate and every invoice coming in tagged and with the workflow that's coming in, and then similarly on your revenue to cash process about the ability to automate how you're sending out receivables and those invoices through AI, you can see a lot further around the corners. The patterns become more clear, the data's more laid out. And so each of these is unique, but I say the places to start is on the automation of invoices coming in and invoices coming out and leaving yourselves the ability to do human intervention to be able to have trust in the system about where it's going. There's a lot going on with Gen AI and other speakers are going to be speaking about that today. I'm talking about back to operationalizing how you just laid in the first three steps and learn to automate things coming in. I could spend all day on this. I've done several webinars and got a lot of all this again. Feel free to reach out to me or put some questions in to talk further about all this. And when you've done all this and laid it all out, this is where Nirvana comes in. Beautiful metrics, metrics you can trust, metrics that show you where the business is going, what the success looks like. We come in with all this metrics that are specific to your model, modified and how the algorithms are pulling the data together to see what you need to accomplish. And time and time again, when someone says they want a dashboard in order to manage all this and why I'm doing this session, why Ray asked me to do this session is you need all that infrastructure to go through those four steps beforehand to get to this point, to get in here. And I'm not going to drill into the metrics. You're going to hear that from many of the other sessions that are here. My job is to help you operationalize and act upon what's possible in order to make that happen. And I want to tell a story that pulls us together. This was our, because nothing makes things come more alive than the success of a peer that's got something done. Springbuk was our customer of the year last year, just an amazing firm. James Norris, the CFO, an incredible fintech firm. To give you context, what they do is they help firms bring on the insurance policy where they're both dealing with the small firms and large firms, so two different billing models, and they bring in a lot of AI analytics in order to help you decide what insurance plans to roll out, what those employee costs are going to look like for you. So incredible product market fit, massive scaling, but the ability to understand the top row, their top two billing models upon what they were going to do and how they're unique to the two different customer segments that they have of small companies, large companies, allowed them to have the analytics to take churn down 7%. And then being thoughtful about their tech stack was starting with the GL and how they're going to layer in some other systems, give them a much more fluid operational flow in order to reduce DSO 50% and accelerate cashflow a million dollars back into the business. And having those pieces laid out across the billing model, being thoughtful on the GL and how they're going to handle the different billing models allowed them to automate REV RAC and the tax and pass the audit, just like that. And then having that infrastructure in place allowed them to have far more, and using some AI to far more efficiently cut the close down where, I love this, back to Nirvana, they build their financial reports in seconds, not days, and so they spend a lot more time talking about what to do, you know, working on the business as opposed to in the business on doing that. And then all that infrastructure, ability to forecast and having that data really truly tracking cash going in and cash going out and having those analytics allowed them to get deeper into second order, third order decisions on the business. So they found some areas to improve cost efficiency and engineering. So they improved gross margin 8% and then took their variance down on their cashflow forecasting by 50%, which then gives more confidence in order to go where you invest in the business and best manage their cashflow. And so that's pulling five steps together to operationalize it. And I want to give some visuals about what all this happens, but when you put, when every transaction comes in with a dimensional report on things, this is where you're able to best manage what's happening with billing to be able to track when you've got great integration across your CRM system, HubSpot, Salesforce, Pipedrive, others, ChartMogul, you're able to then best track the bookings and the revenues out of one financial system or another systems in record. When you've got all the data that shows what the historicals are, what the dimensional tag is, it's coming in a lot faster with a much faster close. This is where it allows you to see budget versus actuals. Similarly, same thing as to be able to anticipate what you think the billing forecast is, both across the traditional enterprise model, PLG model, or consumption or usage where you find the median baseline that comes in. When you've got all those pieces in place and you know what's happened in the past and uses some AI to do the analytics against what's happened in the past, you're able to then do renewal forecasting to anticipate where there's issues and downstream in the business. When you've got the dimensional tag for every transaction that comes in and be your chart of accounts, you're able to build your customer cohorts, then manage the different cohorts over time to see the different metrics that come in from all this. Similarly, then you're able to do your cash forecast. You're able to do a better headcount forecast anticipating against the cash position that you're in. You're able to do MRR forecasting and drop in the different assumptions and elements that you think are going to happen, or pull this in because that data is in the system. You're not spending your time managing as many exceptions as possible where to put those pieces in. You're able to do the same thing with bookings. You've got the flow, the P&L to share with your P&L leader. This is the nirvana back to that last step of if you put the right infrastructure in place, you can get there. Workiva is an amazing IPO on us. Putting all this infrastructure in place allows you to get that kind of growth, that kind of predictability in what happens in your revenue, your multi-entity consolidations, your foreign exchange, your revenue recognition, and then pass public market audits and have a great growth in your business. Even as you add, as they did, even as you have incessant contract amendments, as you roll out new billing models, and as you make acquisitions and create even more of a platform, that's the outcome that can happen. There's the five steps. Where are you in the process maturity lifecycle, so the expectation of what it is you need to accomplish today and anticipate what it is investors are going to look for tomorrow. How do you go through those six steps of being thoughtful of your tech stack checklist as a leadership team, leveraging the insights of others that have gone before you? Where's the value that your customers are getting from your product and thus with the billing use case? Now, I'm going to need that in the subsequent revenue scenario that you do. Because you've been thoughtful on all that, where do you leverage the new technologies to drive automation with AI? Again, my very strong counsel being on automating how data's coming in and data's coming out, cash coming in, cash coming out to get started, and how that infrastructure allows you to get very deep into doing your non-GAAP SaaS metrics that many of the other speakers are discussing and talking about, and then that the other traditional financial reporting that you need to do, or a very capital-efficient market in the uncertain economic time that you've got. The more thoughtful you are at putting all these things together into your tech stack, we can then, as inclusive as possible, the least amount of handoff and reconciliations is what produces that single system of record. And there's some classic metrics that you need to accomplish that your peers have broken down. Again, I'm happy to send this out to anybody who wishes to see it and ask the questions that you have, excuse me, to anticipate what the metrics are and then work your way backwards again to, okay, so how are we building for this and how we set GL codes in order to do this, and where can we automate, get this put in place, and then take out the exceptions that happen across all this. Again, I'm not going to spend as much time on this, because you're going to hear this from many of the other speakers, finds to be very operational-minded, and that's focused for all of you. Well, I want to say thank you, Ray. Thank you. It's been an amazing journey getting started with you since you've had such a great career within building this firm, and what Benchmark and AI is able to accomplish, what you pulled together was such a wonderful community with SaaS Metrics Palooza. I appreciate our many customers for their input that's helped us make us better in order to bring these best practices to all of you, and again, if you have questions, I'm david.apple.appel. If you wish to learn more, simply go to sage.com slash us slash sageahead. Ray, back over to you. David, thank you so much. So much information and insights in 28 minutes, but we have 50% of our audience here are finance leaders, but another 25% are, I would say, mid-stage crisis companies, 10 million to 100 million. So here's a question for you. You've had, I think, 200, 300 at least conversations in the last year or two with finance leaders who have said, I need to maybe apply more rigor and get my financial operations in shape. Are there two or three kind of common symptoms that it might be time to step back and re-look on our financial operations infrastructure and rigor? Absolutely. I don't even need to tell them, you might be each of you feeling that pain that you're going through right now, and they're different upon each stage. I'm going to way oversimplify it. I'm going to go back to that slide, Ray, so we can refer back to it. Here it's, you're starting to get success in the selling model. So how are you going to automate billing? Here it's, there's a lot, I'm going to focus on the cash side, not on the payable side. Here it's a lot about contract amendments coming in and then changing the revenue rules and then perhaps rolling out consumption usage billing and how do you get predictability out of all that. Here it's how do you take all that baseline and understand what it is and then figure out where the inefficiencies are to make it even more profitable with your FP&A model. Here it's handling all the international aspects about handling foreign exchange and currency translation and then of course across all this tax. And each of these, I could keep going, has a compliance factor that the board's going to want to see that comes in. But those are just some initial ones, Ray, for everybody that, and I'll simply say, again, having earned the business over 2,000 clients, doing this earlier rather than later has such a higher success rate on successful systems deployment. Yeah. I'll double down on that because some research we did with Sage, we found like 74% of companies, this is a cross size. We're still using Excel as our primary SaaS metrics calculation instrument. And it's really hard when you're investors at board meetings or you're getting ready for that next round of financing that you don't have consistent and real-time visibility and insights into those SaaS metrics without several days in a manual process, which also very prone to errors. David, thank you so much, number one, for being a speaker. Number two, to Sage for being a platinum sponsor at SaaS Metrics Palooza 23 and for everything you're doing for the industry. Thanks, David. Pleasure, Ray. Again, everybody, thank you for the questions in the chat. We'll respond to them. Feel free to reach out. Good luck with the rest of the Palooza.