One of the most enjoyable things about putting together SaaS Metrics Palooza is the opportunity to get to know people pretty well in the industry who I view as thought leaders. And one of the first people that I met about three years ago when I started on this journey around benchmarking and metrics was Ben Murray, who's known as the SaaS CFO and has one of the leading course curriculums about SaaS metrics and finance at the SaaS Academy. So with that, Ben, I think most people know you, but welcome to SaaS Metrics Palooza 23. Thanks Ray. Great to be here again. All right. Well, welcome everybody. My name is Ben Murray and I hope you're enjoying SaaS Metrics Palooza today. So a little bit about myself, if we have not met through my courses, my content, webinars, et cetera. I am a CFO by trade, SaaS CFO, nine plus years now as a SaaS CFO and like Ray, founding member of the SaaS Metrics Standards Board. My journey in content creation and teaching SaaS Metrics started seven years ago when I started my blog at the SaaScfo.com, sharing SaaS metrics, guides, templates, et cetera, and then now teach courses at the SaaS Academy. And my email's right here if you ever want to reach out to me. So today talking about the SaaS metrics playbook to improve and really support your SaaS valuations. So how do we go about supporting our SaaS valuations when those important discussions come up? So what's your number? How do we know? It's hard to just pick that out of the air and support a SaaS valuation. And again, if we're talking to an acquirer or someone who wants to invest or just the third party trying to understand our business, it's important to have a framework in place to support our valuation and those discussions. Or are we at 3x error multiple? Or are we at 10x? There's a wide range of valuations if we're looking to exit, looking for growth equity, and we need to support that number. Because we know valuation is art and science. There's a little bit of math in there, but there's also a lot of art and a lot of time on the acquirer's side, we don't know that secret sauce, those proprietary formulas that they built. We haven't seen the proprietary metrics. They're taking CAC payback and taking a spin on that to figure out the health of your SaaS business. But we can put tools in place to support our valuation. We can put these tools in place. So we have a foundation to have intelligent discussions around the health and valuation of our business. And this is my SaaS, my five color SaaS metrics framework that I put in place for my clients that I teach in my coaching session. And then I teach in my SaaS Academy and my SaaS metrics foundation course. So now hundreds of students, I think about 1000 students have implemented this for their SaaS business to improve their business and also to support those valuation discussions. And in this presentation today, I'll talk about some common mistakes that I see when we're trying to pair our data both internally just to operate our business. And then also when we're talking to third parties around valuations, around due diligence. So again, two reasons to implement this framework, whoops, going a little too fast one to operate our business, right? We need those numbers to make data driven decisions as we scale our business, right? We start off with gut decisions, but eventually it becomes too complex. We need data points to drive our business forward. And then also we're preparing that data for us to operate our business. And then it also serves to prepare for due diligence or any third party discussion. Any third party who is looking at our business, trying to understand how our business works, what's our business model. So it serves two purposes to implement that SaaS metrics framework. So again, this is our roadmap. The five pillar SaaS metrics framework is our roadmap to systematic reporting. So important. And the rubber really hits the road. If you've gone through due diligence lately, you may feel like, boy, I've got my SaaS metrics nailed. I've got all this data nailed. But then you hand your data over to that third party and they've got the team of analysts who are scrutinizing your data every which way. And even for SaaS CFOs, that makes me a little bit nervous that we've put a systematic, a consistent process in place to create the data, the metrics that we need to drive our business forward and that others are going to use, again, to assess our business. Because we don't want those fire drills. And how common is this? We know all these fire drills that we have, you know, the founder or CEO comes to you and it's like, hey, we need some numbers. We need some metrics. We have some people interested in our business. And then we're hurly calculating these metrics and doing these one-off calculations. And those can be inconsistent. They could have errors. So we want a systematic, consistent process that when that time comes around, we feel confident in those numbers that we're presenting to third parties. Because with the framework, we need to assess. We need to assess what's working and what's not working in our business. And if we're trying to push our business to that higher valuation, say we're trying to get a 10x or double digit error valuation, we need lots of green on the board. And I'll show you how I color code that metrics framework to understand what's working, what's not working in our business. So if we're trying to push to those higher 10x valuations, we need a lot of green on the board. Of course, that makes sense. And then on the yellow side, you know, we can have a little bit of yellow and yellow is where either that metric is improving. It's not quite at standards yet, or it's just a little bit, you know, just a little bit below standard, but it's close. It's not bad. It's not great, but it's close. So we can have a little bit of yellow on the board. And then it's really difficult to achieve those high valuations if we have a lot of red in those boxes in my five pillar metrics framework. So we can have a little bit, but not a lot. And with the yellow and red, if we have color coded ourselves there, and I'll show you how I benchmark SaaS businesses, we need to understand why. It's not just, boy, we're yellow and red in these categories and we don't know why. We need to understand why we're performing that way and what actions we're taking to improve, to move a red to a yellow, to move a yellow to a green. So we can color code, we can benchmark our business either against our own internal goals or against our SaaS peers because we need to connect the dots, right? There's a lot of data flowing through a SaaS business and it seems simple, right? Those formulas seem simple for SaaS metrics, but when we try to pull this together, tons of nuances, what's the period of measurement? Should I allocate? These heads are coded in the wrong department. So when we actually try to do this, it's a little bit harder than we think. So we're trying to connect all these dots into a pattern that then we can move our business forward. Because this is what we want it to look like before a bunch of data, and now we have this nice clean path forward for our business. And also we can have those intelligent discussions with third parties around investing in our business to say, this is what's working, what's not working. Because my biggest fear as a SaaS CFO, I don't want that third party telling me how my business is performing. I need to be telling these third parties and others, this is how our business works. This is our business model. This is what's working. This is what's not working. And this is what we're doing to move our business forward. I want to control those conversations, those discussions, and not have that other person crunch our data and then tell, Hey, Ben, did you know your gross dollar retention is at 78%? Actually, I didn't really know that. And why is that? Well, I'm not really sure. So we want to control our destiny with the data in hand. And there are two pillars of data that I focus on when I'm implementing that framework within SaaS companies. But today talking about first, you know, two of the four pillars, data and technology. Now to build out our finance and accounting process, it's also people and process. So today talking about the importance of data, some of those big data mistakes that we make in SaaS businesses and that tech roadmap that we need to integrate all this data into our processes, because we know due diligence, if we're going through due diligence or any sort of third party review, it is a data intensive process. And we need that toolkit in place, that framework in place that will guide us as far as the data that we need. Otherwise, it's hard to know where to start. So again, this will provide visibility, are we at 3x and we have limited scalability in our business or 10x, we've got a scalable model, things are working nicely. And you can see in this example, this slide, this is where I've color coded, I've benchmarked against some norms. I also like to benchmark with raised data at benchmark.ai, which I'll show you a little bit later. But now we can see what's working, what's green, what's yellow, what's red, against our own internal targets, our general targets, or against our SaaS peers. So now talking about some of the biggest data mistakes that I see that just make our lives kind of miserable if we're going through due diligence, I talked to a lot of founders who have gone through failed due diligence processes, because they didn't have the data in place to then prove out the valuation that they wanted in that process. So one of the biggest SaaS data mistakes that I see is bookings data. I'm going too fast. So bookings data. So if we have that closed one process, outbound motion, we're closing opportunities or those lost opportunities within our CRM system, we need good data integrity in our CRM system. And this is almost right behind financial data as far as due diligence on the numbers side. But bookings data is so important to have this locked in, to have good data integrity, to be tracking the right data in our CRM system, because we know just like our financial data, CRM system is always requested in due diligence, and that'll be sliced and diced in a lot of different ways. So again, usually what I see is no data or poor data from our bookings from our CRM system, and we've got to improve that over time. We have to track the right data, because it's so important. It's going to feed pillar five of my framework, sales and marketing efficiency metrics. It's going to feed data that's going to be required in due diligence. So we have to have nice clean data within our CRM system. So a couple of key elements of bookings data that are always requested that we have to track accurately. So we don't want just this big lump sum number in our CRM system that we really can't do much with. So some of the big things that I see that we need to track in our CRM system, and it's twofold. Again, this is to operate our business. It's not just to serve due diligence, but it hits both of those reasons, is tracking in our CRM system, new bookings. So that brand new MRR, ARR coming from new customers, and then expansion. So we cross-sell, we upsell our customer base. We've got to track those expansion opportunities. Now sometimes we have contraction, right? We go to sell that multi-year contract, the customer decides to drop a few products, and we have contraction. That's still a close one booking. It's a renewal, but we have contraction and that affects our metrics as well. And then also we have to consider our revenue streams. Are we offering pure subscription? Do we have any sort of variable revenue component, usage, processing, transaction, etc.? So we've got to track it by revenue stream or even professional services. So we don't want to lump all these together. We need to know by revenue type what we won in that opportunity. And then the data in that opportunity, ARR versus TCV. We don't want to mix those together. If say in this example, I show $25,000 on the screen, well is that three years? Is that one year? Is it two years? So we have to have good data definition within those product IDs within our CRM system. And then also TCV. This was a recent request and due diligence that I went through. They want to see total billings. They're skipping the whole cash adjusted EBITDA and they want to see your total TCV. So again, we have to isolate ARR, but we also have to track TCV as well. And that was a data point that one private equity firm was looking at. And then metadata, all the other data that we can attach to these opportunities to help us slice and dice these opportunities. So expansion. One tip here in our CRM system, make sure we're tracking expansion on a net basis. So incremental, if they're PNAS 10 and now they're PNAS 12, we need to track that too. Gross tracking will skew your metrics and performance tracking. So gross, okay, it's good to have that, but we need to know net expansion because that's going to feed our sales and marketing efficiency metrics. It's going to feed our revenue forecast. That's that true number that we're going after. And then booking state. This is interesting. This came up in one of my courses. In my history, I'd love to hear Ray's thoughts on this at the end if we have time, but I always counted a booking at that closed one date when both parties signed the contract or maybe issued that PO. So I ran a couple of polls and there are two camps here with booking state. One is contract execution date, then that booking shows up in our bookings report, and then subscription start date. So those seem to be the two main camps as far as what booking state we're recording. And this is the second time I ran that poll and it was pretty consistent. And I bring this up because one, we have to be careful with that booking state. One just for those third party readers reading our bookings report, if we show 50K in July, was that booked in July or was that a subscription start date that triggered that? And then we have to watch out for any mismatch in timing between bookings date and CAC expenses. Let's say we booked that deal six months ago and they finally went live today. Well, now we have sales and marketing expenses to get that customer from that previous time period, but then we have that bookings number showing up today and then potentially flowing into our sales and marketing efficiency metrics. So we want to make sure timing is aligned depending on how we're recording that booking state. And then metadata. Now no longer say in the due diligence processes, just, Hey, give me all the ARR that you won this year for the past couple of years. Now they want to slice and dice that by pricing plan, by invoicing terms. So are most of these bookings multi-year contracts, annual contracts, month to month, and also firmographics. Any data points around that customer base that helps you make better decisions. So we need certain metadata points tracked in our CRM system that then we can slice and dice the bookings day there further. Now you're probably saying, Hey Ben, all right, that's great. That's SaaS data mistake number one, but I don't speak the bookings language. Yeah, I've got a self-service business, low price point, high volume, but the concept is still applicable. We still need to know where our growth is coming from. Is it new? Is it expansion? Do we have any contraction? So even if you don't have that process, a CRM system, we still need to track that same data. And so if you have a close one process, you have CRM system, then we have to implement this next step as well. And then the self-service companies, you may be able to skip part of that CRM process, but now step number two here, and this is a huge one, a huge SaaS data mistake that comes up in due diligence all the time, and that is our MRR schedule. And we have to treat this data like gold because this really supports our SaaS valuation and the health of our recurring revenue. So an MRR schedule, this is what it is. It's revenue by customer by month at its most basic level. This is always requested in due diligence. Even if you run cohort analysis and bookings based retention and different ways to do things, that third party is going to request your MRR schedule. And what they're going to do with that is slice and dice it and create their own retention schedules. They're going to look at customer retention. They're going to look at gross revenue retention, net revenue retention. Then they're going to ask you for metadata. Now I want to see it by product line. I want to see it by geo, by ICP, SMB versus enterprise. So treat this data like gold. So this is a big test. If say due diligence or some event is a little bit further down the road, can you produce this MRR schedule? Can you produce a schedule that looks like this? Because you should treat this data like gold because it's a key pillar in due diligence and it's a key pillar in my five pillar SaaS metrics framework because one, it's going to help us with pillar two and it's going to help us with pillar five as well for sales and marketing efficiency metrics. So MRR schedule, so important that we have this in place. Now this is where things start to fall down. This is where the conversations become very difficult to support those revenue retention numbers that we're touting that then are pushing our valuations higher or supporting that nice valuation number is holes in our MRR schedule. So we have customer revenue by month, but then you can see we've got a two month gap here, a two month gap, a one month gap, and maybe we forgot to invoice that customer. Maybe we don't have revenue recognition in place or some just poor practice as far as invoicing and revenue management. And this is where I say revenue retention does not lie in an aggregate level. This wreaks havoc with your retention. When we run the formula process on this MRR schedule, it's going to show churn. And it's going to show a new customer or maybe it's going to show a downgrade depending on what the data shows in your MRR schedule. And then you're going to have to explain this or you're going to have to clean it and fix this. So this will save you so much pain to focus on your MRR schedule because it's always requested in due diligence. But this is such a common thing. We think, you know, this seems like it's such an easy concept, but it comes up over and over, you know, as far as just, you know, just operating our business and then in due diligence. So again, biggest data mistakes with our MRR schedule, poor invoicing practices. We're not invoicing on a consistent basis. We're not posting those invoices in the correct month, which creates gaps in that MRR schedule. Or maybe we're in the cash basis and we're catching up and they're just being posted in which, you know, whatever month that we actually received that cash or we have no revenue recognition in place, you know, we invoice that annual subscription and we see that ARR MRR amount, you know, showing up just once a year. And it's hard to understand our retention if we have no revenue recognition in place. And then finally on the accounting side, reconciling your deferred sub ledger, just such a key process that we do monthly. So we don't get caught in an audit process, a quality of earnings process that our sub ledger or deferred revenue matches what we show on the balance sheet. Getting a little, you know, kind of technical accounting here, but that's so important when those mistakes crop up, then we start questioning the data. We start questioning that MRR schedule. And then again, metadata for the MRR schedule by pricing plan, by product line, maybe at a SKU level, I could run my MRR schedule by SKU to see how all those different features are performing, invoicing terms, firmographics. So again, it's becoming a little bit more complex or sophisticated as far as requests around that MRR schedule, not just one aggregate schedule, but now we need to slice and dice it by those important data points that will determine the health of our recurring revenue. And then pillar three, the final pillar three SaaS data mistake that I'll go through, and I talk about this all the time, the SaaS PNL, so fundamental to manage our business. And also it makes it very easily consumable for third parties to understand our business. And this PNL on the left, I'm sure this is a very, you know, probably could look familiar, but this is not a SaaS PNL that we can manage our business, poorly organized, unclear revenue streams, definitely not calculating the correct gross margin. We are not able to calculate margins by revenue stream. We're not sure how our OPEX profile is performing as a percent of revenue, but very common within SaaS companies. And we've got to move this towards a correct SaaS PNL, and this is what I call the modern SaaS PNL. We're at the top, clear and distinct revenue streams. We're not mixing variable revenue with subscription revenue. One time with recurring, clear and distinct. And then we have the correct COGS departments. And now pricing models are evolving within SaaS where it's no longer maybe just support and DevOps. You know, do we have any sort of variable revenue and we need a variable cost center? Are we selling hardware along with that software? So COGS departments sometimes dictated by how many revenue streams that we have. And then finally, our OPEX section, no creativity needed here, R&D, sales and marketing, and G&A in the OPEX area to manage our PNL. So tips and tricks with the PNL, again, clear and distinct revenue streams. We're not mixing recurring revenue with one time. We're not mixing subscription with variable revenue. Really important that we have that proper accounting foundation in place. And then we have the correct COGS versus OPEX. So we can really rely on that gross profit number. And then with this PNL setup, we can calculate margins by revenue stream. So for example, if we're running a 60% gross profit, where should we focus? Is it subscription contributing to that? Is it services? Is our variable revenue stream? So margins by revenue stream, so important. And then finally, I'll wrap up here with a couple more slides, just checking the time here. So obviously, a lot of us start in spreadsheets, but we have to move to tech. As our company scales, we have to keep up with that pace of data to manage that data, to produce these metrics, to produce forecasts, to produce the data that we need to manage our business. And the finance roadmap, just like our dev team has their product roadmap, on the finance side we have our own product roadmap. One, it's our accounting software. Are we happy with that accounting foundation, the software that we're using there? And then next is subscription management. So think invoicing and RevRec. Sometimes our accounting system can't handle the complexity of our invoicing. So we need a subscription management system. And those usually have some sort of revenue management system. So we can apply RevRec to those annual invoices, for example. And then CRM data, so important. And then finally, HRIS data. So these are the four data sources that I'm always pulling into my five pillar SaaS metrics framework, putting in place for my clients, and teaching in my course. So we have the data and ultimately the metrics that we need to manage our business. And then benchmarks, so important. Okay, we calculated our metrics. How are we performing against our peers? And one thing I like to say is aggregate benchmarks are dangerous to our SaaS health. Those are great reports that you see out there. I love to read them. But we can't benchmark our business, analyze our performance against our peers with aggregate benchmarks. So of course, I use a lot of raised data at benchmark.ai, you know, with my clients and my courses, etc. So really important to now benchmark our business. We've calculated our SaaS metrics framework and those metrics. Now we need, how are we performing against our peers? So one thing, net dollar retention, of course, our net revenue retention, a favorite SaaS metric. So we're seeing numbers that, you know, this is the total population. Really interesting. Okay. Meeting at 103, top performers at 110. Great to see, but there are certain metrics that we need to benchmark at the lower level based on the profile of our firm. And I like to benchmark based on ACV coming out of benchmark.ai. And this is top quartile performers and generally moving lower left to the upper right. You know, so if you have a smaller price point product, the benchmark is a little bit lower. But it's harder to have retention, say above a hundred. We're probably not going to hit 120 best in class for those mid market enterprise customers. So again, we have to benchmark our business based on the profile that relates back to us. Now, saying that there are certain benchmarks, you know, that are overall benchmarks, gross profit, gross margin. One of those, we're typically, and it's supported by raised data here, that best in class SaaS companies operate around 80% overall gross profit. Great number to look at. Of course, early on, we may not hit that, but it's a target that we're shooting for down the road. So again, I'll wrap up here, right metrics for the right stage, because everyone's on a different path. Some of you may be at a million ARR, some may be a hundred million ARR. So again, this is the metrics framework that I'm going to implement. So if you're above 10 million, I expect you to have a metric in each one of these boxes. But again, right metrics for the right stage of our business. If you're less than 1 million ARR, traditionally what I'm looking at is calculating the growth area, you know, CRM data, my MRR schedule, when we hit, whoops, 1 to 3 million, you know, looking at growth. And now we're keeping those customers in the form of retention. Maybe we're starting to look at margins, maybe it's a little bit early, but then greater than 3 million ARR, there is potential depending on your data setup and your type of business that we can calculate metrics in each of these boxes. And then above 10 million, yes, you should have a metric in each of these boxes. And then you looking at segmentation, SMB versus enterprise, ICP. So we have these metrics. Now we're segmenting the metrics that were even better clarity within our business, because we want to produce something that we know how we stand, because we know this profile doesn't work a lot of red on the board, but at least we know, at least we know where to focus, or maybe we have this profile that gets us closer to a 10x ARR valuation. So we know where to focus and how to have those conversations with third parties around the health of our business. So wrapping up here, Ray, really enjoyed it today. Hope everyone's having a great time at SaaS Metrics Palooza. Some resources, I've got my SaaS Academy site, my blog, and of course I've got a SaaS community can share those links elsewhere. But again, thank you. Really appreciate it. And here's some more information of where you can find me. Okay, Ben. Thank you so much. And by the way, you asked me a question halfway through your presentation. So I'm going to answer it. Bookings starts at contract signature date. And then I looked at billings, of course, that's going to be upon invoice date. And then my ARR and revenue recognition is on subscription start date, which sometimes is the same as the contract date, but often it's delayed, whether it's delayed until the beginning of the next month or the large enterprise, maybe it's three or six months until appointment is completed. Yeah, I think, you know, Ray, one of those underestimated areas is the contracting process and how you write contracts and all those terms, payment terms, right to use the software, all that stuff. So it's definitely an area of focus as well. So we have a very broad distribution of audience from 100 million plus, our sweet spot is kind of in that 10 to 50 million. We have quite a few in 50 to 100. And of course, because of the number of younger companies, we will have a lot in the less than 5 million. So here's my question. A lot of the things that you just talked about may seem like, ah, this is only what's younger, smaller companies encounter. But as you get to 10, 20 and above, are there a couple of common data challenges or mistakes that you've run into, Ben? Yeah, definitely. You know, and it's so funny, they're all shapes and sizes of SaaS. I talked to so many SaaS companies, some, you know, 100 million ARR, and they're still trying to figure out that proper SaaS P&L, you know, so I see all shapes and sizes, even 15 to 20 million, where they're still figuring this out. They're still wanting to put those metrics in place. And sometimes they, I say, kind of get away with it a bit because they've been profitable. They've had a nice business model where they haven't really had to scrutinize those numbers. So I've seen anywhere from 10 to 50 to 100, where they're still working on the SaaS P&L or putting those metrics in place, or maybe they're still transitioning from a perpetual license model and now starting to sell SaaS. And all those metrics related to subscriptions are kind of new to them, you know, so it's just, yeah, different shapes and sizes. And honestly, it's not just, you know, say a 3 million ARR business that's experiencing some of these pains. One of the common challenges I've seen over the last couple of years is the addition of usage-based pricing. And in your recommendations, you showed variable as a unique chart of account line item on that P&L. I'm finding so many companies are just pushing that into subscription. Can you talk a little bit more why you recommend that dedicated line item? Yeah, two things there, Ray. One, sometimes we're just not able to distinguish that, you know, we can't get that data out of our SaaS application and know when that usage comes into play. So sometimes it's the architecture of the product, but we need to separate those because that's going to affect how we calculate metrics. For example, retention. I want to calculate retention based on my revenue stream, so that pure subscription and then my usage. And then if it's going to tell a better story, I'm going to combine those and see if it's moving up and to the right. And also then that flows into sales and marketing efficiency metrics. So it can really skew our numbers if we have usage and subscription combined, because then we don't know, well, are we having all this contraction because it's usage or is it because people are downgrading our subscription product? So that really gets the accounting foundation that we track that revenue at a detailed level. And then we have that flexibility if we want to roll it up all to recurring, fine. But we have that detail for when people ask. Another thing, it seems simple, but I do a lot of benchmark and metrics assessment engagements and just something as simple as having sales and marketing as unique line items on that income statement is so valuable to a lot of the SaaS metrics. But I would say 50% plus it's sales and marketing on that. Is that what you see, Ben? Oh, oh yeah. I always separate that. Always separate your sales and marketing cost centers into two unique cost centers, because we're going to budget differently. We're going to forecast those differently. If I'm looking at a forecast and I see that all combined, I don't really know the story that's going on within those numbers. And then when we get into CAC, payback, sales and marketing efficiency, we have to allocate sales and marketing expenses and we're not going to use the same allocation for sales as we do for marketing. And then the last thing, I saw that you put customer success in your cost of goods sold, which the great debate, right? Where does customer success? Uniquely, or interestingly, I should say, you didn't have customer success on the OPEX line also. Do you think that it should be in both because we allocate that cost and it's so important to metrics like CAC ratio of what's OPEX versus what's COGS? Yeah, Ray, I should put an asterisk there because it's a highly debated topic. So for me, just at a basic level, customer success, if they don't sell, focused on product adoption retention up in COGS. And if they do have that expansion responsibility, they've got a quota, they get paid commission to expand a customer and bring that contract to close. Then we need that down in sales or maybe if they're doing both, we're going to allocate between COGS and OPEX. Well, everyone, that's the end of this session with Ben Murray, the SaaS CFO, the founder and leader of the SaaS Academy. Ben, thank you so much for being at SaaSmetricspalooza23. Yeah, I appreciate it, Ray. It was great being here. Yeah, I hope everyone's enjoying it today. Thanks, everyone. Next session will be up in a few minutes.