After nearly 2 amazing years at OneTrust I’m excited to share I am joining Super.AI as CFO starting today!

I’ve had the privilege of working with the core CFO org across OneTrust and Celonis, and I consider many not only colleagues but also close friends. Thank you specifically to Guido for bringing me on as an SFA four short years ago, pushing me, mentoring me, and giving me opportunities to expand rapidly. I have tremendous respect for the entire team and look forward to continuing to learn from them as I progress in my career. 

As demand for AI explodes, the need to unlock unstructured data to operate, support business decisions and train LLMs is growing like never before

There are billions of pages of operational and transactional data, most of which are unstructured. Historically, companies have had to hire data entry specialists or use BPOs, which is obviously very costly. Version 2.0 used OCR or RPA, which, while cheaper, tended to be clunky and broke easily.

Super.AI created a compound system that uses the best of each public LLM to process documents faster, cheaper, and more accurately

The rise of LLMs has now given organizations the power to intelligently extract information from documents they previously never had access to. The problem, like any proprietary platform, is deciding which platform to choose and dealing with the inherent weaknesses of a given platform. Super has solved this by intelligently chunking each document and routing each part to the LLM best suited for the task. The result: companies save money while gaining access to previously locked data for analysis, revenue generation and other AI use cases. So far, we’ve had great success within the logistics, financial, manufacturing, and insurance verticals.

📢🤝 I’m looking forward to diving further into AI in the coming months. If you’re looking to automate your business, an AI operator or VC/Investor who wants to connect, please reach out!

And if you want some of my latest thoughts on the AI industry, you can read my interview with Bowery Capital:

I recently sat down with Patrick McGovern at Bowery Capital to chat all things SaaS, the impact of AI, and what that means for building a valuable business.

As always would love to hear your thoughts!

This was originally published on the Bowery Capital Blog.

SaaS companies have underperformed broader technology stocks (and especially AI-related companies) in 2024. What is going on in SaaS right now?

Simply put, 2024 is highlighting that there will be relative winners and losers in SaaS. We’re in the heat of Q2 results, but here’s how I would synthesize Q1: Companies like Workday and ServiceNow (the bellwethers of SaaS) reported longer lead times, deal scrutiny, and shrinking deal sizes. Bespoke put out a great tweet that CRM missed consensus revenue estimates for the first time since February 2006 (73 quarters). On the opposite side, we saw the hyperscalers report their first revenue growth acceleration in a long time, but still with more muted growth performance from the big usage-based names (SNOW, DDOG, CFLT, MDB)... showcased by MDB falling 24% the day after it reported earnings. While that sounds bad, there are clearly relative winners as well. And I see two main ones: 1) Anyone focused on selling picks and shovels for AI seems to be winners (mainly hyperscalers) and 2) cyber security… with the added qualification that you must be a best-in-class platform to be winning share.

I think it's safe to say we are in a moderately growing IT spending environment… some would say a “tight” environment but I have seen low-single-digit growth across a number of surveys. Is this slower than prior years? Yes. Is this closer to “normal” growth rates? I would also guess yes plus or minus some margin of error. In this type of environment, CIOs and CFOs are going to prioritize projects within their budgets - not expand their budgets to fit their priorities. In fact, this is a normal dynamic for any vendor / customer relationship - software just isn’t used to it. AI is clearly a priority across all organizations and it is one of the few projects that are likely mandated and pushed by the board and CEO level. These will obviously take priority in the CIO budget and the question is this incremental spend or taking share? The MS CIO survey said it was 50/50, but maybe recent results are suggesting it’s more shifting than incremental.

I would expect cyber security to remain a top spending priority for at least the next few years given the increase in quantity and sophistication of threat actors (also boosted by AI), increased regulations and disclosure requirements, and the growing perception of the downside of breaches (operationally and from being made public). The cyber security market seems to be a few years behind software in terms of moving towards platforms and CFO deal scrutiny given the added technology complexity, but it does seem like buyers are becoming more discerning here and will likely trend in that direction in the future.

How do you think about AI companies and their attempts to price their products in terms of efficiency gains (i.e., we will save you the work of three people)? This can lead to ACVs way beyond what many of these software companies previously commanded.

I’m in the camp that AI will have massive societal impacts long-term, but how that plays out near-term in terms of economics beneficiaries and timing remains highly uncertain. There’s a few obvious use cases with various levels of complexity, timing, and potential ROI.

The easiest and most-common is giving your employees a “copilot” to become more efficient. Microsoft Office is obviously one of the more common options given the ubiquity of the office platform. The return on Office Copilot still seems uncertain, however, the ROI seems much clearer if you’re on the coding side but only for coding problems that are easy to solve and don’t require novel solutions. As part of that, I would include enabling sales and marketing as well. Using AI to enhance marketing ROI seems like an obvious use case. Using GenAI to enhance outbound sales seems like another.

I would say the 2nd layer is automating repeatable, low-value operational tasks which are currently completed using employees or BPOs. Many investors have been labeling this group the “process layer”, which helps clarify it as a system that automates a specific process, not a general use robot designed to replace a human 1:1. It’s hard to measure how many processes (and therefore man-hours) could be replaced using this methodology, but there seems to be clear use cases in customer success and document processing with usage beginning to creep into traditionally more “white collar” corporate jobs such as legal and finance.

The 3rd layer is the most valuable, but the most nascent and hardest to implement — thus the most uncertain ROI. This is using AI to independently drive results and generate new insights. Many of the most beneficial use cases require allowing AI to not only access a significant portion of your data estate but also allowing AI to automatically and independently edit the data and systems without human intervention. If you ask any C-suite executive now, few are comfortable giving the necessary access to their data estate to fully unlock the potential here. 

How each of these flush out in terms of economics and pricing remain uncertain with the big platforms being the only clear beneficiaries so far. While platforms can command an AI premium, many verticals will need to incorporate AI functionality just as table stakes. There will undoubtedly be some large companies in the 2nd layer especially those architected to be AI native and can scale to be a broader platform. Unlocking the 3rd layer would likely allow companies to capture incremental economics but data governance and security concerns as well as broad integration requirements still suggest this bucket is well off in the future. Like many nascent industries the net benefit to customers will likely outweigh the net ROI of the vendors until mass adoption is reached which makes choosing the winners as investors very cloudy.

From your perspective, what are the most significant challenges facing Enterprise SaaS companies in achieving scalable growth? What strategies do you believe are most effective for SaaS companies looking to expand into new markets or verticals?

Durable growth has been the topic du jour lately with the discussion of growth vs. profitability a close second. Both of these have replaced the growth at all costs mindset we saw during the COVID / ZIRP period. I think it is important to frame this question in the framework of growth and ROI. I think what has really changed and what has historically been missing from SaaS companies is a focus on profitable growth. Normally how investors express this sentiment is saying they want you to be profitable. This high level view is fine when the situation is dire and the operating executives and investors don’t have a better methodology. But the reality is that you need to be efficient, and preferably ruthlessly efficient, in each of your growth curves.

What does this mean? When you develop a new product you need to invest significant R&D expense upfront to build the product from scratch and this might continue for a few years as you add major new features and especially if you’re building a new category. Eventually (this might be 3 years or this might be 10) you should end up with a product which is competitive and stable with only incremental fixes and additions needed going forward. Maybe this means 100 engineers were needed to go from 0-9, but only 10 are needed to go from 9-10. Realizing where you are at in the product cycle will guide you on how and when to shift resources to the next growth priority.

A similar exercise also needs to be continuously happening in your GTM motion. At its most basic level, there are account executives who are closing deals and then support teams helping and enabling them (there are also ops and partner teams which we will leave out of this for now). Account executives are well known to operate on a power law and also take time to ramp up to full productivity. These tend to be more significant variables as you get into larger, enterprise contracts. There are a few primary metrics you want to track to assess GTM efficiency including average productivity, ACV/OTE, and support ratios. You should also do this by customer segment, region, and any other way you can think of, to make sure different splits are comparable. If they are not - it is time to look at pruning low performers and ensuring support ratios match those observed in your most profitable segments. At a higher level, you can also look at “Cost-to-Book” which was pioneered by John Cummings at Salesforce which is simply S&M / new ACV.

So how do you operate within this efficiency framework that I am describing? First of all, you need to have a strategy and a plan to execute your strategy. There are many ways to win as a software company - the most important part is to build a strategy which is expected to win in your respective competitive environment. Second, you need to match your internal investments to that strategy and its expected outcome. A lot of the issues we are seeing now is an outgrowth of a mismatch between investment/spend with growth prospects, as well as a mismatch between how the allocation of that spend has been apportioned towards maintenance vs. growth in the organization. Another part of this efficiency framework is assigning risk levels to outcomes and understanding which are higher-risk bets vs. more sure things and to make sure the potential outcomes are compensating you for the risk you are taking. Companies need to answer those questions for themselves on a case by case basis and the answer will differ depending on many circumstances, but this framework can help answer the questions of product line vs. platform vs. new market, etc. (i.e., should you be hiring incremental AEs to add capacity to a current geo/segment line vs. launching a 5-year R&D effort to build a whole new product).

You have written some great stuff on your blog where you take a “DCF for Dummies” approach to help the tech crowd better understand the drivers of company valuation - what is your prediction on multiples and interest rates over the back half of 2024 and into 2025?

If I could predict interest rates and multiples I would be in a different business. But seriously, as an investor-facing operator, it is part of my job to keep a tight pulse on interest rates, industry multiples, and other market drivers and also help my boss/CFO and the broader executive team and company understand those.

I would also say that no matter what is going on with interest rates, market multiples, and the relative valuation of any specific company, the only way to increase the true value of your company is to build a plan for long-term FCF generation, have investors believe in and buy into your plan, and execute against it. Everything else is just noise. So within that framework we try to keep everyone updated on the latest in capital markets trends as in many cases that can drive real business impacts while still staying grounded that long term the dog wags the tail.

All that said, interest rates have a real (fundamental) and relative (valuation) impact - especially on high-growth, FCF burning software companies. When interest rates are low, this is generally considered to support the economy and improve executive confidence, which increases long-term investments with software being one of those investments. Many of the companies buying software today are themselves software companies which benefited from expanding budgets due to loose VC funding. This environment also seemed to create pull-forward demand which the industry is now realizing with hindsight and digesting. While difficult to see in real time, companies were also investing in talent while underwriting these temporarily favorable growth economics and likely doing so inefficiently due to easy access to capital. The industry is working through all these misallocations now, but I would generally expect future interest rate impacts on fundamentals to be much more muted with industry demand more driven by customer needs and ROI, and market share being captured by the companies with the best execution within their given verticals.

On the valuation side, changing interest rates impact valuations due to how a basic DCF formula is structured. All else equal, the higher your discount rate (which is in part based on treasury rates), the lower your valuation will be. If a large part of your company’s value is based on FCF well out in the future, then interest rates will have an outsize impact on your relative valuation. This turns into a fundamental impact when you are trying to raise money and/or IPO as your valuation will likely be pegged to an industry EV/Revenue multiple and adjusted for your relative growth rate (and more and more your relative economics). I wrote a post, Interest Rates and SaaS Multiples, that goes into more detail on the interplay of interest rates and valuation.

You write a lot about public scale B2B software companies - is there one you particularly admire at the moment in terms of how they run their business?

I try to be very explicit with never recommending companies as investments but instead try to understand how internal strategic decisions and external communications translate to fundamental performance and how I can learn from best-practices in the industry.

Within that framework, I like to look at Datadog a lot (I wrote $DDOG: Derisking the Growth Story highlighting what they do right). Datadog made a massive investment in their platform which was wildly unpopular with investors at that time, but has now translated to R&D agility, sustained NRR, and ultimately durable revenue growth (at least so far). They were also one of the first companies to benefit from the usage-based pricing strategy which paired well with the easy expansion capabilities built into your platform. In addition, they always have clear communications on what their product does, the pain point it solves, their product strategy and how these will combine to drive future growth. All of this has been in plain english (which tends to be lacking in a lot of tech) so it’s easy for investors to understand and ultimately underwrite in their valuation models.

I would also note this clear communication helps current employees understand the direction of the company and helps attract future employees who can understand the drivers of the company and get excited by its prospects.

In addition to your great newsletter, you also have a day job. As a growth stage finance professional - what are some of the biggest lessons you’ve taken away from the last 24 months?

I think the biggest thing I have learned is to always learn new lessons! I obviously spend a lot of my time observing other companies, investors, and operators and the blog has been a great way to not only meet these people, but also share my learnings. It also makes me better at my job which is helping map strategic decisions to our long term model and ultimately planning and preparing a successful exit for our investors and employees. While cliche, finding the right team can not be understated. The competition is tough, the job is tough. Finding a team that works hard and you enjoy being around and building things with is very important.

I’m also working on a new side project and it's my first time working with someone to own and build my own piece of software instead of just analyzing someone else's. It’s a competitive intelligence platform which aggregates news and delivers insights to investors, investor relations, strategy/corp dev, account executives, and competitive intelligence teams. I use it every day to keep track of industry news and competitive releases and now I am looking for some early adopters to help me grow! You can learn more about what I am working on at https://app.breakingsaas.news.

If you liked “SaaS Perspectives: Thomas Robb (Hard Mode by Breaking SaaS & SaaS News)” and want to read more content from the Bowery Capital Team, check out other relevant posts from the Bowery Capital Blog.

I had the pleasure to host for a 60-minute interview December 7, 2023.

CJ is most famously known for his blog, Mostly Metrics, and his extremely well received podcast, Running the Numbers.

His day job is CFO of PartsTech where he most recently led the company’s $35M Series C funding in May 2023.

Some of the topics we cover:

• How CJ has been dealing with the 2024 budget season following elevated spending (and easier funding) in recent years

• How investor conversations have changed overtime, especially in regards to balancing growth and profitability

• How CFOs needs to ramp up on understanding new technologies

• How CFOs can balance the endless requests for incremental cybersecurity spending vs. measuring the true risks and ROI

Want more of CJ?

• Running the Numbers

• Twitter

CJ was a guest speaker at our last SaaS101.Live workshop. If you want to interact and network with more amazing speakers, check out our SaaS 101 bootcamp 👇

📣 Starting in 2 days! (+ $100 Off)

If you’re passionate about the SaaS industry and modeling key metrics we have an amazing bootcamp that will jumpstart your investing trajectory:

• SaaS Metrics and Modeling. Understand key SaaS metrics (ACV, ARR NRR), Billings vs. Bookings, and FCF margins. We provide detailed financial models of top SaaS companies and the basics of how to model them.

• Cybersecurity Deep Dive (hosted by ). In-depth overview, frameworks, and analysis into the cloud, endpoint, and network security industries. We also dive into CrowdStrike, Palo Alto Networks and many exciting startups.

• 9 Live Sessions (15-18 hours) filled with interactive content, Q&A, and networking

• Recordings + lifetime access to all sessions to watch on your schedule and advance at your own pace

• Two guest speakers with CISOs & SaaS professionals (we previously hosted and the CISOs of Datadog and Deepmind)

• Slack Community of cybersecurity professionals, researchers and Investors.

• Incredible Networking. Join students from Goldman Sachs, Microsoft, Bank of America, Palo Alto Networks, Cowen, SVB, Scotiabank, VCs, ETF Managers, and many more!

Join us Live! ($100 OFF)

Please enjoy my interview with CJ Gustafson (lightly edited for clarity):

Thomas Robb: Just as a quick intro, I would imagine that most people do know CJ or have read some of his stuff or have seen him across all the social media platforms. He's a CFO of PartsTech. I don't know if that's considered your full-time job or your full-time job is Mostly metrics, but he is a CFO and obviously the writer extraordinaire at Mostly metrics. Last I checked, I think you're at over 30,000 readers on Substack. You've been in finance now for what, 11 or 12 years I think, was obviously creeping on your LinkedIn before this. I think you've touched every single CFO org over your time and has obviously now finished or as currently in the CFO position.

You started your career off at PWC in M&A, moved into private equity and then sounds like you kind of got the itch to go into FP&A, moved to Snyk, which I think a lot of people are very familiar with in 2019. I think you were there for about three years in director of FP&A and finishing as director of IR and now the CFO of PartsTech. I think the most recent you guys just raised, and I'm assuming you did 99.5% of the job of raising that Series C led by OpenView. Obviously you can hear CJ is a big runner and boxing and just had his second daughter, so he is a very, very busy man. 

CJ Gustafson: This is the nicest intro anyone's ever given me. I got to tell my wife all the nice things. Can I pay you to send this to her? Thank you.

I'll be your hype man. I'll just follow you around, give all the introductions and stuff like that. But did I miss anything? That's the most important part. I said a lot of things.

No. I was ecstatic to join because first of all, Thomas and Francis are two of the smartest people I know within the finance and cybersecurity spaces and both kind of worlds have collided for me in the past and their topics near and dear to my heart. So anytime I can hang out with people who are passionate about the same stuff, fellow nerds of the internet, I'm always game. Plus I got out of doing bath time tonight, so I'm here.

Okay. Maybe just to kick us off just since it's probably top of mind for you at the moment, but what have been the biggest challenges as you're going through this budget season? 

Obviously we're coming off of two or three years of a lot of exuberance and free money and that helped all of us hide a lot of probably poor decisions in the past and I think a lot of people are now trying to fix that in this coming year and probably the past year as well. So just curious how that's going for you so far, especially when you think about all the other annual budgeting processes you previously led.

Yeah, I've probably been a part of at least seven that I can remember, and a lot of people joke that budgeting isn't sexy and whatnot, but I look at it as the training camp to get the org into shape for the following year and it forces you to come to grips with wherever the bodies are buried. And I think the biggest challenge, or not really a challenge, but really theme is just getting people to agree that every hire we make has to count. And a lot of people use COVID as this demarcation in the sand of when things change. But I was a part of orgs before then where I think there was this tendency, maybe not to the same extent, but it was definitely there, this tendency to let people just build fiefdoms or miniature empires within companies. And managers over the last five to seven years, I firmly believe have only been taught how to hire, they haven't been taught what to do when times aren't as good or how to get more stuff done and elevate the people they currently have. And that's an entirely different skillset.

So I've worked directly with people who were amazing at building, like I said, fiefdoms, but that's an entirely different ball game than actually just running a company with the people you have. So I think it really all starts with just not hiring people you don't need to in the first place. And I think one of the common misconceptions that startups and even big companies is that having more people means you can do more. I actually believe that to a certain extent, you're just adding more nodes to the network and that's another person that you have to communicate in order to get stuff done. So I've been at companies, like I was at Snyk, I was employee 150 and then when I left it was 1500. So we literally 10X-

In three years?

Yeah, in three years. It was just over 1500.

Every six months I say it was a new company, it really was. And I was redefining my role for better or worse with whatever what was going on at the time. But I was responsible for two and a half operating plans you could call it. I mean, if you throw in the COVID operating plans, that was like five operating plans in one year. I was doing all those too. But at the end of the day, I think I'm a firm believer that a small group of determined smart people can get a lot done and that's the ethos that I try to bring to the companies that I'm at now.

Yeah, that's super interesting. And I've definitely experienced a lot of the same. How we have always framed it is there are peacetime CFOs and executive teams and there's wartime exec teams and CFOs and one can be much more fun than the other. But in reality, long-term over time, running a business is always going to be making hard decisions and it's either you're going to do it or you're going to have to kick the can down the road and someone else is going to have to make a much, much harder decision later in time.

And a lot of people budgeting were doing it from the perspective of what's great for next year, and they were over optimizing for one year. They weren't looking at it in a three and five year timeframe. So you had asked what the biggest challenges or themes were. I think the other one would be getting people to say, okay, if I give you that, what does this set you up for in 18 months? What does this set you up for in 24 months? Because I always look at it, like next year is great, but what's the year after the year? Because I don't want to do something next year that jeopardizes that.

Yeah, exactly. And I think that's part of my next question really, is again, going back two or three years, we're in this time of free money to fuel growth at all costs. And you said earlier that all that growth typically was, okay, I'm a manager. The only thing I've ever been taught is hire more people and they kind of get the exact same stuff done. In reality, it's usually a decrease in productivity because you have too many cooks in the kitchen kind of thing. 

How have your conversations with investors changed over that time and trying to balance, like yes, we do need to obviously make smarter decisions and I think a lot of investors will obviously look from the outside, look at and consider some broader metrics, but maybe not necessarily understand the super nitty-gritty details. And sometimes you have to bridge the, we're going to burn a little bit more than you think, but there is a good reason that we're doing this. How have you found balancing like, hey, we need to keep the investments going. Obviously we're a tech company, we have a lot of TAM to go after versus like, hey, we can't just give up our market share gains and stuff like that. How have you balanced that?

I think I position a lot of it around the burn multiple. So I'm saying, I'm committing to give you this many millions in top line while going through this many millions in cash that you've given me and then the year after, this is how it's going to be more efficient than before. But I think, and this may be a contrarian opinion that not all investors actually want you to be all that efficient. And I don't mean it in a negative way. I mean that they're in a home run game, they're not in it for singles. And if every company was super efficient and cashflow positive, they wouldn't have a job. Investors are quite literally in the business of giving you cash.

So I always find it funny when they say that investors want you to be super efficient, get the cashflow positive as soon as possible. That's a soundbite that a lot of investors give, but we wouldn't need your money then and you wouldn't be on the cap table. So I think, yeah, efficient, it means don't make dumb decisions to me. That's what I read it as. It's not, don't burn money.

Right. And I'm curious, so now you've been at a couple different startups. I don't know if you were involved in capital raising at the company before Snyk, but would you say that your exposure to investors so far has been more VC or more growth equity or have they been half-and-half or?

It's funny, I've worked backwards my whole career from, like when I was at Providence Equity on the PE side, it was later stage investments within media and entertainment and tech. And then I went to Veeam and they were backed by Insight and it was their growth-ish later stage fund. And then I went to Snyk, which was VC, but then eventually we got to the crossovers. So I did go the whole spectrum and they each kind of have a different forte. 

And it also depends on who the investor is because I looked at each... Each investment firm is kind of having a skew because we covered how investors in the business of giving you capital, but some are really good at recruiting, they can help you with that. That's one thing that a startup needs. Others are really good at giving you interest to customers. That's another thing a startup needs. Others are really good at optimizing your go-to-market motion. That's another thing a startup needs. 

And others, honestly, the skew that they're selling is their brand name. So I'm sure Sequoia is good at a lot of things, but that's one of the reasons why people go to Sequoia. It's lended credibility that you're getting before you have it. That's their skew that they're giving you. So that's another way that I look at investing. What am I getting from this investor other than money? And also by the way, one of the skews is just shutting up and giving me cash. It's literally just like, you won't even find an inbox to reach us at, but we'll give you the cash. So they're all kind of different in that sense. How do you differentiate yourself?

Yeah, I feel like the later stage growth investors for a while were like, oh, we're just like, you guys are doing great. Keep it up. We're just going to give you, like it seems like you guys have figured out, we'll just throw an extra 300 million on top of it. We just need the board deck at the end of the quarter and otherwise we're like super hands off. And now I think it's quickly change around like, oh, actually we want to make sure that that 300 million does become more than 300 million, not a 100 million kind of thing. 

I actually want to loop back to something else you started with as well on you guiding those conversations with the business on thinking about who do you actually need to achieve an outcome and kind of a business objective, not necessarily the old school mentality of like, oh, I'm a VP, I expect to increase my org by 10% every year until I die, and that's how I measure success. 

I feel like at startups it kind of leans a little bit more towards people who are more flexible and fast and maybe not as levered to that, but I think both times it's like, you usually get both groups of people. How have you tried to balance that? Almost, I guess it'd probably be more like politically, obviously telling other senior people in the organization like, hey, you're not going to get the headcount you want and I expect you to achieve more this year. And it's not really a discussion anymore.

I think the conversation is actually had, Thomas, when you hire them. So I've been lucky enough to be a part of a lot of hiring decisions where I frame it up as, is this person a hands on the keyboard type person? And you all know them. Like, have you worked with maybe a CFO or a VP of finance who never even opens an Excel workbook or if they had to screen share, would they be able to maneuver around and do math with you? I look for that in a marketing person. I look for that in a sales VP person because I know that I can't afford to hire you to hire 17 people to then do the job. I need you to be really smart to help the org, but I'm doing things I don't want to do either day to day, but that's just the nature of a startup.

I've also worked at places too and I've seen it go not so good where you hire those people who, they're iPad people, they check emails in their iPads and they're not opening the computer to get in the Excel workbook with you. And that's not really the type of person that you need at a certain scale. I think that's maybe for when times are going really good, but I always test for like, if push comes to shove, is this person also going to be a player coach? And I think that's where you get a lot of the efficiency that hungry people who are willing to get into the weeds.

Yeah. And it's like, not necessarily removing that middle management while that also tends to help speed things up, certainly, but it's just that you're not only a manager. When you say that player coach is a super... It's actually, you're probably 180% of a person and you're doing 80% coaching and a hundred percent playing.

And what's nice about that is you know that person's going to hire someone just like them. So it cascades down. The worst is when you hire people who don't actually do the work, they're just doing the theory of work and then they hire other people like that and it's just this cascading effect. And you wake up one day and you have a legal team of 27 people.

Yeah, I feel like it's almost like, I don't know, it's almost like a VC cliche to always don't hire above where you're at. And it's like you don't need the senior vice president of whatever software company XYZ to come lead a group of 50 people when you don't have a go-to-market strategy. And it's like they haven't done hands-on, block and tackling, in the trenches for the last 25 years. 

It's ugly and people forget sometimes how ugly it is when you come backwards in stage of like, oh, where's my stuff? I need my stuff to perform here. And I mean it's hard. It's hard running companies. I'm biased.

As we would expect. I think kind of looping back to working with your business partners and stuff, I think that's always a very interesting dynamic. And I think again, it's kind of going back to three or four years ago, it was like, we need finance to raise the money and then we're just going to let the operators kind of charge full speed ahead and not have a lot of guide rails or anything like that blocking them. And that's quickly changed now to, okay, almost every decision that you're going to make is probably going to be checked by at least some sort of finance business partner that rolls up to a CFO to kind of make sure that we are meeting those OKRs and every hiring decision is supporting what you agree that you do.

And so it seems like that relationship has kind of shifted or at least changed dramatically over the last couple years. And I know you said that one of the things that you did was you make sure you hire correctly and that's probably gets you probably a long way, is like, what are some of the other things that you found helpful when you're communicating with non-finance people to get them on board with these kind of things or vice versa where maybe a business partner reached out to you in a helpful way as well?

So on my podcast, quick plug, it's called Run the Numbers, but I interview CFOs and I had Sinohe Terrero on and he's the CFO and COO of Envoy. And what he had told me is, because I said, "In our job you have to say no a lot and saying no gracefully is a skill that I would love to get better at. And I was like, how do you manage that?" And he said, "I call it the butter and the knife. You got to butter them up before you beat them up or cut them and say no." And what he meant was, you want to be a person who has exemplified to their peers that they will knock down walls for them when they need you to, and that you will help them with little things, to grease the skids. So then later when you say no, they realize, oh well CJ's actually a reasonable person. He's helped me out on other stuff. If he's saying no, I know he doesn't want to say no, he's doing it out of a place of like we agreed to achieve these things.

And I've tried to do that more recently as well. So say like, the HR person needs to pull forward a hire like three months. It's not in the plan. Is it going to completely mess up the budget? No. Do I want it to happen? No. But I will help them do that to show that I'm in their corner and then later in the year when something comes up and they want to upgrade from workable to greenhouse and I don't have the budget for it, I'm like, "Listen, I'm trying to help you, but we agreed that we were going to come out to this burn and I have to cut back the software spending here a bit." And so I've tried to do little things that help people understand that I'm on their side and CFOs do have a unfair advantage that they can throw money at problems. And I don't mean throwing millions of dollars, but sometimes you have a person who's making 250K and they have a $10,000 problem that's blocking them from being 25% more efficient. That's a math problem in my head.

I can throw this amount of money at it and solve it, maybe hiring them someone from Upwork to get this data for them, and I'm going to get so much more out of this person over here. So that's at least how I try to look at it to not just say no to things. It's more of a balanced approach of like, I'm trying to say yes to a lot of things, but I can't say yes to everything.

That's super interesting because I think that's when it goes from CFOs considered, or kind of old school as a more of a bookkeeper, guy just ties all the numbers out, keeps quiet until something goes wrong, to being a true strategic partner and more from the science to the art side of actually building those relationships and making sure your ability to drive value does mean that you need those great relationships.

And it's easier to say no, or can we wait on this? If they've seen you in the business with them every day and that you've been trying to learn what their department does, if you just show up as some guy out of the back room who's there to say, "No." You are the CF no, like I get it. I wouldn't like me either. But if I'm meeting with the product team, because I care about what we're building, they think I'm interested and then I'm making decisions based on, is this a sunk cost? Are we going to get more revenue out of this? And it's a more holistic view.

High level to bridge back to maybe from the finance side back into cybersecurity, how are you looking at your, and you mentioned software spend specifically. I'm assuming a large part of that usually ends up being security of some sort. How has that budget been trending for you and what are some of the technologies that are absolutely necessary now that maybe weren't before or some of the vendors that might be up and coming that are almost required buys and stuff like that?

Yeah, I actually got a decent amount of pressure from my investors for us to step up our security posture over the last 12 months. And me having been the person who worked at a security company, I always heard that was the case, but didn't actually believe it. You think it's just this theoretical, but they were like, "No, you got to buy stuff." Like, this isn't good. You need to... It's not like we were doing something totally bad, but they were like, "We've seen stuff go wrong at other portfolio companies based on that and we know what you have in the building. Can you please do something about it?" Actually, when we raised our Series C, I had to say what we were going to do with the funding and one of the line items in there was this amount for security vendors. And at the time, I didn't know what they all would be, but I definitely had a line item for that. And we picked out some vendors.

I guess how did you guys go about actually deciding the entire strategy? Do you actually have a CISO in-house or?

We don't have one yet. So my org is, we will be 150 people next year. So it's still under our CTO right now. So I would say once you hit probably 400 people, you start bringing on a CISO unless it's like the product you're actually selling. But yeah, so we bought Snyk for the developers to embed it within the code as they go. And then we bought Jamf for endpoint, whoever's using a Mac, we bought Kaseya for anyone who's using a PC. And then we're assessing a lot of other vendors.

And just when I talk to my other CFO friends who are in it, I understand why a company like a Palo Alto is so successful. So people always criticize, it's just a bunch of M&A and none of it's connected and whatnot. But if I can get a better discount and I don't have to review as many contracts and get as many vendor approvals done and go through paperwork for each individual one, I get it. I'm finally understanding that firsthand, and this is for anyone who is involved with an org that has to sell to CFOs too. It's so much paperwork that I have to review that I didn't anticipate, its overwhelming 

That's why you have an accounts payable team or procurement team and all that stuff.

Yeah. So I understand why people are consolidating on vendors, and if I had to guess just with how a lot of security companies raised super premium valuations, I would guess there'll be some consolidation next year. 

Q&A Section

Francis Odum: One question for you real quick was, MSPs, our last class had that conversation, and I wonder if for your startup, for example, you guys talking to MSPs at all to help or you guys just buy your own products generally?

Oh, well, we work with Tropic for procurement, so I'm a big fan of them and they also have a price check tool that helps me. So it's kind of like a middle zone between them, but we don't contract specifically with any MSPs, but a lot of them hit me up. I just don't have the bandwidth to choose one right now or pay for one.

So how are the conversations with the CISOs when you need to say no as a CFO, but at the same time, how can you be more cognizant that it's for something very critical, like making an org more secure?

That's a good one. I'm somewhat biased thinking that a lot of CISOs buy, this is probably very controversial, that a lot of CISOs buy tools as kind of a cover-your-ass to say they had it in place, which is true to a certain extent. You can't protect it if you don't have anything in place. But then a non-technical person like me, like I kind of know cybersecurity, but for me to say, you say we need this to make us safe, I guess so, I'm not going to say I don't want to be safe, but then after I see 10 of these things, then I'm saying, okay, well are we safe enough and what's the difference between all these?

But I am biased in thinking that a lot of them just buy a proliferation of tools to say, well, look, we bought it, so how can you blame us? And then after a while it's like, but this many, this is a crazy amount of tools to have. So it does kind of bring me back to that Palo Alto comment of, can you get some of these through the same vendor or can you just give me one price tag so I don't have to keep approving $50 to $75k spends every couple months because that wears on you over time.

That's the follow-up question I have because I almost always see this as a leader, technology leader. Beginning of the year, we are always shut down on a decent budget for security, cybersecurity, but as we progress, if some incident happens, damage is done, you get a lot of budget approved by the CFO, special funds. Go for it. Get all the tools. Why is it that kind of reactive behavior from the CFOs? I mean, I'm not saying you are someone who's doing it, but as a technology leader.

It's because we don't want to get fired.

Like with my organization recently, we had a security instant. We were literally not spending a lot of focus on that, but suddenly, guess what? We have a lot of budget now. It is the top priority because we had gone through that instance. So why is it always like that, CFO's mindset?

I think it's true because you always have to come up with a plan to present back to the board of why it happened and what you're doing to mitigate that risk. And a lot of times what's in that is processes, but the other part of it is what you're going to buy. And then when you tell that to the board of how you're going to make it so it doesn't happen again, they look at it like a shareholder value conversation. Like, do we want to spend 250,000 on new tools? Absolutely not. But do we want to lose 25 million in valuation? No, we don't want that. And you've made this plan that we asked you to do, so we're not going to say no to it either. And I think that's where the circular reference comes in, at least in my experience. Once you make the plan that they ask for, they're not going to say no. They're like, oh, well, we asked you to come up with a plan of how to make this not happen again.

Or is it the thought process that, "Hey, we do have a cyber insurance covered. Let the things happen, don't worry, we have the insurance covered." How do you as a CFO decide what would be my cyber insurance? 

Yeah. Well, usually it's in your fundraising docs of what the minimum is that you have to have for every policy. So even the director's insurance, it tells me it's going to be $2 million, whatever. So I'll have at least a benchmark. But I always find cybersecurity, sorry, cyber, not cybersecurity, cyber insurance kind of funny for cybersecurity companies, because if a cybersecurity company gets breached and it's that bad, it's kind of a binary outcome, right? You're probably going to go out of business. So it's like, oh, my $50 million policy, did that do anything? So I think you end up buying... This is where it becomes to me more art than science. If you're buying up whatever makes you sleep at night and then what your appetite is for risk, because CFOs, in my mind at a lot of companies, is the chief risk officer, there isn't someone responsible just for risk every day.

So every day you're doing business, you're making a calculated bet, are we going to get hacked today? Is somebody going to get hit by a bus? Are all our customers going to leave? And it's like, how do I take that piece of risk and kind of arbitrage or hedge my bets to a way that I feel comfortable enough to sleep at night? But I haven't found a formula yet for it. So if someone has one that I can use rather than a bit of benchmarking what I'm required to and my gut feel, I'll take it.

Thomas Robb: I was going to say, the few conversations that I've been a part of specifically on the IT side is like we always call it the FUD. So fear, uncertainty, and doubt. And then it's up to the CFO to make some sort of decision that typically they don't have native experience to do, right? It's like CFO's good at making a budget, growing a business, and to CJ's point, you're the risk officer as well. That's almost more of a new term, but that's like your side gig as well at the company, right?

So you have to make a decision like, all right, do I think this is more fear, uncertainty and doubt, or do I think this is a real risk case. And so it's like if you think it's FUD and you're like, okay, I'm going to give him 500K and that's 6 million, and then something actually happens to XYZ’s point, it's like, oh, obviously you should have pre invested in it. 

While to CJ's point, it's probably like, well, it's almost kind of like IT insurance, right? It's like you don't actually need to pay that much until you actually need to pay that much kind of thing. And I think it's just like, you probably have the Palo Altos of the world so good at marketing that they're like, "Hey, you don't have to buy from us, but when you have to report to the board that a security incident happened at your company, we will take that phone call too though."

Yeah, you nailed it.

Let's say you're my CFO. We're a SaaS company, 150 employees. I'm coming in from the SecOps side and I want to have good conversations with finance when I'm asking for budget for security initiatives. We're trying to figure out what to implement first. So I'm not necessarily going for one vendor. I'm trying to figure out operationally what makes sense for our specific case so that I have conducive conversations with finance. What should I bring to this conversation?

Yeah, that's a great one. So my biggest fear when it comes to buying software is that I give you the budget to buy it, you buy it and then it sits on the shelf and people don't implement it. So if you're coming to me with that plan, I want to know which tools are being purchased in which quarter, when they'll be implemented up and running by. And for you to put a name of someone on your team next to each tool, because I'll give you an example. I just bought Snyk at my company. If I asked them next week if they've started implementing it and I bought it last month and no one's touched it, I'm going to be pretty pissed because you begged me to buy it. And we agreed that this was something that we were going to do together.

And then the other thing would be to not take on... Oh, sorry. The last thing would be to, if there's an implementation cost associated with it, lay out that too. So it's not just about the software, it's about which experts are you bringing to help you put it in, because that can actually be more costly than the first year of software itself.

Thomas Robb: Going to say, it's also probably reading your CFO and how they like to manage as well. I feel like your methodology is very strict with like, it almost comes from a project management background, probably that chief of staff in you, man. And I'm sure a lot of it is, if you don't have that, technical skillset to make sure that that usage gets driven to completion. 

Do you see a need for CFOs to be more tech-savvy now? 

Yeah, I think it's definitely true and I spend a lot of my time, I've spent a lot of my time actually on the BI side of things recently, learning how our org does that and I'm paying for Snowflake, walk me through actually what this is doing for us. Walk me through how it links to Sigma. Walk me through why you want Fivetran instead of Stitch. Walk me through this ecosystem and then let me know where the other data pieces are coming from because I'm consuming the data all day. I want to know how it's being produced, and I also want to be able to separate between the production data of what our product is doing and the financial data and then the sales data and then how to link all of that up. So I don't think that was something that a lot of CFOs spent time on in the past. I don't know if I answered your question, but I'm trying to be more forward leaning about what we're actually buying.

Yeah, I think that's very well said. I think it's more than the tech tech, it's more on the cybersecurity per se. When it comes to the CISO getting an approval for budget on the security initiatives is not fancy as CTO gets on the new product features or the business initiatives. So I think having CFO with that kind of knowledge on the cybersecurity I think would help CISOs as well as the organization I believe. I think I've been in the last two, three organizations, I have moved, I closely worked with CISOs and also the vice presidents of the security. They always struggled. They always struggle to get us the right focus until there is an instant or there is some noise is there.

Well, I think a lot of CFOs are afraid to ask, to be the stupid person in the room and ask, what does this actually do? So my strategy other than calling Francis when I have a question is to say like, you want this tool, okay, what does that tool do? Does network security. Okay, explain to me what network security is like I'm a five-year-old, and then tell me why that's a risk to our business. And if you come to it from the perspective of like, yeah, I'm sure the CFO is smart in finance and stuff, but just assume that they have no idea what this security thing does and they're not going to be offended by that. I'm telling you now, they'll be totally cool if you make it really simple for them.

But when I think about security spend, I want you to tell me a story more qualitatively of what the areas of risk are, what the vendors are linked to that, so then I can get in my head. Because if you don't understand and you're just coming at it from like, I need Orca, I need Wiz, I need Snyk, and you're just putting a number next to it, they're just going to look at it like a number. So don't just present it like a number, present it like a story that someone can understand who doesn't know how to code.

Francis Odum: Increasingly, actually, this is something that I've heard multiple times is justifying ROI. Because in security it's also difficult, like if you're buying a sales product or a revenue product or a cost-cutting product, it's very easy for you to calculate the ROI on those products. But with security, it's extremely difficult. The endpoint devices that you're buying, how do you know specifically the ROI on, are you going to get some return investment back? So that's very difficult. The CTO might point to you, okay, these are the risk areas, but obviously as a CFO you're constantly thinking about how do we get our money back or in some way, how does this help the organization move forward?

I'm also thinking though about where does this risk stack up with the others? If I only have enough runway for eight months, that's a bigger risk to me than getting breached tomorrow. So it all depends relative to your audience.

Thomas Robb: I was going to say part of that too is like, I mean CJ and all CFOs are also responding or reporting back always to the CEO and the board. And so if you ask them, or like the percentage of questions that you get on growing revenue, building products and hiring sales teams versus are you using Palo or Fivetran? It is probably going to be, again, 95% is going be focused on that. And so if the chief product officer or chief technology officer comes in and says, I need 15 more product people and 20 more engineers, and they need some software, it's like, yeah, because that's what the board wants. That's what the CEO wants. And so in the background, there's always that thing of like, it's layers of people who are probably just not as focused on it precisely because it's probably the less, like not as sexy as the other parts.

You've had so many different jobs within the CFO org and I guess some of that's even outside the CFO org as well, and you rewind yourself five, 10 years, has your goal or career aspirations always been to be a CFO or did you take a winding path to this or how have you thought about that?

I'm making it up as I go along. I don't know what I'm doing. I interviewed Jenny Decker, CFO of Front on the Run the Numbers podcast, and she had a great comment. She said, "You write your story in reverse." So what she meant is you're making all these moves along the way that at the time you're sitting there in your house on your couch, you're like, "Holy, I don't know if this is the right move for me." But then you find a way to craft that story in which it all seems to make sense when you look backwards. And I think each step, I was just looking at what can I do that's most interesting to me at the time? And I've had shiny ball syndrome at certain points. When I went to the private equity side, I thought it was so cool to be able to say I worked in that.

And then I woke up one morning and I realized I wasn't even that good at it. And that was one thing to grapple with. And then it was like, well, where do I have more fun? And I found it was actually hanging out with salespeople, discussing how to grow the business, talking about building stuff and not being a financial tourist. So I think on paper I saw a lot of things along the way that looked like they'd be really cool. And then it was up to me to figure out if I actually had like CJ product market fit there, some things I did, some things I didn't. But each one along the way, I think I got a story out of it.

Francis Odum: Yeah. I wonder, by the way, because I remember when you were at Snyk, we would always chat about some of your ambitions with CFO roles and stuff like that, if you remember those days. I wonder what's it been like, obviously being a CFO of a startup now, obviously you've been in this seat for over a year now. You've been here over a year. What do you feel like, what's maybe been the biggest takeaway or something maybe you didn't appreciate as much or you've appreciated as much about that role?

One's a hard skill. One's a soft skill. The hard skill is procurement. I'm in procurement. So if I don't read the contract, it's on me. There's no backstop.

Thomas Robb: Is this sponsored by Tropical right now or is this a-

The podcast is, but it's not just procurement with software though too, it's procurement with hiring people, agreeing on people's salaries. It's procurement in the sense of anything that's a dollar going out the door. You want to know what you're signing up for because before you're a CFO, you've always had a CFO to check stuff. Now there's no one besides the CEO to check for you, and they're doing their own job. They're doing a thousand other things that you're not doing, so they can't always check. So that's been one thing. And then most importantly, just reading contracts around things, even with suppliers. So that's not even buying stuff, just like relationships or partnerships and the economic impact of that. That's been something I've been working on. But the soft skill side is that I feel the higher up you go, it's less about IQ and it's more about EQ.

I know some of the people on my team are probably smarter than me, book smart wise. What I have to be good at is putting them in a position to succeed and breaking down barriers with other people they work with so they can do really good stuff. And it's also me making sure that I build relationships, not just the CEO, which I think like I came in on day one, I'm like, this is my only customer, this is my only customer, this is my only customer. And then I realized, no, you got to be cool with everybody else who's your peer. Like, CPO has to like you or your job's going to kind of suck. CMO has to like you. And that's been a big thing for me as well, to build those relationships because it's really about, it sounds cliche that it's about the people, but it is about the people you work with. You can't actually get things done if people don't want to work with you.

Yeah, that makes sense. I guess, any preliminary thoughts on what's, obviously you're, what? You have another 35 years of your career ahead of you, you're already a CFO. Do you think the CFO spot is what you'll be doing longer term? So you have aspirations to move more to running the business or starting your own business or you're just going to buy CNN or what are you going to do?

I don't know. I think I'll write that story in reverse as well. But I've always had an entrepreneurial streak to me as well. Like, the newsletter, Mostly Metrics is my way to scratch that itch in a lot of ways. And I mean, I've also tried to start a business and failed at that, so that's something fun. But yeah, I think being a CEO at some point would be cool. But I wake up every day and there's something new to work on, whether it's podcast, newsletter or being a CFO. What I do know is that there's a lot that I don't know, and I'm trying to scratch that itch with a lot of the stuff I write about. And what's cool about writing in particular is that I would never met you guys if I didn't write online and put myself out there. I made friends through that, that are smart, that helped me.

And I feel like it's the last true democracy in a lot of ways that you can write your way into any room and smart people on the internet will find each other and you'll have stuff to talk about. So for me, it's just keep making content because it makes you smarter, but it also introduces you to people who could give you a cool opportunity.

I could not agree more, I think, yeah, at least from my own personal perspective too, is just like, at first was like, I need to ramp back up on this industry. And then it was like, oh, you start meeting cool people and you can be humble and be like, okay, this person's 15 times smarter than me. I'm going to read their stuff and hang out with them and slowly get better. And that opens more doors because now you're getting smarter and stuff.

It's a virtuous cycle.

And I think it's one of those things where it's beneficial on its own. I found it super helpful at work as well, where not only do... Like if you're writing about what you do at work, it forces you to actually think about it, which in reality, once you try to actually write about what you do at work, you start to realize like, oh, maybe I actually don't understand everything.

I agree with you, Thomas. I don't feel like I own anything until I can write about it and explain it to somebody. Other than that, I'm just borrowing it.

Yeah, and I think I'm imagining that you probably have gone through some of the same things where I just sit down, I'm like, topic that I do all day, I got this. And then your two paragraphs in you're like, actually, I don't understand.

I don't actually know what this is. Yeah.

And again, I'm assuming you've kind of had the same thing where I think it does lend credibility to the organization that you work at too. I think it's like, people obviously want, smart people want to work with other smart people, and I think that helps put that beacon out that there's intellectual curiosity and we do things intelligently here and stuff like that.

I also look at it like, if anybody ever wanted to hire me down the road, my resume is online. That's what it is. I could give you a PDF or you could just read some of the stuff I wrote and I'm for some people, not for other people. And you think it's interesting, but it's kind of like just having something to show for it online.

When you do think about your time as an investor at Providence, how has that kind of shaped your career as more on the operational leadership side?

So I was more on the valuation and the fundraising side at Providence, which was a unique spot to be in because I had to learn the portfolio companies after we brought them in. And that made me look at how they all made money, and that's where I first learned about SaaS. So I was like, oh, this is a pretty beautiful thing that the revenue just keeps showing up every year. And I got to look at a lot of different business models. I also looked at telecom. I was responsible for a telecom business in India, another one in Brazil. And for me it was like, you have this box, now how does it make money within that box? And then what are the costs? And so I got a lot of reps at that modeling it out.

What I didn't have was, and I learned it when I did FP&A, it was like actually talking to people to come up with the plan. So I was digesting the plan and coming up with a valuation off of it and then telling a story of why we should be able to raise another fund. But I used the term before, I was a bit of a financial Taurus that I was going company to company and didn't have to actually stick around for the results of just one of them and be the one who was responsible for it. So I learned about how it works, but I didn't actually learn how to do it until I got to a real company.

CJ, any specific podcasts or recent writings you want to highlight or plug before we let you go here?

Audience may like this, but mostlymetrics.com. Today I came out with a post on what companies report versus guide to. So I took 20 companies and I looked at their earnings and I said, well, what do they disclose? Some companies stopped disclosing that dollar retention. It's like, oh wow, okay. And then other companies will guide to ARR and it's like, whoa, that's aggressive. And so some of those were kind of eyeopening to me. And I think there's a game theory behind why companies either do or don't disclose stuff. So that was one that I had a lot of fun with.

Thank you! Any final advice for the crowd? 

The only advice I would say is that someone once told me to try to stack my talents and I didn't understand what it meant at first, but it's just finding things like you guys did with the course to create a one-on-one, to make it so you become the defacto for what people need to get done. So I've tried to do that in my career. So I wrote a whole post about this, but my whole skew was that I knew how to tell a story with numbers. Like, you could go to the product marketing team for part of that, you go to the finance team for part of that and six other people in New York, but I could do an 85 good percent job at doing all of it at once, and you could just come to me and I can do it.

And I've tried to just do that throughout my career where try to find places within the org that you become the only person who can kind of do something. And that's where I think people look at you as really valuable.

I'm so happy you guys are doing a successful course and it was something that was needed, like when you told me that you were blending finance and cybersecurity, I was like, this is kick-ass. This is a one-on-one type thing. And that's what I think is awesome when people aren't just doing like, here's AI and finance. It's like, no, this is real core shit that people need. And it's like blending two very important things. So congrats.

Absolutely. Well CJ, again, I really appreciate you taking the time today. Always super great to catch up and hear about all your thoughts across everything from the annual budgeting process to career advice.

Disclosures: I do not currently have a position in PANW. None of this is investment advice and is solely my own views. Do you own due diligence.

LOTS of moving pieces in Palo's FCF Billings

Palo Alto has been the topic du jour over the past week as the company announced earnings with FY24 billings guidance missing consensus and guidance.

In response to the earnings report, the stock fell 25%+ (25-30bn mkt cap) as investors digested if “Platformization” is real or these declines represent organic headwinds.

With the guide lower, the majority of recent commentary has been focused on the company’s product strategy and industry competition, but I’ll be focusing on the key trends impacting Billings, Collections and FCF.

A major part of this is the company’s use of its relatively new Palo Alto Networks Financial Services (PANFS) which is captured as Financing Receivables on the balance sheet but generally lacks any other disclosures.

Note: I don’t touch on RPO trends and Revenue/Rev Rec which is another important part of this story

After all the chaos, PANW has partially recovered since sell side broadly supports the story and retail rushed in (led by none other than Nancy Pelosi).

FY24 Billings Guidance Missed Consensus and Guidance

• Reports Q2:

  • Revenue $1.98B vs $1.97B Cons/Guidance

  • Billings $2.35B vs $2.36B Cons/Guidance vs. ~$2.40 buyside

• Q3 Guidance:

  • Revenue $1.95-1.98B vs $2.04B Cons

  • Billings $2.30-2.35B vs $2.62B Cons vs. ~$2.63B buyside

• FY Guidance (Jul 2024):

  • Revenue $7.95-8.00B vs prior guidance $8.15-8.20B and $8.19B Cons

  • Billings $10.10-10.20B vs prior guidance $10.7-10.8B and $10.74B Cons and ~$11B buyside

More analysis below 👇

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Importance of consistency

The power of the SaaS model is that investors receive multiple GAAP metrics which help frame and quantify the consistency and growth profile of the company.

Namely these are Bookings, Billings, Revenue, and Collections. For every contract a SaaS company sells, all these metrics will eventually be equal. Any sort of deviation is just timing.

When these external metrics all move in unison, investors have greater clarity on the underlying growth of the company which helps derisk their forecast and can help support a higher multiple.

If a company makes operational changes which changes or makes these relationships volatile, the opposite is likely to happen.

Said another way, as soon as investors start talking about billing linearity and rev rec policies instead of growth, you’re screwed (the opposite of what DDOG did at their analyst day)

I presented this chart at our SKO a couple weeks ago which helps visualize what Palo Alto investors started to experience over the past few quarters:

Palo Alto was trading at 13-14x heading into earnings implying investors had high expectations for FUTURE growth.

Check out my analysis on how to tell your growth story:

The changing story and underlying supporting metrics (mainly billings) made investors rapidly discount that the underlying growth was more uncertain before. In Palo Alto’s case it was wither lower industry demand, worse pricing, or “buyer fatigue”.

Net net, this miss and story change caused a rapid multiple contraction to ~10x (which has now partially recovered).

Rising Cost of Money

Starting in Q4 FY23, Palo started to discuss customers’ increased desire to have friendlier billings terms than the standard full TCV up front.

These deferred payment terms are delivered in the form of annual billing plans and through our PANFS financing capability. The percent of bookings that included deferred payments increased approximately 45% year-over-year. Additionally, the proportion of our bookings have included billing plans more than doubled from Q3 to Q4. This quarter-to-quarter increase negatively impacted our billings as compared to what we forecasted 90 days ago in our guidance.

A byproduct of the rise in deferred payments is greater predictability of our cash flow over time. For example, we now expect about $1 billion in cash flow from deals entered into in prior years where payments will now come in fiscal year '24. This $1 billion is twice what it was in contribution to our fiscal year '23 cash flow when we entered the year. Summarizing cash flow, I'm confident we can maintain a baseline of 37% free cash flow margins over the next 3 years after accounting for the factors I noted

Lots to untangle here… which I suspect is part of the goal.

But deferred payments is obviously going up. This is defined as companies moving to annual billings (vs. TCV up front) and using PANFS.

In Q1, the company also called out “Volatility in contact duration” as one of the impacts of the companies being more conscious about their billings terms. As a note, bears are saying the company has been using contract duration to keep billings growth strong while using PANFS to finance it.

Financing Receivables

In conjunction with Q4 earnings, Palo started to also disclose the amount of financing receivables on their balance sheet.

The important part to remember about using financing receivables vs. normal deferred billings is it shows up very differently on external billings (a key share price driver and exec payment metric):

• Using PANFS allows Palo Alto to continue reporting the full Billings number in external financials, but customers still get the benefit of paying annually.

• In normal “deferred payment plans” Palo will just bill a portion of the contract (normally 1-year) then rebill on an annual basis. This again gives customers the benefit of paying annually

• Note: all else equal, this either of these mechanisms does not impact Palo’s revenue or “run rate business”

PANFS was heavily pushed in FY23 but that seems to be shifting towards straight annual billings in FY24:

• What we can tell is that receivables were generally flat until Q1 FY23

• Receivables started to increase rapidly starting in Q2 before peaking, on a dollar value and cash flow impact in Q4, meshing with their overall commentary.

• After Q4, it appears customers interest/capacity in using PANFS started to decline as receivables peaked which coincided with billings guidance falling. I would assume because this shows up as a lability on their balance sheet.

Assuming new Financing has stabilized back at prior levels (flattish total receivables), we could expect this to be a considerable cash inflow in 2H FY24 as the first payments of the receivables issues in 2H FY23 come due.

This assumes a 3 year TOTAL average contract length with the first year paid up front and the final 2 years financed with equal payments due over the next 2 years.

High-level I’m calculating a $740M net headwind in FY23 flips to a ~$260M benefit in FY24 and ~$350M benefit in FY25.

Using this methodology, we can also create a proxy for “Collectable” Billings which adjusts reported billings for the net cash impact from financing and allows us to better understand collections potential.

As we can see below Collectable billings was BELOW reported billings in FY23 (creating a cash drag) while this flips in FY24, supporting cash collection vs. reported billings.

Using Collectable billings and adjusting collections by the same amount, we see a high correlation between collections and billings and which more accurately reflects the cash flow trends of the business.

Applying all this to guidance

Applying these collections trends to their guided reported billings estimates + my assumption for financing collectables, I’m getting collections between $9.2-$10B. Again this assumes no increase again in receivables and they don’t see another sudden shift in their billings terms.

Swagging the rest of their cash guidance, it seems like their FCF Margin guidance includes plenty of room for cash expenses. I’m calculating 103%-115% of Non-GAAP expenses (based on guidance) vs. 100-102% in prior periods.

It looks like prior guidance included an even bigger buffer for cash expenses which also explains why they were able to absorb the lower net billings while keeping FCF guidance flat.

This also doesn’t include any beats to EBIT Margins/Expenses, Interest Income, Cash Taxes, and CapEx (which they have a history of also beating).

As usual, please reach out with any comments/feedback (especially negative) or if you’re doing any digging of your own!

With Blessings of Strong NRR,

Thomas

Disclosures (reminder): I do not currently have a position in PANW. None of this is investment advice and is solely my own views. Do you own due diligence.

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The art and science of story telling

Datadog hosted their investor Day February 15th

I LOVED the simplicity and cohesiveness of the growth story

Not only is the company’s strategy and growth to-date impressive, the story telling during the event was succinct and actionable for investors and analysts

Overall, it’s a great case study on explaining how industry tailwinds and the company’s strategy translate to growth and shareholder returns… all in very simple terms.

The event also received great feedback from the sell-side community

A Few high-level thoughts/notes:

• Dead Simple. In the first ~35 minutes, the CEO, CTO and VP Product were able to lay out the entire vision of the company, how they’re expanding, the importance of the platform, and how they’re benefitting from AI

• LOTS of focus on the Platform. Team made a conscious decision to do the hard work early to build a platform. While this was questioned at the start, it is now paying off in faster innovation and market share (50% of R&D is spent on the platform)

• Clear financial ROI. David Obstler (OneTrust Board Member) finished with how the company’s strategy will manifest as 5 growth pillars in the future.

Everyone SAYS they will drive growth the same way

And honestly it isn’t rocket science… land new customers + expand usage + expand products

The difference being the executive team told a compelling story how and why they would be able to execute

AND that they already are

Current Business + New business

Growth companies drive their value from 2 parts: 1) the current book of business (RPO + renewals) and 2) their future growth from new ACV

A company’s RPO and GRR provide it with a (hopefully) solid base so its CAC is primarily focused on net new ARR not replacing churned customers

Growth is the lifeblood of high-multiple companies. For investors to continue paying such a high multiple, investors must have a high degree of confidence that massive amounts of incremental ARR will continue over many years

DDOG is currency trading >15x NTM Sales so there’s a lot riding on them to tell a compelling growth story

And to reiterate - this has to be long-term growth. Investors don’t pay 15x for only one year of growth to have it fizzle out after.

I wrote a piece on how growth impacts SaaS multiples you can check out:

So how can an investor get confidence in future growth?

• How strong is your current product/what is your current TAM?

• Do you have the opportunity to expand into new products/can you expand your TAM?

• Do you have the right to win/do you have the PMF + strategy to win the market?

• Will you be able to capture the TAM efficiently/profitability?

Yes, a lot of this is more art than science

But Datadog did a great job telling the story in a way analysts could see (model) the growth for the next few years in the future:

• Their core product can 5-10x from here (we’re in a massive market)

• They have multiple products which are already driving meaningful ACV growth (not a one trick pony)

• They have many bets in place to expand their TAM in the future (we’re making smart growth investments)

• Their Platform gives them a unique competitive advantage to capture this TAM (right to win / we won’t be displaced by other competitors)

• All making it easier for investors to underwrite continued growth into the future = Higher multiple now

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Building a Juggernaut

Oliver Pomel, Co-Founder & CEO, sets the stage describing the 2 ingredients Datadog uses to build a wildly successful product (and business): 1) an open-ended unified platform, and 2) building simple, easily-adopted product (as easy as a spreadsheet).

Everything else flows and elaborates from here.

So how do we build products? Well, obviously, there are many ingredients to building a successful product such as Datadog.

But if I were to reduce it to the most -- the 2 most important ideas, it would be, first, that we built Datadog from day 1 as an open-ended unified platform. All of our products are tightly and deeply integrated at the architectural, at the data and at the user interface layer. And the same platform serves end-to-end use cases from one data set to another, from one product to another and across team boundaries.

The second key ingredient would be our relentless focus on delivering a product that can be easily adopted. We call it simple but not simplistic, simple because our product should be deployable in minutes by meer mortals without extensive training and service engagements, and this should show value extremely quickly.

One way to put it is that Datadog should be as approachable and as easy to adopt as a spreadsheet - Olivier Pomel

Step 1: Building a Unified Platform

All of this was started by a singular decision to build a horizontal, infrastructure platform early in the company’s life.

But the defense at Datadog is that we actually decided to build the platform first, as you heard from Olivier. And actually, at the beginning, that was actually seen at the wrong choice. It's even made it pretty hard to get the company funded in the first days. - Yrieix Garnier

The crazy part is they admit that the solution is not that differentiated - just no one else built it:

The platform is basically a data store where you store your data, layers when you actually collect that data and then visualization layer like dashboard or just being able to expose that data, all of that be able to serve a specific use case or a product. Out of this, there's really nothing specific. It's actually, I would even say every solution in the market will provide the same kind of capabilities. - Yrieix Garnier

Taking that risk 14 years ago has allowed the company to build a broad moat around their business:

Step 2: Maximize Usage and Reach

Once you have a platform, you want to make sure its used as broadly and frequently as possible in the organization:

Every month, about 600,000 active users log into Datadog to analyze, coordinate and take action on their company's issues across development, operations and security. - Olivier Pomel

Having more users, use the product more frequently creates a growth flywheel:

• The product becomes more sticky

• There’s more people to sell adjacent products to

As the company focuses on larger customers this flywheel becomes even stronger:

Remember that the majority of our customers are quite small, and the bottom half of our customers is about 1% to 2% of our revenue.

What it looks like for large customers is that hundreds of users on average use our platform every month. And this number is growing.

Despite the last couple of years, being years of contraction for headcount at larger companies, we keep expanding as we spread across more use cases and more teams. - Olivier Pomel

Step 3: Build (Multiple) Products that Create (Massive) Value

When investors think about modeling future growth it helps to have a sure thing and a number of side bets.

Think of this as “multiple ways to win”

This shows:

• I don’t NEED other products (representing execution risk) to win

• But the company has also shown an ability to diversify (stability and upside)

Oliver expects their core observability platform to 5-10x from here:

So we think that observability alone on its own holds the next 5x or 10x scale-up for us. - Olivier Pomel

But also noted APM and Log management are independently generating $500M of ARR already:

We've also made progress in delivering a unified platform. As I discussed in the recent earnings call, we now have over $1 billion of AR in infrastructure monitoring and over $0.5 billion of AR in both APM Suite and log management - Olivier Pomel

Part of their strategy is simply building products that solve problems for customers.

This seems obvious, but customers need a massive ROI to justify the expense/execution risk of implementing new software.

This is a powerful graphic to help contextualize the value Datadog is bringing to customers

Step 4: Build your Moat

Advantage #1: AI Positioning

The Datadog infrastructure positioning gives the company a unique and highly-valuable dataset to better use AI.

This advantage comes down to contextual data tagging and would would obviously not be available publicly:

We also know how our customers name things, and this is surprisingly important.

So when a customer says Hogwarts, for instance, in a chat with Bits AI, Bits AI knows that it's not about Harry Potter, but it's about a web service that fronts their billing system, for instance. And the key insight here is that you cannot derive this kind of insight from public data. It just doesn't exist. It's not in a publicly available training data set.

That's very important during the incident response. And we also get a lot of context from our customers' stacks from their documentation, from their SureScore, group chat and so on and so forth. - Alexis Le-Quoc

Advantage #2: A unified platfrom creates R&D efficiency

A consistent theme the Datadog team talks about is the rapid pace of innovation at the company.

The company spent considerable time discussing the value of the platform in this pursuit and noted half of engineers are focused on the platform:

I said half of our engineers work on the platform, the other half work on all those various products. And thanks to the platform, those products actually -- those product teams are actually very lean. - Yrieix Garnier

Turns to find out, “very lean” is a wild understatement

Their $500M ARR Log management business has only 30 engineers and their cloud cost management product was built with 10 engineers (while it would take other companies 100).

If I'm taking the example of the log management team, this is $0.5 billion business and it actually only has about 30 engineers working on it.

Another example, cloud cost management, CCM, it's solution for the FinOps personas and if I look at other companies out there, they have, I would say, hundreds of engineers to build that kind of solutions just to build a cloud cost management solutions. At Datadog we were able to do it really quickly with only 10 engineers, again, because we are leveraging all the power of the platform underneath. - Yrieix Garnier

The outcome is a rapidly expanding product map at very high cost efficiency:

Step 5: Close Loop + Collect Value

Bringing this all together the company is building a broad platform with multiple scaled products, multiple ways to win, and a value proposition few will be able to displace.

As Oliver put it, the unique advantages they are creating is helping them build an “Unstoppable Juggernaut”

Our ultimate goal is to collect the data from numerous sources and to provide a single source of truth to break down silos across all users, deployed everywhere, used by everyone. - Yrieix Garnier

We do think that the combined platform, though, ends up being an unstoppable juggernaut when everything comes together, and that's what we're building out. - Olivier Pomel

A side note on the power of a great one-liner

The response from analysts was broadly positive with nearly all of them quoting the company’s “Juggernaut” comment and the importance of building a broad, unified platform.

From JPMorgan:

In our view the most critical and consistent message both from the company and customers is the word “Unified”: Datadog is the leading modern, unified Observability platform consolidating point solutions and gaining share, with Datadog commenting that over the long-term its platform can become “an unstoppable juggernaut when everything comes together.”

Numerous comments sprinkled throughout the event hint at Datadog’s confidence in the growth runway ahead of it, including: “we think Observability alone… holds the next 5x or 10x scale up for us”

From Morgan Stanley

In essence, access to rich, highly contextualized, real-time data gives Datadog the license to enter these categories. At the same time, management acknowledged that its capabilities in these areas will have to get better over time. Ultimately, Datadog is confident that taking a platform approach to delivering this highly integrated set of capabilities will serve the company well over the long term and management is not willing to take shortcuts just to better monetize in the near term.

Our assessment is that despite what seems to be an all-encompassing vision, we were highly encouraged to hear management state that the business has the potential to scale 5-10x from current levels on just the observability opportunity alone.

With Blessings of Strong NRR,

Thomas

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Interest in AI is Accelerating

Reuters did an analysis of companies reporting Q4 to-date and noted 38% of companies in the S&P 500 have discussed AI on their conference calls. This continues the spike in interest starting in Q2 of this year as companies rush to take advantage of the benefits of AI (savings and insights).

What is most-interesting is the broad impact and relevance this technology has across the US economy (and globally). This is NOT just a tech product.

The terms "AI" or "artificial intelligence" have been uttered on 38% of conference calls held by S&P 500 companies in January, a Reuters analysis of LSEG transcripts shows. That is up from 34% at the same point during the third-quarter reporting season, and it is in line with the second quarter, when discussion of AI spiked.

"AI" has been mentioned an average of 3.5 times per S&P 500 call so far in January, up from 3.3 times per call at the same point during the October quarter. - Reuters

Large tech companies, led by the hyperscalers, are discussing the most-frequently AND increased the most since last quarter.

• Alphabet increased 15%

• Microsoft increased 11%

• ServiceNow increased 6%

3 main trends being discussed:

1. CEOs are driving the purchasing decisions and shrinking sales cycles

2. Pricing is outperforming expectations AND have significant room to expand

3. Tech companies are going to have to spend a lot of money to take a piece of this pie

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The $3 Trillion Opportunity

Why are companies talking about the AI opportunity so much?

Garner expects $3 Trillion to be spent on AI through 2027. And this massive budget increase is coming at the expense of slower growth outside of software and IT services.

Gartner estimates $5 trillion in tech spending in 2024, growing to $6.5 trillion by 2027. That means that spending will grow another $1 trillion in only 2 years, accelerating from the decade plus it took for us to get to $5 trillion. For the first time in a decade, IT services will become bigger than communication services in 2024. Gartner estimates that by 2027, nearly all of the growth in worldwide IT spending will come from software and IT services. And when you drill deeper into the Gartner forecast between 2023 and 2027, $3 trillion will be spent on AI. What we have here is a strong, durable market being supercharged by a once-in-a-generation secular trend.

- Bill McDermott (NOW Q4 Conference Call)

Amazon alone expects to drive tens of billions of dollars over the next several years.

Gen AI is and will continue to be an area of pervasive focus and investment across Amazon primarily because there are a few initiatives, if any, that give us the chance to reinvent so many of our customer experiences and processes, and we believe it will ultimately drive tens of billions of dollars of revenue for Amazon over the next several years.

- Andy Jassy (AMZN Q4 Converene Call)

Companies are shifting resources to win AI market share

Building out the infrastructure to capture this value is going to be wildly expensive and executives are willing to cut unnecessary projects and divert funding to make sure they remain competitive WHILE balancing margins.

“2023 was our ‘year of efficiency’ which focused on making Meta a stronger technology company and improving our business to give us the stability to deliver our ambitious long-term vision for AI and the metaverse,” Zuckerberg said on the earnings call.

Nadella told investors that Microsoft is committed to scaling AI investment and cloud efforts, even if it means looking closely at expenses in other departments, with “disciplined cost management across every team.”

Microsoft CFO Amy Hood underlined the “consistency of repivoting our workforce toward the AI-first work we’re doing without adding material number of people to the workforce,” and said the company will continue to prioritize investing in AI as “the thing that’s going to shape the next decade.”

The theme was similar at Alphabet, where Sundar Pichai spoke of his company’s “focus and discipline” as it prioritizes scaling up AI for Search, YouTube, Google Cloud and beyond. He said investing in infrastructure such as data centers is “key to realizing our big AI ambitions,” adding that the company had cut nonpriority projects and invested in automating certain processes. - CNBC

CEOs are driving purchasing decisions and shortening sales cycles

Bill noted all the conversations he’s having with CEOs and the intense interest in utilizing the efficiency gains.

He also noted that since the CEO is typically involved in the purchases, it significantly decreases sales cycles.

Things are going very well out there, and the momentum is terrific. What's really happening, and I can say this after 186 CEO meetings in the last 6 months, the CEOs are now getting very involved with the gen AI revolution.

- Bill McDermott (NOW Q4 Conference Call)

Demand is underpinned by massive efficiency gains

ServiceNow noted productivity is increasing 40-50% efficiency gains which is helping the product grow rapidly.

As I said, that SKU has outsold any other new introduction we put into the marketplace. So there's a real appetite to invest in Gen AI, and there's no price sensitivity around it because the business cases are so unbelievable. I mean if you're improving productivity, 40%, 50%, it just sells itself.

- Bill McDermott (NOW Q4 Conference Call)

And Microsoft characterized Copilot as not necessarily an operating expense but a development activity to invest in the productivity of your dev team.

And so yes, I think all up, I do see this as a new vector for us in what I'll call the next phase of knowledge work and frontline work even and their productivity and how we participate. And I think GitHub Copilot, I never thought of the tools business as fundamentally participating in the operating expenses of a company's spend on, let's say, development activity. And now you're seeing that transition. It's just not tools. It's about productivity of your dev team.

- Satya Nadella (MSFT Q2 Conference Call)

Companies expect to capture a greater share of the value

When many of the large tech companies originally released pricing, it was higher than expected, which I wrote about here:

ServiceNow mentioned their pricing uplift from their AI SKUs are even outperforming their expectations:

So Mike, first of all, the Pro SKUs, as you know, that we launched it in 2018 Q3, so we have 5 years of consistent trajectory and measures on how we did on Pro across ITSM, CSM and so on. And that we shared at Financial Analyst Day in May. Gina shared that number that we got 25% uplift. When I look at Pro Plus, first, just to underscore what Gina said that it definitely exceeded our expectation, did really, really well and the fastest growth. We have launched so many products over so many years. This definitely exceeded our expectations.

- Chirantan Jitendra Desai, President & COO (NOW Q4 Conference Call)

Satya specifically calls out that their average ARPU remains low and expects Copilot to allow the companies to take some share of the 2-3ppts of OpEx leverage as pricing power.

So you are going to start seeing people think of these tools as productivity enhancers, right? I mean, if I look at it, our ARPUs have been great but they're pretty low. But frankly, even though we've had a lot of success, it's not like we are a high-priced ARPU company.

I think what you're going to start finding is, whether it's Sales Copilot or Service Copilot or GitHub Copilot or Security Copilot, they are going to fundamentally capture some of the value they drive in terms of the productivity of the OpEx, right? So it's like 2 points, 3 points of OpEx leverage would go to some software spend. I think that's a pretty straightforward value equation.

- Satya Nadella (MSFT Q2 Conference Call)

With Blessings of Strong NRR,

Thomas

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A Few Layers Deeper

Some great stuff has been written on the topic of interest rates’ impact on SaaS multiples. In particular, I liked David Spitz’s LinkedIn posts on the topic here, here, and here. Not to mention Jamin Ball’s great charts every week at Clouded Judgement.

I recently helped Guido Torrini (my boss and CFO at OneTrust) build a presentation helping distill the concept for the entire organization in his quarterly “Inside the Numbers” all-hands. Here, I wanted to take the thought experiment a few layers deeper. (And thanks for the inspiration 😉)

On a surface level, the relationship is obvious. You discount future cash flows at a higher rate, and you end up with a lower current value. Simple enough.

Taking it one step further, an unprofitable, high-growth company (such as SaaS) has an even higher percentage of its value in future periods/terminal value (~95%), so its value gets hit even more by this mechanism.

However, this high-level framework doesn’t consider the on-the-ground impact of SaaS fundamentals and investors’ perceptions of such (some caused by higher rates via reflexivity). These include: 1) growth trajectory, 2) long-term margins, and 3) and higher perceived risk.

Watch out for part 2 where we’ll discuss more details on:

• Slower growth trajectory (consumption, SMB, and Unicorn spending unwind)

• Lower margins (higher payback periods)

To help follow along with the framework and calculations, I built an Interactive Worksheet.

Breaking Apart a DCF

There are 5 primary variables impacting a DCF valuation:

1. Interest Rates

2. Equity risk vs. Bonds (Equity Risk Premium)

3. Relative Risk of Security vs. Equity (Beta)

4. FCF trajectory

5. Exit multiple/Terminal Value

Components 1-3 combine to form the discount rate. (Google it).

In the simple framework, the primary variable being flexed is Interest Rates.

But Risk and FCF trajectory actually seem to play a larger role in multiple compression (high level, I find it to be 60-70% vs. 30-40% interest rates alone).

Note, some people have suggested that updating the exit multiple for lower current market valuations also causes a double hit in this calculation. I would argue for an ongoing company valuation, only long-term average market multiples should be used for exit multiples and only applied once the company reaches a steady state. Therefore, this variable should not play a significant independent role in the multiple compression. This calculus may change if the company is being considered for near-term M&A or an LBO where current market multiples might be used to reflect multiple arbitrage for exits 3-5 years from now.

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Scenarios: Interest Rates vs. FCF Trends

I built 4 scenarios of a hypothetical SaaS company to gauge the relative impact between the different variables. I used high-level numbers for the change in discount rates over time and used growth decay as the mechanism to lower future growth estimates.

Overall, I got discount rates between 6.5% and 12%. Again, the analysis is more so the relative change in discount rates, not necessarily the correct absolute value (which would also change by each security).

If you want to dive in deeper on discount rates:

• David Skok suggested ~10% when rates were lower. David Spitz used this framework with 15% for higher rates now in his LinkedIn post.

• Kroll suggests an ERP of 5.5% and a risk-free rate of a minimum of 3.5% or the current 20-year rate.

• Aswath Damodaran currently calculates an ERP of ~4.5%

Beta or relative risk is also an important variable to consider when calculating the discount rate. I discuss this more in the next section.

In each scenario, I assume the company starts with $100, grows 50% in Year 1, and then apply the growth decay factor to the annual growth rate thereafter. I assume margins start at (20%) and expand to +20% by 10ppts annually in all scenarios. You can find the full set of assumptions in the sheet below.

To account for investors’ lower fundamental expectations, we lower the growth decay factor to 85% in the final scenario, which causes a 37% cut to Year 10 FCF (with no change in margin assumptions. (Note: you can check out a quick growth decay/discounting visualization in the following section).

To visualize and test out the different variables, I created a spreadsheet for you to follow along:

Interactive Worksheet

As you can see, investors’ perception of risk and lower FCF trajectory play a larger combined impact to multiple contraction than the interest rate alone.

Monopoly Bond to Commoditized Risk Asset

One of the parts that fewer people discuss (and is harder to quantify) is the risk appetite and confidence investors baked into multiples and share prices at the peak.

With high growth multiples trading as high as 40x at its peak and the top 5 public companies trading at 70-80x, this is pricing in some combination of, and likely all of the following: 1) dominant market position growth, 2) monopolistic long-term margins, and 3) minimal risk of achieving this end state.

Investors were pricing high-risk companies as sure bet bonds. While the SaaS business model is seductive, it is far from foolproof.

Further, with a large portion of SaaS companies trading at these multiples, investors were likely pricing in multiple monopolistic companies per software niche, which is impossible alone.

I pulled historical Betas for a few high-growth (SNOW and DDOG) and “Staple” (WDAY and CRM) SaaS companies and compared them to the 10-year rate over time.

A few observations:

1. Betas were extremely low (~1-1.1) through 2021 likely due to generalists flooding the space driven by the popularity of high-growth SaaS IPOs and better understanding of the economics of SaaS businesses.

2. Beta’s started to spike as interest rates started to increase

3. The spread between the Betas of high-growth (and likely riskier) and Staple (mature, steady economics) has widened. Again, likely much healthier.

We can see that investors were previously valuing growth at all costs, but now realize many high-growth companies are commoditized and don’t have profitable unit margins.

Markets Discount “Perpetuity” to Today

David Spitz had a great visual of how compounding small near-term changes to fundamentals impacts long-term FCF and DCF values. In this case, it was investors’ perception of Confluent growth rates over time.

This is similar to our growth decay mechanism in our scenarios above. Investors don’t just adjust one year, they adjust ALL future periods which have a massive compounding effect.

Interest Rates Sparked a (Likely Healthy) Repricing

The net impact of rates and its related fundamental impacts have caused median SaaS multiples to contract from ~20x in 2020 to ~6x today while 10-year rates have expanded from <1% to 4-5% in recent months.

I would guess raising rates sparked the reckoning and repricing of SaaS businesses, but the correlation seems to have become less impactful more recently (as measured by vibes, not quantitative).

With the rising spreads between betas and more company-specific trends dominating recent commentary, I would expect this trend to continue through 2024.

We’ll discuss more of the impacts to fundamentals in part 2!

With Blessings of Strong NRR,

Thomas

Any questions? Respond to this email or drop them in the comments 👇

☁ SaaS 101 Live Bootcamp

• Dive into SaaS ARR & revenue metrics (long-term FCF margins, Bookings, billings, and collections

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Let’s jump right in.

You’ve seen 348 diagrams of how to calculate GRR and NRR

But how do operators actually convert raw contract data into everyone’s favorite SaaS metrics?

Don’t worry…

Today we’re diving into 3 super important metrics (Renewal Rates, GRR, and NRR), the differences with other common metrics and common pitfalls.

Download the free worksheet to follow along and test out your skills 👇

ARR Book 101 Worksheet

This is the second part of my series on building an ARR Book. If you didn’t check out Part 1: ☁ How to Build an ARR Book (Part 1)

☁ LAST CALL: 30% Off SaaS Modeling Bootcamp

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Renewal Rates

Renewal rates aren’t as widely discussed externally as its cousin GRR as it’s largely an internal operating metric. The metric measures how much ARR is renewed vs. contracts ending and is commonly tracked by management and GTM teams.

Renewal Rate = Renewed ARR / ARR up for Renewal in period

ARR up for Renewal is simply the value of contracts that are ending, and therefore must be renewed in any given period. 

While its highly related to a company’s GRR, GRR is a balance metric while up for renewal is a transaction metric. Your GRR will reflect the cumulative renewed balance over time as measured by the Renewal metric.

An important distinction is that ARR must be renewed BEFORE the contract ends. So the Renewed ARR (and the flip slide, Churned ARR), is a generally captured a period before Lost ARR is reflected in your ARR bridge and therefore the GRR calculation.

You will also be able to track which months will require more renewal effort (typically at the end of quarters and fiscal year) and what products/segments are coming up for renewal which much impact your overall churn.

Gross Revenue Retention (GRR)

Gross retention is how much ARR is retained (excluding upsells and lands) from the starting balancing 12 months ago 

GRR = Retained ARR from logos 12 months ago / ARR 12 months ago

As discussed above, it reflects the cumulative churn from the up for renewal contracts over the past 12 months which is why the 2 metrics are important to consider and analyze together. 

The other important difference GRR accounts for is different length contracts. If a company only sells 1 year contract, that means 100% of their ARR must be renewed every year. Not only does this leave more opportunities for customers to churn, it also strains the company on an operational level as most contracts require a touch point from the GTM team as some varying level to renew the contract. 

If a SaaS company expands their average contract to 2 years, that means only half of their ARR is up for renewal every year. As an example, an 80% renewal rate would then be reflected by a 90% GRR.

Net Revenue Retention (NRR)

Net Revenue Retention is generally considered the holy grail of SaaS metrics as it reflects how much incremental revenue you’re able to drive from current customers.

NRR = Total ARR / ARR from logos 12 months ago 

GTM teams are frequently divided between “hunters” and “farmers”. Hunters go out and find new logos while “farmers” try to expand current customers. 

Landing new customers tend to have longer sales cycles and require more resources to close (from BDRs, SE, legal, and other support). Overall this makes new logos generally more expensive relative to incremental ARR contribution than expansion ARR. 

In theory, once you land a customer, expansion should be easier as the relationship and trust has already been built (again, “in theory is pulling a lot of weight here). This leads to shorter sales cycles and fewer other internal resources needed to close a contract. In expansion deals, the primary expense is enabling customers to increase usage, add new seats, or purchase incremental modules. 

All else equal, a higher NRR should enable higher long-term margins especially as growth slows and new logos become a smaller % of total ARR growth.

Comparing vs. Gross retention and Net Retention rates

Anyone close to SaaS metrics knows there are millions of ways to calculate these metrics, with each company using their own internal spin on each of the metrics.

When you think about the number of inputs going into each of these calculations, you can quickly picture the number of assumptions that must go into each stage. 

As such, there are many other commonly used terms that are sometimes used to reflect the above metrics, derivatives of such or shortcuts to represent similar trends. 

The most common of these is using the ARR book / Leaky Bucket to calculate in-period expansion and churn and gross these up to an annual metric. ICONIQ uses a similar methodology in their SaaS Glossary ARR Funnel template.

While keeping track of these metrics may be helpful it’s important to remember they hide a lot of underlying information that a full ARR book would uncover.

As a note, top VCs and growth equity investors generally require a full ARR book as part of their normal due diligence precisely for this reason. 

Reminder: Real Life is MESSY

Its easy to build excel examples to show how to calculate these metrics in hypothetical situations, but as discussed above, real life if always messier. 

I could write a full post on the intricacies of ARR books and contract data but to quickly highlight a few complexities our simple example doesn’t cover: 

1. MRR vs. ARR. Most metrics force monthly contracts into annual metrics such as GRR and NRR. I generally speak more to annual contracts and metrics as my experience is in the b2b space.

2. Large contract complexity. When companies offer many products or pricing tiers to large customers, there’s frequently movement between these products, with different timing, and varying discounting across products. Its important to member all these metrics are cohorted on a logo basis to net out any of these impacts. 

3. Active ARR vs. Contracted ARR. Again there are many differed terms for the same thing, but Contracted ARR may reflect all contracts signed, even those that have not started yet, while active ARR is only contracts which have started and are actively contributing to revenue. 

4. Discounts/Ramp deals. There’s always operational reasons (i.e. a massive customer wants to pay you a lot of money) to discount your products or provide varying levels of service throughout the contract. When possible, your ARR should seek to represent your revenue recognition in the period as closely as possible.

And make sure to check out my other SaaS 101 posts:

• ☁ Benchmarking 101

• ☁ SaaS Revenue 101

• ☁ Diving into RPO Metrics

• ☁ Digging into SaaS FCF

With Blessings of Strong NRR,

Thomas

Any questions? Respond to this email or drop them in the comments 👇

☁ LAST CALL: 30% Off SaaS Modeling Bootcamp

• 📣 2 AMAZING Guest Speakers. SaaS metric master and CFO, CJ Gustafson covering all your metric questions and Mike Privette, CISO and editor of Return on Security.

• Dive into SaaS ARR & revenue metrics (long-term FCF margins, Bookings, billings, and collections)

🚨 Workshop starts TOMORROW! (reply to this email with any questions)

Join Live Cohort! (30% Off)

The ARR book is a cornerstone of SaaS analysis, but few ever really learn how to build from scratch and utilize properly. Many seem to get the general idea, but I’ve received many questions and requests to go through the process in detail.

To help you practice along the way, I built a supplemental and interactive Google Sheet you can use to while you read through the post. Why? Because I need more hobbies 😉

Once you’re done with the post and worksheet, you’ll be able to:

• Convert raw contract data to an ARR Book

• Allocate all the changes between land, expand, churn, and contraction ARR

• Pivot it out to a SaaS “Leaky Bucket”

In part 2, we’ll dive into the many metrics you can calculate off of an ARR Book

👇 Download the worksheet here (at the bottom of the event page)

Free Worksheet

Check out my other SaaS 101 posts:

• ☁ Benchmarking 101

• ☁ SaaS Revenue 101

• ☁ Diving into RPO Metrics

• ☁ Digging into SaaS FCF

📣 NEXT SATURDAY: Free 2-Hour Workshop

If ​you’re passionate about Cybersecurity and SaaS Modeling, Francis (Software Analyst)and I are hosting a free 2-hour workshop to introduce ourselves and give you an idea what to expect from our full bootcamp:

• Intro to Cybersecurity Featuring Palo Alto Networks

• Intro to SaaS metrics & ARR Modeling with Datadog

• Live Q&A Session & Networking

• Saturday, November 18 @ 10am ET

• Good opportunity to preview the full, in-depth course we host on Maven (Cohort #3 Starting 11/27).

Join us for Free!

What is an ARR Book?

An ARR book is a common internal analysis tool as well as a standard due diligence request when VCs and Growth investors are looking to make an investment.

In short, it provides your ARR by customer by period overtime allowing you to calculate key SaaS metrics as well as run analysis on what is working and not working.

Below is a simple example which would allow operators and investors to calculate New, Expand, Contract, and Lost ARR. As well as look at trends by Region and Segment/Customer Size.

What is the Underlying Data? Customer Contracts

ARR is always an output from your customer contract source of truth. These typically originate from Salesforce but may be consolidated and pulled from your primary datalake such as Snowflake and piped into an analytics platform or downloaded for excel.

A customer contract includes all the information required to track customer deliverables as well as additional information which may impact revenue recognition.

This may include:

• Customer Name (as well as Unique ID)

• Customer Description (Size, Region, Industry)

• GTM Descriptions (Customer Segment, Sales Theater)

• Product Name (as well as internal roll ups/tags)

• Contract Close, Start and End Dates

• Size of Contract/ARR

💻 How do I convert contract data to an ARR Book?

Once you have a cleaned dataset (always an herculean effort in itself in early-stage companies), it’s pretty easy:

1. Make a list of all your logos (rows)

2. Determine a sequence of periods (months or quarters are most common) you would like to calculate (columns)

3. Calculate if each logo was generating ARR at the end of any given period

4. Note: If a logo churns at the end of the period, its typically assumed it’s still in ARR (i.e. 12/31 churn remains counted in December ARR and then reflects $0 in January/the next period)

Boom. Done.

Want to check out the math live? Download it here (link at the bottom of the event page):

Free Worksheet

The SaaS “Leaky bucket”

The primary output from an ARR book is building a proper ARR Build or Roll Forward, commonly known as the “Leaky Bucket”.

This framework breaks net change in ARR from one period to another into 4 main categories:

• New Logo ARR. Driven by new customer growth that the company didn’t have previously.

• Expansion ARR. Captures when existing customer add seats, increase usage, or add additional products to their contracts.

• Logo Churn. This captures when customer decide to fully end their relationships with the company.

• Downsell/Shrinkage. Represents the opposite of expansion ARR. If customer reduce seats, usage or cut products.

ICONIQ provided a visual representation in their SaaS Glossary report:

Why do we break our ARR into these buckets? ARR, and by extension, change in ARR, hides a lot of nuance in business performance. Using the SaaS Leaky bucket allows you to properly compensate results and address issues:

Dave Kellogg provides a good scenario on how different operators in a business would respond to different line items based on their overall responsibilities:

If it’s the VP of New Customer Sales, then very angry. They landed at 60% of plan, not 96%.

If it’s the VP of Sales (responsible for all new ARR), then still pretty angry. They landed at 73% of plan, not 96%.

If it’s the VP of Customer Success (and they’re responsible only for churn), then not angry at all. They were spot on plan though we had a little more shrinkage ARR and a little less lost ARR than plan.  Good job, but I have a few questions.

If it’s the CRO, responsible for both new and churn ARR, then back to very angry.  Net new ARR (new ARR – churn ARR) was $825K, 60% of plan, not 96%.

- Dave Kellogg / Kellblog

In my bootcamp with , we simplify the concept even further to New + Churn for beginners:

💻 How do I calculate my leaky bucket from an ARR Book?

Once you have a completed ARR book, you must now divide the change in each periods ARR by each logo within the 4 categories described above.

There are MANY ways to skin this cat. The theory behind how you choose to calculate lays in this being a MECE exercise and you must design your spreadsheet/formulas to reflect as such. Everyone will likely do this a bit differently based on what they’re comfortable with and how you like to build your formulas.

Here’s my current favorite:

1. Add net change columns. I’ve found this intermediate step simplifies some of the formulas below as splitting the categories between net add vs. net negative is an important step. (In the worksheet, I’ve already done this for you).

2. New Logo ARR. Change = Positive and P0 = 0 (there was no ARR previously)

3. Expansion ARR. Change = Positive and P0 > 0

4. Logo Churn. Change = Negative and P1 = 0 (ends with no ARR)

5. Downsell/Shrinkage. Change = Negative and P1 > 0 (ARR declined, but logo remains)

Once you’ve calculated each of these steps, you can link everything back to the Summary / Leaky Bucket tab.

Hint: Remember adding up all these changes in each period should equal the ending ARR from your ARR book in each period.

Again follow along live (free link at the bottom of the event page):

Test Out Live

Watch out for Part 2!

Part 2 will dive into the many different metrics you can use your ARR Book to calculate including:

• ASP/Customer economics

• Logo bridges

• Renewal Rates

• GRR/NRR

• Contract lengths

☁ Ready to learn more?

3-Week SaaS + Cybersecurity Bootcamp on Starting 11/27

• Dive into SaaS ACV vs. ARR, Revenue metrics, Billings, and Bookings

• Understand long-term FCF margins

• Live Q&A + interactive models/worksheets

• Slack networking group

Join students from Goldman Sachs, Microsoft, Bank of America, Palo Alto Networks, Cowen, SVB, Scotiabank, VCs, ETF Managers, and many more!

SaaS Bootcamp

Rumor has it that Harry actually left the royal family because he disagreed with Elizabeth on the proper benchmarks for guiding royal expectations.

While this is common, it doesn’t have to happen to you.

Benchmarking is in vogue as companies battle with VC demands to reach profitability and ramp up FY25 annual planning.

Unfortunately, most people don’t do this right.

Let’s chat about choosing the right benchmarks and where to find them 👇

📣📣 SaaS Bootcamp: 3 Amazing Guest Speakers!

1. Stan Zlotsky, SVP, Head of Finance and IR at Pendo.io. Previously ED, Software Equity Research at Morgan Stanley. Stan led the famous SaaS X-Ray report and is a thought leader on long-term SaaS margins and pivoting from Wall Street to Corporate Finance.

2. Vijay Bolina, CISO at Deepmind, Google. Previously the CISO at BlackHawk Network and a contractor responding to data breaches perpetrated by some of the world’s most sophisticated nation-states targeting the US Military.

3. Emilio Escobar, CISO at Datadog. Previously VP, InfoSec at Hulu, and started his career interning at the US DoD in the aftermath of September 11. Emilio provides insightful context on how dynamic corporations view the risks/opportunities around AI and securing networks.

Join students from Goldman Sachs, Microsoft, Bank of America, Palo Alto Networks, Cowen, SVB, Scotiabank, VCs, ETF Managers, and many more!

🚨🚨 Workshop starts TOMORROW

Join Live Cohort!

What is benchmarking?

Benchmarking is using peer financial and operating data to understand how your company is performing relative to your industry, set targets, and enable executives to make strategic decisions based on the information.

Like anything in finance and corporate strategy, there’s a bad way and a good way (what my readers aim for) to use benchmarking:

• The Lazy Way. Looking simply at Absolute $ spend or % of revenue with no regard to other relevant information (“Company XYZ spent $50M on G&A, so should we”.

• The Strategic Way. Incorporating additional information to make strategic decisions. (“Company ABC was able to more rapidly drive efficiency when they were at our scale”)

Benchmarks are also heavily used by investors as it is a simplified framework to analyze their portfolio companies against each other and against peers. When they see large deltas, be prepared to answer for the deltas (or explain why it’s not as relevant).

How do I choose which benchmarks to use?

The more difficult part is making sure to choose the RIGHT benchmarks. If you sell cars, Meta is not your benchmark. Similarly, if you’re a SaaS company, you should be looking at other SaaS companies.

Benchmarks are also commonly used in capital markets to judge relative valuations. In this case, you typically try to compare yourself to “best-in-class” metrics to maximize your valuation.

There are 3 common considerations for choosing the right benchmark:

• Size. Scale plays an important factor in determining how much a company spends, especially as a percentage of revenue. This is frequently the case in G&A.

• Growth. The faster a company grows, the more it must spend in Sales & Marketing. In a dynamic industry, a company also has to invest more in developing new products to remain competitive.

• Time. Popular in capital market considerations such as “time to IPO” and size of recent M&A.

We have a section on benchmarking when thinking about long-term margins in our SaaS Bootcamp:

Where do I find benchmarks?

There are 2 main sources of benchmarks:

1. Public company SEC filings (I’ve been a huge fan of BamSEC recently, but you can also just go old-school EDGAR)

2. Aggregated Private company reports

Attaining private company data is always much more difficult, but many VCs periodically publish relevant insights from their portfolios. Your friendly, neighborhood investment banker may also have some helpful resources, but these are usually harder to get your hands on.

The metric maven himself, , d.b.a. Mostly Metrics, put out a great report last week aggregating all the top private reports.

He also has a great call out about our workshop 👇

Mostly metrics

My favorite benchmarking reports

As a child I collected baseball cards. As an adult, I now collect benchmarking reports. Do yourself a favor and bookmark this list in your Chrome browser (or Microsoft Edge browser, if you’re a complete psychopath) for ease of reference. It will be especially helpful during annual planning season when you’l…

Read more

3 years ago · 21 likes · 4 comments · CJ Gustafson

With Blessings of Strong NRR,

Thomas

Any questions? Respond to this email or drop them in the comments 👇

☁ TOMORROW: SaaS Modeling Bootcamp

• Dive into SaaS ACV vs. ARR, Revenue metrics, Billings, and Bookings

• Understand long-term FCF margins

• Live Q&A + interactive models/worksheets

• Slack networking group

🚨 Workshop starts TOMORROW

Join Live Cohort!

Klaviyo is the first pure-play SaaS IPO in ~2 years, and, at first take, looks like it will take its seat as one with top-tier metrics.

🤑 Fun fact: Kaviyo co-founder, Andrew Bialecki, still owns 38.1% of the company. To put this in perspective, Jason Lemkin at SaaStr put out a great analysis of founder share at the time of IPO. He found the largest founder on average owned <15%.

The Klaviyo Recipe of Success:

• “Efficiency is part of our DNA”. CAC Payback of 29 months with a Rule of 40 of 65 (benchmarking included below).

• + Strong NRR. Klaviyo sports 119% NRR vs. ~120% Top Quartile. Boosted by price increases, customer contact growth, and new product launches. (Klaviyo gave a great cohort chart included below)

• = $650M ARR with minimal burn. Despite aggressive growth and scale, the company has only burned $15M of the $454.8M total they raised 🤯

As usual, I would highly recommend the deep dives from the which I pulled from extensively: Alex Clayton & Team at Meritech, Jamin Ball at Clouded Judgement / Altimeter, and Janelle Teng at Next Big Teng / Bessemer. If you’re feeling wild, check out the full Klaviyo S-1.

☁ Love SaaS Metrics? You’ll love my SaaS Modeling Bootcamp

• Dive into SaaS ARR & revenue metrics

• Learn to model SaaS expenses

• Understand long-term FCF margins

• Bookings, billings, and collections

• Join students from Goldman Sachs, Microsoft, Bank of America, Palo Alto Networks, Cowen, SVB, Scotiabank, VCs, and ETF Managers

🚨 Workshop starts in 10 days! (reply to this email with any questions)

Join Live Cohort!

Bootstrapped in 2012, Now They Here… with $650M of ARR

Klaviyo was founded in 2012, but didn’t start to raise significant capital until 2019. Since that time, the company has launched new products to build out the platform and partnerships to diversify away from Shopify (~78% of ARR). The company now serves 130k customers and recently launched CDP and reviews. The company is now runrating above $650M (quarterly revenue * 4, Meritech has some great comps below).

Data Layer + Application

Klaviyo is a SaaS platform combining a data layer with a platform layer to enable online stores to effectively use first-party data to market to their customers via Email, SMS, and push notifications.

Data Layer

• Creates a single source of truth for real-time customer event data to allow granular segmentation and real-time interaction

• 300 native integrations (Shopify, Salesforce Commerce Cloud, WooCommerce, loyalty, social media, customer service, and shipping solutions)

• Launching new verticals new verticals, including Mindbody, Zenoti, and Olo

Application Layer

• Provides data analytics without the need for in-house engineers

• Allows customers to create and manage targeted marketing campaigns

• AI allows businesses to estimate consumer lifetime value, predict a consumer’s next order date, and calculate potential churn risk.

• “We believe our software is highly extensible across a broad range of functions for B2C and B2B businesses alike”

The company wins by being a purpose-built, full-stack data, analytics, and marketing platform:

Other software solutions were not purpose-built to harness customers’ first-party data to deliver impactful consumer experiences. Data-focused offerings, such as cloud data warehouses or operational databases, provide the ability to store and analyze significant volumes of data for general-purpose use cases but are not purpose-built for consumer data and lack the front-end application layer. Marketing solutions are insufficient because they lack the underlying data intelligence. Simple marketing solutions use a flattened and narrowed view of a consumer’s historical data. This basic profile data alone significantly limits the granularity of segmentation businesses can use. Profile data is also difficult to combine with event data, which includes all digital touch points of a consumer’s engagement with a brand and provides necessary real-time information. Point marketing solutions tend to focus on single engagement channels, driving inconsistent and disjointed consumer experiences across digital channels. In an attempt to bridge this gap, other marketing solutions use a patchwork of third-party technologies, such as separate consumer data, learning, and messaging applications. These solutions often require significant technical expertise to implement, operate, and maintain, which limits flexibility, reduces speed, and increases costs. Furthermore, these solutions are not able to provide clear revenue attribution, minimizing ROI.

Pricing and Pricing Increases

At its core, Klaviyo charges based on the products you want (Email or Email + SMS), the number of contacts you have, and how often you message them. At its heart, the product is a SMB offering starting at $45-60/month, but scales quickly with the size of the business and usage of the product (i.e. PLG).

Klaviyo also announced a price increase for its largest customers (first time in 10 years) in July 2022, with pricing impact taking into effect on September 2022 (hat tip to Rob Litterst for posting). Some marketing blogs estimated this would come out to a 15-50% increase based on the number of emails you sent.

And for reverence, here’s their prior pricing (vs. it’s competitors Omnisend) before the pricing increase.

From their blog post on July 18th:

Today we’ve announced changes to our pricing, with increases for our largest customers, updates to our SMS pricing model, and the introduction of a new enterprise-grade tier. - Klaviyo Blog

And quantifying the revenue impact in their S-1:

We estimate our price increase in September 2022 represented a mid-teens percentage increase of incremental revenue dollars in the first six months of 2023. We anticipate a relatively smaller impact from this price increase in the third quarter of 2023, given that the price increase took effect in the third month of the third quarter of 2022. We estimated this by taking the pricing-related monthly subscription changes for impacted accounts and applying those changes to the remaining months of the year. - Klaviyo S-1

119% NRR

This efficient upsell and pricing at the higher end fuels the company’s strong NRR (119% vs. top quartile 120%). And even though COVID boosted the company’s NRR, it pointed out that its cohorts continued to grow well even after the one-time bump.

Our Q1 2022 Cohort expanded by 128% in the first 12 months;

Our Q1 2021 Cohort expanded by 123% in the first 12 months and 150% after 24 months; and

Our Q1 2020 Cohort expanded by 133% in the first 12 months and 162% after 24 months and 192% after 36 months.

Amazing Efficiency

The efficient upsell (like any PLG) also supports a very healthy CAC payback. As a reminder, most SaaS companies require an account executive to actually increase contract value (typically via increased seats or products) which takes time ($$) and usually commissions (also $$). PLG allows customers to expand organically within the product, which allows much more efficient growth but typically more R&D upfront (need to design UI/UX well enough so minimal services/support is required).

This dynamic is shining through Klaviyo’s impressive CAC Payback of 29 months (consistent with top quarterly and much better than the Median of 41 months).

And also shows in the company’s rule of 40 of 65 (vs. 45 top quartile and 34 median). This is primarily a function of the company’s operating leverage as it scales.

What’s next for the IPO Market?

Janelle Teng at Next Big Teng / Bessemer wrote a great piece on whats driving the IPO market and which companies typically lead out of a downturn/into an IPO Re-Opening.

Themes for Reopening Tech IPOs (Via Bank of America):

• Category Leaders

• Attractive growth profiles

• Strong scale and profitability

My spidey sense tells me Klaviyo will do a great shop shaking open the stagnant IPO market through the rest of the year.

With Blessings of Strong NRR,

Thomas

Any questions? Respond to this email or drop them in the comments 👇

☁ Looking to learn more about SaaS Revenue forecasting?

• Learn to analyze and model SaaS companies

• Live Q&A + interactive models/worksheets

• Slack networking group

• Amazing guest speakers (including the CISO of Datadog)

🚨 Workshop starts September 12th (reply to this email with any questions)

Join us live!

Live view of analysts attempting to forecast revenue.

Revenue forecasting seems complex (and many times is), but can be broken more simply into 3 main pieces:

1. New revenue

2. Renewal Revenue

3. Contracted revenue

4. Usage-based

How analysts forecast these different pieces is largely based on whether they are internal (Corporate FP&A) or external (sell-side/buy-side analyst).

• Internal teams. Have the benefit of knowing complete information of #3, partial knowledge of #2 (know the timing of renewals and trends), and at least pipeline/sales calls of #1.

• External teams. Must battle with limited knowledge of all 3 while using historically reported external metrics to fill in the gaps (especially for contracted and renewal). Hedge funds will use their usual “how’s the quarter going?” call at conferences to get management’s relative guidance on #1.

☁ SaaS Modeling Bootcamp

• Dive into SaaS ARR & revenue metrics

• Learn to model SaaS expenses

• Understand long-term FCF margins

• Bookings, billings, and collections

• Join students from Goldman Sachs, Microsoft, Bank of America, Palo Alto Networks, Cowen, SVB, Scotiabank, VCs, and ETF Managers

🚨 Workshop starts in 3 weeks! (reply to this email with any questions)

Join Live Cohort!

Internal Forecasting

The building block of any internal revenue forecast is your contracted revenue schedule. This tells you how much revenue will be recognized if nothing else happens.

Next, you will want to tease out contracts up for renewals by period. This will allow you to apply a churn assumption to those contracts and properly forecast the revenue.

Finally is new ACV. Every company has a full-year forecast (typically by month/quarter), but usually includes some sort of execution risk / “go-get” assumptions. Further, timing (even internally) plays a huge factor in revenue recognition as deals may slip from month to month.

As a bonus complexity, many SaaS companies now include a usage-based component to their pricing strategy which must be included separately.

Step 1: Contracted Revenue

When you close a SaaS contract, a revenue schedule should be built to document how revenue will be recognized over the life of a contract. Most SaaS contracts are straightforward and recognized ratably over the life of the contract, but some may be more complex if they are designed to ramp or have variable performance obligations over the life of the contract. Either way, your accounting team typically builds the official schedule.

As a side note, this is slightly different than your deferred revenue schedule. The deferred revenue schedule only includes contracts that have been billed so would miss non-standard billing terms and known rebills (multi-year contracts with annual billings terms).

Step 2: Renewals

The beauty of SaaS is the recurring revenue nature of the contracts and business. Once you sign, a contract you know the exact revenue you’ll recognize over time, and assuming your customer finds value, your company should have a high probability of renewing the contract (and hopefully upselling). This is where the term GRR (gross retention rate) comes into play.

ServiceNow ran into this complexity 2 quarters ago as lower early renewal+upsell contracts was lower than in prior periods pushing new bookings below analyst estimates.

☁ Diving into RPO Metrics

Step 3: New ACV

Once you have the value of your current book of business (contracted + forecasted renewals), it’s time to layer on new contracts.

The mechanics of new contracts are fairly straightforward, but face the greatest risks and uncertainty in regards to sales execution, timing, and macro factors (companies are less likely to buy new software in a recession).

We can see these 3 steps in action below. This assumes all contracts are signed at the first day of the month so ARR / 4 can accurately calculate revenue.

Bonus: Usage Based

Many SaaS companies have embraced Usage-based pricing as a way to more closely align pricing with the value delivered to customers. This allows customers to only be charged for what they use but also allows customers to expand organically and with less friction as use expands. While this dynamic tends to create a net benefit for the company overtime, it also adds a layer of complexity to the forecasting as companies are able to ramp and cut usage as needed.

SentinalOne missed guidance last quarter as the company assumed usage-based trends would continue at current levels even though the company was entering a seasonally lower period and enterprise spending was well-known to be decelerating.

☁ Thoughts on SentinelOne

External Forecasting

Godspeed and good luck 🤷‍♂️

External forecasting comes down to piecing together the few pieces of information companies provide in their financial statements with guidance (if the company provides it).

The important part to remember is that all external metrics are linked together to one core contract so it’s the job of the analyst to understand how everything is pieced together and how the business dynamics of each company impacts each metric provided.

Every contract goes through a similar life cycle:

Revenue = Bookings = Billings = Collections

Everything else is just timing.

There are 3 main metrics to be aware of each with benefits, downsides, and caveats:

• ARR

• Bookings/RPO

• Billings/Deferred Revenue

ARR

ARR indicates the current annual run rate revenue of signed contracts.

Many companies don’t actually report ARR but can be a powerful metric for forecasting if available.

All else equal, Average ARR / 12 should give you a close approximation of monthly revenue. This value can swing based on timing of new business, delays in renewals, FX rates (many ARR numbers are reported in constant currency), inclusion of future start contracts, and many other one-offs. These variances will be even higher when working to convert ARR to quarterly revenue.

You can see this methodology in the charts above.

Bookings/RPO

Bookings and RPO represent the value of all signed contracts that have not been recognized as revenue yet.

The derivative Current RPO (cRPO) represents only the revenue that will be recognized within the next 12 months.

Analysts also use calculated bookings as an indicator of business closed within the period.

The important thing to remember about bookings is if a company sells multiyear contracts this bookings number may not be helpful for forecasting revenue. And the complexity multiplies if the percentage of multi-year bookings fluctuates.

You can see below how consistent terms can be used to adjust the forecast, but if you mismatch between bookings and revenue recognition you can get too high or too low of an estimate.

For 1Y contracts you can take bookings / 4, and for 3Y contracts you can take bookings / 12.

Billings/Deferred Revenue

SaaS contracts are typically billed annually upfront giving analysts another methodology to estimate future revenue.

Billings face similar challenges to bookings as not all contracts have consistent billings terms and some companies bill usage in arrears instead of in advance.

Again if these trends are consistent you can still use the information but many times they fluctuate. Similar to bookings if a company bills quarterly, but you model it annually, you’ll underestimate revenue.

With Blessings of Strong NRR,

Thomas

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Move fast… and request regulations?

All new technologies follow a similar path: Build as fast as possible and ask questions later (i.e. pick up the pieces). AI seems to be moving through this hype cycle even faster than prior technologies given the immense rush of funding and the potential for significant impacts the technology has on the economy and society.

Efforts are already underway across stakeholders (government and private corporations) to make sure the technology is being used properly and protected from outside threats.

A few thoughts on:

• What is the White House doing?

• AI Attack surface map

• The 10 ways to attack LLMs

• Vulnerabilities by LLM touch points

Marc Benioff remains one of AI’s most ardent supports while calling for guidelines before we face a “Hiroshima” moment to discover how dangerous the technology might be:

Technologies are never good or bad, it’s what we do with them that matters. Nobody wants a Hiroshima moment to understand how dangerous AI is. We want to be able to kind of get our heads around the tremendous consequences of the technology that we are working with. And that’s going to require a multi-stakeholder approach — companies, governments, non-governmental organizations and others to put together the guidelines for this technology. - Marc Benioff, CEO Salesforce (Associated Press)

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Rushing AI Products carries risks… especially within security

Nick Schneider, CEO of Artic Wolf, is concerned about rushing new AI products to market, especially within the cybersecurity industry where companies (rightfully) have a low risk tolerance for new technologies:

People are rushing to get things into market and a lot of times what’s being delivered is beta or really early versions of AI within their tool sets. In certain industries, that’s fine because there’s very low risk to there being an issue or an error. But in cybersecurity where time, accuracy and efficacy of what’s being delivered to the end user is paramount, especially in the situation where they might be having an issue, you just want to make sure that what’s being put into the market has been vetted properly and has been given enough time to soak within the data. It’s a cautiously optimistic view. - Nick Schneider, CEO Arctic Wolf (CRN)

The White House Response

Lining up early support for new regulation

Tech is well known for “moving fast and a breaking things”. With the sheer power of AI and the potential to disrupt massive parts of society, governments have been stepping in proactively to head off any significant downsides.

Governments (especially the White House) are concerned across multiple vectors including labor (potential for layoffs / dislocations) and human rights (inherent biases, etc.). The Biden-Harris administration secured voluntary commitments from top tech companies to ensure AI products are developed safely and

The meeting and commitments were attended by and secured from 7 top tech and AI firms:

• Microsoft president Brad Smith

• OpenAI president Greg Brockman

• Google president Kent Walker

• Meta president Nick Clegg

• Anthropic CEO Dario Amodei

• Amazon Web Services CEO Adam Selipsky

• Inflection AI CEO Mustafa Suleyman

Why is this necessary? Because you never trust people (especially if said people are large tech companies) to grade their own homework. Derek du Preez summed up the state of AI development, potential risks, and coming battle with regulators:

If recent history tells us anything it’s that technology companies can’t necessarily be trusted to mark their own homework, when it comes to responsible development. This is coupled with the fact that fines and penalties from governments are sometimes seen simply as a ‘cost of doing business’ in the pursuit of greater market share. There are going to be serious mistakes made along the way and the real test will be how governments respond to those and correct industry along the path towards safe and fair AI use. Responsible development and use of AI is critical to all of us, but the pace of development will inevitably outpace the creation of effective legislation. Lawyers and the courts will be busy over the next decade, no doubt, but it’s also worth remembering that AI use cases are often already covered by established regulations - including human rights and labor laws. My hope is that these companies recognize that if they to be leaders in AI, building in trust for users from the start is essential. - Derek du Preez (Diginomica)

3 Frameworks for AI Risk

Categorizing High Risk Scenarios

Sandesh Mysore Anand wrote a great piece in Boring AppSec on categorizing and building a framework for protecting and mitigating “high risk” scenarios.

Sandesh noted risks will change based on private vs. public LLMs and how AI is actually being used (individuals prompts vs. integrating APIs).

I would highly recommend reading his full piece and the subsequent resources were found via is “References” section:

AI Attack Surface Map

Daniel Miessler created an AI Attack Surface map on his blog Unsupervised Learning. He stresses the risk AI poses stretches well beyond just the model and gets much closer to every day life via AI Assistants.

He concludes:

AI is coming fast, and we need to know how to assess AI-based systems as they start integrating into society

There are many components to AI beyond just the LLMs and Models

It’s important that we think of the entire AI-powered ecosystem for a given system, and not just the LLM when we consider how to attack and defend such a system

We especially need to think about where AI systems intersect with our standard business systems, such as at the Agent and Tool layers, as those are the systems that can take actions in the real world - Daniel Miessler

The 10 ways to attack LLMs

Finally, The Open Worldwide Application Security Project (OWASP) published a report on the 10 ways to attack LLMs. OWASP is a nonprofit foundation that works to improve the security of software.

With Blessings of Strong NRR,

Thomas

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Databricks’ $1.3B Bet on Enterprise AI

Databricks’ made a bold bet last month (June 26th) acquiring MosaicML for a headline $1.3B.

Here’s what I’m thinking about 👇

• What is MosaicML and how are they different?

• The importance and price of efficiency (especially with GPUs)

• LLMs are becoming table stakes

• Open source models and the importance of privacy

• What is Microsoft doing?

Overall, I see this acquisition and the early bets by large data companies as foundational to the enterprise AI space moving forward.

What is MosaicML?

MosaicML is known for its state-of-the-art MPT large language models (LLMs). With over 3.3 million downloads of MPT-7B and the recent release of MPT-30B, MosaicML has showcased how organizations can quickly build and train their own state-of-the-art models using their data in a cost-effective way. Customers such as AI2 (Allen Institute for AI), Generally Intelligent, Hippocratic AI, Replit and Scatter Labs leverage MosaicML for a wide variety of generative AI use cases. - Databricks

What does MosaicML do in English?

While the AI models Mosaic’s customers use are less sophisticated than OpenAI’s, they’re typically less expensive to run and more tailored to companies’ internal use cases, such as retrieving internal information for employees, according to Mosaic customers like Replit, a software development tool provider, and Glean AI, which makes software that tracks company spending and suggests ways to cut costs. - The Information

(Replit did a full analysis on the benefits of training your own LLM Model)

Unpopular Opinion: Your LLM doesn’t need to philosophize about how Rome fell:

The startup released its MPT-7B model in May, which cost $200,000 to build. 

“It’s not $100 million,” Rao emphasized of the price tag. “Everyone needs to get that out of their mind.”

As he put it, models don’t need to have the capability to philosophize about topics such as how Rome fell. Organizations just need to ensure general capabilities and correctness for their particular use cases. “That’s not necessarily what OpenAI has built,” he said. - Venture Beat

At $20M ARR, the acquisition was quoted at 65x… but closer to 33x at today’s pricing

In 6 months, MosaicML ARR went from $1M to $20M. Run rate growth would be hard to calculate at this scale, but suffice to say its fast and clearly the demand is there. Further you get Naveen Rao and 62 AI/ML experts in one of the hottest fields around.

In reality, the value of the deal is much less. Databricks is paying for Mosaic in stock at the same share price as Databricks’ last equity financing round, in 2021, which valued it at $38 billion, Ghodsi said. That period was the peak of startup valuations, so Databricks’ current valuation may be closer to half that. If Databricks’ share price were to be cut in half, the Mosaic deal would be worth closer to $650 million, or 32.5 times revenue. That’s well above the value put on most enterprise software deals outside AI, which lately have been done at less than 10 times next year’s revenue. - The Information

Efficiency as a competitive advantage

Efficiency has real life consequences when code starts to hit real life (silicon)

Mosaic’s models are cheaper but are also able to more effectively use GPU capacity which is highly valuable with limited industry capacity:

He also said that MosaicML was one of the few labs that have access to Nvidia H100 GPUs, which increased the throughput-per-GPU by over 2.4 times and resulted in a faster finish time. - Venture Beat

Nvidia has historically been the leader in LLM GPUs but Mosaic released a study noting the gap between Nvidia and AMD was within 73-80%:

Performance was competitive with our existing A100 systems. We profiled training throughput of MPT models from 1B to 13B parameters and found that the per-GPU throughput of MI250 was within 80% of the A100-40GB and within 73% of the A100-80GB. We expect this gap will close as AMD software improves. - MosaicML

SemiAnalysis pointed out that you really only need Nvidia at these speeds if you’re running 1000s of GPUs, giving Mosaic/AMD a competitive advantage as Nvidia GPUs are currently hard to source:

Mosaic’s stack, much of which is open source, is an obvious choice unless every last drop needs to be squeezed out with many dedicated scaling engineers for clusters of 10,000s of GPUs.

Now, MosaicML is going to be able to offer the same with AMD hardware. They have only just gotten their Instinct MI250 GPUs this quarter, but they are already close to matching Nvidia. - SemiAnalysis

Side note: SemiAnalysis is hella deep - would highly recommend if you’re heavy on the semi/hardware side

This is not Rao’s first rodeo building an ML/AI company which helps accelerate hardware. He was also the founder and CEO of Nervana which built open-sourced software and customized computer chips for machine learning applications. He sold the company to Intel in 2016.

Nervana brings expertise on the software side that can be used right away as well as designs for specialized chips that can be accelerated with Intel’s chipmaking know-how. - Fortune

LLM as Table Stakes + Importance of Privacy

Which all brings us back to the enteral life question (at least in SaaS): What is a moat?

Ben Thompson made a great point that with the simplification of models (plus lower cost and easier access to GPUs), LLMs are now just table stakes for data companies. Data remains the only moat:

It seems apparent — and frankly, makes intuitive sense — that the data storage providers see providing LLM capabilities as table stakes. At the same time, it also reinforces the idea that it’s not apparent that LLM capabilities will in-and-of-themselves be a long-term differentiator, or moat; what is a moat, though, is holding a company’s data — storage is sticky, which is to say that LLMs may attract customers, but inertia and egress costs will keep them. - Stratechery

While MosaicML focuses on open source, Snowflake has gone the opposite direction:

Neeva/Snowflake and MosaicML/Databricks also seem like the right pairing: Snowflake abstracts everything away from customers in Snowflake managed storage with a Snowflake proprietary schema, and it makes sense to add on a natural language interface on top of that (even if that interface is powered by an open source LLM, or OpenAI). Databricks, meanwhile, has an open source heritage (Apache Spark), and more of a picks-and-shovels approach; this very much fits with MosaicML which offers both open source models and tools for training data (Databricks is also much stronger in terms of storing data for machine learning). - Stratechery

Ghodsi and Rao were drawn together by the same belief that private and open-source models would be the future:

Both Ghodsi and Rao wanted to democratize AI and enable lots of people to have access to these models, both knew security was a big issue for their customers, and neither was interested in building a consumer app. This is counter to all the publicity generated by ChatGPT, the AI chatbox from Microsoft or Google’s Bard. “We’re not interested in that. We are focused on enterprises and big organizations, B2B, which is usually something very boring that no one cares about,” Ghodsi said. - Fortune

On the security front, Microsoft is planning to release a private version of ChatGPT that will cost 10x but keep data separate from other customers:

Later this quarter Microsoft’s Azure cloud server unit plans to sell a version of ChatGPT that runs on dedicated cloud servers where the data will be kept separate from those of other customers, according to two people with knowledge of the upcoming announcement. The idea is to give customers peace of mind that their secrets won’t leak to the main ChatGPT system, the people said. But it will come at a price: The product could cost as much as 10 times what customers currently pay to use the regular version of ChatGPT, one of these people said. - The Information

The fun part of any strategic analysis is figuring out where Microsoft stands and, even more importantly, what is their next move. Microsoft seems to have partnerships with EVERYONE which in the database world includes Snowflake AND Databricks. I snagged the below chart from @SouthernValue95 which was in a presentation leaked in a FTC inquiry (highlighting their’s).

I don’t have context beyond the slide, but what seems clear (at least as of FY22) is they want to clearly differentiate against Snowflake and seem more closely aligned with Databricks:

"Deliver the “Data Cloud”: Create a new data platform category by integrating databases, analytics, AI, an data governance into a comprehensive offering that further differentiates against point players such as Snowflake.

With Blessings of Strong NRR,

Thomas

Twitter | LinkedIn

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The tech-dominant index is up ~40% YTD which stands out even more vs. the ~25% gain for Nasdaq’s equal-weighted cousin and only ~19% gain for the S&P 500.

Some equate the outsized relative gains to rate hike pauses and recession fears subsiding, others are simply pointing to the massive opportunity for big tech to take outsize shares of a massive AI market:

Big tech is a “torchbearer” for the stock boom around AI, says Dan Ives, managing director at US financial company Wedbush Securities, who predicts that spending on such ventures could reach as much as $800bn over the next decade.

“Heading into the second half of 2023, we see a much broader tech rally ahead as investors further digest the ramifications of this $800bn AI spending wave on the horizon” - The Guardian

Two of the biggest tech/SaaS names announced AI-driven price increases over the past 2 weeks driving a ~$120B of market cap gains:

• Microsoft (~$100B increase). On Tuesday (7/18), Microsoft announced Microsoft 365 Copilot would cost an incremental $30/user/month.

• Salesforce (~$20B increase since list price increase). The following day (7/19), Salesforce announced Sales GPT and Service GPT will both be priced at an incremental $50/user/month and includes a limited number of Einstein GPT credits. This comes just a week after they announced list prices would increase 9% across the board starting in August (7/11).

These massive gains (also helped by the likes of Nvidia and Tesla) are leading to a “special rebalancing” of the Nasdaq-100 today as big tech has officially become too big:

The Nasdaq-100 is getting rebalanced after its biggest tech companies got too big. The move was triggered earlier this month when the companies in the index with weightings exceeding 4.5% saw their combined weighting surpass 48%, according to Cameron Lilja, global head of index product and operations at Nasdaq. The rebalance, which takes effect Monday, will cap their combined weighting at 40% in accordance with the index’s methodology.

Shares of megacap tech companies have been on a tear this year, with Nvidia’s stock more than tripling and Tesla’s more than doubling. - WSJ

Goldman Sachs Chief US Equity Strategist, David Kostin, had a quick podcast on the “special rebalance.”

More Below 👇

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Microsoft Copilot Pricing higher than expected

Satya Nadella was hyping up the opportunity for AI at the company’s Inspire event for partners:

“If you have an economy that’s around $100 trillion, we may have $7- to $10-trillion more of GDP growth driven by this next generation of AI technology,” Nadella said, describing it as “a massive partner opportunity.” - GeekWire

“Look, we are already as an ecosystem participating in a $4 trillion opportunity that could in fact, grow by another 50 percent,” Nadella said. “So that means approximately $2 to $2.5 trillion dollars of additional TAM is going to be addressable by all the innovation across our partner ecosystem.” - CRN

The $30/month price tag was well above Street expectations with Morgan Stanley noting the list pricing supported ~40% upside to their original base case:

Our original monetization framework yielded a $10 billion Base Case Office 365 Copilot opportunity in FY25 based on a 100% uplift to an ex-Copilot average ARPU of $113 (projected for FY25) for Office 365 and a 20% penetration of installed base.

A 20% Copilot penetration of this eligible installed base of users at $30/month ($360 ARPU) implies M365 Copilot revenue of ~$13.9 billion, or ~40% upside to the $10 billion Base Case from our first pass

Bottom line, today's pricing, based on the value proposition of the technology, is higher than most expected and represents a more bullish opportunity for Microsoft.

Other analysts across the street were universally excited by the pricing announcement with many increasing their price targets on the news:

Mizuho Securities analyst Gregg Moskowitz said the news of the $30 per user per month add-on for Microsoft 365 Copilot could boost revenue by fiscal 2025 by as much as $9B, assuming 20% of customers sign-up for the voluntary add-on, or $19B, if 40% of them do.

Moskowitz, who also boosted his per-share price target to $420 from $390 following the news, added that Mizuho is confident on the company's growth opportunities, including "significant" monetization of its generative artificial intelligence capabilities.

Barclays analyst Raimo Lenschow wrote in an investor note that the pricing announcement "suggests that the product is finally ready for prime time" and that it is a "major next step for MSFT’s AI monetization." 

TD Cowen analyst Derrick Wood, who also boosted his per-share price target on Microsoft (MSFT) to $390 from $330, said that he expects Microsoft's (MSFT) generative AI capabilities to offset some mixed demand data points. - Seeking Alpha

Salesforce leaning into Einstein GPT Trust Layer

Salesforce has announced a flurry of AI-related products and associated price increases over the past 5-6 weeks with a heavy focus on Trust and Governance.

June 12th: Announced AI Cloud

AI Cloud will enable sales reps to quickly auto-generate personalized emails tailored to their customer’s needs, and service teams to auto-generate personalized agent chat replies and case summaries. Marketers can auto-generate personalized content to engage customers and prospects across email, mobile, web, and advertising. Commerce teams can auto-generate insights and recommendations to deliver customized commerce experiences at every step of the buyer’s journey. And, developers can auto-generate code, predict potential bugs in code, and suggest fixes.

June 29th: Announced Sales GPT and Service GPT at World Tour London: AI Day

Sales GPT will embed generative AI in the flow of work, auto-generating customer emails, call summaries, account research, and more. Service GPT will auto-generate service replies, automatically summarize customer interactions into easy-to-access knowledge articles for service teams, and better prepare field service agents before they arrive at service locations.

July 11th: Announced 9% average list price increases

Salesforce’s last list price increase was seven years ago, and since then the company has delivered 22 new releases, thousands of new features—including recent generative AI innovations—and invested more than $20 billion in research and development.

July 19th: Salesforce Announces General Availability and Pricing for Service GPT, Sales GPT

Sales GPT is included in Sales Cloud Einstein at $50/user/month and includes a limited number of Einstein GPT credits.

Service GPT is included in Service Cloud Einstein at $50/user/month and includes a limited number of Einstein GPT credits.

As usage grows, customers can purchase Enterprise Expansion packs for Sales and Service teams.

Initially, only customers who have purchased Unlimited Edition, which includes the Einstein SKU, will be eligible to use GPT-powered capabilities.

Kathy Baxter, Principal Architect of Ethical AI Practice at Salesforce was on Bloomberg discussing the importance of AI to fight biases and how consumers need to be able to make informed decisions when interacting with AI.

Bob Evans questioned the possible competitive impacts from Oracle and SAP in a crowded market with software companies hungry to take share:

Oracle and SAP are looking to position their CX offerings as superior to those of Salesforce by virtue of their adjacency to the ERP and supply chain apps and data that are central to the app businesses of those rivals. And, Oracle in particular has been attempting to position Salesforce as a tool that tells sales managers what’s happening in the field, while Oracle CX can actually help salespeople sell more. - Acceleration Economy

We believe companies will continue to do business with partners who offer real value. Today’s business leaders need intelligence and agility to meet rapidly changing customer expectations and market conditions. What differentiates SAP and our intelligent CX solutions in this crowded field is our ability to deliver value by bringing together disparate data insights from across the entire enterprise. Whether consumers are buying or subscribing, or they already own or use a product or service, SAP provides our customers with solutions that align to every touchpoint of the consumer journey and delivers amazing customer experiences, every time.” - Ritu Bhargava, President & CPO for SAP Industries & CX via Acceleration Economy

With Blessings of Strong NRR,

Thomas

Twitter | LinkedIn

This provides an AMAZING case study applicable to CFO orgs across the SaaS industry:

• The dangers and downsides of publicly reporting ARR (or any non-GAAP operating metric)

• The importance of properly setting up your CRM

• Having a proper forecasting methodology (and ongoing revenue reconciliation process)

• Building conservatism into your guidance methodology

• The multiple impact of going from an inflecting growth asset to a “show me” story

A few notes before we begin: 1) I haven’t talked to the company, nor sell-side analysts so all details are based on the public conference call transcript and my personal experience/assumptions, 2) none of this is a reflection of my own personal outlook on the company/technology/financials.

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ARR Changes & Errors

The company made a $27M, one-time adjustment to their reported ARR metric driven by 2 changes:

1. Changing the ARR methodology to only included contracted ARR on consumption based contracts. This removes the volatility associated with these contracts (especially in unfavorable macro environment) and better matches ARR to revenue. While kind of shady to change when a tailwind turns to headwind, this is probably the correct methodology to use.

2. Fixing a subset of renewal+upsell contracts where the company included the renewal portion as incremental ARR, inflating reported ARR (contributed to majority of revenue miss in the quarter)

These by themselves are pretty shocking, but it’s when you double click, these changes appear to reflect an internal controls issue vs. concerns of a simple guidance miss:

• Revenue forecasts appear to be based on ARR forecast (forecasted ARR/12?), not a deferred revenue schedule + new ACV + renewal bookings + consumption assumption (less clarity on this, but other reported GAAP metrics did not need restating)

• ARR forecasts were based on consumption remaining flat/continuing to increase

• The ARR double counting came from a CRM implementation issue, but also seems to imply the ARR roll was not cross checked with a final contracted ARR number (again less clarity on this)

• Problems were only found AFTER quarter-end during a rev rec exercise (of all 10.7k customers)

• Public guidance was all predicated on the faulty data/forecasting methodology and did not seem to include a healthy amount of conservatism for known industry/macro factors

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Wall Street Whipsawed

The company had been signaling a strong quarter to investors through the period, leaving the street whipsawed and destroying confidence in management execution.

Wolfe Research noted the company appeared bullish to investors during the quarter:

So a lot of the feedback that I'm getting from investors is just that it seems like you guys came across pretty bullish during the quarter. And clearly, the tone is changing here on this call. So I apologize if I missed this, but I'm just trying to understand when exactly did you notice the slowdown in the business really pick up? And maybe even like when did you guys notice the issues with the historical ARR disclosure?

With the CEO responding they were surprised by the suddenness and magnitude of the consumption change at the end of the quarter:

I think generally, when we look at it, we see kind of the end of the quarter is the point where we started noticing more and more pronounced consumption changing. To us, that was a point where couple that with a couple of deals slips and suddenly, you're looking at a very different outcome for the quarter. So I think, generally, if you just look at our new and upsell target for the quarter, it was pretty much in line with what we expected. But when you couple that with that downsizing of consumption, then you just arrive at a very, very different result. And to us, I mean, once again, win rates sustained, revenue still growing about 70%. I think if you take out that consumption element, I mean, things would have looked very, very different. So that, I think, is kind of the reason where parts of the business here are really humming. And suddenly, we saw this, which, frankly, we were surprised by and we were surprised by the magnitude, and that's where we are today.

Leading Deutsche Bank to publicly ask for the CEO’s “End game” with all the volatility:

The ARR statement is very unfortunate. The environment is very tough. I think you guys have said it yourselves, and you're being asked a lot of tough questions. So I mean, as long as we're in this forum, I'm going to add to those, which I guess, for you, Tomer, most appropriately, what is your strategic end game? You're facing an increasingly hostile macro and competitive end market. You're still burning a good amount of cash. I mean, it just seems like, in a recession, you're in a bit of a tough spot. And you, yourself, I think, said in many different ways that conditions are worsening. So when you think about what you envision for the business years ago, coming to the public market versus where you are today and what you can see a few years out on the horizon, how do you think about the different alternatives out there?

Analyst notes after the call were equally concerned by the methodology changes and control issues and expect this to turn into a “show me” story for the next few quarters.

Morgan Stanley see’s SentinelOne “Stuck in the Penalty” Box:

Coming into the FQ1 print, investor sentiment in SentinelOne had been meaningfully improving. Now, we think that investor confidence will be hard and take a while to regain… Restating and revising down prior-year ARR by 5%, putting more scrutiny on the quality of disclosure and internal controls

JP Morgan expects the investors will require a couple quarters to regain confidence:

The company discovered historical upsell and renewal recording inaccuracies relating to ARR and certain subscription and consumption contracts. There were no changes to GAAP reported numbers, but the company made a one-time F4Q23 ARR reduction of $27mm, or approximately 5% of ARR. The inaccuracies have now been corrected, but we think it will take a couple of quarters of solid execution to regain investor confidence.

$1.5B of Confidence

Confidence is fickle and expensive on wall street.

Zooming out:

• SentinelOne was a SaaS darling at it’s IPO in 2021 per CNBC: SentinelOne closes up 21% in NYSE debut as highest-valued cybersecurity IPO ever.

• And if anyone needs reminding of the SaaS IPO market in 2021 you can have a lookback at the Meritech 2021 SaaS IPO Review.

• Based on data from Koyfin, NTM EV/Sales peaked at 95x in September 2021.

• How the turntables.

Zooming In:

• multiples troughed at ~5x in January, coinciding with the broader SaaS industry

• Multiples re-expanded to nearly 9x before earnings, consistent with high-growth peers as tracked by Jamin Ball

• Following the disappointing results, guidance reduction (2nd time for ARR), and metric issues, multiples have come full circle back to ~5x (below the current SaaS median)

• Now a “show me” story. Investors are waiting to see if there are any other control/reporting/metric issues and digesting the sudden change in consumption trends. Investors will want more clarity before stepping in again

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More Quotes on ARR Changes from Conference Call:

On consumption impact:

• First, some context. In the past few years, we had seen steadily increasing usage and consumption patterns by our large customers, which we accounted for real-time in quarterly ARR. However, as the first quarter progressed, we experienced a notable decline in usage, which continued in May. In light of the current macro environment, we expect these lower usage and consumption trends to persist. Due to this new dynamic, we elected to tighten the methodology for calculating ARR for consumption and usage-based agreements to reflect committed contract values. This provides a cleaner view of growth for fiscal '24 and beyond. By making this change now, we expect ARR and revenue to be more closely aligned. It should also reduce volatility in ARR compared to the prior methodology, where usage and consumption changes could have a magnified impact on ARR.

• But what we had been seeing historically was we were seeing customers that were signing up for contracts, using the data in excess of what they were committed to, renewing early, and we were reflecting that in the ARR balance. As something that we've seen going into late Q4 and early Q1 and then throughout Q1, even into May and likely into June, we're seeing a decrease in that and customers rightsizing their spend to get back to committed total. So we were seeing an outsized swing to the opposite end that we had seen prior. So what had happened was we're -- by doing this, we're trying to tighten the definition of ARR to eliminate these swings to our favor or to our detriment and basically lock it to the committed contract. So we believe that this is -- this, going forward, it's going to reduce the quarterly ARR fluctuations. It's going to more correlate ARR and revenue.

• But basically, the ARR adjustment that we've done was realizing that both we've had consumption is kind of an ad hoc element to our ARR, which basically drives up ARR as consumption goes up, but it drives down ARR significantly when consumption is not continuing to grow. In the past couple of years, consumption for us was always on the up and up, and it created that overstatement of ARR, so to speak, which created an expectation for revenue for us internally as well. So when the quarter ended, the dust settled, when he started kind of figuring out, hey, why aren't we seeing that revenue, a big part of it was the ARR was reflecting consumption that was now going down. And that impacted what we should have seen in revenue. And couple that with, again, some CRM inaccuracies that Dave mentioned as well, and that was mainly the reason for the revenue miss

On calculation error:

• As we reviewed the methodology, we also discovered historical upsell and renewal recording inaccuracies relating to ARR on certain subscription and consumption contracts, which are now corrected. After considering these factors, this adjustment resulted in a onetime ARR reduction of $27 million or approximately 5% of ARR, resulting in Q4 fiscal '23 ending ARR of $522 million.

• The reason it doesn't change any historical bookings, in terms of the other side of this, which was historical upsell and renewal inaccuracies, what we were seeing was we had upsell motions that included a renewal, and we were adding that to the historical ARR versus adding just the upsell component of it.

• This was an error in our CRM. We have fixed this and should not have that error going forward. That's why it didn't affect revenue, didn't affect overall total bookings, didn't affect cash flows, didn't affect the income statement. But what it did do is it set external expectations for what revenue should be going forward, both externally and internally, that's why we made this adjustment now.

• And in terms of evaluating the ARR, I guess, rebasing restatement. When we really were diving into that, it was because I was investigating why revenue was coming up as a shortfall. So it started out, and we did a deep dive into revenue. And obviously, you would assume that about 1/4 of ARR goes into revenue absent some churn, absent some slower deployments, things like that, that are typical. But I still, obviously, based on our Q1 results, had a shortfall. So to understand that, we did a deeper dive by scrubbing everything in ARR, all 10,700 customers, to evaluate why that was. And that's where we noticed that we essentially had an uplift that we expected because of renewals that had not been essentially moved out of the system because they were treated as upsell. So I was essentially stacking an upsell on top of an existing renewal without removing the previous renewal. And this was an error in our CRM. We fixed it, but that's where that came up, and that was obviously later on in the quarter and actually post quarter end when we really had fully identified it and been able to scrub all the customers.

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SaaS 101 Replay

DDOG Q4 Transcript

🍺 Want to be BFFs IRL? Thinking of hosting a NYC SaaS beers. Respond to this email or DM on twitter if interested!

I had the pleasure of meeting Mike Brown of Bowery Capital after he responded to a prior BreakingSaaS post.

As leaders in vertical SaaS investing, I thought Mike and the team would have great insights on how vertical software companies would be uniquely impacted by the end of low-interest rates and lengthening of investment timelines.

🔮 No doubt their crystal ball did not disappoint!

Would like to thank Pat for all the help brainstorming, writing, and editing 🙏

Hope you Enjoy! 👇

Intro to Bowery Capital 

• Bowery Capital: Bowery Capital is an early-stage venture capital fund focused exclusively on business software. 

• Michael Brown: After starting his career in equity research, Michael learned the venture business as an early-stage investor at Virgin Group and at AOL Ventures before going on to found Bowery Capital. 

• Pat McGovern: Pat started his career as a diligence consultant before getting an MBA and working as an independent advisor to early-stage B2B businesses. Pat initially interned at Bowery during graduate school and has since joined the firm full-time as a senior associate. 

• Bowery’s Focus: The firm was founded as a specialist in business software. We primarily spend time at the application and infrastructure layer leading pre-seed and seed rounds of funding. Most of the major software “buying” buckets are covered here at the firm - software sold to the offices of the CTO, CMO, CIO, CFO, and beyond. Recently we have expanded our remit to include some of the trends occurring around software verticalization in various industry cloud segments. In addition, we have been spending a lot of time in business eCommerce or what has been referred to as B2B Marketplaces.

• Past Vertical SaaS Investments: Alchemy (vSaaS for the specialty chemicals industry); Fero Labs (vSaaS for process manufacturing), Oncue (vSaaS for the moving industry); Castiron (vSaaS for culinary creators); Klir (vSaaS for water management) and others.  

• How to get in touch: If you are building or want to trade ideas feel free to reach out to Mike (mike.brown@bowerycap.com) or Pat (patrick.mcgovern@bowerycap.com).

Bowery Capital’s Investment Philosophy: “At Bowery Capital, we are firm believers in the power of vertical SaaS and have seen firsthand how its adoption has spread across a wide range of industries. Vertical SaaS is a broad term but we tend to define it as being a SaaS business where >80% of the revenues come from buyers in a particular NAICS code. As we evaluate opportunities to invest in vertical SaaS businesses, we have a few key tailwinds we look for.” 💯

1) Industry REALLY Matters

Bowery is widely recognized as a leader in vertical SaaS investing. 

Every year they publish a vertical SaaS report diving into market size, sector growth, competition, and the catalysts for adoption. 

Their goal? Game out which verticals are most promising at a given point in time. 

Check out the full report here:

Opportunities in Vertical Software

What dynamics should you watch out for? 

• Offline is the new Online. Bowery is excited about investing in software companies targeting ‘offline’ industries that are going ‘online’. Think mom and pop industries where the potential of switching to mobile unlocks significant value (i.e., in-field service professionals going from phones to mobile apps). We often see this shift being sparked by outside factors like generational ownership changes or supply chain issues (e.g., SMBs experimenting with new marketplaces to source supplies during COVID). 

• Inflection points in legacy industries. Industry-level cloud migration is a common inflection point driving a shift to vertical software. This might shock you – many industries still operate using on-prem point solutions which are 10-20 years old. It gives us heartburn too. 

Which verticals is Bowery focused on? 

• Transport & Logistics. Increased concern among companies with regards to securing their supply chain and dealing with logistics bottlenecks have led businesses to look more deeply at software to solve these challenges. We think the potential for vertical SaaS to help revamp supply chains to make them more resilient and more cost-efficient is a long-term opportunity. Examples: Kargo, Flexport, Starship.

• Travel & Hospitality. Gen 1.0 on-prem solutions are now facing increased pressure from cloud-native challengers in the travel sector. The adoption of POS/CRM technology allows companies to better understand their customers and enable automation and efficiency in light of growing labor costs and worker shortages. Examples: CloudBeds, Revinate, and ResQ. 

2) Plan Ahead to Eat Your Cake

With a narrow industry focus, it’s important to always be planning the next act. 

System of record. Your goal is to become a system of record for the business you sell to. Vertical SaaS companies often start out as glorified point solutions which solve for a few key functions. Over time they add additional features until they become an OS for a business. 

Once you provide enough value to an industry, your product becomes the ‘default option’ for that particular sector (and therefore irreplaceable). 

This expansion strategy is commonly referred to as a “layer cake” approach - this is a term coined by Bessemer and is a helpful heuristic for thinking about feature expansion.

The two most-famous examples: 

• Toast - Expanded features (POS > reservations -> payroll -> restaurant financing) but stayed locked in on restaurants as their category.

• ServiceTitan - Expanded features while also expanding categories by growing from HVAC professionals into pest management, lawn care, electrician, plumbing, etc.

Example: So you Want to Layercake your Vertical Software Company?

Landscapr, a (hypothetical) software company selling into the landscaping market wants to expand. 

Goal: The CEO wants to increase ASP. How would they do it? 

1. CRM. The company starts off as a CRM to help landscapers track track and contact leads and customers. 

2. Payments. Given its roots are as a customer engagement suite, the company expands into helping operators with invoicing and processing customer payments.

3. Scheduling. From the FinOps side, Landscapr could then expand into operations by offering scheduling, productivity, and communications (think a white-labled app).

4. Payroll. Now that Landscapr is handling scheduling, it has all the data and process to expand into offering payroll (they could also offer payroll advances to the workers supported by its payroll offering).

5. Asset Tracking + Marketplace. Along with helping management track and manage human capital, it can quickly expand into tracking maintenance and repairs on physical machinery (they may even roll out a marketplace to source new equipment and sell used machinery).

6. Financing. Landscapr could begin to offer financing to landscaping companies - it could also partner with an insurtech company to sell them business and workers’ comp insurance.

7. Pricing/BI Tools. Offer anonymized benchmarks to help users understand how to optimize pricing for their product.  

Feature Laying vs. Category expansion. Companies can also expand by offering the same feature set into adjacent categories. In our Landscapr example, they could expand into snow removal or pest control. The key is to look for sectors with similar fragmentation, seller types, and end customers. 

Bowery Note: “Founder-market fit is so important given how deeply a successful vertical SaaS platform needs to integrate with its users’ businesses - we tend to gravitate towards founders who have direct experience in the sectors they are building for as they can understand which features to prioritize and how to build them optimally for their industry use cases.”

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3) Looking for Decade Founders

Value accrues slower in Vertical SaaS. A hot data or cybersecurity company can reach a $1B valuation much faster than a traditional vertical SaaS company ever will.

Why? While many B2B SaaS companies only need to successfully launch one product or platform, a vertical SaaS company needs to engineer and develop an entire tech stack across a company. 

>10 Years. The time to exit for vertical SaaS companies that make it to an IPO is often >10 years. Founders and investors should understand that they are signing up for a long journey if they are chasing the largest possible outcome (i.e., a multi-billion dollar public company) 

How long exactly? The VERY BEST still take 9 years from inception to IPO.

Long-term founder vision will start to play a bigger role. Will this timeline lead vertical SaaS founders to go a more capital efficient path and eventually exit to PE? 

10 years + questionable economics = rise of the PE exit.  The possibility of 10 years is hard to stomach even for the most ardent founder. 

Founder game theory: 

• Can I become an OS? Every vertical SaaS founder initially envisions that they will become a platform for their particular industry. Achieving this ‘default’ status (like Toast in restaurants, Procore in construction, etc.) is typically necessary to reach an IPO scale outcome.

• Should I exit? In today’s funding environment, companies won’t be raising money without tier 1 customer economics. With an IPO-scale exit out of the question, founders need to optimize for the second best option which is frequently a smaller strategic or PE exit. Often PE firms will allow founders to roll over some equity and acquisitions usually include an equity component to continue to drive upside for founders. 

• Can I continue solo? If you realize mid-way that it’s not going to happen, it's important to reduce spend and reach for profitability. This offers the alternative of being taken out by private equity or rolled up into the leading platform, but might mean slower growth/smaller scale.

Bowery Note: “We expect speed to exit will increase as i) the vertical software playbook is more well understood and ii) ‘offline’ industries are more receptive to and reliant on vertical solutions. Nevertheless, the often lengthy hold periods observed among the recent vertical SaaS success stories is something that many seed funds who invest in this space are weighing as they evaluate the next crop of vertical SaaS businesses going out to fundraise.”  

4) $100M May Be the New NYSE Bell

Free put option is no longer available. The pathway to IPO has always been littered with ambitious founders who never quite made it. Over the past 5 years, low interest rates basically gave founders a free put option impacting a variety of behaviors: 

• Gave founders the appearance that downside was limited

• Potential pathway to IPO was much more clear

• License to hire aggressively and address competition with headcount

• Reduced need to consider long-term profitability

Now that the free-money exuberance is over, we would expect to see many Vertical SaaS companies be built for profitability and mid-sized scale in mind, rather than burning heaps of cash to try to attain (and sustain) industry dominance.

While we all want to believe EVERY SaaS company will win, the market reality remains that the majority of value accrues to a few and what looks like a dominant market position today may be competed away by another company. Not to mention a platform could eventually just give your primary product away for free. 

What does this mean for founders? 

• Historically: IPO vs. Constellation (multi-million exit). Vertical SaaS companies often ended up with two types of exits - either an IPO-scale outcome, or a sub-$10MM sale to Constellation or other aggregators of specialty software.

• Now: PE Exit. As founders realize growth funding for non-market leaders is unavailable, alternatives to the growth at all costs philosophy will arise (think $100-$500MM exits vs. $1B-$5B exits). In fact, TechCrunch was talking about the PE Exit trend back in 2018.

Key traits PE would be looking for? 

• Vertical SaaS company with sticky recurring cash flows (able to be levered).

• Moderately efficient (need to be able to squeeze cost efficiencies). 

• Mid-sized (not many PEs have appetite for whale SaaS acquisitions yet).

• Industry where cloud adoption is increasing (want an industry tailwind).

Jason Lemkin at SaaStr was discussing the future rise of the $100M exit last month: 

$20m ARR with 100%+ NRR and even just so-so growth gets a lot of folks interested in an acquisition as long as you are doing something reasonably important in SaaS.

Now it’s not always a huge multiple.  A $20m ARR startup growing 40% getting acquired for $100m ARR isn’t an amazing outcome for VCs.

And showed a pathway to a great exit even at a reasonable ARR and multiple: 

5) Investor Reckoning

Putting all these dynamics together, investors will also have to adapt to the new realities and expected outcomes.

Returns over the last few years were heavily influenced by expanding multiples and mark to market valuations as new capital flooded the market. 

As market funding dynamics return to normal, the importance of company (founder team) and industry selection will return as the key return drivers. 

Seed investors will be relatively insulated. For seed investors, exits to private equity in the $250MM-$750MM range are still quite successful in terms of MOIC. However, this same size outcome may be viewed as a bad investment by a growth investor. 

Investors need to have reasonable public valuation expectations. Vertical SaaS multiples today are in the high single digits on average - in our sample below the average EV/Rev sits at about 9x. Only a few vertical SaaS companies have achieved public scale so this multiple is probably artificially high compared to the wider universe of mid and late-stage private companies that employ a vSaaS business model.

How will this impact appetite for growth capital overall? If more vertical SaaS companies end up selling to PE in the growth stage instead of chasing an IPO, this could impact the growth appetite for this business model which could lead to a feedback loop where founders have fewer options in terms of VCs investing in their company beyond Series A.

Net / Net - Final Advice for Founders

1. Start with an industry tailwind. Understand you will be competing in a given industry for 10+ years so you want to make sure you choose an industry with catalysts (e.g., ownership changes and mom-and-pops moving to the cloud). 

2. Plan your long-term product roadmap. Every Vertical SaaS company aims to become the Operating System of a given industry. This usually starts with more modest ambitions and expands across the enterprise overtime. 

3. Be prepared to be committed. The very best Vertical SaaS companies took at least 9 years to IPO. Is this really what you want to do? Plan accordingly. 

4. Know your exit opportunities. Ringing the Opening Bell is every founder's dream, but keep your eyes open for the $100M Exit. 

5. Understand the new realities for investors. Realize that investors will want to understand your expected timeline and how your preferred exit impacts spending and fundraising strategy.

With Blessings of Strong NRR,

Thomas

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We’re doing something a bit different today:

1. Announcing SaaS 101 Live! Francis Odum (@InvestiAnalyst) and I are working on a series of education workshops combining an in-depth overview of an industry/technology/company and how to model the different drivers on the financial statements. We’re launching in beta with a free a 2-hour workshop covering Datadog.

2. Breaking DDOG Transcript. The latest full Datadog transcript was 9,000 words. Mine is 1,500 and you can read in <10 minutes. Thinking of doing these more regularly. Let me know what you think! (P.S. reading the transcript will help you in our workshop 😉)

SaaS 101 Live (Beta launch)

Diving into Datadog

ROUGH SCHEDULE:

• Track 1 (~45 Mins): Francis Odum (@InvestiAnalyst) walks through an easy framework for understanding the complex world of cybersecurity and observability. Building on this, he does a walkthrough of Datadog’s technology, business model and GTM strategy.

• Track 2 (~45 Mins): Thomas Robb (@BreakingSaaS) walks through how to model ARR, think about risks, research market multiples, and apply them to your own forecasts. Participants will be sent a google sheet to work through together live! (Note: We’ve only got 45 mins so this will be very 101 level)

• Track 3 (~30 Mins): Q&A

Disclosures: All information is presented for educational purposes only and may not reflect the opinions of the authors or their respective employers. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. To determine which investments may be appropriate for you, consult your financial advisor prior to investing.

Free DDOG Workshop

Ready to start modeling? Read the latest management commentary in <10 minutes below 👇

Breaking Datadog Q4 Transcript

Designed to cut out the fluff and only include incremental information not found in the press release or financial statements. Want the whole thing? Full Transcript

Q4 TL;DR

• Strongest new logo quarter to date with the support of a healthy pipeline.

• Ended the year with 17 generally available products, up from 13 at the end of 2021. The product suite included broadened observability capabilities and the company remains early days in cloud security and developer experience.

• Experienced larger than normal seasonal headwind finishing December, driving headwind to Q1 growth.

• FY23 Guidance assumes spending optimization of large customers continues through 2023. Management expects tailwinds to subside, but is unsure when.

• Guidance assumes NRR falls below 130% in FY23

• Getting early interest in Cloud Cost Management (signed 6-figure commit). Datadog has a lot of experience running a very large cloud operation internally and believes it can build something ambitious and differentiated.

Usage Trends & Logo Growth

• Q4 usage vs. Q2/Q3. Existing customer usage growth was overall slightly lower than Q2 and Q3. Driven by the continuation of cloud cost optimization by larger spending customers

• December usage vs. Oct/Nov. Saw existing customer usage growth in October and November at a similar level to trends in Q2 and Q3. December experienced more pronounced seasonal slowdown than in previous years.

• Large vs. Small growth. Similar to Q2 and Q3, larger-spending customers growing slower than smaller-spending customers.

• Headwinds in ECom and Food Delivery. As with Q2 and Q3, relatively more deceleration in the consumer discretionary vertical, particularly in e-commerce and food delivery.

• New Logos. Strongest new logo quarter. Record level of new logo ARR bookings. Sales pipeline remains healthy. Demand growing along with investments in go-to market.

• Large customer penetration. As of January 2023, 37% of the Fortune 500 are Datadog customers, up from 30% last year. Median deal is 100’s of thousands = large opportunity to grow

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Q4 Financial Results Commentary

Billings/RPO

• Billings. $536 million, up 31% YoY. Billings duration was slightly lower year-over-year.

• RPO. $1.06 billion, up 30% year-over-year, and RPO duration declined on a year-over-year basis. Current RPO growth was in the high 30s year-over-year.

Income Statement

• Q4 Gross Margin. Drove efficiencies in cloud costs. In the mid- to long-term, continue to expect gross margin to be in the high 70s range.

• Q4 OpEx. Continue to grow headcount in R&D and go-to-market.

• YoY Op Income. Year ago operating margin benefited from lack of in-person office, travel and event costs due to COVID policies during the pandemic.

FY23 Guidance Commentary

Key Guidance Considerations:

• Conservative organic growth assumptions compared to historical periods

• Basing near-term guidance on recent customer activity

• Incorporating seasonally weaker Q1 growth due to subdued December growth creating a lower growth trajectory to start Q1

• Assuming cloud optimization continues to affect expansion rate in 2023

Q1 Guidance

• Q1 Revenue. $466 million to $470 million

• Q1 Non-GAAP EBIT. $68 million to $72 million

• Q1 Non-GAAP EPS. $0.22 to $0.24

• Q1 DWAC. ~348M

FY23 Outlook

• FY23 Revenue. $2.07 billion to $2.09 billion

• Non-GAAP EBIT. $300 million to $320 million

• Non-GAAP EPS. $1.02 to $1.09

• DWAC. ~351 million

• Grow R&D and GTM, but slower than in previous years.

• OpEx (Ex. COGS). low 30s percent range year-over-year.

• HC. mid-20s percent range year-over-year vs. ~50% last year

• Net Interest/Other Income. ~$75 million.

• Tax expense. $11 million to $13 million.

• CapEx/CapSW. 4% to 5% of revenue

Q&A

AWS monitoring & relative growth between hyperscalers.

• Not one for one with what happens on infrastructure vs. revenue growth.

• Seeing some of the same trends where their growth slowed down throughout Q4.

• In terms of the mix shift, don't see anything that differs from the trends from prior year.

• They are also seeing optimization from their largest customers

• Expect optimization to continue in FY23 and is included in guidance

Drivers of new logos.

• Seeing greenfield/new projects/new workloads opportunity for replacing legacy and open-source tools.

• Also seeing consolidation. Customers moving to Datadog for platform efficiencies

• Still early in cloud migration and digital transformation. Not seeing a slowdown in shift to cloud.

• Larger customers that are further along in this cloud transformation that have large workloads trying to optimize because that's where they can meaningfully save cost

Product Innovation. Strategy of complicated suite vs. point solutions.

• Lots of products are early in life cycle and helping win larger consolidation deals and drive more efficiencies. Need to move further to suite, not less.

• In times of budget tightening, might be difficult to gain high level traction so working to maximize number of customers and grow ARR later

Contract enforcement. Rolling over credit, enforcing commitments, take or pay aspects

• Always work to build long-term relationship. Work to structure contracts to make a better outcome with companies facing business hardships.

• Customers tend to undercommit relative to what they eventually get to, so minimal unused commitments.

Q1 Billings comp.

• Billings can move above revenue, but not planning for billings. ARR is the main economic driver.

Growth vs. hyperscalers.

• Continuing to solve bigger and bigger problems for customers = expanding TAM

• Cloud migration/digital transformation might be a bit more of a headwind over the next few quarters, but strongly believe that it will become a tailwind again in the future.

Growth by product.

• Interesting early signs of Cloud Cost Management, which is still only billed to a handful of customers at this point but already seeing very large commitments from some of these customers.

• More focused on major products 2-4 years out and becoming a platform.

Security adoptions vs. Infra/APM.

• Not comparable to infra.

• Similar to APM – required high investment and have an ambition/differentiated approach.

• Seeing adoption by a very large customers, but still a lot of work to do

Confidence in small customer usage patterns.

• On large customer side, cloud spending is 1-2x more than observability so focus is on bigger spend

• On the smaller side, customers tend to be large customers that are fairly new to cloud adoption.

• On the very low end of customer base, see impact of the macro environment. Little more churn, which is driving down customer adds. Very small impact on overall GRR

Gross margin Strength. Pricing?

• Optimizing hosting spend on the engineering side.

• Changing prices by 1% based on that doesn't make a big difference for customers.

• Goal is to address scaling with products that provide more options to process data and align pricing with value.

2023 guidance conservatism vs. 2022.

• Continue to use same approach.

• Historically, faster existing customer growth drove better performance vs. guidance

• Assume optimization seen in last few quarters continues.

Sales capacity.

• Growing GTM capacity in-line with overall headcount growth guidance

• Still have segments and geographies to cover. Sales interactions are productive and drives ROI.

• GTM HC growth of >25% in 2022 drives capacity growth in Fy23 due to ramping.

Product splits.

• APM, logs and infrastructure monitoring at major scale today and seeing similar growth in Q4

• Some optimization happen at cloud provider level. Seeing some aspects of APM that are transaction-based being optimized

Implied NRR dip below 130% in guidance.

• Guidance implies dip below 130%

• Having headwinds in YoY NRR comps starting in Q2 driven by lower organic growth rates.

Layoffs impacting growth.

• Not seeing layoffs as a key driver of weakness. Easier to save money on cloud bills. Multiple knobs you can use to optimize

• If you’re running everything on-prem as all the costs are sunk already and confined to the future.

• Vast majority of products infrastructure usage or data volumes, not per seat. Not directly impacted by layoffs.

Cloud Cost Management/FinOps.

• The larger the customer, the more they’re interested in FinOps.

• Directly relates to the work they do in cloud optimization.

• Still a very nascent category today.

• Datadog a lot of experience internally running a very large cloud operation across all of the large cost cloud providers. Believe they can build something that is fairly differentiated there.

Early 2023 Commentary.

• December slowdown happens in most years, people go on vacation and reduce logs. Tends to rebound in January. Seeing a bit of that, but cautions reading into that given the market volatility

• Q4 was more front-loaded in terms of rev rec. Entered Q1 at a lower level driving slower sequential growth.

• Slow down has been seasonally higher.

• Early 2023 is so far consistent with 2H22/Q4.

• Already factored into guidance, assuming optimization has not stopped

FedRAMP.

• Can't give specific numbers there. Seeing wins and engagements from government agencies.

• Still need to build out GTM and the product side. More levels of certification to reach.

• proof points that DDOG has product-market fit and there is a real market

Normal optimization timeline.

• Not one size fits all

• Have seen customers having multiple rounds of layoffs having to adjust multiple times

• Fastest thing they can do to have an impact on their bills is to adjust some of the data they send in logs or in APM.

• Cloud has higher $ impact, but also takes a little bit more time because they need to reorganize some of their workloads and invest more engineering time in doing that.

• Some customers have gone through the process and started to grow again

Linearity of headwinds.

• Not including any inflection in guidance. Assuming same contribution vs. actual of last year

• Would expect a reacceleration but given macro uncertainty, can’t tell when.

Cash Flow/Payment Terms.

• FCF usually slightly higher than EBIT.

• not seen any material changes in payment terms

DDOG + Modeling Workshop

With Blessings of Strong NRR,

Thomas

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