MongoDB, Inc. (NASDAQ:MDB) 44th Annual William Blair Growth Stock Conference June 5, 2024 5:40 PM ET
Company Participants
Michael Gordon – CFO & COO
Serge Tanjga – SVP Finance
Conference Call Participants
Jason Ader – William Blair
Jason Ader
With that said, I’m Jason Ader with William Blair. Very pleased to have Michael Gordon, COO and CFO of MongoDB; and Serge Tanjga, SVP of Finance. Regulars at this conference now. I appreciate it.
Michael Gordon
Yes. Great to be here. Thanks for having us.
Jason Ader
Before we begin, I’m required to inform you that a complete list of research disclosures, potential conflicts of interest is available on our website at williamblair.com. And we’re just going to do a fireside chat format here. And hopefully, we’ll have some time at the end for Q&A. But like I said, at 5:10, we’ll cut the webcast, and then, we’ll have a little bit more of a kind of interactive discussion.
Question-and-Answer Session
Q – Jason Ader
To start out, Michael or Serge, I think maybe you’re, because Michael’s voice is going, you might want to do this.
Michael Gordon
I apologize. I’m losing my voice.
Jason Ader
For investors less familiar with Mongo, I’m sure most people here are familiar, but we may get a few that are not super familiar. Can you just give a quick history lesson on the company and tell us what pain points you saw for customers?
Serge Tanjga
Excellent. I’m happy to start. So we play in the database software space that’s $80-plus billion market as per IDC last year, one of the largest markets in software. And then also interestingly enough, still a very fast growing market, still a double-digit growth market.
And the reason why that dynamic is in place is because of many of these things that you’ve heard over the years, such as every company is becoming a software company, software is eating the world, competitive advantage in every industry is increasingly dependent on the software experience that they provide to our customers.
And that market is dominated by 50-year old technologyknown as relational or SQL, it’s literally 50 years old. I think the original paper that created it was either 1969 or 1971. So, a technology built before mobile, built before Internet, obviously before cloud and obviously before AI.
And we, among others were founded roughly 15 years ago to address some of the challenges of that technology that became increasingly clear to developers. And those are the legacy technology is not terribly developer friendly. It’s hard to work and reason about and doesn’t work well with modern programming languages.
And then generally, the second bucket is performance. It’s hard to scale. It’s impossible to scale horizontally. So, it’s expensive to scale vertically. It’s hard to create performing and globally distributed applications. And as applications that we’re used to become more and more demanding, these challenges become more and more apparent to developers and ultimately to the customers.
We were founded in 2007, because our founders were developers who are working with relational databases and realized all these challenges and realized that that’s not a tenable state of a large industry in the long-term. We were founded on a completely different paradigm than those of the document model, which solves the pain points of the customers that we just described.
We’ve been around for 15 years, went public in 2017, did roughly $1.7 billion of revenue and guiding to roughly $1.9 billion this year. Call it, roughly 2% share in this market. That’s fundamentally what gets us excited about this opportunity, which is that despite the amount of success that we’ve had so far, the traction that we have in the market, the opportunity ahead of us is still enormous.
We think of ourselves as the general purpose solution, i.e. all but some of the niches use cases can and should be built on MongoDB and we have this great opportunity to continue growing and gaining market share over the years.
Jason Ader
Okay, great. Thank you. I’m sure in everybody’s mind, what happened in Q1.
Michael Gordon
Yes. Thanks for the question. For those who know, we reported results last Thursday. It was a more mixed quarter for us. That’s not normally our pattern. There are plenty to talk about, particularly to call out some of the unusual things. Maybe just to run through some of the summary key metrics, 22% year-over-year growth on a top-line.
Our database-as-a-service offering, which we call Atlas, grew at 32% on a year-over-year basis. That’s now 70% of the total business, up from virtually nothing at the time of the IPO, just to give a sense to folks. We beat the high-end of our guidance, although as I’m sure we can get into, not by as much as you all are used to us beating it by. And so, we can talk about what the surprise is positive and negative for us within the quarter and run through that.
If you think through working the way down the P&L from an overall standpoint, outperformed. Most of the revenue outperformance flow through to the bottom line, 7% margin on the bottom-line in the quarter and generally upside surprise there as well.
I think the key thing that we look about we think about the business and the way that we’ve talked about the business for the last several years now is helpful just to remind folks, maybe ground them as we talk about Q1 and sort of the business going forward. It’s really helpful to think about the business in terms of the growth in consumption of applications we’ve already won. Those are sort of existing workloads that we’ve already won from our customer base.
Separately, to think about new business that we win in the quarter and that new business can be from additional workloads from existing customers or new logos, someone who is paying us for the first time.
The dynamic that we’ve seen for about two years now on the existing applications is slower growth, impacted mostly by the macro slowdown. We talked about this a couple of years ago. We were one of the first to point this out to see what we see. What really drives the equation there is, we have a consumption model from a revenue recognition standpoint.
As people use and consume the service, that’s what triggers the revenue recognition. That consumption is very closely tied to the underlying usage of the database. If you think about the reads and writes, the transactions at the database level, not transactions like e-commerce transactions, but like the actual transactions at the database layer. That’s what drives the underlying consumption.
And so, what we’ve seen is, we’ve seen that grow more slowly, reflecting the macro environment, it’s sort of not necessarily maybe macro in the way that you think of it. I can’t ascribe a coefficient to GDP versus labor force participation versus inflation, et cetera, et cetera. But it’s all related to the underlying actual business activity that’s occurring in this broad set of workloads across closing in on 50,000 customers. It’s fairly diversified across industries, geographies, use cases, et cetera, et cetera. So, that’s sort of the dynamic on the existing workloads.
And the growth of existing workloads just given the size of the company now, as Serge said, had a little over $1.7 billion last year, is the greatest factor in the short-term of results. Over the long term, the most important thing is how many new workloads we win, given that we’re low-single-digit share in a $80-plus billion market.
But in the short term basis, so in Q1, what we saw on the growth of existing workloads was we saw slower growth than we had expected. So we guided at the beginning of March. So obviously, we had February in the bag. And so February was an actual.
So no surprises, positive or negative in February because we kind of knew what that was when we guided. What we’ve typically seen over these last few years is a seasonal uptick in the underlying usage and consumption in March and April. And we did not see that rebound to the extent that we typically do and when you go back and look on a year-over-year basis to quarterly underlying usage that we grade volume in Q1 it did grow at a slower rate than it did a year ago. And so that’s sort of what we’re talking about in terms of sort of the incremental macro impact on existing workloads.
As it relates to new workloads, despite sort of some of the challenging macro conditions over the last couple of years, we’ve been able to execute well against that front. We’ve been able to continue to win new business. We have this as Serge said, very large market. We have a value proposition that’s very strong and resonates within the market. And then in general, the team has been able to pair that with really solid execution to sort of win despite more challenging environments.
And so, we haven’t talked about over the last couple of years or sort of this sales cycle elongation, deal slippage and other things that some other companies have had to talk about. So in general, the new business side has gone very well for us. We did talk about sort of one foot fault self-inflicted miss on Q1, such as the new business wasn’t as strong in Q1, not having anything to do with market factors or anything.
But we just in the part of our annual cycle, planning cycle of signing quotas and comp plans and territories and promotions and everything. We just were a couple of weeks slow on that. So just kind of slow start to the quarter. But again, that’s sort of a very specific thing as it relates to the new business side. So there are a bunch more to talk about, maybe I’ll just stop there. But at least wanted to try and get into some of the things that will likely be on people’s minds.
Jason Ader
And the other thing that seems like I’m I don’t call it self-inflicted or something that was unexpected was the sales comp change at the beginning of last fiscal year. Can you just talk about what you guys did there and how you’re tweaking that now?
Michael Gordon
Yes, sure. Happy to talk about that. So we’ve been on a multiyear journey since we probably first started this in our fiscal ’21, which would have started in February of 2020, to move towards a more consumption oriented model and less focused on maximizing upfront commitments. And there are really a couple of different theories around this or a couple of different drivers of the approach, but the core of it is we have this very large market. We have a relatively small footprint of sales coverage relative to the opportunity.
And particularly, when you think about negotiating a contract, Jason, if you’re about to launch an application and my incentive as a sales representative is focused on maximizing your commitment. You and I are going to do a whole bunch of rounds, where we’re going to negotiate and trade red lines.
I’m going to try and get you to commit to a higher spend level than you are used to or want to. You don’t even know because it’s a new application and we’ll go back and forth. I’ll waste all this time on this. None of which will change what the application does or how much usage the application actually consumes.
And so, we wanted to be on this journey to reduce that upfront friction. It has the very specific and strategic purpose of synthetically expanding the capacity of our sales force, right? Because I can increase velocity, do more deals with less time spent per deal that will allow us to capture kind of more market share. In general, over the last several years, that’s worked exactly as described and intended.
This past year, in fiscal ’24, we took sort of one of the final kind of key steps for that, which was you remove the incentive for sales representatives, for selling one year commit deals. Again, the final piece of that is, in that focus on workloads and let’s focus on workloads and let’s not worry about big commitment deals that could span multiple workloads, but let’s go win as many workloads as we can. What you saw over the course of fiscal ’24 is we actually had a great year in terms of the workloads.
What Jason is referring to is, when we looked and started analyzing the data. Obviously, one of the challenges when you’re getting data off of quarterly cohorts is, you need to give them a little bit of time to sort of play out and see where the cohorts are growing. Those cohorts started off at sort of expected and grew at expected levels for the first couple of quarters. But what we’re starting to see as some of those cohorts were growing.
The growth was slowing sooner than normal. They weren’t exhibiting the typical sort of pattern or trajectory. One of the things that we did or observed is that, in removing the that sort of final incentive around upfront commissions.
Again, if Jason and I are negotiating a deal, if I’m not looking for a commitment, I’m going to lose a whole bunch of information, or not have access to a whole bunch of information, not gain a whole bunch of information that Jason is going to disclose to me as we’re going back and forth negotiating.
And so, I just kind of went in and grabbed a handful of workloads. They went on not having sort of what I’ll call kind of like a representative portfolio of growth. But change that we made coming into this coming year was to be a little bit more prescriptive and not just ensure that you’re looking only for workloads and to recognize that not all workloads are created equal, but be a little bit more prescriptive to say, let’s get some that look like, A, let’s call these T-shirt sizes, some mediums, some largest and some extra larges rather than just saying, a bunch of mediums or better.
These are minimum dollar spend thresholds that qualify as workloads. That was a tweak that we made, based on what we were seeing and we’re constantly iterating as we’ve done over these last four or five years in terms of the go-to-market, but that’s sort of specifically what was happening there.
Jason Ader
Was that change made at the beginning of Q1 or the beginning of Q2?
Michael Gordon
We make all the comp plan changes at the beginning of the year. They get implemented effectively in Q1. But those are — and part of the reason why you do it is comp plans are annual plans. You’ve don’t always have all the information that you need at the time, but you’ve got sort of your one bite a year to have a chance to go and do that.
Each year, we take our best guess and say, what are the tweaks that we want to make this year to try and improve the outcomes. Obviously, some of the incremental data after comp plans rollout, you get incremental data. And so, we continue to get data and I think it’s sort of verified that this is a good change to make.
Jason Ader
Is that one of the reasons why it took a little bit longer to kind of get rolling on the comp plans?
Michael Gordon
I wouldn’t quite directly link them. To me, I think about it more in the context. Fiscal ’24 was the first year that we had a workload oriented, model plan and all the associated territories and everything that goes with that. But we didn’t have any data, which means there’s really not much to debate. This past fiscal year was the first year that we got new data.
And so is and the part of doing comp plans and everything for fiscal ’25, we issued some data to debate and try interpret and get into. And so it’s not exactly related to what you’re describing, but in general, that was an adjustment period that we just took too long to do that.
Jason Ader
And I guess, what’s your sense? I mean, what’s your confidence level that the new comp plan is sort of Goldilocks?
Serge Tanjga
So maybe I’ll say they say a couple of things. Number one is reality is we won’t know. We won’t know for about a year because what Michael was saying, the workload cohorts we acquired last year is exceptionally strong in volume and the original growth characteristics were aligned with our expectations. We got to give it a little bit of time. Maybe one encouraging factor was that it was a very easy change to roll out to the field.
Once we sort of have the sort of the new matrix in terms of the types of workloads that you need to go after. The feedback that we got was no questions, no concerns, not a tremendous amount of angst. More just like, I get it. Now let’s go do it. So that’s incrementally encouraging, but the reality is it will take us a couple of quarters.
Jason Ader
And then one question that’s come up has been, is it possible that the reason you’re seeing kind of lower quality workloads, because a lot of the higher quality workloads have already been you know, picked below hanging fruit kind of metaphor.
Michael Gordon
Quality is a word I tend not to use in this context just because it can mean so many different things to different people, but I think I understand, like, the point of the conversation or question. I don’t find that plausible. And first of all, no one internally has offered up that as an excuse. And secondly, if I even were trying to think about it, it’s just in a market that’s $80-plus billion growing at $10 billion to $12 billion a year.
When you’re a 2% market share player, that’s just sort of not credible that like all the good ones have been bit, right? Like I think about the conversations we have with our customers, the workloads that we win every year, the pipeline, the conversations that we’re having, and I just I don’t find that incredible. I think it’s more just sort of accidental based on not having as much insider intelligence into kind of what was behind it as a result of removing commitments.
Jason Ader
And then two more sort of devil’s advocate questions on like what could have gone wrong. One is sort of the sort of AI pause where customers are just taking a step back. And I think I know the answer to that, but I want to propose that one.
And then the second one is the idea that maybe there’s a competitive issue with some of the hyperscalers, especially they are your number one competitor, I believe, correct? And customers are coalescing around their GenAI development on those hyperscaler platforms. So maybe that’s sort of creating some halo effect and sucking in some market share. How do you think about this?
Serge Tanjga
Yes, let’s take those two in order. So first, this AI distraction factor, let’s call it that. So we think that that’s a plausible theory. And just the question is like what’s the magnitude and how would it affect us. So let us first tell you what we see. We see tremendous amount of interest in AI in our customers and it sort of spans the gamut from the developers all the way up to C-suite.
So at the C-suite level, there’s focus and pressure, sometimes even from the Board to come up with an AI strategy. There’s a tremendous amount of sort of confusion because the space is so rapidly moving and it’s sort of like a blank canvas and it’s really started to hard painting on it. We see based on the conversations that we’re participating in, a meaningful amount of mental energy is going into this this conundrum, if you will.
On the developer side, we see them tinkering. We see them building proof-of-concepts. We see them wanting to learn the technology because it’s new to them as well. Most of the pieces of the AI stack didn’t exist a year ago. And then, on top of that, they want to build applications to start to test in terms of how well do they work and can they be scaled, can they have the kind of ROI that they would need to actually productionalize.
In other words, we see focus on AI across the board. Could there be some amount of distraction in the version of focus? Absolutely. But going back to — if that were the thing, the way it would impact us, it would impact our ability to win new business. Meaning that, developers and their bosses were not building as many traditional new applications if you will, because they were focused experimenting with AI. We don’t think that, there will be a plausible reason to impact our new business.
What do I mean by that? Again, what Michael and I were talking about, it’s an enormous market. We have 2% share. Even in terms of incremental dollars, we’re still in the mid-single-digits. This AI distraction factor would need to be enormous. It would have to be something more like freezing or paralysis in order to shrink the available market so much to us that we couldn’t reach our target.
Dave said it on the call, you wouldn’t accept that as a plausible explanation or excuse, if somebody offered it to him for our new business performance. Our new business performance in Q1 is sort of we stubbed our own toe. We haven’t changed our assumptions for the rest of the year, even this AI distraction factor should not be distracting enough to impact our ability to win new business.
Michael Gordon
Before we go to the competition point, let me just try it back for people simplistically. If remember, I talked about two factors in terms of driving the business, right? Growth of existing workloads that we’ve already won and then new business. Even the hypothetical of sort of an AI distraction factor has no bearing on the consumption of existing workloads, right?
And then secondly, it’s not been a factor in our new business to date and you’d have to make some pretty herculean assumptions. Doesn’t mean that, it can’t be what other people are saying or maybe others folks or business lines that aren’t as where AI is more orthogonal to what they’re doing or they’re a lower priority, but it’s not what we’ve seen.
Jason Ader
What is the split between the [indiscernible] within ARR between the account growth in the existing workloads versus the new business? Have you disclosed that split [indiscernible] every quarter?
Serge Tanjga
We have not. But conceptually, if you freeze your time at any given moment and then look at it before and after, the workloads that you have at the beginning of that period drive a significant portion of the growth in the near-term, but those slow over time because they age. You need to be adding incremental workloads and those present a larger and larger percentage of growth the longer you go on.
Michael Gordon
Yes. For those who haven’t seen it, we walked through this and you picked the chart in our Investor Day back in May. That sort of gives kind of an eight quarter view and says, okay, here’s what the installed base does. If you are going to assume stable growth over the time period, ignore seasonality and like those things.
As the base matures, you’ll see a slower and slower growth rate, then you layer in workloads that you win in a given fiscal year, if we just can train, it can say that fiscal years and then in year or two will have an impact. But the base is obviously, especially at our revenue size and scale in the short term is the biggest factor.
Jason Ader
Yes. But I mean, in a given quarter, the vast majority of the business has to be coming from existing workloads, right?
Serge Tanjga
Right. But within that, remember the prior years is a meaningful portion of that because those are the ones that are still they’re large enough to matter and still growing quickly.
Jason Ader
That’s what they’re considered new business.
Serge Tanjga
Well, if it’s last year’s, it’s considered old business. But in the base, I think the difference, how old is it matters.
Jason Ader
And then the competitive question?
Serge Tanjga
Yes. So we’ve heard this one come up a lot, and sort of the maybe to paraphrase or repeat the question is something along the lines of the cloud providers seem to be ahead of the game in terms of the AI pieces.
They have more of them or all of them. So, could they be winning all of those workloads and potentially even non-AI workloads because they’re giving customers incremental sort of comfort to standardize on their platform, use just get out of the box solutions and therefore you are seeing it in your news business or rather that’s why your news business is hurting, because stuff you would’ve gotten in the past, you’re not seeing anymore, because the cloud guys are boxing you out. Is that a fair sort of summary?
So we sort of come at it in two ways. One is what we hear from customers when it comes to AI. So, well, sorry, before we go there, we are effectively saying is cloud guys are bundling and boxing you out. And bundling has always been the strategy of the cloud players, right? They have the benefit of business walks into the door for them because they’re there to buy infrastructure and they’ve been using that for the case of AWS for a decade already to sell them other stuff.
That other stuff is database layer of any of the other 200-plus services that they sell, that’s been the bundling strategy. And obviously, we’ve done reasonably well. We would argue over the years against that bundling strategy with our best-in-class platform independent approach. We’re fine, but maybe the world is different now. Maybe AI is the extra sort of secret sauce that makes bundling that much more competitive.
And what we hear from customers makes us doubt that or not believe that. And the reason why I would say that is, number one is by picking a platform, even the one that seems meaningfully ahead of everybody else right now, you’re sort of betting that they are the technology winner in perpetuity, which let’s be honest, this AI cycle is what 15, 18 months old. You all are students of technology cycles and you know that the early winner is sometimes the winner and sometimes not. And so by locking yourself in, you’re foregoing that type of optionality.
So the second piece associated with it is that, you are increasing your dependence on that player. Like, IT departments and CIOs know what the cost of a lock in is when one vendor has more of your business, they have pricing power overdue in the long-term. And that’s generally been the reason why they have multi cloud strategies. And one of the reasons that we’ve been successful is sort of an independent player. So now you’re tripling on your on your platform lock in if you start building your entire AI stack there.
Then the final one, a nontrivial one, cloud providers are offering proprietary solutions, not open source and they’re offering solutions to customers to us tell us are expensive. So ROI is one of the reasons why a lot of the development is happening at a lot of the AI action is happening at the infrastructure layer but hasn’t yet quite made it into large enterprise application layer, because some of the pieces of the stack are reasonably expensive.
So you are foregoing the cost optionality if you’re going with the all the bells and whistles player, which the cloud providers is what they’re bundling currently. Maybe we’ll come at it in another way, which is, let’s just say that that’s true.
Let’s just say that it is in fact true that we’re being boxed out of more deals and seeing less new business, because the cloud providers are keeping it more to themselves. We both partner and compete with them and it stands to reason if they were more successful in competition, they’ll be less inclined to partner.
And like historically, we’ve talked about how those relationships ebbs and flow. But what we see currently is our cloud provider partnerships are stronger than they’ve ever been. That’s not just executive-to-executive, it’s all the way down the field. To kind of do it bluntly, if they were killing it with their first party services, we would be calling them to partner on deals and they wouldn’t be picking up the phone or they wouldn’t be calling us or they wouldn’t be releasing marketing funds to go help us win incremental customers together and we’re not seeing that. We’re deducing that this is not happening, but obviously, we’re very mindful of it and keeping an eye on it.
Jason Ader
Good. I think maybe one more question before we wrap-up the formal session. This is kind of a couple of parts. I’ll try to make it quick. But I know you guys are using or thinking about using GenAI to help the migration or help accelerate the migration of relational databases to MongoDB or relational workloads to MongoDB.
My question is, there a risk that with Gen AI, it just becomes easier in general to switch out the database? Therefore, we’ve always known the database to be this super sticky space. Does Gen AI kind of fundamentally change the stickiness factor of databases?
Serge Tanjga
That’s a good question. We are certainly working hard to reduce the stickiness of the relational database. That’s been one of the key inhibiting factors that are gaining market share, which is that for all their challenges, databases and relational databases are exceptionally sticky and taking these like in some cases, decades old application on your platforming. It comes with a lot of labor and a lot of risk.
But your question is fair. If that sort of friction can reduce why wouldn’t all friction reduce, effectively? And to which we would say when they make it much more likely that the best database wins. And then you’re back to performance, ability to scale, distributed data, developer friendliness, all the things that AI fundamentally doesn’t change. We think that’s leveling of the playing field only improves to the benefit of a 2% share player, who happens to be best-in-class.
Jason Ader
Okay. I guess, we had 30 seconds left. Any questions from the audience? Last 30 seconds.
Serge Tanjga
I’m happy. I’ll repeat the question. The question is how do large language models change our business and maybe our industry? I would say, generally speaking, we think of the benefit of large language models or AI to kind of come in three flavors for us.
First, large language models will make developers more productive, writing applications will become easier. We think that will result in more applications being built than they would be otherwise. Our entire industry is going to grow and the rising tide will help all boats including our own. That’s bucket number one and sometimes easy to forget because it’s relatively simple.
The second one is, specific AI-powered applications will be built over time. We are seeing some early signs of that, although it’s relatively early. But back to some of our prior conversations, we expect that the database underlying those applications will need to be flexible, able to handle differentiated types of data, perform and distribute and so forth, which means, it’s more likely that we win it versus the relational alternative.
The final piece is, maybe related to the first one is, we believe that, we can use large language models and other tools to make it much easier to re-platform the existing estate of applications onto a modern platform like cars, which means that, the part of the market that is accessible only as applications reach end of life, becomes more accessible in a shorter period of time. That’s also another thing that we’re working on and try to benefit from.
Jason Ader
All right. I think we’ll cut the webcast now. And, thank you guys for being here. Thank you, everybody, for staying late.
Michael Gordon
Thank you for having