AvePoint, Inc. (AVPT) Earnings Call Transcript & Summary

Good morning. Thank you, everyone, for joining us at the Goldman Sachs Communacopia & Technology Conference. I'm Gabriela Borges, I head up the emerging software vertical here at GS. I'm delighted to have on stage with me TJ, Co-Founder and CEO of AvePoint, and Jim Caci, CFO. Thank you so much for taking the time.

So TJ, I'd love to start with a little bit of the history of the company because 2001 had deep expertise in building this company over that time. What has stood out to you about the way the industry has evolved since 2001? And help level set us a little bit, where does AvePoint sit in the enterprise technology stack relative to the ecosystem of Microsoft and some of the other companies that folks are familiar with.

Well, great question. So the industry go through this cycle of centralization and decentralization. So since 2001, we actually now have gone from the distributor model to now the cloud, which is a centralized model. And more importantly, recently, obviously, with aggregation data and massive increase in compute and cheap cost of storage, we now have generative AI, large language models, that's driving further growth. So we have seen tremendous change in the dynamics of enterprise environment, we have been helping our enterprise customers managing their information for the last 20-plus years. So we get to know all the legacy data stores, all the different data silos, hybrid deployments, multi-cloud deployment scenarios across enterprise. So we were able to invest early, that's kind of the luck part of it, into Microsoft Cloud back into [indiscernible] days and build that sophistication and maturity globally for the audience. And also because we started in the enterprise content management space, which historically has been very large enterprise and focused on highly regulated industry and public sector governments. That was, by nature, a very high-touch experience. So we had to expand to 18 countries and serve those customers in person. Fast forward to now, we have a global platform. So we're in Asia, we're in Western Europe, we're in U.S. and North Canada expanding to Latam and Middle East and Africa through our channel program. So we are able to basically cover the global market, but also very importantly, extend the segmentation coverage from just large enterprise regulated industry in our medium to small businesses because of SaaS nature. So more accessible, software deployments to the smaller customers because they don't have to install, maintain, worry about security. And very importantly, out of the SMB segment, there's also managed service providers, which is a new trend as well in the last few years. That's the fastest-growing segment for us. What that is, is essentially small to medium businesses don't have IT. They are now aggregating IT expertise by these consulting companies, and they are then providing the management capabilities for businesses. Of course, the MSPs industry vertical is also consolidating. So we also see very large MSPs, global MSPs and we actually also recently signed up a few of them as well. Crayon is one such example, SoftwareONE's another, they have global MSP practice. So that's the new development. Of course, the latest evolution as AI, my background is in machine learning and AI. So it's near and dear to my heart. And machine learning is no stranger to AvePoint. We've been actually a long-time consumer of cognitive services, which is in Azure, which is basically the front-end API to Open AI, and we have done great projects, especially in Singapore, around EdTech, around GovTech, around leveraging and machine learning AI. But with any AI initiatives, 80% of the workload is information management, is data cleansing, data classification, data consolidation, aggregation, analytics. So when we talk about AI-ready information management practice and that's where we now see massive demand as every enterprise is racing to build proprietary data models to better their business on top of the large language models. So those are some of the kind of evolution key points in the last 20 years.

So let's stay on that last point. AvePoint as an AI-ready information management practice.

That's right.

Tell us a little bit about what that means in practice, and what are some of the conversations you're having with customers that will help them be more AI ready?

That's right. So if you take open AI and even Google Bard, right? When you actually interact with it using parameters, it behaves very much like a very good summer intern. And the issue with summer intern is that it doesn't have domain-specific knowledge nor knowledge about your enterprise. What makes a difference for Actionable AI, for companies is to be able to leverage the decades worth of domain data that your company have accumulated and build another layer on top, right, of the Generative models to make it more actionable and purposeful for your business. Even something as simple as handling inbound customer requests, right, customer support, case queries, Tier 1, Tier 2 and auto routing. But the foundation of AI is, clean data in, actionable data out. Otherwise, you have trash in, trash out problem. If you have out of date, trivial, redundant data, then you will have more cases of incorrect answers coming out, right? This in our industry called hallucinations, right? So that's something that you have to try to control. So what we do then is because we have been in this business of information management, enterprise content management, started from there, we understand data from all different data repositories, whether it's Fileshares, Documentum, e-mail systems, OpenText, chats, right? Slack, teams. We're able to help companies aggregate that data and be able to correctly classify, label and then tie a data life cycle and governance capability to it. And very importantly, also access control, who has access to this set of data internally and externally and then make it actionable. So for example, we actually mentioned this at our latest earnings, we're able to help Australia government agencies achieve 20x productivity improvement by properly labeling all these information records that they put in cloud and then make it available to the public. And then they're now in the process of building AI models. I was actually just in Singapore last week talking to GovTech which is their government technology arm. They actually said to us for all their government agencies, they don't want to use cloud, right? There's this data segregation issue and data security issue. So they want to build their own LLM, large language model, so on top of their data. And wouldn't you know it, vast majority of the unstructured data that the government of Singapore stores is in SharePoint, which is where we came from.

Of Course. Yes.

So they say, "Hey, AvePoint, can you come in and help us build a LLM on top of all the SharePoint data stores across government?" We're like, yes, that's a fantastic project. And the premise is that asked was they want to improve the search capabilities across enterprise unstructured data. So it's basically how to find things better and also leverage machine learning AI to actually draw more context of your domain-specific knowledge. So these kind of asks are coming in fast and furious and this is where we talk about AI-ready information management. Otherwise, you're going to have trash in, trash out problem across your proprietary data set.

So It's really interesting context. And this is a question for Jim as well. How do you compare the potential deal size for an enterprise customer that is working through some of the quality data in, quality data out problem? How do you compare the deal size of one of those engagements today to a typical engagement if we were just to take your ARR and divide it by a number of customers? In other words, because customers are going through this life cycle, what are the implications for your normalized growth over the next 3 to 5 years?

I think that question is for you. You can go first and I'll go.

So when we -- it's interesting, if we -- maybe to break that question down into a couple of pieces. First, we have three segments right? In terms of -- we have enterprise customers, we have mid-market and we have SMB. And so if you looked at our -- if you just took our ARR and divide it by the number of customers, that probably wouldn't give you the right picture in terms of an average because we really have those three segments. And when we look at those three segments, the average ARR per customer is different per segment, as you would expect. Our enterprise customers are generally larger, our mid-market is a little bit less, and then obviously, the SMB customer would be our smallest. But kind of what your -- so that's the first setup for us. And then what we're seeing, as you talk about kind of the projects or what we're going to be doing for some of these customers, that's really where we're first seeing it is in the enterprise or the government customers that TJ alluded to. And those projects generally are larger and some of those projects not only include our basic software, but then are including services as well because they're really looking for something beyond even what the current software would allow for. And so we're seeing those projects being much larger in terms of dollars and scope. But they're really -- it's interesting because those projects then lead to future development and almost our R&D projects because it's helping fund then that knowledge gained is embedded then into the next generation of the software that we're producing. So it's a nice -- it's helping those enterprise customers kind of get to the next level, but then also creating software for the not only enterprise group but the mid-market and SMB will benefit from that same knowledge that we gained and we provided to that enterprise customer and now make it available to everyone.

Yes. So my commentary to add is we are working to increase the ARR per customer across the segments. The easiest to think about our Confidence Platform, it's a SaaS. We are actually today, Microsoft Cloud ecosystem's largest SaaS data management governance player, so our Confidence Platform is that layer. Now we're layering on top of that additional vertical solution capabilities. So information -- AI-ready information management is such vertical capability. Of course, we also have EdTech, that's training management, digital assessment and learning management also on top of this data layer platform that's fully integrated with teams, with Office Cloud, that name is [ Dynamics Swagger ] which is the CRM cloud as well as Azure, which is the compute cloud. Today, we're actually a 8-digit consumer of Azure. That puts us already -- actually, we just met with a Microsoft senior leadership in Silicon Valley two weeks ago, we're top 24 of their global 240,000 product partners, and we're top 3 of their global 400-plus education product partners. So in this multi-trillion dollar ecosystem, we have the sophistication and maturity that we built up over the last 22 years. We know how to really navigate and provide additional strategic value within the Microsoft ecosystem to help customers ultimately, this year is the theme of optimization, ultimately to optimize and maximize the return on investment on top of Microsoft Cloud.

So that leads to two interesting questions. I think the first is you do know Microsoft incredibly well. And so I would love to get your thoughts being one of their close partners, how do you view your strategy as being complementary to Microsoft's strategy as they continue to invest in what is also arguably an AI-ready information management practice?

Yes. So to play in a hyperscaler space, whether it's Microsoft or Google or AWS, the role of a strategic vendor partner is to add value to the customers, to enrich that ecosystem. We all say the hyperscalers -- they will continue to improve the baseline infrastructure, right? So when they have the Syntex release, when they have this base-level API augmentation improvement on backup, for example, we will leverage our services. It's akin to utility company layering bigger pipes in the street. At the same time, as you improve your house, right, your digital transformation projects, you still need to go to prime contractor like AvePoint to then go help you improve that, actually services and capabilities that actually land Microsoft Cloud into your organization and enterprise. And that's really the analogy here. And Microsoft, when they -- their latest partner conference to talk about, again, this whole construction and interior design kind of analogy of every layer have different type of the C-suites and then the director level and then across different departments, there's different capabilities that we can bring to bear on top of the Microsoft platform and this is where the value add that we bring to the table. Having said that, the world is multi-cloud. We have actually a big conference -- industry conference called SHIFT happens in D.C. in October. If you say fast enough, it also means something else.

No, we got it.

But that's technology, right? So it's actually established as an industry conference where we have customers, industry expertise. So we will talk about that. We also showcased some of our AI capabilities on Google platform. So we're actually going to showcase a lot of new products, the capabilities, leveraging AI across a multi-cloud fashion because our customers by definition, are multi-cloud. There's very, very few customers that's only using one hyperscaler's environment for the expressive purpose of business continuity and also data sovereignty, as the world also increasingly going towards anti-polarization, more localized approach, multi-cloud, multi-vendor approach and also data sovereign hybrid deployment, that complexity favors vendors like us that's been there and seen it and done it. So I think those are good macro trends, tailwinds for us.

So the second part of the question is, your comment on helping customers optimize their usage with the Microsoft ecosystem. And I think it's especially pertinent in this environment where we've heard so much about optimizing IT costs. And so talk to us about how that's a part of your go-to-market? What are the types of savings and what are the types of costs buckets where you're helping customers? And what is the order of magnitude of savings that you're helping customers by?

That's a great question. I think there's multilayer approach to cost savings, right? Even look at just within the Microsoft Cloud context itself, you have different licensing types on Office Cloud, right? There's F1, E1, E3, E5. And the magnitude, price differentials could be as big as 4, 5x, right? And then now you have Copilot, which is another whack at a $30 per user per month. So that, will effectively double your spend if you're already at $30 per user per month spend level, which is about E3 levels. So we actually help customers kind of coalesce entitlement management, license management, also optimized feature set so that not everyone in the organization have to go to the most cadillac version of Microsoft offering but still make sense of the data holistic management, right? So for example, you take Walmart. They -- a vast majority of their workers, employees are not desktop-based person. They're remote device operated workers, and there's many temporary seasonal workers. So Microsoft has a term for that called retail license, right, F1 license. So how do you then actually make sure that the data that's generated out of these population of employees and the data generated from your C-suite, which are E5, are treated the same, right? With the same holistic life cycle, access control and management. Of course, for a partner, especially MSP who help small businesses manage different licenses, how do you actually have a more holistic approach to manage these licenses? So that one goal is cost savings, right? So we're talking about potentially 30% cost savings for the customer right there. And then, of course, you have other cloud vendors like take Box , for example, right? Box is $30 to $40 per user per month. Effectively, we are now actually doing a campaign with Microsoft to say, "Hey, if you already pay $30 to $40 on Office 365, what do you need another cloud storage vendor for" when actually in addition to Microsoft capabilities and AvePoint capabilities, you already achieved that for a fraction of the cost, right? There, we can actually save customers another easily, like 60%, 70% of cost. So we see consolidation of different systems of different providers, and we also see cost optimization. So -- and also at the same time, storage costs. So if you actually look at the data's cost, just putting your data into SharePoint, right, which is Office 365 and OneDrive, there's a cost to that, and that storage footprint continued to increase very quickly, faster at the rate of declining storage costs. Now with AI, especially. So, Satya Nadella actually recently talked about by 2025 which is very near, 10% all data generated will be done by generative AI, so this is on top of the pace which we're already operating at, which is every two years, we are generating as a specie, generating more data than all data accumulated since beginning of mankind. So we're talking about exponential data generation already. So that means there's even more urgency to have proper data management so that you can get rid of a lot of these out of date or trivial data. So that -- this term in our industry called dark data, right? Vast majority time, 90% of your data are actually not actionable. So all these combined, so we actually -- in cases of we have a case study on this with WPP, which is one of the largest advertising company in the world by signing on us, we're saving them millions in storage costs. Again, optimizing the storage out, at the same time, providing data resiliency and business continuity because we're actually storing that critical data onto different platforms than just a singular one hyperscaler cloud platform. So that's also part of the requirement. So we don't only operate and live within the Microsoft cloud ecosystem, we already have significant footprint of data estate on AWS as well as on Google and also on-prem. So there lies the complexity, right? There also lies the need for enterprise customers and any business customers that don't want to rely only on one vendor to provide everything that they need.

So it leads into your question on demand because software investors have been debating for a couple of years now. Where are customers in their IT optimization cycles and you all made some interesting comments on your earnings call, which is signs of life or early signs of demand stabilization. And so tell us what the signs are, where do you think customers are, both in optimizing their overall IT ecosystem budgets? And then more specifically for AvePoint and your pipeline in your business, what are the early signs of demand stabilization?

Yes. Maybe on the demand stabilization, what we were referring to during earnings was that last year, we saw elongation of sales cycles. We saw really that kind of really being the forefront of people's IT spend, right? They're being very critical over their spend. And so we kind of planned that in '23. We kind of built that into our models, right, and our expectations. And so what we saw in Q1 and then further in Q2 was none of that getting worse, which to us was a really good sign, right, that we didn't see this continuation of more scrutiny or elongation further, we saw stabilization that our deal cycles seem to lock in and they got longer in Q4, but that did not continue in Q1 and Q2. Which was very encouraging, right? Because we didn't -- we had planned that they would kind of stabilize and they in fact did. So that was really good. So when we think about that demand stabilization, that's kind of what we're referring to. So that was a really good sign. And when we think about the journey of where people are, I think TJ's example of WPP is a good one. I think people are still in the early stages of now realizing they have massive amounts of data and the costs are now starting to become real and they're dealing with it, right? And fortunately, we have an opportunity to help them. WPP is a great example. We worked with them for several months working through the ROI, working through the potential savings, and it became a very cost-effective solution for them to implant, which we were able to help them do. And as TJ said, it's going to save them millions of dollars over the course of the first year, and that only gets bigger as the amount of data expands. So I think we're still in the early stages of people really focused on optimization but we are seeing it this year. I mean, we're doing it in our own business. I'm sure most of you guys are doing the same thing, focused on controlling costs and data is obviously a big component of that.

Yes, I agree. I think we're still in early stages. Even a recent study that's, again, cited by Satya was 85% of the C-suite see the need for digital transformation and going to cloud, and yet, only 18% of them are satisfied with their own firm's progress. We see that during COVID, everyone's going to cloud in a hurry because that's the only way to scale and enable hybrid work. We know customers, for example, they only use VPN in Japan, they have the lineup at like 5:00 a.m. to get on to VPN queue, it doesn't scale, right? But now that post COVID -- so in during COVID, everyone go to cloud just so that they can work, right? What that then triggered is, this is a big transformation agent that's actually COVID, not any management books or management edict, but then once everyone go to cloud, then the concern about who has access to what, right? So this governance thing became a very important thing, security became very important. Now the next wave, of course, is with AI. So people also realize not only do I need to work, going to cloud to scale my work, but I actually have to go to cloud to leverage the latest and greatest of technology has to offer, which is now generative AI, whereas if I just stuck with my on-prem data centers, which, yes, it's more cost effective because I already have my OpEx spend done. That's not where the action is happening. That's not where the latest iteration where you, as a business, have to innovate, otherwise you'll be left behind. So there's a second push towards using cloud in a bigger way. But of course, at the same time, you have to have the guardrails. We talk about information management, we talk about security access rights so that you also don't, at the same time, lose the crown jewel of the business, by submitting a lot of proprietary data to open a larger language model query and also potentially some of your query information could be embedded into some of other people's answers. So there's concern around security, around where you feed these larger language models, both proprietary as well as public ones. It's what's driving a lot of the debates among our customers and demands as well.

So how do you reconcile some of the big-picture trends that are happening with your go-to-market in practice? Because you've got the three product pillars, you've got 40-plus products. So I'll ask the question in two ways: One is how do you enable the go-to-market for your sales [ folks ]; and two is out of the 40-plus products, is there one or two that investors should be focused on that you think can really [indiscernible].

Yes. So our customers don't actually see the 40-plus products. The 40-plus product covers different deployment scenarios and different data sources, right? So the way we actually sell is we sell functional areas, and we sell platform. We think platform is really the way our customers are thinking about things in terms of consolidation. So we don't have a holistic competitor, we have [ point ] competitors across these major functional areas. But then our power comes from the platform play. We can get into accounts various ways, right? I talk about during COVID, everyone rushed to cloud, and the tip of the spear became the governance story. And then prior to that, the tip of the spear was a migration story, right, data analytics migration. And now with AI, they become the data analytics story and the information management story. So we have different ways to get into engagements and then leveraging the power of the platform, be able to then upsell and cross-sell. And we'll do more of this. So again, pitch for the October SHIFT Conference in D.C., you will see how we're actually embedding more AI capabilities across our products so that they actually work better together to incentivize more upsell, cross-sell and ultimately to improve NR, right? But the ultimate goal, of course, is to provide more value to our customers. So they do see by betting bigger with a platform provider like AvePoint, we can actually overall save them cost and get better returns on their investment on Microsoft Cloud.

All right. I'm going to pause for a moment and go to the audience, please.

[indiscernible]

Yes, that's a great question. So using our business -- so obviously, we're imbuing more AI capabilities into our product to improve the top line. But in tuning for our own business is to improve bottom line. So for example, we talk about the support use case. So we actually, in our industry, have the -- recently one of the outside organizations surveyed 2,000 enterprises out there, and they find that we offer the best support as well as the best backup service offering in the Microsoft cloud ecosystem. And we do have a 24/7 support organization and it's located around the world for data sovereignty reasons. So we see that the ability to automate Tier 1 support to actually provide actionable routing in [ case ] that application, essentially suck in 20-year worth of support data as well as all of our extensive user documentation into AI model and provide that support, create extensive savings for us. The other side of it is coding. So we use Copilot, GitHub Copilot. But we do find that similar to what Microsoft is reporting to us privately as well. It's very helpful for senior developers to use GitHub Copilot to actually generate automatic routines. But it's not very helpful for junior developers, new developers, because you have to be able to cross-check and understand what's generated before you deploy it, right? Because there's always a chance of based on probability and statistics that what's generated is actually part of that "hallucination", right? So it's helpful. We see productivity improvements up to 30% for our senior developers. For junior developers, they should still learn the ropes and learn how to actually do proper DevOps and containerization and cloud development capabilities. One thing we do see is that also in cloud, everything has a cost, right? Compute, bandwidth, storage. There's a joke in the industry -- not so much a joke anymore, that Microsoft is laying off a lot of people to buy more GPUs. GPU is very expensive, so Microsoft has a Copilot program, preview for Office 365. Not only do they have to hand select, this is all done by the business side, you have to be a big enough client for them. To become part of that Copilot program, the minimum entrance fee is USD 100,000. So it's expensive, right? So even if you're running experiments with GPUs like what we do with Google and Microsoft is also costing them. So everyone has become very, very cost conscious. So what that means translated to code optimization is that you need to make sure your code is optimized in such a way that it's cost conscious. Every time you query AI model, there's a cost to it, right? When you do the parameterization, so also the best practice in prompting for something like OpenAI, Chat GPT is that your prompt should be no less than 400 words to be a good product. But when you pump-in a lot of long-term prompts into the engine, every few tokens has a cost because of the way transformer works, right? It will run the model. So all of these, your programming have to be very cost conscious. And that's also something that yes, you can use AI to cross-check for you. But ultimately, there's also some type of training for our developers to be mindful of. But yes, absolutely, there's bottom line improvements, leveraging AI for the business.

And then maybe just one added to that is on the kind of say, admin side of the house or our internal uses, we've been doing a lot of automation over the past several years. And so AI is almost just the next generation of that. So we're using it in some of our accounting systems, some of our operations. We're trying to embed as much AI as we can to make and streamline kind of the admin side of the house as much as possible. So we're evaluating a lot of tools, and we're starting to implement some of that stuff now.

[indiscernible]

We think it's only going to be positive. As I mentioned earlier, any AI project, 80% of the lift is actually data cleansing, data aggregation and data analytics. And that's our wheelhouse for the last 22 years. So this is why you will see new messaging from us in term of highlighting this AI-ready information management practice. It's really about aggregating of all your corporate enterprise data from different silos, hybrid and multi-cloud approaches to make them clean data set to pump into proprietary data model. So we think AI is going to drive further need to go to cloud and also further need to raise awareness of information management and governance even more than before. So we think it's only a net positive.

All right. I think we can end with a finance question. Target of Rule of 40 in 2025 and breakeven from a profitability standpoint. What are the one or two biggest needle movers that will get you there from a unit economic standpoint?

Yes. So maybe just to level set again, for us, Rule of 40 is ARR growth plus our non-GAAP operating as a percentage of revenue. So those two components make up our Rule of 40. So when we think about '25 and getting there, I think there's a couple of things that are really going to help drive that. First is our kind of shift to the channel and our focus on channel because that does two things for us. We're seeing real good success in terms of driving growth and helping us on the topline so it's helping us with that ARR growth number. But it's also helping us from an efficiency play, too, for our sales and marketing. So it does two things for us that have been at least so far have been very positive, and we would expect that to continue. And then again, on efficiency side, when we look at our G&A costs, some of the -- the question before about AI and really just automation. So we spent a lot of money, obviously, from a G&A point of view becoming public company ready. And over the past several years have really invested and built an organization that not only can be and support us being a public company. But we don't need to make those same investments that we've made in the infrastructure going forward at the same levels. So we do expect to see significant improvement in our G&A efficiency as well moving forward. So really, those two key drivers help us not only achieve the topline growth, but also the bottom line profitability.

Fantastic. We'll leave it there. Thank you both for your time.

Thank you.

Thanks, Gabriela.