Filed by Elastic N.V. Pursuant to Rule 425
Under the Securities Act of 1933
And Deemed Filed Pursuant toRule 14a-12
Under the Securities Exchange Act of 1934
Subject Company: Endgame, Inc.
Commission File No.:001-38675
The following communications are being filed in connection with the acquisition of Endgame, Inc. by Elastic N.V.
The following is a transcript of a presentation by Elastic N.V. at the Bank of America Merrill Lynch Global Technology Conference on June 6, 2019. While every effort has been made to provide an accurate transcription, there may be typographical mistakes, inaudible statements, errors, omissions or inaccuracies in the transcript. Elastic believes that none of these is material.
CORPORATE PARTICIPANTS
Janesh Moorjani, Elastic N.V. - CFO
CONFERENCE CALL PARTICIPANTS
Kasthuri Gopalan Rangan, BofA Merrill Lynch, Research Division - MD and Head of Software
Kasthuri Gopalan Rangan: It’s always the best, isn’t it? I don’t know how many of you got to see the panel just before. You were there. I saw you. And your panel is going to be awesome, too. Elastic’s going to be awesome, too. So, real delight to be able to finish up the third day with very high quality companies. But before we get to the meat of the discussion, I have to sing a song for you guys. I’m kidding. Yeah. It’s Anthony Script is the original author of this wonderful song that I’m just going to read out.
Forward-looking statements: Statements made in these discussions, which are not statements of historical fact are forward-looking statements based upon current expectations. Actual results could differ materially from those projected due to a number of factors, including those referenced in Elastic’s most recent SEC filings.
That’s a great composition. I’ve got to hand it to you. Yeah. Even without the rhythm and rest of the instrumentation, it sounded very good.
So, Janesh, congratulations on becoming a public company.
Janesh Moorjani: Thank you.
Kasthuri Gopalan Rangan: So, glad to have you join us. Maybe since you guys had your earnings report yesterday, you could recap for us what were the things that really stood out in the quarter that we should pay attention to, things that might have
been otherwise lost in translation, because the entirety of the tech investing world was at the BAML Global Tech Conference, so we may have missed the most salient aspects of what drove your business strength in the most recent quarter.
Janesh Moorjani: Sure. First off, thanks for having me. It really has become quite the event, so I’m not surprised at all that you had spectacular attendance yesterday and throughout the conference.
For us, broadly speaking, a couple of things. One is just in terms of our fiscal fourth quarter and how it played out. It was just a very strong quarter for us across the board. So, if I think about all the typical metrics that you would look at in terms of revenue growth, we delivered 68% constant currency growth. Billings was exceptionally strong as well at 63% constant currency and that’s on the back of a particularly strong Q4 of last year.
So, we are quite pleased with how all of that played out. And that was all visible in the customer metrics, too. When I look at new customer additions or the way customers have expanded their spend with us over time, so we grew to have more than 8,100 paying subscription customers, more than 440 of whom now have spent more than $100K with us in ACV terms.
So, across the board it was just a very successful quarter. Our net expansion rate continues to be pretty robust at more than 130%. And even if I think about the rest of the P&L, even in terms of the operating margin we came in ahead of where we expected to be. We did accelerate investments quite a bit, as we said we would this quarter. We added 195 people back in Q4 and so we continue that pace of investment.
But the other big highlight on the call yesterday was the announcement of the acquisition of Endgame or joining forces with Endgame as we like to say. So, that’s an exciting announcement for us. And we look forward to actually extending our presence in the security space. A lot of what we do with Endgame was – or what Endgame does I should say was on our roadmap anyway. And so, this is a great opportunity for us accelerate our future into the present. And so, we’re super excited about that.
Kasthuri Gopalan Rangan: Maybe just you could talk about Endgame a little bit more. You probably didn’t get a chance to go into it in tremendous detail on the call yesterday. So, what was the rationale? What is the new opportunity set that is opening up to the company and who are the competitors that should be – that we should be watchful as you execute?
Janesh Moorjani: Yeah, sure. So, the space is the traditional EPP and EDR space. But if I think about our own roadmap and strategy, I think everyone knows we already have ambitions in the SIEM space and we are working on our SIEM product. We also invest quite heavily in agent-based technology to be able to ship data into
Elasticsearch. And actually, it is all about the data at the end of the day. And what Endgame has is the endpoint technology that does obviously the detection, but also the remediation. And they’re actually built on top of Elasticsearch.
So, it was a great opportunity for us to take our future roadmap even beyond SIEM around building purpose-built shippers for data for security events and raw security events that would ordinarily ship into Elasticsearch and to pull that forward. So, we can use Endgame for that. And then really to integrate the SIEM and endpoint capabilities and integrate all of those and then to that apply our community-basedbottom-up adoption model that we’ve been so successful with in terms of driving much wider adoption at scale. So, that’s really the core thesis and that’s what excites us about it.
Kasthuri Gopalan Rangan: Excellent. So, Shay could not be here, but for the benefit of folks that may not be completely familiar with Elastic, such as myself, I’m kidding. Just wanted to throw that out as a joke. Just give us an overview of Elastic, its origins, what was the key business problem that Shay wanted to solve? I remember that from our first meeting.
Janesh Moorjani: Very funny.
Kasthuri Gopalan Rangan: And where are you headed, just your overall framework?
Janesh Moorjani: Yeah, happy to. So, you joke about yourself not being familiar with the company or you’re deeply familiar with the company and know so much more about us than many others. But, the roots of Elastic actually started, oddly enough the anecdote that Kesh was referring to was Shay wanted to build a recipe search application for his wife. She’s a chef and he started out trying to solve that problem for her. And he started writing some lines of code to build a search box for his wife to search for essentially recipes and other culinary terms. And I think she’s still waiting for him to deliver on that product, by the way, because he got distracted and ended up building, writing the first few lines of Elasticsearch and then a lot more lines of Elasticsearch. And then eventually, we built the company around that.
So, the company itself was launched in 2012. The product was put out into the open source community back in 2010, but the company itself was built in 2012. And over the last seven years, it’s been quite a ride. We’ve grown the company quite successfully, obviously went public last year. So, a lot of that historical growth trajectory is well-documented in terms of the things that we didn’t have we grew.
But when Elastic was first started, the idea that you can ship data from a variety of different sources, index that data and then return near real-time search results on that data, it’s a really compelling argument. And you see that playing out in so many different ways, whether you look at websites where you are doing a search,
which is what most people typically think of when they think of a search, so the traditional site search application. Or, if you’re looking at a mobile device and you look at your text box in a mobile device, chances are for most of the mobile applications that people have today when you fire that up, one of the first things you do is search for something within that application. That’s an example called app search.
And so, we started with a set of search-related use cases and over a period of time we wanted to make sure that we don’t find ourselves boxed into any one particular area. So, app search, site search or even enterprise search more broadly, we wanted to make sure we don’t find ourselves boxed into that. And the technology started to get adopted within the community because it was put out there into the open source community.
Creative minds started to use the technology in many different ways. And someone said, “Hey, what happens if I take a bunch of logs and I start to apply this search technology against logs?” Because at the end of the day, logging is exactly that. You’re indexing large volumes of data and returning search results on that pretty quickly. And that became our logging use case. And that individual is now with Elastic. And we looked at ways in which we could visualize that data through Kibana and Rashid, who was the founder and creator of Kibana, who wrote Kibana essentially, is with Elastic as well.
So, along the way we kept expanding in terms of our features and functionality, but, more importantly, in terms of the use cases to which Elastic could be extended. We eventually extended into areas around security. And today we are used, broadly speaking, if I had to think about where we currently stand, it’s three broad sets of use cases. One is a set of traditional search-related use cases for app search, site search, and enterprise search. The second I would describe broadly as observability-related use cases, which combine logging and extending into areas like metrics and APM. And the third broadly is around security-related use cases. So, it’s been quite the journey for us over the last seven years.
Kasthuri Gopalan Rangan: Excellent. How has been thepost-IPO experience? What are the things that have surprised you positively or not so positively being a public company?
Janesh Moorjani: Very little surprise I should say because through the process, at the end of the day...
Kasthuri Gopalan Rangan: And Janesh, by the way was at public companies in his prior life, so he is not a stranger. You were at Cisco. You were at VMware, which is where we got acquainted.
Janesh Moorjani: That’s right. So, yeah, I’ve been exposed to the public financial track for quite some time. And so, no real surprises. I think along the way in the IPO
process, it was clear to us that we want to make sure that we have a story that’s simple and that people can understand what we actually do. We, when we went public, we tried our best to avoid jargon and terms that just get thrown around in the industry that really don’t have much meaning and kept it fairly simple and grounded so people would be able to understand the story.
And no real surprises. We’ve done what we said we would do. We had a strong fiscal Q2 coming out of the gate shortly after the IPO, did the same in Q3, consistently met the beat and raise cadence that people have come to expect for newly minted IPO companies. And so, I think we are proud of what we’ve delivered over the last couple of quarters.
Kasthuri Gopalan Rangan: Congratulations.
Janesh Moorjani: Thank you.
Kasthuri Gopalan Rangan: It’s not an easy one to build a company to the point where you can actually take it public and still continue to put up solid results. Use cases, I want to talk about the use cases. When investors ask me, “So what does Elastic do,” I talk about two or three use cases. I use the Yelp. I use the Uber one. But as you, sitting where you are having this very broad perspective...
Janesh Moorjani: I thought you might use the Tinder one, by the way, because that is an example of search. It’s one human being searching for another.
Kasthuri Gopalan Rangan: Yeah. Just not relevant in my world, if my wife is listening to this webcast is going to be the straight answer. Where do you see the use cases going in the future? I mean enterprise search is one. It’s very easy to understand. And even there, you could just pick up a really high scale at scale customer example that uses Elastic for enterprise search and just talk about a couple of these things so people can visualize and understand how the rubber meets the road.
Janesh Moorjani: Sure. So, we had a number of examples that we had provided previously. Uber itself is actually a pretty good example. When you first fire up the Uber application, you’re located within the four walls of – the four edges of your device. So that’s an example of how you’regeo-located and you are searched for. And then there are vehicles. Once you enter your destination, the rider has to be matched to the driver. That’s another example of search.
And then when you hop into the vehicle and you are driving along the way, every few seconds the location of that vehicle is being sent back to Uber. And when that’s being sent back, that’s used for all kinds of analytics. So, should we introduce surge pricing, should we not introduce surge pricing is one example based on traffic patterns that you see.
So, there’s a number of different ways in which Uber as a company uses Elastic. And what starts off as a use case in one particular domain, quickly the lines start to blur. So, maybe I’ll give you a more generic example.
If you fire up an application and you go to a website. And let’s just say it’s a drugstore and you’re searching for Band-Aids. And you type in Band-Aids into the search box and that’s an example of site search. It pulls up a bunch of things that you can buy and it maybe even has a recommendation engine. And you start to – you add those Band-Aids to your cart and you start to checkout. Now you’re talking about ane-commerce-related example.
By the way, the steps that you take along the way are being tracked and logged and each of those webpages is generating logs and all those logs can get stored in Elasticsearch. And that’s an example of then how that extends to logging, so that if a certain page...
Kasthuri Gopalan Rangan: If I used Tinder I would need more than aBand-Aid. I’d be in the hospital.
Janesh Moorjani: So, those pages generate logs and all of those logs need to be analyzed. If the app misbehaves or if there is a delay, why is that? What happened? So, what starts – and of course the security then plays right into that as well. And so, as you think about all these different examples, all the use cases start to blur a little bit and what starts off as one quickly becomes another use case.
Kasthuri Gopalan Rangan: Yeah. That’s very useful. And how do you see this playing out. So if you look at Splunk, seven years back they went public. It was largely SIEM-related bunch of use cases and now there are dozens and dozens of use cases. The possibilities have seemingly grown endless.
In your case, if you were to look three, four years out, how do you see the technology becoming more pervasive than it is? It’s already quite pervasive. When you say Uber, Yelp and Walgreens, that’s a pretty awesome set of examples at scale. But how do you see the use cases panning out in the next four to five years?
Janesh Moorjani: Yeah. I think from our perspective it’s we’ve really doubled down in all of these broad areas. So, we are super-excited about the search-related use cases. Yesterday, we talked about how we’ve released a preview version of an on-prem enterprise search capability that now actually allows you to search in a single search box across multiple assets in the cloud. So, think Dropbox, GitHub, Salesforce. You could search across all of those applications with a single search box.
Kasthuri Gopalan Rangan: Give us an example. What would you possibly search for?
Janesh Moorjani: You’re searching for, let’s say you’re searching for an example or you’re searching for interactions that you’ve had with a particular customer for aCRM-related matter and you type in the account name and you will get the results from Salesforce, but you’ll also get any assets that might be stored in Dropbox that relates to that customer, presentations that you might have made or other assets that the enterprise has. Or you could – and in the same result, you’ll also get examples of pull requests or requests that have been made on behalf of that customer for features that they may have requested and what have you. So, in a single search box you could get results that relate to that particular enterprise or that particular customer from all of these different data sources.
Kasthuri Gopalan Rangan: Got it. When the company went public, you talked about a very large TAM. If you could pick apart two or three use cases that are going to help you realize the TAM over the next four to five years, what would those be?
Janesh Moorjani: Yeah. So, I’d say broadly it’s the – we doubled down on the search-related use cases, extending observability and then with security, which is still nascent for us. It’s still – it’s a pretty powerful use case for us. A lot of people do use us for security today. But with SIEM and with Endgame we are going to take it to a whole new level.
Kasthuri Gopalan Rangan: Is there any way at all that we could think about segmentation of revenue or bookings or any other way to think of how these different use cases are contributing to bookings and the volume of business?
Janesh Moorjani: Yeah. It’s a great question and one that actually comes up often, so worth spending a minute or so on. So, as I think about our monetization model, when we talk about features or products that we launch, these are typically not dedicated SKUs. So, we don’t have an APM SKU that we go sell, or we don’t have a machine learning SKU or an enterprise search SKU. And so, in terms of disaggregating the revenue, it becomes hard to do it on a product by product basis or feature by feature basis.
The way a customer consumes a technology, these are all capabilities that are extended and added to the Elastic Stack. And as they use the technology for all of these different use cases, it drives additional demand for the stack overall. And so, we see that in the form of increased nodes and increased volume. And our price models, our subscription models are based on essentially node-based pricing and whether you have a gold subscription or platinum subscription or an enterprise subscription, there are different features in each and different levels of price points in each.
So, it becomes a little bit hard to disaggregate. But, we end up – sorry, my throat’s acting up – but we...
Kasthuri Gopalan Rangan: Mine too, after three days of nonstop talking.
Janesh Moorjani: What we end up doing is because we don’t have dedicated SKUs we rely a lot on internal reporting from our own tools or our own sales reps tell us. And broadly speaking, I think all of these use cases or trends we saw in Q4 are consistent with what we saw in Q3 in terms of the distribution of business.
Kasthuri Gopalan Rangan: Got it. So, maybe you could just take this opportunity to help us understand where does open source free version stop, where does the commercial paid version begin? And as you have more incorporation of features into the open source version, the free version, how do you really charge the customer for thevalue-add?
Janesh Moorjani: Yeah. It’s – and there’s a nuance there because we also have the middle swim lane, which is proprietary features that are completely free to use. So, there’s two versions of free, if you will, what’s in the open source domain and then what’s proprietary but free. That’s what we refer to as our basic subscription. And then the gold, platinum and enterprise are, of course, the monetized versions.
And so, as we think about the capabilities, a lot of the foundational capabilities that are needed to make the adoption frictionless, to drive adoption, to help users be up and running fairly quickly, and basic capabilities that they would expect in terms of being able to derive some level of value from the product, a lot of those go into the basic swim lane or some into the open source. And then as we think about advanced features, which might be used for advanced applications that are mission-critical in nature, a lot of those go into the paid swim lanes. And it’s not just at the feature level or at the technology level. It can actually get even more granular than that.
So, within APM, for example, the UI is in basic, but some of the agents might be in some of the more advanced swim lanes. And so, we have some pretty fine-grained thinking around the approach in terms of which capabilities go into which swim lanes.
Kasthuri Gopalan Rangan: Got it. So moving on to public cloud and the big public cloud players that also have been doing some work in this space, let’s talk about AWS. It looks like they’re trying to create their own version of Elastic. Well, it’s supposed to be one common open source, but it looks like it could fork. What are you doing about this and what are the implications of what AWS is doing?
Janesh Moorjani: Yeah. I mean, honestly, we don’t need to do anything about it. Our strategy is working, which is why they had to react the way they did. So, to step back and provide...
Kasthuri Gopalan Rangan: Why do you think they did that? I’m just curious.
Janesh Moorjani: Yeah. So, I’ll just step back and provide a bit of context for the folks. So, when we – this idea of a set of free and proprietary features, it’s something that we had for some time. But I want to say in the middle part of last year we decided to open the code of our proprietary features as well and there’s a variety of reasons why we did that in terms of the interaction model that we have with the community and with our customers.
But, we opened the code and made the code visible for our proprietary features and folded them into the download. So, now there’s a unified download where you have in the same download the open source features and you have the basic features as well and the advanced features and they’re very clearly delineated and you can choose which ones you want to adopt and which ones you don’t. And that was very well-received in the community when we made that announcement.
One of the effects of that is that it further starts to create differentiation between pure open source and basic. And so, what Amazon offers is just the open source bits and when a user downloads the default distributions from Elastic, they get all the open source bits but also the free proprietary features and they can start using those right away. And so, if you think about the feature differentiation, and this is actually most visible in 6.7 and beyond, where the number of features that we have in basic versus what Amazon has purely in the open source, when you look at the two productsside-by-side...
Kasthuri Gopalan Rangan: They’re different.
Janesh Moorjani: ...they’re completely different. And Amazon started to see this happening and so that’s why they came out and launched what they call Open Distro. But, since the launch of Open Distro, we’ve seen no impact at all in our business. It was relatively a minor thing and completenon-event for us from our – from the perspective of our SaaS business and what we see in terms of adoption and so forth. The fact that they didn’t have a better reaction and that this is the approach that they had to take shows you that our strategy was working.
Kasthuri Gopalan Rangan: Good. That is your decision to bundle the pure open source with the proprietary free features.
Janesh Moorjani: Exactly.
Kasthuri Gopalan Rangan: So the customer can easily go from the open source version to the free open source and to the commercial version. It’s a very seamless process.
Janesh Moorjani: It’s very. In fact, in many cases when they first download they just start using all the basic features. This is anecdotal, but it’s pretty compelling. But, when you see messages from users or customers posted on developer forums or elsewhere and said – and they are messages that say, hey, I just downloaded this from Elastic and I had all these features and I’m looking at the Amazon one and
that just seems deficient. And why don’t they have these features? That just shows you that it’s resonating very clearly in the community.
Kasthuri Gopalan Rangan: Not to pine on this. I mean you’re a Chief Financial Officer, but you speak with the proficiency of a Chief Technology Officer. But...
Janesh Moorjani: I’ve been well-trained by Shay.
Kasthuri Gopalan Rangan: I’m going to exploit this. Janesh, you’re doing great, seriously. Now, is there – if I used Amazon’s open source version, can I – will this work with the Elastic free proprietary and then the commercial version of Elastic as well? They’re compatible in other words.
Janesh Moorjani: Yeah. So, there’s a couple of things. If you’re using the AWS Elastic – Elasticsearch on AWS, which is only the open source, and you want to start using the more advanced features, you can’t use those on AWS’s offering. You have to run – be on the Elastic Cloud. But it is so frictionless, because there’s – the data migration, it’s from moving from Elasticsearch to Elasticsearch. So, there’s no complexity over there.
Elastic Cloud, what we offer, we actually sell it on Amazon as the underlying infrastructure, one of the underlying infrastructure providers for us. We’re also available on GCP and a couple of others. And so, you could be right there on AWS and get the Elastic Cloud service.
Kasthuri Gopalan Rangan: Switching from one...
Janesh Moorjani: And it should be a pretty easy move.
Kasthuri Gopalan Rangan: So, we did a survey. Shankar has this brilliant idea. We did a survey of customers using different search technologies. And what we found was it was a very fragmented marketplace, right Shankar? There were six different search technologies, legacy technologies, and there was also an increasing intention on the part of customers to consider switching out of those products into something like your commercial version of Elastic.
Do you see the same way as our survey results see when you look at the market opportunities, that’s something your salespeople are coming back to you saying, “I’ve got this. It’s a replacement cycle boss, Shay and Janesh.” Like or is it something different?
Janesh Moorjani: No. It varies quite a bit by use case. But in the traditional enterprise search use cases, I think you’re right. It is incredibly fragmented. None of the companies of the old generation were really that successful. Customers are using a wide variety of things and the old enterprise search capabilities never really lived up to their promise. And I think part of it was because you just had fundamentally
different data structures and so many different applications that were built on-prem that if you tried to move all of the data into a data lake and you said, great, now I’ve got a data lake, now what do I do with it?
But the promise of enterprise search in its new avatar is incredibly powerful because that problem has been addressed because now most of the data actually lives in SaaS applications. And so, there’s very common data structures that you have across those which makes it much more, much easier for customers to think about enterprise search differently than was done before. And as a result of that, when we – I talked, for example, about the capabilities that we just previewed that allows you to search across SaaS applications, it makes it that much more compelling.
Kasthuri Gopalan Rangan: Like your Salesforce and Dropbox example.
Janesh Moorjani: Yeah. That’s exactly right.
Kasthuri Gopalan Rangan: And that is a feature that’s specific to the latest release of...
Janesh Moorjani: Correct, yes.
Kasthuri Gopalan Rangan: ...6.7 or something.
Janesh Moorjani: The release is actually 7.1.
Kasthuri Gopalan Rangan: See, I have a hard time keeping up. We cover only 45 companies and so they all have...
Janesh Moorjani: Well then, the pace of innovation at Elastic is incredible and we’ve just doubled-down on it because we are accelerating investments even further in R&D. But we like to say we drop a major and a minor every single time and we...
Kasthuri Gopalan Rangan: Very musical.
Janesh Moorjani: That’s right. And we launch releases every – every six to eight weeks there’s something coming out. And so, it’s hard for anyone to keep pace with that.
Kasthuri Gopalan Rangan: We’d start off with the first octave, A minor, and then that’ll give you the 28 releases or whatever four octaves. You could keep going, yeah.
I want to talk about competition. Who are the key competitors that you’ve run into from abake-off standpoint and how is that likely to change in the future?
Janesh Moorjani: Yeah. It’s one of the compelling things about ourgo-to-market model is that we almost never find ourselves in a situation where we have to go and prove and explain features and benefits to customers. And we have an internal saying
that if we’re doing a POC then there’s something wrong with our approach, because the power of thebottom-up model for adoption is you put it out there, let users be successful.
Very often when our salespeople actually engage in customer conversations, the customers are already up and running, some of them in test/dev, some of them even in production. And the conversation is more about you’re using our technology already and help me understand. We want to move to a commercial relationship. So how do I do that?
I’ll give you another anecdote. My very first customer meeting as the CFO of the company, this goes back almost 1.5 years ago, this was a global CIO of a large corporation. If I gave you the name, everyone would recognize it instantly. He started the meeting by saying, “You’re used in so many different places within the enterprise. Before we go through the agenda of this meeting, can you just help me understand where you’re used so that I can then figure out how we can take this relationship to the next level?” And that’s music.
Kasthuri Gopalan Rangan: Yeah. So, maybe just this is a very appropriate juncture to talk about the number of free downloads. I think it was 250 million at the time of the IPO.
Janesh Moorjani: Yeah. Three hundred and fifty million at the time of the IPO.
Kasthuri Gopalan Rangan: Yeah. And how does that process go from free to evaluation to paid to expansion?
Janesh Moorjani: Yeah. It’s the way we approach it is the – we want to make the customer adoption as frictionless as possible. So, the downloads are not gated in any way. You don’t have to provide us with all of your information if you choose not to. And there’s in some instances privacy regulations around that, too.
But, broadly speaking, we don’t require you to provide us with a whole lot of information. And so, those downloads numbers really take off and people start to use the technology. And then they engage with us in different ways. They’ll engage with us for things like documentation or questions that they have in terms of how they’re using it or they’ll show up tomeet-ups where we have communities of like-minded users who are interested in the technologies, who will come to those sessions. Or they’ll come to an Elastic{ON} in a city near them. We end up doing a good five to seven of these every quarter. So, basically there’s one every couple of weeks that we have somewhere in the world.
And so, they’ll come to all of these events and from there we will start to learn more about them and how they are using the technology. And the goal is just to help them. And if they ask one question, we will provide them with more information. We’ll provide them with things, information and content that will
help them scale pretty quickly. We don’t turn around and start to telemarket to them or start to pound them with sales messaging. I mean, it’s a very nurturing relationship and over a period of time as we help them become successful, then they sort of naturally gravitate towards wanting a commercial relationship in cases where they are using the features or see the value in the support or both.
Kasthuri Gopalan Rangan: Quick pulse check. Any questions? Yeah, David. Then we’ll come to you.
| Q: | Yes. Thanks, Kesh. So, my question is in the enterprisecross-app search is very compelling. And so my question is if you go back an era of client server compute and the strategic role of an Oracle database, a Sybase, SQL server database, what’s happening to the role of the database and the traditional search queries that were done in that architecture versus cross-cloud, which is what you’re doing now? And has that value proposition changed as a result? |
Janesh Moorjani: Yeah. It’s a good question. So, I think the databases and the world of data stores themselves are continuing to evolve. By the way, we refer to ourselves as a data store. We like to be intellectually honest about these things. So, we’re not a database company. We do get used as databases sometimes and we show up in database rankings and so forth. But we are a data store.
But, thinking back to the client server era and how data was used back then, a couple of thoughts. One is, as I mentioned, the data structures across all of these different applications tended to be very different. So, it was very difficult to realize the promise of true cross-application enterprise search.
The second is many of these applications were never really constructed with the intent of search in mind. If you talk to an Oracle developer or an Oracle DB rather and you sort of – if you tell somebody who is running the Oracle applications and managing them for you and you say I’d like to run these search queries and I’d like the results in about three seconds or two seconds or one second or a fraction of a second, they’d just look at you like you’ve got four eyes and 12 ears and they just shake their heads and go away. It’s just not possible in the way the applications themselves are constructed.
Kasthuri Gopalan Rangan: Yeah, please.
| Q: | Hi. I have two questions. One is as you accelerate investments, how much of your R&D effort is going to open source features as opposed to proprietary features? And then the second question is that I think we were all somewhat surprised to see Netflix and Expedia on the Amazon press release. How has that relationship developed and why do you think they agreed to be on that press release? |
Janesh Moorjani: Sure. So, in terms of the mix of R&D, it’s a difficult question to answer because as I said earlier the approach that we take in terms of deciding which
features go in which swim lanes, it’s a much more fine-grained approach. And so, the level of effort that goes into developing a certain set of capabilities, it’s the same engineers that are working on it. And then we sort of disaggregate that and say we’d like these capabilities in open source, these capabilities in basic, and these in proprietary.
So, it becomes allocation math, if you will, which is always a little bit goofy. And so, we really don’t think of it in that sense. The way we think about it is more around what capabilities do we need to build to enhance the foundational capabilities of the stack and then how do we make the adoption easier in the form of the solutions and the use cases to help customers actually get up and running on specific use cases very quickly with those solutions. And so, we’ve got teams that we have across all of them and teams that are focused on features across all of these.
In terms of the question about Netflix and Expedia, at the end of the day when you step back, I think when you peel back the onion on any press releases that you see out there and you think about individuals and the relationships that they have, you will start to see how some of that comes together. I think in at least one of those cases, the individual at Amazon who is responsible for running that particular part of the product came from one of those companies. And so there are – it’s not that hard to get a quote if you need to get a quote from someone at a firm level. But, what really happens is the battles are won on the ground.
Kasthuri Gopalan Rangan: Matches are won on the ground. I’m reminded of World Cup Cricket. I know cricket is not relevant to America. Second most popular sport. Janesh, Shankar and I and Pranay can all relate to it.
So, anyway, I also wanted to ask you a question on the business model. What are the levers that – one of the things that struck me is how efficient your sales and marketing engine is with respect to how much it takes to acquire a piece of new business. How do you see the operating leverage playing out in the future? What are the things that drive profitability?
Janesh Moorjani: Sure. So, for us I think a couple of dimensions. One is we will start to see some level of operating leverage in sales and marketing and G&A in fiscal ’20. The approach we are taking is to reinvest that in R&D and double down. And that’s what’s really driving the model for fiscal ’20. And so, some of those investments that we make then derive returns or will deliver return to us in the medium-term and long-term.
The market opportunity that we see ahead of us is enormous. And so, we think the right thing to do is to invest to capture that growth for the future and secure that growth. And that’s the approach that we’ve taken.
Now, as an alternative, it would be pretty easy for us to turn around and say, wait a minute, we’re going to cut back on R&D spending, we’re going to cut back on sales and marketing spending, we’re going to run this business for profitability. But that means you just end up sacrificing growth in the future.
So, the way we see it is the better decision is to actually invest now and grow the business in the future. As we move forward, as we go through fiscal ’20 and as we continue to see the market opportunity unfold and if we are able to successfully deliver against that, then we will keep maintaining an investment profile that’s consistent with the relevant growth rate that – rate of growth that we see in the business.
Kasthuri Gopalan Rangan: Yeah. I just keep bringing up Splunk because the comparison from a numbers perspective is just very striking. They kept growing their sales income pretty significantly and did not slow down and went from $200 million to$2.3-$2.4 billion. So, clearly you guys have the potential to be a multi-billion dollar revenue company. How do you look at it? As you enter the new fiscal year, how do you view the shape of sales quota carrying headcount growth rate?
Janesh Moorjani: Yeah. So, if I look back over fiscal ’19, we grew the total headcount in the company by about 67%. The distribution across R&D, sales and marketing, G&A functions was roughly even give or take. In terms of the increase that we made in fiscal ’19, I think a little bit more than a third of our total company resources are in sales and marketing.
So, broadly speaking, as I look ahead at fiscal ’20, I think we will maintain the pace of investments as we had in fiscal ’19 around sales and marketing and I would expect that to grow at a pretty healthy level. The way we get leverage is by scale on the top line, not by cutting back sales and marketing investments.
Kasthuri Gopalan Rangan: On that note, let’s give a round of applause to Janesh.
Janesh Moorjani: Thank you.
[Applause]
END
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Elastic N.V. (“Elastic”) plans to file with the Securities and Exchange Commission (the “SEC”), and the parties plan to furnish to the security holders of Endgame, Inc. (“Endgame”) and Elastic, a Registration Statement on FormS-4, which will constitute a prospectus of Elastic and will include a proxy statement of Elastic in connection with the proposed merger of Avenger Acquisition Corp., a Delaware corporation and a direct wholly-owned subsidiary of Elastic
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