Snowflake SNOW Q3 2021 Earnings Call Transcript

Operator: (00:01) Ladies and gentlemen, thank you for standing by, and welcome to the Snowflake Q3 FY21 earnings call. At this time, all participants are in listen-only mode. After the speaker's presentation, there will be a question and answer session. To ask a question during the session, you will need to press star-one on your telephone. Please be advised that today's conference is being recorded. If you require any further assistance, please press star-zero. I would now [inaudible 00:00:27] hand the conference over to your speaker today, Jimmy Sexton, head of investor relations. Thank you. Please, go ahead. Jimmy Sexton: (00:34) Good afternoon, and thank you for joining us on Snowflake's Q3 fiscal 2021 earnings call. Joining me are Frank Slootman, our chairman and chief executive officer, and Mike Scarpelli, our chief financial officer. During today's call, we will review our financial results for third quarter fiscal 2021 and discuss our guidance for the fourth quarter and full year of fiscal 2021. During today's call, we will make forward-looking statements, including statements related to the expected performance of our business, future financial results, strategy, long-term growth, and overall future prospects. These statements are subject to risks and uncertainties, which could cause them to differ materially from actual results. Information concerning those risks is available in our earnings press release distributed after market close today and in our SEC filings, including the Form 10-Q for the quarter ended October 31st, 2020. That will be filed with the SEC today. Jimmy Sexton: (01:24) We caution you not to place undue reliance on forward-looking statements and undertake no duty or obligation to update any forward-looking statements as a result of new information, future events, or changes in our expectations. We'd also like to point out that on today's call, we will report both GAAP and non-GAAP results. We use these non-GAAP financial measures internally for financial and operational decision making purposes and as a means to evaluate period to period comparisons. Non-GAAP financial measures are presented in addition to and not as a substitute to [inaudible 00:01:54] for financial measures calculated in accordance with GAAP. To see the reconciliation of these non-GAAP measures, please refer to our earnings press release distributed earlier today and our investor presentation, which are posted at investors.snowflake.com. A replay of today's call will also be posted on the website. With that, I would now like to turn the call over to Frank. Frank Slootman: (02:14) Thanks, Jimmy. Good afternoon, everybody. Thanks for joining us on our inaugural earnings call. Let's review the results. We saw strong consumption trends across our customer base in Q3, but product revenue growing 115% a year on a year to $148 million and a net revenue retention rate of 162%. Coupled with this rapid growth, we continue to see improving unit economics, cashflow, and operating efficiency. Frank Slootman: (02:42) Our growth is driven by long-term secular trends in data science and analytics enabled by cloud scale computing. With the onslaught of digital transformation, data operations have become the beating heart of the modern enterprise. Dynamic has been more or less neutral to our business. Some businesses were negatively affected in terms of demand sentiment, but others stepped up their data strategy given the new complexities of the health crisis and economic effects. Frank Slootman: (03:09) It bears repeating that Snowflake is not a SaaS business model. We're a consumption company, and our reported revenue has a direct relationship with the consumption of our platform during the period. The consumption model is variable, not fixed, meaning our model places no limits on how much of our platform a customer can consume, and this contributes to our strong revenue retention rate. The technology is not ahead of people's ability to take advantage of virtually unbounded capacity, scale, and performance. Frank Slootman: (03:40) Over the past year, Snowflake has augmented its selling motion to campaign some of the largest enterprises in the institutions in the world. Snowflake is well-represented now in eight of the Fortune 10, and we add a 12th Fortune 500 customers in Q3, including Fiserv and Geico. Interest in Snowflake is growing. We hosted our Snowflake Data Cloud Summit two weeks ago at over 40,000 registrations, up from 15,000 registrations at our Snowflake summit in June. Frank Slootman: (04:13) Shifting gears, I'd like to take a moment here and introduce you to the Snowflake Data Cloud because it's the centerpiece of our mission and strategy. As an industry, we have struggled to mobilize our data, meaning that it's been hard to put data in the service of our enterprise, and we set out to change that. As we see it, we've never had a data cloud in the history of computing. We are used to SaaS applications as application clouds, and the massive infrastructure clouds like AWS, Azure, and Google Cloud, servers and storage you can consume by the [inaudible 00:04:46], but data lives absolutely everywhere, in millions of places held hostage by machines, applications, networks and clouds. Frank Slootman: (04:56) We have long needed to blend and join disparate data sets. That's why we built data warehouses in the first place. They were expensive, capacity constraint, and required tons of data preparation and manipulation prior to use. Only the largest most demanding data estates could afford these platforms so they were never pervasive. Snowflake change that, drastically scaling down to the smallest jobs and radically changing the economics with a highly elastic utility model. Not only did everybody now afford these great powers, Snowflake also remove constraints on data volume, plus to performance and concurrent workload execution. Even high-end users reported being able to cut their existing spend and expend their workload dramatically at the same time. Frank Slootman: (05:42) As compelling Snowflake was to turbo existing workloads, old habits die hard. Many customers are still evaluating data platforms one workload at a time, basically limiting data operations to their silos. That's like steering a ship by its wake. Future workload will look different with machine learning and data science becoming ultimate users. The workload-at-a-time mentality leads to building the silos of the future. Frank Slootman: (06:09) The Snowflake Data Cloud is a data universe, a global data orbit where Snowflake users effortlessly plug in, discover, explore, and access data from an incredible growing variety of sources. It's a different way of thinking about the data needs of the future. The Snowflake Data Cloud combines world-class execution with unfettered data access. Customers need both. We're seeing promising signs of adoption with already 23% of our customers using our data sharing capabilities. We also continue to onboard new data providers, and in Q3, we added Standard & Poor Global, Morningstar, and CoreLogic, to name a few, and we now have over a hundred data providers on our marketplace. Frank Slootman: (06:55) At the Snowflake Data Cloud Summit, we heard from customers about the impact that the Data Cloud is having on their business. A retail rewards customer has fully embraced the Data Cloud. Snowflake allows them to securely share data sets with media partners and power customer-facing applications to target end users. Frank Slootman: (07:14) Commercial data providers are also turning to Snowflake to reach new customers and monetize their data by making the data available in the Snowflake data marketplace. Experian helped retail customers accelerate their digital transformation efforts. Experian retail customers are now reaching shoppers online with insights garnered from Snowflake data marketplace to reach customers who are no longer shopping in stores. Frank Slootman: (07:41) Let's highlight some product announcements from the Snowflake Data Cloud Summit in November. First, we've grown a partnership with Salesforce. The previously announced upper connector is now generally available and allows customers to more easily sync Salesforce data with Snowflake. Secondly, we announced Snowpark, our new developer experience. Snowpark will enable users to write code in their preferred language to build data transformations and score machine learning models, all processed by Snowflake. Third, we announced support for unstructured data in addition to our longstanding support for structured and semi-structured data. Lastly, we are enhancing our data governance strategy with the introduction of row access policies, tagging, and column masking. This will help our customers control access by user type in a highly granular fashion. Very important given the heightened sensitivity around data governance. Frank Slootman: (08:37) These capabilities are essential to grow on the data cloud, and you can expect more platform enhancements from us going forward. Before closing, I would like to highlight the upcoming release of our book, The Rise of the Data Clouds. We wrote this book because the data cloud is the centerpiece of our strategy and vision. We showcase many customers in numerous vertical industries with their Snowflake journeys and experiences. It's meant to enlighten and inspire, something we call the art of the possible. The book will be available on December 4th. In closing, we're pleased with our quarter and excited about the momentum coming out of the Data Cloud Summit as we head into the final quarter of our fiscal year. With that, I will turn the call over to Mike. Michael Scarpelli: (09:21) Thank you, Frank. Before I discuss the results and guidance, I would like to spend some time discussing our unique and powerful business model. We are not a SaaS model. We are a consumption model. Our business model is a key differentiator for us and is designed to drive customer success. Our customers purchase credits, and when those credits are consumed, we recognize the revenue. Unlike a ratable model, we only recognize revenue if the customer uses our platform. For this reason, there is no shelfware in our revenue. Michael Scarpelli: (09:52) For many customers, it takes several months until they're up and running at full capacity, and this model gives them the flexibility to purchase the amount they plan on using without wasting credits or exceeding their original contract if they consume more than planned. For these reasons, we did not focus on the same metrics that a SaaS business would. We focus on product revenue and remaining performance obligation. Michael Scarpelli: (10:18) Product revenue, which excludes professional services and other revenue, is the most transparent disclosure we offer. It gives full insight into how our customers are actually using our product in the period reported. If a customer purchases credits and does not consume, their revenue will be $0. Michael Scarpelli: (10:36) We also focus on remaining performance obligation, or RPO. RPO represents all the contracted revenue not yet recognized, including both deferred revenue and non-cancelable contracted amounts that will be invoiced and recognized as revenue in future periods. Unlike most SaaS businesses, billings is not a meaningful metric for us because it is less correlated to product revenue due to the variability of consumption. I would also like to mention that our GAAP financials will now be available on the Snowflake marketplace, and we encourage all of you to consume our financial information in a new way. Going forward, we'll be publishing our quarterly results on the marketplace in conjunction with our earnings. Michael Scarpelli: (11:20) Now let's turn to our results and guidance. For Q3, product revenues were $148 million representing 115% year over year growth. Our remaining performance obligation was $928 million representing 240% year over year growth with a weighted average life per multi-year contracts of 2.5 years. This strong performance is driven by our customer base realizing the value of our platform for their existing use cases while also embracing the Snowflake Data Cloud vision. Michael Scarpelli: (11:54) As I mentioned earlier, our business model allows our customers to consume their entire contract before the end of the term, which is what we often see. We also continue to see customers willing to make more multi-year commitments with us, which is a direct result of the value our customers can create with us. As we continue to scale, we are increasingly focused on moving upmarket, and you can see the benefits of those investments paying off. Michael Scarpelli: (12:21) On Q3, we saw the number of customers consuming greater than $1 million in trailing 12-month product revenue increase to 65, up from 31 in the same period last year. Turning to margins, on a non-GAAP basis, our product gross margin was 70%, positively impacted by one time credits received from a cloud service provider in connection with our new agreements. Operating margin was negative 30%, benefiting from lower than expected employee-related costs. Our adjusted free cashflow margin was negative 23%, positively impacted by pre-payments coming in lower than expected and stronger operating margin. Michael Scarpelli: (13:02) Going forward, we will report and guide non-GAAP adjusted free cash flow. Adjusted free cash flow will exclude the impact of cash paid for employer payroll tax items on employee stock transactions. This quarter, we saw $800,000 impact from those items. For detailed bridge of cashflow from operations to adjusted free cashflow, please refer to our investor presentation on our IR website. Michael Scarpelli: (13:29) We ended the quarter in a strong cash position with approximately $5.1 billion in cash, cash equivalents in short-term and long-term investments. This capital allows us to invest in new strategic initiatives such as Snowflake Ventures, which we announced last month. Snowflake Ventures' mission is to enable more organizations to harness the power of the Data Cloud. To do this, we will invest in growth stage companies that demonstrate a commitment to mobilizing data, providing value to our customers, and expand opportunities for the Data Cloud. Similarly, we will continue to evaluate strategic tuck-in opportunities and M&A focused on talent and technology. Our acquisition strategy aligns with our product strategy. Everything must be done the Snowflake way, and that means delivering as one native product. Michael Scarpelli: (14:19) Now I would like to give an update on how we view COVID impacting our forecast. Our forecast assumes our employees will continue to be working remotely for the foreseeable future. We have proven our ability to maintain productivity during the pandemic and no rush to return back to the office and regular travel. Leveraging Zoom and other collaboration tools, we have been operating at a high level and able to smoothly onboard over 500 employees during the pandemic. We believe we will eventually be back in the office, but until we have new information and can guarantee our employees' safety, we will continue to work remotely. Michael Scarpelli: (14:54) Now let's turn to guidance for the fourth quarter and full year fiscal 2021. For the fourth quarter ending January 31st, 2021, we expect product revenues between 162 and $167 million representing year over year growth between 97 and 103%. Turning to margins, we expect on a non-GAAP basis an implied 70% product gross margin, negative 30% operating margin, and negative 8% adjusted free cashflow margin. We expect 283 million weighted average shares outstanding. Michael Scarpelli: (15:32) I would like to remind everyone that because of our consumption-based business model, we do not recognize revenue immediately after a deal is booked. For this reason, you may not see a revenue beat flowing through to the next quarter like you would in a ratable business model. Just because a customer consumes in a certain pattern one quarter, it does not necessarily mean they will continue those patterns going forward. For the full year fiscal 2021, we expect product revenues between 538 and $543 million representing year over year growth of between 113 and 115%. Turning to margins, we expect on a non-GAAP basis, 68% product gross margin, negative 40% operating margin, and negative 18% adjusted free cashflow margin, and we expect 255 million weighted average shares outstanding. With that, operator, you can now come to the line for questions. Operator: (16:32) At this time, I would like to remind everyone in order to ask a question, press star-one on your telephone keypad. To withdraw your question, press the pound or hash key. Please stand by while we compile the Q&A roster. Your first question comes from the line of Heather Bellini from Goldman Sachs. Please, go ahead. Heather Bellini: (16:54) Great. Thank you very much, gentlemen. Congratulations on the first quarter out of the gate. I wanted to ask a little bit, you mentioned, you know, kind- Heather: (17:03) I wanted to ask a little bit, you mentioned kind of keeping your employees home as a result of COVID until things are safe, but wondering if you could share with us a little bit about what you've noticed in terms of the pace of consumption trends as the pandemic has been going on and as people prioritize moving to the cloud. Can you share with us any anecdotal data about how customers might be even accelerating their pace of capacity usage with Snowflake? Thank you. Frank Slootman: (17:32) Yeah, Heather, it's Frank. I'll I'll I'll give you one example. We have a data set on our Snowflake data marketplace that's listed by a company called Star Schema, which provides detailed incident and fatality rates, very, very detailed, and they're updated continually. And we saw almost our entire customer base access that data within days and weeks. And when that happens, it drives consumption. That access has continued to this day. So sometimes you have catalysts in specific data sets that really help customers overlay data, run models, understand their demand environment better and so on. Frank Slootman: (18:22) It's hard to generalize. This is just sort of a single anecdote that kind of stands out to us. Michael Scarpelli: (18:26) I would add, Heather, that we do see in certain industries, like we have some customers that are in the travel industry, we see their consumption down. But we have a lot of customers that are kind of more in the online consumer world that their business is booming, and their consumption is much higher than they were forecasting. So it all depends upon the industry they're in, but on average, we're seeing our customers consume more than we would expect. That's why we ended up beating by what we did. It was a higher beat than I was expecting for the quarter. Heather: (18:57) Great. Thank you so much, guys. Speaker 1: (19:04) Your next question comes from the line of Raimo Lenschow from Barclays. Please go ahead. Raimo Lenschow: (19:09) Hey, thanks. Congrats from me as well as for the first quarter, and thanks for the presentation as well. Lots of useful information in there. Frank, question for you. You mentioned earlier about the data formulas that you were kind of dealing with and that is now doing also unstructured data. Can you talk a little bit about the evolution? Because when Snowflake started out, it was a very good relational cloud data warehouse, and then when you guys came, it became much broader. Can you talk a little bit how do you see that evolving and how quickly you see customer adoptions in the other areas of working with data coming through as well? And then I have one follow-up for Mike. Frank Slootman: (19:50) Yeah. So the overarching theme here is that we have evolved Snowflake from being a data warehouse in the cloud to being a cloud data platform. And the distinction there is a much expanding scope of workloads. For those who have not been following Snowflake that long, we actually got our start processing semi-structured data. That was really a big differentiator for us going back to 2015-16. But certainly structured relational data is a mainstay of our business. Frank Slootman: (20:30) But when you're following a cloud data platform strategy, what happens is, is that our customers are seeking broader workload support, broader data support. And we also announced a couple of quarters ago support for geospatial data and the uptake of that new data type is just enormous. So there is ferocious appetite in our customer base for us expanding the scope of our capabilities, both in terms of workloads and in terms of data types and our ability to use external services. So it's a very broadly capable platform. Customers don't want a multitude of platforms in their environments. They're very, very intent on bringing as much data as they possibly can onto Snowflake and running as many workloads as they can on Snowflake. And we're running hard to enable that. Raimo Lenschow: (21:23) Okay, perfect. Thank you. And then Mike, on gross margins, I got the 68% on gross margin kind of comment. Can you talk a little bit about the puts and take in terms of that in the long run? Obviously with bigger scale, you have more negotiation power with the big cloud vendors. How's that going to change the margins going forward? Thank you. Michael Scarpelli: (21:43) Sure. So as you saw for the most recent quarter, we just did 70% gross margin, and the implied for the full year is 68. But we're actually going to do 70% margin in Q4. As we talked about when we were going public, I do think longer term in terms of model, we can get to the mid seventies, and it's really driven by a few things. One is better pricing with our cloud vendors. We did just renegotiate deals with two of our cloud vendors, AWS and Azure, who are the majority of virtually all of our businesses running there today. And I do think as we can continue to grow, we'll be able to renegotiate those again. Michael Scarpelli: (22:27) Scale. We have a lot of deployments around the world where we're not even close to being at scale. And especially as Amia is starting to take off for us, we'll get more scale in those, which will drive better margins for us. And then also we're seeing a lot better discipline in our field around discounting. And if you see the average price per credit we're getting, it continues to increase. So those are the three things that are really going to drive that margin. Raimo Lenschow: (22:54) Okay, perfect. Thank you. Congrats. Speaker 1: (22:58) Your next question comes from Brent Bracelin from Piper Sandler. Please go ahead. Operator: (23:05) I think one for Frank and a quick follow up for Mike here. Frank, we're starting to pick up broader interests for Snowflake in the insurance industry, which makes sense, given this industry tends to be more data intensive. But I guess could you talk about the opportunity you see for Snowflake and insurance or any other vertical maybe that stood out this quarter where you're seeing outsized traction? Frank Slootman: (23:25) Yeah, I've been involved in a lot of insurance campaigns, and they're very keen on transformation more so than just modernization. Many of the larger insurance companies have very broad cross selling capabilities but do not have the systems to, from an analytical data standpoint, to support that. So they're very, very intent on sort of dislodging themselves from their historical on-premise systems and really gained the promise of data sciences and machine learning. And you need to have platforms like Snowflake to be able to enable that. Frank Slootman: (24:08) So yeah, we announced a number of insurance accounts this quarter, and we love that industry, because as you said, they're very data rich. There's enormous potential for us to really digitize if you will, really bring digital transformation to the insurance industry. So super exciting. Operator: (24:29) Okay, great. And then Mike, just as we think about the RPO momentum this quarter, I mean, much stronger than I think we had kind of thought it would be. And I know you had the benefit of a very large contract last quarter. So what drove the acceleration and kind of new bookings this quarter in that upside in RPO? Michael Scarpelli: (24:52) As we're moving more into large enterprises, and large enterprises really want to do multi-year deals. They're not interested in having to go to procurement every year. And so we saw a number of large enterprises commit to multi-year deals with us, and we think that is going to be a trend that will continue. And I will say it wasn't until this year that our sales force really started pushing more three year deals. And we're going to continue to do that going forward in the future. Operator: (25:26) Good to see you. Thank you. Speaker 1: (25:31) Your next question comes from the line of Patrick Colville from Deutsche Bank. Please go ahead. Patrick Colville: (25:38) Hey there. Congrats on the quarter. I mean, very impressive. Can I talk about the competitive environment in my first question. Just what do you guys see? Who do you see as the kind of competitors right now? And I guess what's happening in the on-premise world? Is that still kind of low hanging fruit? Frank Slootman: (26:02) Yeah, Patrick, this is Frank. Our competitive environment is very much dominated by the public cloud vendors. I think we said that over and over, and that really hasn't changed. The only time that we're dealing with on-premise environments is customers are trying to figure out how to modernize and move off of those platforms. They're really not viewed as options going forward. So it may be that our filter is biased because our competitive dynamic is very much centered on the three public cloud platforms. That is the vast majority of competitive interaction that we have. Michael Scarpelli: (26:47) I would say the only time we really come across the on-prem is where they're the incumbent, and they're really not considering keeping them, and they're evaluating with us and the other public cloud players. That's why we don't necessarily see them as competitive, but they're definitely the incumbent in many of the cases. Patrick Colville: (27:07) Got it. That's very clear. And I guess my second question is around the support for unstructured data. I mean, that's a really interesting announcement. I guess what kind of use cases are you expecting I guess in that category in the mid-term. I mean, what will be a couple of kind of things that you think customers will likely, what sort of data will they likely start with? Frank Slootman: (27:31) For sure image data, video data, I mean, PDFs, social media. And by the way, this is also where ML services are going to become, machine learning services, are going to be really interesting because say I have image scans from a webcam somewhere in the city and and I'm able to shoot that over to an external service, have it scan to whether that's a recognizable known person. There's going to be enormous potential for this. And we're, as I said earlier, we're getting pushed by our customers to really bring these different data types onto our platform, because they're just part of the types of analytical processes that they want to run. So that's sort of where we see these initially going, and these data types are large. This will definitely add to the storage side of the equation as well. Patrick Colville: (28:31) That's great. Thank you very much for taking my questions. Speaker 1: (28:36) Your next question comes from the line of Keith Weiss from Morgan Stanley. Please go ahead. Keith Weiss: (28:42) Excellent. Thank you guys for taking the question. I want to talk a little bit about Snowpark, another kind of recent announcement that you guys made. One, in terms of how does this evolve kind of the broader story, in terms of overall Snowflake and sort of getting more people engaged with the overall platform? Then if we think about the monetization strategy for something like Snowpark, and I guess the same extends to sort of the overall marketplace, is this all in the service of just sort of the more workloads you can get on the platform, the more consumption of that data, it's just more compute that, and that's really how you get paid on all of these? Frank Slootman: (29:24) Yeah, Keith, Frank. Basically we're enabling a whole different range of users on the Snowflake query engine. I mean, there are the the data engineers that use the SQL language to interrogate data. And then there's a whole another world out there that interrogates data through their language of choice. Now, notably Java and Python, and they use things like data frames to process their data. It's a very different mode. It's highly programmatic. A lot of data transformations, a lot of machine learning type of processes are executed through these languages. So what Snowpark will do is we will start hosting the language runtime inside Snowflake platform. So as it becomes a completely optimized experience. It dramatically expands the workload scope of Snowflake. So it's a very, very important direction for us. Frank Slootman: (30:29) And you're correct in terms of how does this help. Our entire business model, as we've said over and over, is based on consumption. So anything that drives consumption of our query engine in our platform accrues to our business model. So our whole strategy is driven to move the dial on consumption. And this is definitely going to do it. We have many large customers who said that this is really, really important to them because what Snowflake will bring to these environments is highly optimized efficiency in terms of the economics. Secondly, very, very highly optimized performance. And performance and efficiency are highly related. And the third one, this is super important, is governance. And that relates to privacy and security. So for customers, they really want to lock down their environments from a governance standpoint with a single platform. So to the extent that we can enable these workloads and consolidate these workloads onto Snowflake, that's really helping them. Keith Weiss: (31:33) Got it. And maybe I can sneak in one follow-up. In terms of the partnerships that you guys have been signing, any ones in particular that have been ramping up more aggressively than others, or maybe helping you guys with the new customer acquisition and moving up market that we should be keeping an eye on? Frank Slootman: (31:53) Well, the number one partner that has helped stimulate tons of activity has been the relationship with Salesforce. And we knew that was going to happen because the Salesforce data is incredibly prominent in the Snowflake world because that data typically gets overlayed with all kinds of different types of marketing data. Salesforce is not an island, and I think that's one of the reasons why our relationship with Salesforce evolved so productively because they understood that, that the data gets blended and overlayed with data coming from other sources. And that really then translates into new insight, new signals that help drive the business. So Salesforce has driven a lot of new looks and new interest in our combined offerings. Keith Weiss: (32:42) Got it. That's super interesting. Thank you guys. Speaker 1: (32:46) Your next question comes from the line of Brad Reback from Stifel. Brad Reback: (32:53) Great, thanks very much. Mike, your comment earlier around less discounting, how much of that is a function of better execution in the field by the sales force versus customers just being much more comfortable migrating to the cloud and you being able to take advantage of that? Thanks. Michael Scarpelli: (33:09) I would say a lot of it is our better discipline in our sales force, but then also given the reference customers we have and what customers are seeing, they're more willing to move to Snowflake as well, too, which makes the procurement process easier. Brad Reback: (33:26) Great. Michael Scarpelli: (33:26) But what I will also tell you today is the product we're delivering today is so much better than what it was three years ago or two years ago, continues. And it becomes more, the performance on it every year, it gets better. And better as a result, customers should pay more for it. Frank Slootman: (33:43) Because they save money. Michael Scarpelli: (33:43) Yeah. Correct. Brad Reback: (33:46) Excellent. Thank you very much. Speaker 1: (33:51) Your next question comes from Mark Murphy from JP Morgan. Please go ahead. Mark Murphy: (33:57) Yeah, thank you. I'll add my congrats. Frank, where do you think we are in the cycle of COVID- Speaker 2: (34:03) Frank, where do you think we are in the cycle of COVID impacts, specifically on the new logo bookings piece? I think Chris Degnan had said recently that customers that were hesitant to start their cloud journey are moving faster now. And so it looks like a bit better quarter for your customer ads. Are you feeling that incremental improvement in the new logo bookings kind of relative to where that was in the March and April timeframe? Frank Slootman: (34:30) Yeah, I think that the demand sentiment was stronger in this period than it was in the first half of the year. And COVID is definitely one aspect to it. I certainly think that our public market debut also added to sort of the general energy and notoriety, if you will. It just became kind of a catalyst for a lot of people wanting to find out more and really take the relationship to the next level. So it was a tailwind in many ways. And that certainly was one of the reasons why we wanted to take the company public is to really raise the stature of the company in the marketplace because we sell to the largest institutions and enterprises in the world and we're also competing as the largest ones in the world also. So these things are important. Speaker 2: (35:24) As a quick follow-up, could you help us understand how aggressive your ambitions are going to be in the data science arena? You've touched on it a bit, but for building machine learning models and AI algorithms, and also for handling log data or machine data, is there equal customer pull in both areas or is it stronger in one or the other? Frank Slootman: (35:48) Oh, we do a ton of work in machine data and log data. I mean, security data lakes is a growing use case that has a ton of interest. And we already have very, very large deployments of those. So that's an area that is very exciting. As far as machine learning goes, what we are doing is we're really enabling ourselves architecturally to really accommodate machine learning services so that we can really take advantage of all the technology that is out there. Frank Slootman: (36:21) What you're not going to see from Snowflake is that we're going to go buy something and then go it alone and sort of shut out our partners. Our architecture and our strategy is very much on embracing and enabling all the capabilities that are out there. Customers have tons and tons of choice. They can use ready-made services, they can use the tooling for building new services. And that is in contrast to some of the competitors that we have in the marketplace. So we have very specifically chosen for that path. But we're going to be super, super active in data sciences. Why? Because they are the ultimate consumer of the Snowflake cloud data platform. Speaker 2: (36:59) Thank you. Speaker 1: (37:03) Your next question comes from the line of Patrick Walravens from JMP. Please go ahead. Patrick Walravens: (37:09) Oh, great. Thank you. And let me add my congratulations. So Frank, you mentioned at the beginning that you're very focused on moving up market. And I'm just wondering if you could provide some more color on what that entails from an operational perspective for you guys. Frank Slootman: (37:24) Yeah, so we talked about this quite a bit during the road show, but when we joined the company, Snowflake was mostly a sort of a commercial selling, high density, relatively small contracts, but high volume and fast sales cycles, quick adoption. And the company really liked that model. But us coming in says we just know that in order to win in markets, you have to knock down the biggest, most iconic logos in whatever city, state, country, or region that you're in. So we really had to pivot our organization, or I should say enhance the organization to layer in a selling strategy and a sales motion that campaigns these very large institutions. And we went as far, and we were a private company at the time, of course, we did open heart surgery on our sales organization and we separated it between a sales organization that campaigns the largest 250 accounts in the world and then sort of the rest of the large enterprises and them were segmented even more on the low end also. Frank Slootman: (38:38) And that's because the type of salespeople that we have to hire, the type of legal contractual posture that we have, the security team that we have, the type of compensation plans that we need to have, all different when you're comparing these large accounts because they're long campaigns, they're very political, they're very challenging, and requires tons of senior management involvement. So we've been working on these now for well over a year. And as Mike pointed out in his prepared remarks, we make a ton of progress. And we expect to become very heavily weighted over time to sort of that part of our business. Because these large accounts, I mean, the amount of money that they will spending on these type of services is going to be much larger than anybody is currently really understanding because this is becoming so important, so core to how they do things. So yeah, it's a very important shift or evolution, whatever word you you want to use, so that we are very aggressively oriented towards the largest companies and institutions in the world. Patrick Walravens: (39:48) Okay. That's super helpful. Thank you. Speaker 1: (39:52) Your next question comes from the line of Tyler Radke from Citi. Tyler Radke: (39:58) Hey. Thank you very much. And congratulations on the first quarter here. I wanted to ask you a little bit about the guidance for Q4. Obviously, you saw really strong RPO growth in Q3. And I'm curious if there were any factors that had perhaps a short-term negative impact on the Q4 revenue guidance. I know oftentimes when you sign these big deals, customers, through volume-based discounts, get effectively a lower consumption credit rate. So I'm wondering, I guess, if almost the RPO strength that you saw this quarter and the large commitments that were signed, if that had any type of negative impact on your expected growth rate for revenue in Q4. Speaker 3: (40:51) No. You have to remember, a lot of that RPO are new deals that we were booking. And as I said before, it takes customers to ... We have a lot of big on-prem migrations that are going on, and until those ramp up, and it could take six months plus, you don't really see any revenue from that. And so a lot of that won't flow through until next fiscal year. Tyler Radke: (41:18) Got it. Helpful. And on data sharing, I think you talked about it's a pretty healthy mix of your customers that are using Snowflake for data sharing, I guess a couple questions there. What are you seeing more at this point? Is it kind of an internal data sharing use case, or is it the external? I know you did talk about over 100 data providers. And then secondly, I mean, I know it's still early, but have you seen any types of trends where once customers use data sharing, you see consumption rise by orders of magnitude? Or just help us think through how much that consumption potential is once customers move over to a data sharing use case. Frank Slootman: (42:08) It's Frank. The important thing to understand is that both gaining and providing data access to external sources, whether they are inside of an institution or whether they are external, is becoming a main part of data operations. Historically, we used file transfer protocols, APIs, to move data from A to B. It was logistically incredibly hard. It was very governance challenged. People did it because they had to, but they absolutely didn't like it. So here comes Snowflake with a frictionless, seamless data sharing model. And all of a sudden it's like, "Hey, part of our data operations is that we both gain and provide access to, in the case of large financial institutions, they have hundreds and hundreds of data sets that they gain access to and provide access to. So data sharing is part of data operations. We're just, for the first time now, really providing an extremely strong model, operational model, to be able to do that. And I know historically, people just didn't do as much of this because it was so damn hard. Frank Slootman: (43:27) Now I will tell you that job one for most of our customers is, well, first I got to get my data to the cloud. Then I got to, due to database migrations, which is usually the biggest part of the transitions, this is just modernization. And then we start thinking about transformations in terms of running processes that we've never run before, things that are now possible that couldn't be done before. And typically, data sharing is sort of the frontier, if you will, of the new things that are now possible that people have never even considered before. I mean, it was not even an option. And they were so preoccupied with just doing things in terms of blocking and tackling that they had never gotten around to really, fully examining and understanding what is now possible. Frank Slootman: (44:15) So data sharing is usually not the first part of the conversation, it's something that sort of evolves over time. And what we are seeing though is that we're now getting into deals where people already have strong incumbents on the cloud relative to existing workloads. And the data cloud and our data sharing capabilities are busting open accounts where normally, on a workload basis, we would not really have that much opportunity or the customer incentive to even look at us. So the sales force loves that about us. It's a conversation where we're hitting 1000. Okay? It just never fails to trigger a new interest. So we like it for all of those reasons. Speaker 1: (45:05) Your next question comes from the line of Ittai Kidron from Oppenheimer and Company. Please go ahead. Ittai Kidron: (45:12) Thanks. Hey guys, again congrats on the first great quarter out of the gate. A couple of questions from me, maybe one for your first, Frank. I want to go back to the machine learning and comments you made earlier. But I just want to make sure I understand this and get this right. Do you intend or not intend on moving into the machine learning algorithm space like Databricks or DataRobot? I want to make sure I understand your interaction with machine learning. Are you going to be an enabler? Or are you actually going to provide a full stack platform for machine learning? Frank Slootman: (45:45) Well, we're going to do both. What I did say was we don't sort of want to go to the marketplace and say, "You're going to have to ours and that's it." There's enough people that operate in that mode. So we're going to have a much richer, wider, broader set of choices for customers. Certainly what my comments about Snowpark, I mean, considering now we're going to have Java, Python language runtimes on our platform, that's going to be a key enabler for people to develop these kind of capabilities and use the kind of capabilities that they're already using today. So it's really bring those workloads that already exist or that are being newly developed to bring the insight, the Snowflake platform. Frank Slootman: (46:32) So we're not going to be a company where we have our own flavor of everything and our partners are all going to be secondary. We want to actively encourage development on our platform, participation on our platform. Because again, we're a consumption company. So the way we drive our strategy is to drive activity on our platform, right? I mean, if we get activity on our platform, whether our product drives it or a partner drives it, it yields the same result, right? So we'd be crazy to ... Because the way our model works, we don't have to prioritize our own product. Ittai Kidron: (47:11) Got it. And maybe flipping it to the other side of the chain then, clearly your economics get much better and better the more and more data resides on your platform. And so moving data into your platform is absolutely critical for you. How do you think about the data integration layer, which is highly fragmented and has multiple layers to it? How far down the data integration side do you want to move in order to enable better and faster and smoother movement of data into a platform? Frank Slootman: (47:41) Well, the data engineering side of our business is already a huge part of what drives the consumption on our platform. And everything that we're doing with Snowpark is obviously focused on that as well because so much of that work is going to be data transformation oriented. So no, it's a huge thing. But we're going to do things that are Snowflake optimized, right? The sort of processes that run all the way from ingestion and transformation, bringing data on the platform, running analytical process to make that as seamless and as frictionless as possible. And our partners in the ecosystem, they serve many masters. We only serve one, and that's Snowflake. Ittai Kidron: (48:28) Got it. Okay, good. Good luck, guys. Thanks. Speaker 1: (48:33) Your next question comes from the line of Gregg Moscowitz from Mizuho. Go ahead. Gregg Moscowitz: (48:39) Okay, thank you. And I'll add my congratulations as well. I wanted to go back, if I could, to the topic of bringing unstructured data into the Snowflake platform. Frank, as you mentioned, Snowflake began its life as a semi-structured data company. And I'm curious how much of a technical challenge it is or has been for you to support and to transact on images, videos, PDFs, et cetera, and to do so at scale. Frank Slootman: (49:02) Well, it obviously is not a no brainer. Otherwise, we probably would have done it a long time ago. But our technical teams are now heavily invested in making this happen. I mean, our announcement was very much the very first time we've talked about this openly. I expect that in our next Snowflake Summit event, which is in June of next year, we will have extensive technical details and presentations on this topic. So we'll be communicating on this topic in full detail at that time. We're certainly not prepared or ready to do that right now. Gregg Moscowitz: (49:43) Look forward to that. And then just as a follow-up, so 23% of customers already using data sharing. Really impressive for a service that frankly hasn't been available all that long. How does this uptake compare to what your expectations were at this stage? And then also, where can this go longer term? Frank Slootman: (50:01) Yeah, I think longer term, I mean, I think everybody's going to be doing this. As I said earlier, this is going to become core to data operations. The whole notion of a data cloud is you're not just running data sciences against your own data silos. You're going to be running it across disparate data sets, meaning data that lives inside your world, outside of your world. And that's really what data science is all about is to build these highly descriptive models that can then be used predictably and prescriptively to really get closer to business outcomes. So in order for the data cloud to really happen as a concept, this is going to become incredibly mainstream. That is our general strategic assumption about data. It is not going to stay where it has been historically because we were essentially encumbered by technology and we're not anymore. It's wide open. So I think customers are learning and slowly sort of gaining an appreciation what all the new possibilities are. I mean, I said in my prepared- Frank Slootman: (51:03) Gaining an appreciation what all the new possibilities are. I said in my prepared remarks, technology is now well ahead of people's ability to use it. Right? And people are scratching their heads like, "Wow, this is just unbelievable, the scale, the scope, the power that we have. How do we use it?" And that's where we are. Really exciting, but nobody has the playbook ready to throw at it. So it's a bit of a journey that we're on, everybody will be on. Speaker 4: (51:29) That's great. Thank you. Operator: (51:33) Your next question comes from the line of Derrick Wood from Cowen and Company. Derrick Wood: (51:39) Great. Thanks and congrats on a strong quarter. My first question, I wanted to ask about a couple of different regions. First on Europe, I think you guys have been building out a major accounts focus there. Would love to hear about how that's tracking versus what you've been experiencing in the US. And then in the US regarding the Fed, I think you guys have received some new FedRAMP certifications this year. Can you just talk about how you're thinking about the opportunity with the government looking out over the next couple of years? Frank Slootman: (52:11) Yeah. We had a really good quarter in Europe. I was very excited about that. Europe was definitely a region where we had to rerack ourselves in terms of our large account orientation. Europe is even more sensitive to major account campaigning because that's how these markets operate. They really look to their own iconic enterprises to see what kind of positions they make and then the whole market follows. So you simply cannot afford to be in those markets and not campaign those large enterprises. So it's a fairly substantial reboot for us to be able to have that posture. But we're seeing it pay off and I have very high expectations and high hopes for Europe. And we're going to be spending time there when we can travel again to help campaign those institutions. A very, very good opportunity. Frank Slootman: (53:11) As far as the Fed goes, the hard part about federal government is always getting them on the cloud. They just have historically been incredibly slow to move to the cloud for all kinds of reasons. So the FedRAMP certifications, we have achieved some certifications that are helping. We need higher level of certifications to really enable that business further. And that is really the gating factor, if you will, for the Fed really becoming a solid contributor to the business, which they're not yet. Derrick Wood: (53:48) Got it. And then maybe one for Mike. You had a nice rebound in that revenue retention rates, but it has bumped around a bunch over the last few quarters. So can you just walk us through the mechanical drivers in terms of what you've seen in the last couple of quarters and maybe how to think about, how you see those numbers settling out over the next couple of quarters? Michael Scarpelli: (54:08) Well, when we were going public, we had mentioned that we fully expect it would be above 160% this quarter. So came into where we were expecting. These are extremely high net revenue retention rates. I think over time, it will come down, but I'm not expecting a steep decline. And I think it's going to be pretty stable for the next little while. It's not something we're going to be guiding to. I just knew after last quarter that this quarter was going to go higher because it was the cohort that had just come in from the two year ago period, that it was just the nature of some of those customers. Derrick Wood: (54:47) Okay. Got it. Thanks. Operator: (54:51) Your next question comes from the line of Gray Powell from BTIG. Please go ahead. Gray Powell: (54:57) Great. Thanks for taking the questions and yeah, I'll echo my congratulations on the results. So maybe a couple of questions for Mike. As you look at your growth forecast over the next 12 months, how much of it do you feel like you have visibility on from the installed base today, the customers you just won in natural growth and usage? And then how much do you think you have to go out and win? Michael Scarpelli: (55:26) First of all, going into a quarter, virtually all of our revenue is coming from our existing customers. Very little comes from customers that are landed in the quarter. And it's less than 5% come from on-demand customers. Over the next 12 months, it's still the vast majority of our revenue is coming from our existing customers that we have. And we do see the growth in those customers. Sure, we need to sell them more, but most of our growth is coming from our existing customers over the next 12 months. Longer term, it's super important that we acquire new customers because those will be the growth drivers in two, three years out. Gray Powell: (56:10) Got it. Okay. And I just want to make sure I'm thinking about the Q4 guidance correctly. So if I just look at the last two quarters, product revenue increased by about 23 million sequentially in both Q2 and Q3. I would think that usage is seasonally stronger in Q4. So is there any reason why product revenue would only grow by 16 million sequentially in the quarter? How should we think through that? Michael Scarpelli: (56:35) No, it all depends upon the type of customer you are and depending upon what you're doing. There's also a number of our large customers that we've given that are getting more mature right now too. And their growth has slowed down somewhat with some of these large customers. But there is seasonality within our customer base. And I would not assume Q4 is what's driving their consumption at all. Quite frankly, in Q4, there's a lot of holidays. You have Thanksgiving, you have Christmas, and you do see decrease... And then New Years. You do see decreases because employees aren't in the office driving analytics. Gray Powell: (57:14) Got it. Okay. That's really helpful. Thank you very much. Operator: (57:19) Your next question comes from the line of David Hynes from Canaccord. Please go ahead. David Hynes: (57:25) Hey, thanks guys. I want to ask a follow-up on the strong net revenue expansion. Frank, when you talk to CIOs at some of your larger customers, I'm curious if you pick up any concern about the pace at which their Snowflake spend is growing? Or if it's still that there's such price disparity versus the incumbents that it's not an issue? I'm curious how you think about balancing that in a consumption based model. Frank Slootman: (57:51) So here's the important thing. I already mentioned it a little bit earlier. So Snowflake is a variable paradigm. Right? In other words, this is not fixed. People can consume as much and as little as they have appetite and budget for, which is why you have this buoyancy in net revenue retention rate, because people because they now can, they start doing things that they could not do before. The big thing, and by the way, it's not CIOs that are upset. It's usually CFOs that are... They can't understand how you go from one period to the next, and all of a sudden the number has doubled. But the business is telling them, "No, we need to do this." So people are getting used to spending way more money on this class of service than they ever imagined. And the reason is they now can, and they now need to. Right? Frank Slootman: (58:46) Now does that take time? Yeah, it takes time for people that get used to that. I talked to CTL. They said, "We spent 50,000 last year, we're spending a million this year. How the hell do I explain that?" Well, those are the types of transitions that we're going through. We have customers where we are the second largest line item behind Amazon AWS. Right? Well, that's a little getting used to for a customer, that you're spending on a service like this at this rate. But when data becomes the beating heart of your enterprise, it makes total sense. It's just that it doesn't make sense in a historical context. And it takes time for us to transition to this realization that this is pretty damned important. Frank Slootman: (59:29) And that's where sometimes it goes a little bit too quick and people are like, "Holy moly, this thing is a little bit too slick and too fast. And it's just too easy to fire up a cluster, a warehouse. And instead of running this process once a week, we're now running it every night." Because they see advantage in doing it. Right? But we're seeing people getting used to large numbers. Quite honestly, in my own history in software, I'm just stunned by the size of the relationships that we're seeing in this business. And it's an amazing thing. But it does take time for people to really transition to the realization that this is going to have a big economic footprint. David Hynes: (01:00:13) Yeah. Yeah. Makes sense. Obviously good problems to have. Maybe one quick follow up if I could. We've talked about the network effects that come from the data exchange. In the near term, what's more important on that front in your view? Is it getting more customers to participate or is it getting more data providers on the network? Frank Slootman: (01:00:33) Yeah, I don't know that we need to trade those off, quite honestly. We can walk and chew gum, if you will. So we're operating on both those vectors at the same time. In terms of data providers, it's sometimes it's more of a qualitative than a quantitative thing because they're not all created equal. So some of those... And by the way, it's very vertically oriented in the types of data that we're dealing with. So it's a fairly nuanced thing. It's very important for us that we enrich the data cloud with new data all the time, because there's going to be more and more customers who the reason to be on Snowflake is not just the world-class execution capabilities that we have, but they need to be there because their partners are there, their data providers are there. That after a while starts to break the bow because it becomes a no brainer. But we have to have both. If you don't have the workload execution capabilities, the data richness will not save you. You need to have both. David Hynes: (01:01:38) Yeah. Very helpful and congrats. Frank Slootman: (01:01:41) Thank you. Operator: (01:01:43) Your last question comes from the line of Brent Thill from Jefferies. Brent Thill: (01:01:49) Thanks. Just on the go-to-market as we go into next year, in terms of your quota carrying sales capacity, and your build-out, how are you thinking about what you're adding to the system? Are you accelerating the build-out here, given what you're seeing with the economy starting to stabilize a little bit? Just maybe walk us through your thoughts and how you're thinking through the shape of that go-to-market? Michael Scarpelli: (01:02:15) Well, what I would say is we're very much focused on first our geography. AMIA is one that we're investing very heavily in and adding a fair number of people there as a percent of what they have. We're clearly focused on going after the largest enterprises in the world. And so we are continuing to add reps there. I would say we really never slowed down given what was happening in the economy. You can see that year over year. In Q3 of 2020, we had 900 people in our sales organization and we've added roughly almost 300 people into our sales organization over that period of time, a year. And we will continue to add at that similar pace going forward next year. I would say the other area we're really adding as we get more into the major accounts, we're adding a lot more SEs into our organization because majors require more one-to-one relationship, where in the past we never did. So I would say is we are really going after those larger accounts and enterprise more so than SMB. Frank Slootman: (01:03:27) Yeah. The only thing I will add to that is that we're also in a massive push to drive verticalization end to end in our selling motions. And then that means that quota's going to be overlaid an added by vertical as well. So you see our capacity to sell will grow. And it's really nice that we have it geographically, we have it by large enterprise accounts, and we're going to have it by vertical. So we're very excited about the company verticalizing, because we're having great businesses on the technology side, on the media side, retail consumer packaged goods, healthcare. So that's where a lot of our selling capacity is going to grow in the coming years. Michael Scarpelli: (01:04:12) And I'll add, Brent, too that we've really been focused in the last 12 months on the system integrators, the global system integrators. We're adding more people there. The likes of Deloitte and Accenture are building real practices around Snowflake. And we're going to continue to invest in those because they are starting to drive meaningful business to us. Brent Thill: (01:04:34) Great. Welcome back. Thanks. Michael Scarpelli: (01:04:36) Thank you. Operator: (01:04:38) And that was our last question. Ladies and gentlemen, this concludes today's conference call. Thank you for participating. You may now-