Cloudera, Inc.
Q1 2018 Earnings Call Transcript

Published: 9 Jun 2017

[Operator:]

Good afternoon my name is Leandra and I will be your conference operator today. Welcome to the Cloudera first quarter fiscal 2018 quarterly results conference call. All participants' lines have been placed in listen-only mode to prevent back ground noise. After the speakers' remarks there will be an opportunity to ask questions. [Operator Instructions] Please note this conference is being recorded. Your host is Kevin Cook, VP Corporate development and Investor Relations. Kevin, you may begin your conference.

[Kevin Cook:]

Good afternoon and welcome to Cloudera's first quarter fiscal 2018 conference call. We will be discussing the results announced in our press release issued after market closed today. From Cloudera with me are Tom Reilly, Chief Executive Officer, Mick Hollison , Co-Founder, Chairman and Chief Strategy Officer and Jim Frankola, Chief Financial Officer. During the course of this call we will make forward-looking statements regarding future events and the future financial performance of the company. Generally, these statements are identified by the use of words such as expect, believe, anticipate, intent and other words that denote future events. These forward-looking statements are subject to material risk and uncertainties that could cause actual results to differ materially from those in the forward looking statements. We’re caution you to consider the important risk factors that could cause actual results to differ materially from those in the forward looking statements in the press release and this conference call. These risk factors are described in our press release and more fully detailed under the caption, risk factors, in the final prospectus related to our initial public offering and our other periodic filings with the SEC. While there is final prospectus under rule 4/24/b4 which also includes an explanation of our net expansion rate was filed with the SEC on April 28 2017. During this call we will present both GAAP and non-GAAP financial measures. Non-GAAP measures excludes stock based compensation expense amortization of acquiring intangible assets and charitable contributions to the Cloudera foundation. In addition, we provided a non-GAAP weighted average share count that assumes the conversion of our proffered stock and common stock and the weighted impact of common shares issued in our IPO as if the issuance occurred on the date of effectiveness. These non-GAAP measures are not intended to be considered in isolation from substitute for or superior to our GAAP results and we encourage you to consider all measures when analyzing Cloudera's performance. For complete information regarding our non-GAAP financial information the most directly comparable GAAP measures in the quantitative reconciliation of those figures please refer to today's press release regarding our first quarter fiscal 2018 results. The press release has also been furnished to the SEC as part of the Form 8-K. In addition, please note that the date of this conference call is June 8, 2017 and any forward-looking statements that we make today are based on assumptions that we believe to be reasonable as of this date. We undertake no obligation to update these statements as a result of new information or future events. Now I'll turn the call over to Tom.

[Tom Reilly:]

Hello, thank you everyone for joining our inaugural quarterly results call. Our first quarter was a record one where the company delivering outstanding results. We will go into detail in a moment but first I would like to pause and acknowledge our successful IPO as a result of many years of innovation delivered by dedicated employees our ecosystem and partners and community and our global customer base; as we collectively pursue a very large market opportunity driven by major secular trends in cloud, Internet of Things and machine learning. I also wish to thank our investors, both our early investors who have been with us for many years and our new public ones who have recently joined us on this great journey. In Q1 we executed well across the business. We achieved revenue of $79.6, million year-over-year growth of 41% and we delivered a net expansion rate of 142%. Our subscription software revenue grew 59% year-over-year. From a cash flow perspective, we are pleased to report that we generate positive cash flow of $5 million in the quarter due to strong collections. However, we do not expect this to repeat in the near-term. In addition to strong financial performance we continued our technology leadership and cloud analytics, IoT and machine learning innovation. Our engineering team delivered three new product advancements in these areas. We are excited about the initial market response and prospects for these offerings. Mike Olson will explain the importance of these three new products announcements shortly and then Jim Frankola will review our financial results and outlook. Since we cannot visit with all of you on our IPO road show and many of your new Cloudera shareholders, let me quickly levels set on our business and market opportunity. Cloudera is helping to solve some of the world's biggest problems with data. The world is becoming hyper connected. By 2020 there will be 30 billion newly connected devices on the planet. Generating immense amounts of new data at an unprecedented rate. Enterprises need and want to capture and analyze this data and to apply machine learning and advanced analytic techniques so that they can get some data and insight [ph] driven. Our modern platform for machine learning advance analytics empowers organization to collect store and act on vast amount of data from a variety sources including the Internet of Things to transform their businesses. Our customers are transforming their businesses in three areas. First, they are gaining new customer insights to better serve and market to their customers. Second, they are building newly connected products and services in the IoT world gaining new insights into how products are being used, predicting maintenance needs and tailoring services in real time. And third, they are using data and machine learning to manage risk and protect their businesses in the areas of cyber security, fraud, any money laundering, and other regulatory used cases. Traditional technologies for collecting, storing and analyzing data are inadequate and this era of big data and machine learning, they are technically incapable and too expensive. Organizations require a distributor platform that are not only designed for this purpose, but also satisfies the performance, governance, compliance and security demand on a large enterprise or public sector entity. Most of all the platform must scale cost effectively and run anywhere, cloud, multi-cloud and on-premises. Cloudera offers the leading cloud native software platform for machine learning and advance analytics. We allow enterprises to operate, manage and move workloads across multiple architecture, mixing on-premises and cloud environments, including all major public cloud infrastructure providers, including Amazon Web Services, Microsoft Azure and Google Cloud. Our market opportunities expanding its enterprise to shift data and workloads to the cloud, allowing them to address a whole new set of elastic and transient job, that have been impractical to run in the data center. Our hybrid open source software model combines the best of open source software with Cloudera's proprietary software that meets the exact requirements of large global enterprises and public sector entities, especially in the areas of performance, data security and compliance. These capabilities are not available in standalone open source or from other vendors. Based on these enterprise class differentiators, from a go to market perspective, we primarily focus our selling efforts in companies with more than $1 billion of revenue. We call these businesses the Global 8,000. In Q1, we added 15 Global 8,000 customers and now have more than 500 customers in the Global 8,000, an increase of 30% over the fiscal quarter of fiscal '17. This increase puts us on track to achieve our targeted attainment for the full year of approximately 120 net new Global 8,000 customers. One of the most compelling attributes of our machine learning and advance analytics platform is that it has broad applicability across hundreds of used cases. This quarter, we had some great new customer wins while some key existing customers expanded the utilization of our platform through additional use cases. I’ll quickly share three examples that illustrate the diversity of use cases particularly across our focus areas of machine learning, Cloud and IoT. First, an interesting and very topical use case is Thomas Router's news tracer tool, which uses our platform to capture millions of tweets every day and apply machine learning algorithms to help Router's 2,000 global journals and clients identify true breaking new and filter out fake news. Along with an internal knowledge base of reliable sources, tracer generates voracity and news worthiness scores which enables Router's to begin us reporting ahead of the competition with higher quality. Second, Komatsu Mining has built an industrial IoT base service that utilizes Cloudera to collect and analyze data from mining equipment. Operating in some of the most remote locations around the world, where to Komatsu built their datacenter for this, they choose the cloud. A single piece of mining equipment can generate 50,000 unique timestamp records every minute. Analyzing this data in our software platform running on Microsoft Azure, gives Komatsu a complete picture of machine health and operations in its global mines and enable Komatsu together with its customers to identify ways to improved equipment performance and utilization rates. Finally, we believe that the market leader must deliver its platform across the major cloud vendors, as well as on-premises. Experian has long that customer, recently expanding use cases to Amazon web services. Experian uses machine learning algorithms in the Cloudera platform in the cloud and on-premises to solve complex credit underwriting challenges. In addition to these exciting customers used cases, a vibrant ecosystem of partners is building a growing range of applications on our platform that produce powerful network effects on our business. For example, our partner MedStream [ph] is building a healthcare analytics application on the Cloudera platform for rush university medical center to enable two key initiatives. The first relates to incorporating doctor’s note into electronic health record to rapidly identify correlations from more effective patient care. MedStream and Cloudera provide a solution that enables real time analysts of patient records and uses natural language processing on the doctor’s note to enhance accuracy of information and decisions. Through the precision medicine initiative, the second program reduces genomics processing and announce this time, when performing machine learning at scale. And also integrate genomics and clinic data to improved treatment and patient outcomes. As evidenced by the MedStream example, partners generally favor our platform when developing solutions for real world business problems especially mission critical applications in regulated industries. In addition, our large partner ecosystem in more than 2,600 companies globally affords us international reach, industry expertise and technical prowess. These partners accelerate adoption of our platform and create leverage for our sales organization. Before Jim covers a business model and our Q1 financial performance, Mick Hollison will reflect in the founding of the company, innovation, and our role in shaping the industry that he helped to create.

[Jim Frankola:]

Thanks Tom. Hello, everyone. I’d like to add my thanks to our ecosystem of partners and community members. When we founded Cloudera, we believe the data had the potential to help some of world’s very biggest problems. We also believed that the power of the cloud and the merits of open source software development were essential to unlocking that potential. We’ve been fortunate that the market is largely developed as we expected. We’re particularly encouraged that cloud infrastructure has finally come to have the effect on the big data and analytics market that we anticipated at our founding, that’s why we called the company Cloudera. Workloads moving to the cloud take advantage of the native scalability of our platform and our investments in cloud analytics operations and pass over recent years. Today, CIOs insists on open source in their software infrastructure. They want an open infrastructure that’s flexible and can incorporate the latest technologies and is future proof. When we found the company, to ensure that we built an attractive long term commercial enterprise, we knew that we have the complement these open source technologies with proprietary software, tailored to meet the specific requirements of large organizations. We were convinced than a different type of business model was necessary in order to invest in innovation and in making our customers successful. That model Hybrid Open Source software or HOS combines the best of open source software development and technologies with differentiated proprietary software. We make open source software accessible, reliable and safe for large enterprises to deploy in their own data centers and in the cloud. It's our proprietary software that delivers enterprise grade data governance, compliance, management and security including encryption and key management as well as platform as a service cloud offerings. Our innovation in both proprietary and open source creates a unified highly integrated platform that offers our customers and partners compelling benefits. When we choose to innovate in proprietary and in open source software, we do so in order to expand our addressable market and to increase our sustainable competitive advantage. And we benefit from the innovation of the global open source community, as contributors and as members of that community, we embrace fully integrated and incorporate the extraordinary development that occurs in the community. As the category leader, we benefit disproportionally from open source innovation. For example, our software is often used as the enabling platform for traditional enterprises performing machine learning. We were the first data platform vendor to incorporate Apache Spark and open source project known primarily for its machine learning capabilities. We currently have more than 550 customers running Spark to often in combination with third party and open source algorithms on our platform making Cloudera the most popular framework for machine learning in the enterprise. As Tom highlighted at the outset, we recently made three substantial new product introductions relating to IoT, machine learning and Cloud. Early in the quarter, we announce Apache Kudu as generally available for production. Kudu is a one of a kind open source data store designed for IoT, time series and data base applications. Kudu simplifies and streamlines the development and delivery of real time IoT analytic applications. We also introduced Cloudera Data Science Workbench, a self-service data science tool that enables data scientist across in enterprise to collaboratively build scale and deploy machine learning solutions using the most popular programming languages and deep learning frameworks. Our data science Workbench delivers to the market, the IP that we acquired when we bought sense, a data science and machine learning stat up in 2016. Finally, we made a significant advancement in our cloud capabilities with a general availability of Cloudera Altus, our Platform-as-a-Service offering. We are pleased to see strong interest in the GA of Altus and we are optimistic about the roadmap there. Altus is designed to deliver the speed convenient and elasticity of public cloud infrastructure, easing the creation of new cloud workloads and accelerating the migration of existing workloads to our platform running in the cloud. We believe that the release of Altus will accelerate cloud based usage of our platform. You should expect the Altus family in our product portfolio to expands to other offerings and other clouds. I’ll wrap up with a note on our strategic partnership with Intel. The funding that we received from Intel and 2014 allowed us to invest in machine learning before it was fashionable and to invest in cloud ahead of our customer’s willingness to move workloads there. Fundamentally, our partnership and collaboration enable us to optimize our software for the cloud and for hardware architectures of the future. Together, we maintain a joints development roadmap. Cloudera gets insight that allows us to improve performance of our software based on knowledge of chip design and hardware advancements. This gives us significant functionality and time to market advantages. We are extremely excited about some of Intel’s recent initiatives and machine learning autonomous driving and artificial intelligence. We look forward too many years of collaboration with Intel.

[Tom Reilly:]

Thanks Mike. It is a true honor to be a partner as we execute the vision that you and the other founders put in place nine years ago. Jim will now review our business model and discuss our Q1 financial results in detail. Jim, please

[Jim Frankola:]

Hello everyone. I am pleased to report that we had an excellent first quarter. To Provide context, I’ll begin with reminder about the market and Cloudera’s differentiated business model. Our addressable market is large and rapidly growing with both transformative and disruptive elements in scope. We helped create the big data industry have defined a new market category and are extending our category leadership with innovation and meaningful differentiation. As a result, we are growing rapidly at scale. The licensed our software platform primarily on a subscription basis. This results in a sticky recurring revenue stream representing more than 80% of total revenue. Cloudera's hybrid open source software model allows us to benefit from open source development, attract customers more readily and create competitive motes. We are focused on building and enduring enterprise software company. As Mike mentioned, the Intel funding allowed us to invest aggressive in machine learning and cloud together representing roughly half of the future development in R&D. We invested early and aggressively in international expansion and a global partner network giving us more than 90% coverage of our target market. Cloudera’s business model is also shaped by our focus on its largest enterprise in the world. The Globe 8,000 as well as large public sector entities. The word’s data and data related spending is concentrated in these segments and we have optimized our engineering and go-to-market approach for these customers. This focus is built into the fabric of our land and expand model, which we believe to leads to powerful customer lifetime value economics. A high degree of visibility and substantial operating leverage stems from our reoccurring revenue model and strong dollar based net expansion rates. Finally, we benefit from multiple growth vectors. As customer data grows, revenue grows. As new use cases are deployed, revenue grows. And as partners builds applications on our platform, revenue grows. Note that a relatively small portion of revenue relates to professional services and training, which are focused on ensuring customer success and driving expanded use of our platform. Recap in Q1, we measure performance primarily on subscription software revenue and growth. Subscription software revenue in Q1 with 64.7 million, an increase of 59% from a year ago period. This represented 80% of revenue up from 72% last year. In total, revenue was 79.6 million for the first quarter, representing 41% growth over last year. There are couple of metrics that we plan to share each quarter to provide more insight into our business performance. We believe that these are useful indicators of the progress we are making in our landing expand model, although it's important to look at these metrics in the aggregate as we do not intend to optimize for any single metric. We added 15 net new Global 8,000 customers in the quarter. Bringing the quarter end, Global 8,000 customer count to 510, this compares to 393 Global 8,000 customers at the end of Q1 of last year. While we opportunistically added many more customers outside the Global 8,000 we feel that it is most important to track the largest enterprises where we have the greatest expansion opportunity. We were especially pleased to maintain our 142% net expansion rate in Q1. Recall that our net expansion rate factors retention, expansion and churn on a dollar basis. More than any other metric this one exemplifies the power of our land and expand model and its attractive lifetime value economics. We have more than 30 customers with analyzed ARR in excess of $1 million collectively contributing more than 40% of our subscription software revenue. Perhaps most encouraging is that we have plenty of room for growth within this segment, we have penetrated only about 6% of the Global 8,000 and have thus far captured this a small portion of our customer's data related spending. As I review the remainder of the income statements note that unless otherwise stated all references to expenses and operating results are on a non-GAAP basis. Historical non-GAAP results are reconciled to GAAP result in the press release issued earlier today. In Q1 subscription gross margin was 84%, over 500 basis points higher than in the year ago period. As we look ahead we expect continued margin expansion as software revenue grows faster than services revenue and as workload shifts to the cloud. Services gross margin for the quarter was 11% versus 29% a year ago. As compared to our subscription revenue services revenue and margins have a high degree of quarter-to-quarter availability based on the timing of project work, the level of participation from our partner ecosystem and the nature of customer subscription agreements. Services revenue recognition is often differed when services are sold at the same time as subscription software, while services costs are expensed as they are incurred. These timing differences sometimes lead to substantial revenue and margin variability. Importantly, blended total gross margin for Q1 with 70%, up 500 basis points compared to 65% a year ago. Our market is substantial, but still early in its development. We are making disciplined and targeted investment to secure the right customers and extend our category lead. Turning to operating expenses, sales and marketing expense was 49 million for the first quarter or 62% of total revenue, this compares to 78% of revenue in the year ago period. This progress is consistent with our expectations and the unique dynamics of the Cloudera model with higher customer acquisition cost offset by much higher customer lifetime value. As our customer -- as our net expansion rate implies we have to yet to experience a lifetime value of a customer. As we have innovated in machine learning and the cloud, research and development were 28 million for the first quarter or 35% of revenue down from 41% last year. Innovation is vital for long term success in this market. We expect to realize leverage in R&D even as we plan to continue to invest aggressively to define the category and add capabilities to expand our addressable market. G&A was 9 million for the first quarter or 11% of revenue. This was down slightly from 12% of revenue in the first quarter of last year despite cost associated with building the infrastructure to operate as a public company. Overall operating loss was $30 million in Q1 representing a negative operating margin of 38%. This was an improvement of more than 2,700 basis points compared to a year ago quarter, where we had an operating loss of 37 million. Non-GAAP loss per share was $0.27 in the first quarter based on 114 million weighted average shares outstanding, compared to a loss of $0.33 in the first quarter of fiscal year 2017, based on 111 million weighted average shares outstanding. Our IPO became [indiscernible] at the end of the first quarter, but did not formally closed into the beginning of the second quarter. As such for the first quarter, we are providing a non-GAAP share count that assumes the conversion of preferred stock into common stock and includes the weighted impact of common shares issued in our IPO as if the issuance occurs on the effective date. Note, that due to our IPO, we recorded a cumulative stock-based compensation expense in the first quarter that represents several years work of stock-based compensation. The charge stems from the satisfaction of vesting condition for employee RSUs. Going forward, employee RSUs will vest with time and we do not expect a multi-year catch-up as occurred with the IPO. Please review the tables in today’s press release for additional information regarding historical and forward-looking stock-based compensation expenses and shares outstanding. Now turning to the balance sheet and cash flow. We exited Q1 with 370 million in cash, cash equivalents, marketable security and restricted cash, up from 270 million at the end of Q4. The Q1 cash balance does not include IPO proceeds of 233 million as the offering closed in Q2. Operating cash flows for the first quarter was positive 5 million, this compares to negative operating cash flow of 24 million in the year ago period. Capital expenditures were $175,000 in the quarter. We are very pleased with the cash generation in the quarter, which was driven by strong collection and continued improvement in operating efficiency. I would like to note that operating cash flow has meaningful variability on a quarter-to-quarter basis, primarily due to the seasonal nature of collections. Total deferred revenue was 213 million at the end of the first quarter, up 32% year-over-year. Short-term deferred revenue was 187 million, up 30% year-over-year and down 3% sequentially consistent with seasonal pattern. Note, we believe revenue is the best measure of financial and operating performance. Some observers will examine the change in deferred revenue to impute billings, which is then used as a proxy for bookings. A simple imputed billings calculation for any particular period is affected by the timing of billings and can be a misleading. For example, in the fourth quarter of fiscal year '16, we had a greater than average amount of bookings that were billed in the following quarter or Q1 of fiscal year ’17. This resulted in a Q4 '17 imputed billings growth rate of 83% and a Q1 of '18 imputed billings growth rate of 26%. Average in this metric over several periods provides a better representation of underlying business results. I will conclude by providing initial guidance for fiscal Q2 and for the full year fiscal '18. Based on the solid performance in Q1, as well as our momentum in the marketplace. We are optimistic about our prospects for the remainder of the year. We currently expect Q2 total revenue to be between $85 million and $86 million representing 32% to 33% growth compared to Q2 of last year. With subscription revenue in the range of 70 million to 71 million, up approximately 38% to 40% year-over-year. Non-GAAP net loss per share is expected to be $0.26 to $0.24 based on approximately 137 million weighted average shares outstanding. Due to the high degree of availability associated with customer collections, we do not intent to provide guidance as to quarterly operating flow. We do intent provide operating cash flow guidance on an annual basis. Nonetheless, since Q1 operating cash flow was particularly strong, we provide the following outlook for Q2 operating cash flow. Second quarter fiscal '18 operating cash flow is expected to be negative 30 million to 27 million. Included in this cash flow guidance is a 2.4 million charitable contribution as Cloudera donated 1% of IPO proceeds to the Cloudera Foundation. This donation is not factored in our non-GAAP per share guidance. Capital expenditures are expected to be approximately 5 million primarily due to the build out of our new offices. For fiscal '18, we expect total revenues to be between 345 million and 350 million representing 32% to 34% growth with subscription software revenue in a range of $280 million and $285 million, up approximately 40% to 42% year-over-year. We expect services revenue mix to be flat to slightly down as compared to the first quarter. We expect non-GAAP net loss per share of $1.07 to $1.04 based on approximately 133 million weighted average shares outstanding. And finally, we expect operating cash flow for the year to be negative 27 million to negative 68 million. This is a substantial improvement over the last year and reflects the leverage in our operating model. We anticipate capital expenditures for the year to be around $14 million driven by lease hold improvements. I’ll turn over to Tom for some concluding remarks.

[Tom Reilly:]

Thank you, Jim. We had a great first quarter as a public company. We are fortunate that the market is developing a way that favors Cloudera, especially as you consider our early leads and competitive advantages in machine learning and cloud. We are benefitting from the shift to workloads to the cloud generally, but our launch of Cloudera Altus platform as a service would help generate even more workloads in the cloud for our customers and us. With the release of Cloudera Data Science Workbench, we are bringing more data scientist to the platform to develop machine learning applications and solutions. Apart from quarter-to-quarter financial results, what has me most excited is that we continue to do right things to position Cloudera to be a much larger successful company built for the long run. The team and I are extremely grateful to our customers, our developer community, our many partners and to our investor base. Working together, we can use data to make what is impossible today possible tomorrow. Operator, let’s begin the Q&A portion of the call please.

[Operator:]

[Operator Instruction] Your first question comes from the line of Sandeep Singh with Morgan Stanley. Your line is open.

[Sandeep Singh:]

Just to start off, Jim maybe I guess one the things I am just trying to understand and I think you have alluded in your script is sort of the discrepancy between the really good net expansion rate of 142%, your rate subscription revenue guidance versus the billings [indiscernible] which came in a little lighter than our expectations, particularly on the current billing side. So I wanted to see if there was anything -- if you can just sort of go through that explanation one more time? And was there any impact from a faster transition to cloud that where revenue get sort of bypassed the balance sheet because you are recognizing that revenue more in real time?

[Jim Frankola:]

I’ll address both those things. So first let me give you context. Our focus is selling to the Global 8,000. As such relative to a business itself to small and medium sized companies we do relatively fewer transactions that are relatively larger, therefore you have quarter-over-quarter impact that tends to be more variable when it comes to things like billings. To recap when you go back to Q4 of fiscal year '17, we had a greater amount of bookings shift the billings into Q1 of fiscal year '17. So therefore, when you are doing the comparisons on a year-over-year basis, if you look at Q4 '17 imputed billings rate is 83% which is higher than the underlying business shifting in to Q1 of '18 because of the year-over-year comp that same number is 26%, which is less than the underlying health the business. So if you average through together or if you average them over a period of several periods, you get a number that much more closely approximates the strength of the underlying business. So it's really a timing difference of when something is booked versus when something is billed. Going through your second point. We did not see any material impact in the cloud, we did see a slight sorting of average contract duration from roughly 20 months, with our old average we did about 18 months in the quarter.

[Sandeep Singh:]

Right, and if I could just have a follow-up maybe one for Mike. Mike, you mentioned in your script that you guys were pretty early on spark. And I just wanted to get an update on some of your views on some of the other open source projects out there, in terms of real-time processing that seems to be gaining some traction wanted to get your view on that and maybe you can a little bit about the opportunity with Kudu?

[Mike Olson:]

Let me hit those points, very briefly we pay attention to point, I will tell you that when we look at our platform, the 26 projects that we bundle and distribute today. We don’t see any substantial feature gaps that would encourage us to go fill in with more project. So we'll watch the innovation going on in the space and certainly if there are new developments we may pull in more projects. In general, I think the addition of Kudu has closed a gap that we needed to close, we can now take on IoT, time series and relational were close that were hard for us to address prior and run some high-performance analytics including machine learning on that and so I think that's a pretty good for us.

[Sandeep Singh:]

Great, thank you guys.

[Operator:]

Your next question comes from the line of Mark Murphy with JPMorgan. Your line is open.

[Mark Murphy:]

Tom, I wanted to touch on the cloud workload mix. Can you just remind us what percentage of your customer workloads are in the public cloud today? And more importantly beyond that where do you see that mix going a longer term. And do you have any approximation of just how your market share looks in the on-premise world versus in the cloud?

[Tom Reilly:]

Thank you, Mark. As we share during the past we're excited about the growth of customers and workloads moving to the cloud. When we last shared numbers here 18% of our customers were operating in the cloud, while our overall business has grown as we just shared, our percentage of customers in the cloud has grown. At the end of Q1 we were at 20% of the customers in the cloud. And we expect that percentage to continue to grow with the introduction of Cloudera Altus our platform-as-a-service, we make it even simpler for our customers to move workloads to the cloud or create new workloads in the cloud. It’s hard to have a long-term projection. I know that it’s gone from 18%, well it’s got from 15% to 18% to 20% and it will continue to grow. I don’t know the ultimate balance, but what we’re seeing is all of our large enterprise customers are desiring a hybrid capability, where they want to have on-premises, they want to take advantage of public cloud, so they want that hybrid capability and they want multi-cloud, they want avoid cloud locking. So we expect that to continue to grow.

[Mark Murphy:]

Okay, great. As well a follow-up for Jim. Jim, I believe, you had just stated that there could be a timing difference of when something booked versus billed. I just wanted to try and you also mentioned there was a shortening of the contract duration in the quarter still we’ll have to run math on that. But are you -- just to be clear, are you saying that there is a material amount of business booked in Q1 that did not get billed in Q1. Or perhaps a different way of asking this. If you were to look at your new ACV bookings growth did that grow at rate that would have been well above what we see in terms of the 26% the billings growth?

[Jim Frankola:]

So let me break that up into two pieces. So first, the answer is yes. There is a substantial amount of booking that do not get billed in the quarter that the deal is sold. So for deals that are done in the last month for the quarter. In many cases, the customer request that the started is on the first of the next month, since we’re in a long-term relationship with them. We don’t try to drive that date into the quarter by a day or two, because it’s not a meaningful economic event. So yes, we'll have bookings in Q1, that will not get build into Q2 and that’s a very natural phenomenon, it happen all the time. And on top of that, we may see contractual terms, where a customer is buying hardware or is working with the service provider and they will not be able to implement the software for several weeks or even a month or two out. And those deals will also cause billings to be deferred into the future. By the way, the deals in the second category tend to be with the larger customer. So they’re looking for a very large transaction, you will sometime see those billings move into the next quarter. So yes, there is a significant phenomenon where billings and bookings are not correlated on a short-term basis, which is why we don’t believe billings is a good measure on a quarter-over-quarter basis. Going to the second part of your question, when we look at billings and we compare to software bookings. Once again, when you measure that over a period of several periods, that is representative of software bookings and the underlying health of the business. In any one quarter, it is not.

[Mark Murphy:]

Okay. A final one if I may for Mike, on the community activity levels. How would you characterize that overall pace of contributions and innovations in the open source Hadoop [indiscernible] and do you feel like that is moving fast enough to continue to fortify the level of differentiation that you have in the platform?

[Mike Olson:]

Yes. Given the breadth of the platform at this point, the 26 projects we’ve got, there is intense activity. And varies project-by-project, you mentioned Hadoop. So old [indiscernible], all HTFS, relatively little is happening there. There is a great deal more action around newer projects, Impala Spark for example, and so unsurprising, but there is still a very vibrant ecosystem. And then of course we are driving investments ourselves in ML, in cloud, in the governance security, compliance, regulatory capabilities that we deliver for a large enterprise clients.

[Operator:]

Your next question comes from the line of Kash Rangan with Bank of America with Merrill Lynch. Your line is open.

[Kash Rangan:]

A couple of questions from my end, one is with respect to bookings the number of customers and net new customers you added multiplied by whatever the average contract value was, did that meet your expectations so we can get little mollified with respect to how the billings could potentially rebound in Q2, that’s an accurate of saying that? And then second on a non-financial side, could you talk about what kind of use cases are these new customers, the 15 new customers you brought on board are engaging Cloudera for, are you seeing any trends with respect to how the business -- the net new business is shaking out from product standpoint between [indiscernible] operational DBR from a customer use case standpoint between customer insights and two other use cases? That will be useful thank you.

[Jim Frankola:]

Let me take the first part on the bookings and I'll turn over to Tom. So whenever you look at the underlying health of the business, there is new customers, their expansion adds up to total amount of software bookings in the quarter. To reiterate we are delighted that our net expansion rate remains at 142. It's a great measure of ongoing engagement. That led to our overall bookings in the quarter being very healthy growth on a year-over-year basis and something that we are very pleased with. And that level of overall software bookings is -- a not consistent with the billings in the quarter, but is more consistent with billing trends over the past two quarters. I’ll let Amr talk about the [technical difficulty].

[Amr Awadallah:]

Kash, as we have shared in the past we are focused on penetrating the largest customers, I shared that we captured 58 Global 8,000 adds, those of the 8,000 company doing over $1 billion in revenue. What I find exciting is that 12 of our newest customers are actually in the Global 2,000 the largest of enterprises, and that’s where we see the greatest growth, in those large enterprises. Used cases are consistently is where we're differentiating ourselves in machine learning and advanced analytics. Our primary verticals where a largely of our customers come from, our financial services, Telco and the public sector. In the use cases, around machine learning, pretty much in those verticals venture are around Customer 360 and Customer Insights as well as a lot of risk used cases. So we are seeing great success in anti-money laundering, fraud and the regulatory used cases, leveraging machine learning and cyber security is also a strong differentiator for us course.

[Kash Rangan:]

Thanks so much. And Jim I’ll take that as an assurance that reported billings which is not really an indication of the bookings in the quarter should react. So right in Q2 than if your bookings were to your satisfaction in Q1?

[Jim Frankola:]

Question/observation.

[Amr Awadallah:]

So am I answer on Q2 is, once again we don’t view billing as a good measure of health of underlying business. So we don’t have a forecast for Q2 billings, so I can't guide you very specifically on where Q2 billings will be.

[Operator:]

The next question comes from the line of Walter Pritchard with Citi. Your line is open.

[Walter Pritchard:]

First, just question for you Jim, on subscription growth accelerated a little bit this quarter from last quarter and I'm wondering is your guidance next quarter obviously doesn’t imply that and is down quite a bit in terms of subscription growth rate. Can you talk about what assumptions are in their specially around the offset rate which was 142% this quarter, what do you expect next quarter that might be driving the deceleration?

[Jim Frankola:]

The biggest driver of the deceleration is going back to Q2 of last year. If you look at the revenue growth from Q1 to Q2 of fiscal year '17, you will see substantial revenue growth. In Q2, we recognized some revenue that was hung-up on the balance sheet due to under delivered elements, on top of that some of our partner driven revenue was at a peak, which makes the comps for this year to be pretty tough. On the other hand when you look at sequential revenue growth as we see sequential revenue growth of 10% in the quarter at the guidance level which is fairly healthy sequential quarter-over-quarter revenue growth. So long story short, tough comps over year-over-year basis we expect the net expansion rate to still remain relatively healthy 142% is at the very high end of the range. But we should still be growing at a rate much higher than most subsequent companies.

[Walter Pritchard:]

Great, and then just maybe this question is for Mike or for Tom. On Altus, I know it's early can you talk about use cases there and are those helping you get into new customers and new use cases or is it mostly a continuation of the kind of drivers you see in use cases you've seen so far with core platform.

[Mike Olson:]

Walter this is Mike, I'll grab that one. Thanks for the question. As I said we are pretty excited about Altus. Specifically, what we released is a product we call Altus data engineering. So a big chunk of enterprise data analytics is data ingest, prep, organization, cleaning. We've built an as a service offering, we manage that service and allow our customers to use those services very, very simply. They don’t need our provision and run a cluster, run their own Cloudera Manager. That process is only one step in the life of data. It is often the case that you want to deliver that data to an Impala engine for query or to Spark for machine learning, we deliver consistent security, data governance, compliance, regulatory support across all of those engines which is a good differentiator for us. Delivering the data engineering SKU made sense. That was an obvious thing for us to do. Most of those workloads are transient and that's a natural for running in the public cloud. I will say that you should expect to see more happen. I said so in the script and I will reiterate here, we are very eager to expand the Altus family at Cloudera, I think it's going to unlock consumption that we would not otherwise see, not just the transient workloads, but in general ease of use matters a lot for enterprise adoption.

[Operator:]

The next question comes from the line of Karl Keirstead with Deutsche Bank. Your line is open.

[Karl Keirstead:]

Maybe one for Tom and one for Jim. Tom, I know you guys don’t give headcount growth each quarter, but I'm just wondering if you could comment even qualitatively on how the hiring went during the quarter especially around capacity build outs for quota carrying reps? I'm sure Q1 is not that the biggest hiring quarter, but any color there might be helpful. And then Jim on the time frame to hit cash flow positive, you may be repeating what the medium-term target? Thanks so much.

[Tom Reilly:]

Hi Carl, this is Tom. So on headcount, first half, I will show you. We’re very pleased with our ability to attract talent. We are an attractive place of people who want to join and we’re highly great well. And more importantly, I am very pleased with our retention, especially of our best talent. Right now, our employee count is over 1,500 people, the number I have in front of me is that's growing 26% year-over-year. So while we continue to higher, we’re not hiring at the rate of our business growth and that’s where we’re getting much to our leverage. And I will point out, we have grown sensibly in international markets, we made that investment a while ago, complete report that we brought on a new season leader for our APJ region. We just opened up a presence in Indonesia. We have now operations in 28 countries, that gives us nearly completion coverage of the Global 8,000. So that expansion is done and now we would just continue to higher our quota carriers to help drive our revenue growth expectations.

[Jim Frankola:]

And so the second part of your question. We are very pleased with the leverage that we’ve seen in the model and you can see a lot of it showing up in Q1. And we believe that we will be cash flow positive on a sustained basis in about six to eight quarters.

[Operator:]

Your next question comes from the line of [indiscernible] with Stifel. Your line is open.

[Unidentified Analyst:]

Jim, can you give us a sense on how the seasonality should play out for Global 8,000 customer adds?

[Jim Frankola:]

Clearly, Q1 is our seasonally lightest quarter of the year, Q4 is are seasonally strongest. I don’t have the numbers in front of us, but I would guess of the top of my head that we would expect to see somewhere between 20% and 25% of our full year acquisition occurring in Q2 and Q3 and North of 30% occurring in Q4.

[Operator:]

Your next question comes from the line of Greg McDowell with JMP Securities. Your line is open.

[Greg McDowell:]

A multi-year break for you from earnings call, so welcome back to the quarterly cadence of talking to us. You guys have a very ambitious vision to be one of the largest infrastructure software companies in the world. And I think many of us understand that infrastructure software companies are different and application software companies. So I was hoping, we can spend a little bit of time maybe digging a little bit deeper into customer economics and the land with the customer and maybe just help us think about how the fly wheel get going and maybe talk about the leverage between your Global 8,000 customers or should I say customer paying more than $1 million in ARR versus your smaller customers and maybe how we should think about leverage among different segments of your customer based? Thanks.

[Tom Reilly:]

Hi Greg, good to be connected with you on this conference call again. This is Tom. So the root of your question is, why we’re so focus on the Global 8,000? This is a land and expands opportunity. Within any enterprise and I was with an auto manufacture today, there are hundreds of potential used cases for machine learning. So thinking of an automated factory, everywhere from their supply chain, their parts, their manufacturing, the quality of the manufacturing to their distribution channel to understanding their customers and ultimately having the connected car. And so the reason we focus on the Global 8,000 is yes our initial projects starts small, we typically start with one or two use cases. We get that platform in, they start to understand how they can employ our technology to solve these types of problems and quickly they start expanding. As Jim share we have well over 30 customers in excess of $1 million AR, some of them well in excess and these customers have not just two or three used cases, they have dozens, if not hundreds of used cases. So part of our strategy is to look long term, stay focused on our target market, win the largest share that we can of the players. With our focus on enterprises, our hybrid capabilities of allowing them to work on-prem and in the cloud and pretty much multi-cloud. We think we have unique sustainable advantage and when our customers are successful as Jim shared in his prepared remarks, as data grows, as used cases grow, as our partners build more applications our relationship grow. And I suspect every Global 8,000 should be north of $1 million relationship for us.

[Greg McDowell:]

And one quick follow up. I think other analyst have appropriately asked about some of the differed and billings and bookings situation. I think one way I would like to ask it is simply, look it's Q1, we know how Q1s are in the software industry, Tom or Jim, I mean, were you happy with your sales team performance in Q1? Were you satisfied with the sales productivity metrics, were they are in line with your expectations? Thanks.

[Tom Reilly:]

Well, so you're from the enterprise software Greg, I mean Q1 are always seasonally your lightest quarter, because that’s where you are bringing on a lot of new reps you are training them, you are doing themselves kick off, you tend to have a lot of things other than you're out there selling. I’ll share with you, I am very pleased with our Global salesforce, I am very pleased with the net new customers that we won, these customers in the Global 2,000 are very, very competitive battles that we are very proud of how we performed. And of course, we won many more customers beyond that. But we feel very good about how our salesforce is doing.

[Operator:]

Your next question comes from the line of Michael [indiscernible] with Raymond James. Your line is open.

[Unidentified Analyst:]

First you talked a lot about the international investments, so can you talk maybe about how traction has been in the international? And then I have a product follow up.

[Tom Reilly:]

Yes, so our international markets are doing extremely well. First and for most, the growth in international markets year-over-year was greater than 40%. So we are seeing high growth in the international markets. We also see fewer competitors in the international markets. We think to our partner Intel had the financial wherewithal to make an early investment and we were able to plant our flag in over 29 countries where we have one largest banks, one largest Telcos, we've got into the government. In the international markets, we come across IBM as a competitor and increasingly IBM wants in [ph]. And if you learn about our strategy and customers learn about our strategy for machine learning and artificial intelligence we deliver an open source platform, a whole ecosystem of partners are developing and delivering to, that's what customers want and we're competing against a black box offering. So I think we will continue to do very well in the international markets against other competitors we see their most presently.

[Unidentified Analyst:]

Thanks. And then the follow-up question again along the machine learning line, but Data Science Workbench build out relatively recently can you tell me how that's going and when you especially to the point where that begins to directly monetize?

[Tom Reilly:]

So Data Science Workbench is an up-sell into our customer base and the uptake has been tremendous. We are building a strong pipeline, we have a lot of trials going on, so we think it will be a growth driver for us in two ways. We license access to the Data Science Workbench, but as we have more data scientist building more applications, it's going to grow our infrastructure underneath. So it's both an incremental driver, but longer-term a strategic driver to grow our relationships with every customer.

[Jim Frankola:]

Tom, if I can, I just want to add a little bit to that. I think you folks know, but Cloudera's strategy is to embrace a polygonal [ph] world. So there are lots of different tools and languages that are popular among data scientist. Data Science Workbench enables all of them. There are lots of machine learning frameworks that snap into Spark, so to think about [indiscernible] and others. We are able to take advantage of all of that innovation in the market and given our presence, given our success in machine learning as a category leader, we think we get an early view in the what's coming and we've got a privileged position.

[Tom Reilly:]

All right, well. Thank you all we came up against the hour, operator so we are going to have to finish the Q&A portion of this. I do want to thank you all for being investors with us, following and asking very good questions about our business. We look forward to giving you an update in the next quarter and sharing what our new products are doing, how our customers are growing how our business continues to grow. So thank you very much for supporting us on our first quarterly conference call.

[Operator:]

This concludes today's conference call. You may now disconnect.