Cloudera, Inc.
Q2 2018 Earnings Call Transcript

Published: 8 Sep 2017

[Operator:]

Good afternoon. My name is Devin and I will be your conference operator today. Welcome to the Cloudera Second Quarter Fiscal 2018 Quarterly Results Conference Call. All participants’ lines have been placed in listen-only mode to prevent background 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:]

Thank you, Devin. Good afternoon and welcome to Cloudera’s second 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; Mike Olson, 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, intend and other words that denote future events. These forward-looking statements are subject to material risks and uncertainties that could cause actual results to differ materially from those in the forward looking statements. We 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 are 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. Cloudera’s final prospectus under Rule 424(b)(4) 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 exclude stock-based compensation expense and amortization of acquired intangible assets. In addition, we provided a non-GAAP weighted average share count that assumes the conversion of our proffered stock into 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 or 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 and the quantitative reconciliation of those figures, please refer to today’s press release regarding our second 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 September 7, 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 Reilly.

[Tom Reilly:]

Hello, everyone. Thank you for being with us today as we discuss our quarterly results. We reported a strong second quarter, driven by much of the new product innovation we’ve recently announced. In Q2, we executed well as a company and we continue to benefit from major secular trends in machine learning, cloud and the Internet of Things. We achieved revenue of $89.8 million representing year-over-year growth 39%. Our subscription software revenue grew 46% year-over-year. In addition, we added 45 net new Global 8000 customers, which along with large public sector entities is our primary target market. As we described last quarter, this increase puts us on a track to achieve our full year goal of adding approximately 120 net new Global 8000 customers. Finally, we are especially pleased to have sustained a net expansion rate of 140% across all of our customers, driven in large part by successful deployment and growth within the Global 8000. For anyone joining us for the first time, I would like to take a moment to share why the opportunity ahead of us is so exciting. A recent edition of The Economist declared on their cover that the world’s most valuable resource is no longer oil, but data. We couldn’t agree more. The organizations that extract the most value from that raw resource will be the winners in a new data-driven economy. Whether developing self-driving vehicles, predicting healthcare outcomes, anticipating customer needs, or preventing fraud, the fuel for making all those things possible is the same, it’s data. The tools of choice for maximizing the value of data are machine learning and analytics, increasingly delivered via the cloud. Cloudera’s modern platform for machine learning analytics provides a flexible enterprise grade environment for organizations to harvest the value of data of all types whether in the cloud or on-premises. Becoming a data-driven enterprise isn’t optional, it is a business imperative. Yet most organizations are very early in their journey. In fact, a recent issue of the Harvard Business Review states that many organizations are leveraging less than 1% of their unstructured data for decision-making. Our software empowers people to transform complex data of any type and to clear an actual insight in three distinct ways. First, we help businesses grow their revenues based on the insights they drive from their customer data. Second, we help enterprises win in the world of IoT by connecting either their products or services to achieve lasting competitive vantage. And third, we help organizations to proactively protect their businesses in ways never previously imagined. Lazy technology is not an option. It lacks the power, scale and economics to support the modern data-driven enterprise, particularly in era where data is doubling in volume every other year. Organizations seeking to capitalize on the value of data and desiring to be more competitive require a robust and flexible platform to collect, analyze and act on the massive volumes of data being produced today, commonly referred to as big data. Cloudera helped create the big data industry, as we know it. Today, we are powering over 1,000 of the world’s most forward-thinking organizations to become data driven via our modern platform for machine learning analytics optimized for the cloud. Our customer ADP illustrates what is possible when an enterprise becomes data-driven. ADP, widely known for its payroll and HR solutions, uses Cloudera to capture billions of records each quarter. It then applies machine learning to deliver a workforce intelligence services their clients use to reduce employee turnover. This service enables clients to quickly identify the most at-risk employees and to target retention efforts where they have the most impact. This service is not only a competitive differentiator for ADP but also a new source of revenue. This is just one of the many applications of ADP. And as this example demonstrates, machine learning is rapidly moving to the mainstream for large enterprises. Cloudera’s customers have been building machine learning analytics applications on our platform for years. Today, we have hundreds of large enterprises introduction of those applications, fundamentally improving the way they grow, connect and protect their businesses. We love those applications. They are simply ravenous for data. Training machine learning models requires big data to actively recognize pattern, detect anomalies and predict future outcomes. For example, DBS Bank, the largest bank in Singapore is shaping the future of banking in the region through the utilization of machine learning solutions built on the Cloudera platform. DBS uses Cloudera in machine learning specifically to provide better customer service by leveraging insights collected through digital customer interactions. The result is a significant increase in revenue per customer, lower cost to service customers and improvements in fraud detection and risk management. DBS Bank is just one example of many across a wide range of industries from health care to telecommunications to manufacturing and public sector. They are all leveraging machine learning and analytics to transform their businesses and differentiate from their competitors. Now, I would like to introduce Mike Olson, our Co-Founder, Chairman and Chief Strategy Officer to share some exciting news about our continuing investment in innovation and more importantly in machine learning in particular. Mike?

[Mike Olson:]

Thanks, Tom. This afternoon, I am excited to share the news that Cloudera has acquired Fast Forward Labs, a leading machine learning and applied AI research company, based in New York. Hilary Mason is the Founder and CEO of Fast Forward Labs and she is a well-known and highly regarded industry expert in data science and machine learning. She and her team form the core of Cloudera Fast Forward Labs, an organization specifically focused on helping customers take advantage of state-of-the-art machine learning capabilities to solve real world problems. Cloudera Fast Forward Labs builds on our substantial organic investments in products and services offerings as well as on earlier acquisitions. For example, our acquisition of Sense brought in the team and the IP that became Cloudera Data Science Workbench for a collaborative data exploration and analytics. And of course, we continue to work closely with the Open Source Community, our first in the industry embrace of Apache Spark has led significant success with literally hundreds of customers running machine learning applications on Cloudera with Spark today. We’ve made these investments, because we believe that machine learning and applied AI are essential to large enterprises. I am thrilled to have Hilary and Fast Forward Labs at Cloudera. I’m confident they’ll accelerate our customers and their embrace of these powerful new capabilities and drive increased consumption of our core platform by allowing enterprises to build ML-based applications on Cloudera. Our modern platform for machine learning and analytics is optimized for the cloud. We’ve invested substantially to allow customers to run on premises or in the public cloud of their choice. Amazon, Microsoft and Google are all important platforms and partners. Having a cloud-native platform fits nicely with enterprises’ desire to shift data and workloads to the cloud, particularly analytic workloads because those workloads are elastic and transient in nature and because much of the new data that enterprises want to analyze is increasingly generated in the cloud. The second quarter also saw growth in the adoption of Cloudera Altus, our platform-as-a-service offering that enables data engineering and data science workloads to run natively and easily in the public cloud. We handle deployment, management and operations; customers concentrate on their data processing and analytic work. With Altus managed service and our unique cloud orchestration and operation software, we’re now addressing a new set of elastic and transient jobs that would otherwise be impractical to run in the data center. We plan to grow the Altus family over time, giving enterprises more flexibility than ever before to run workloads anywhere that they prefer. A more recent news, we’re proud that the Business Intelligence Group has recognized Cloudera Altus with its Stratus Award for cloud computing and the platform-as-a-service for enterprise category. Our customer Cox Automotive, showcases the flexibility of the Cloudera platform, running workloads in whichever environment is most advantageous, cloud, on-premises or both. Cox participates in many segments of the automotive industry. Brands include household names like Auto Trader and Kelley Blue Book. Cox runs the Cloudera platform on premises and in the public cloud, mostly Microsoft Azure today. The company likes our multi-cloud capabilities and frequently [ph] migrate as business demands change. The ability to choose between on-premises and cloud for each use case enabled Cox to reduce total cost of ownership by approximately 50% per terabyte. The analytics they run have improved their marketing efficiency by 37%, saving millions of dollars in advertising spend. As Cox demonstrates, Cloudera and Microsoft represent a potent combination. Azure deployments represent a growing opportunity for us. And we believe that the opportunity is mutual. We’re pleased that Microsoft’s U.S. Enterprise Partner Group names Cloudera as 2017 Data Platform ISV Partner of the Year. As you can see, we have been busy. We continue to learn a great deal from our Global 8000 customers and our partners. This earnings call is too short to share all of the news. So, stay tuned; we’ll be unveiling more exciting product innovation at the Strata Data Conference in New York City in the last week of September including amazing new ways to accelerate and ease the move in of applications across multi-cloud environments. And with that, I will hand back to Tom.

[Tom Reilly:]

Thank you, Mike. These customer stories exemplify the broad applicability of our modern platform for machine learning and analytics across industries and in hundreds of use cases. Customers, partners and our field organization are constantly identifying new use cases to make this powerful technology accessible to large organizations at the technical as well as line of business level. Our strong net expansion rates reflect the success that our customers are experiencing and transforming themselves to data-driven enterprises. The opportunities for enterprises to leverage data for insight are endless. And as a business, we have yet to see the lifetime value of a customer relationship as data volume growth and use case expansion are ongoing. Finally, our large partner ecosystem continues to grow and develop industry-specific applications on the Cloudera platform. With our success penetrating large enterprises, we are rapidly becoming the platform of choice for partners seeking to build industry solutions. As an example, one of our strategic partners Cognizant is a leading global systems integrator with over 250,000 employees worldwide. They have developed a platform powered by Cloudera, called Cognizant BIGDecisions. They enable organizations to improve their customer experience, optimize business processes and reap the benefits of being a data-driven business. Cognizant’s BIGDecisions platform includes industry-specific applications for customers in banking, health care and life sciences, automotive and retail. In Q2 Cloudera and Cognizant BIGDecisions were selected by one of the largest nonprofit health plans in the U.S. to help improve the quality of care and control costs in the communities they serve through increased insights provided by machine learning and analytics. Developing those valuable healthcare solutions simply wouldn’t have been possible without our partnership with Cognizant. Today, our online solutions gallery features over 100 partner developed solutions, many that are industry-specific. As you can see, our partners create a network effect that benefits our business; and broad partner ecosystem provides us with international reach, industry expertise and technical prowess. If data is in fact the new oil, then our partners are essential to refining it and making it more valuable to our customers. Jim will now review our Q2 financial results in detail. Jim, please?

[Jim Frankola:]

Thanks, Tom. Hello, everyone. As Tom indicated, we had a strong second fiscal quarter. Subscription software revenue in Q2 was $74 million, an increase of 46% from last year. This represented 72% of revenue up from 79 -- 82% of revenue up from 79% a year ago. In total, revenue was $89.8 million for the second quarter, representing 39% growth over last year. Before I run through more of the numbers, I want to remind everyone that Cloudera’s land and expand model reflects how large organizations are adopting modern data platforms. Operationally, we focus on making the Global 8000 customers successful and have designed both our customer acquisitions and expansion go-to-market activities for this segment. Execution against the approach is evidenced in several of the key metrics we posted for the quarter. We’re pleased to have added 45 net new Global 8000 customers in the quarter, bringing the quarter-end Global 8000 customer count to 552; this compares to 427 Global 8000 customers at the end of Q2 of last year. The world’s data and data-related spending is concentrated in this segment, and large enterprises exhibit the greatest desire and ability to expand. In Q2, many of these customers increased their utilization of the Cloudera platform, fueling growth and driving our net expansion rate to 140% for the quarter. Recall that net expansion rate factors retention, expansion and churn on a dollar basis. It is this metric that gives us confidence in our land and expand model. Over time, we target Global 8000 customers to expand rapidly and contribute substantial annualized recurring revenue. We now have more than 40 customers with ARR in excess of $1 million, collectively contributing more than 45% of our subscription software revenue. We view this as a validation of the powerful customer lifetime economics in our hybrid open source software business model. More importantly, we have yet to experience the lifetime value of a customer. As I review remainder of the income statement, 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 results in the press release issued earlier today. In Q2, subscription gross margin was 85%, over 200 basis points higher than in the year-ago period. Services gross margin for the quarter was 19%, level with a year-ago. Total gross margin for Q2 was 73%, up 400 basis points compared to 69% a year-ago. Turning to operating expenses. Sales and marketing expense was $49.6 million for the second quarter or 55% of total revenue, this compares to 70% of revenue in the year ago period. This progress is consistent with our expectations. The unique dynamic of the Cloudera model with higher customer acquisition costs offset by much higher customer lifetime value, produces improving sales efficiencies as our customers grow. Research and development was $29.7 million for the second quarter or 33% of revenue, down from 39% last year. G&A was $12 million for the second quarter or 13% of revenue; this was up from 10% of revenue in the second quarter of fiscal year 2017. G&A expense peaked in the quarter due to $2.4 million of expenses associated with the donation of 1% of IPO proceeds to the Cloudera Foundation, coupled with the cost of building the infrastructure to operate as a public company. Overall, operating loss was $25.3 million in Q2, representing a negative operating margin of 28%; this was an improvement of more than 2,200 basis points compared to a year ago quarter, when we had an operating loss of $32.3 million. Non-GAAP loss per share was $0.17 in the second quarter based on 137 million weighted average shares outstanding, compared to a loss of $0.29 in the second quarter of fiscal year 2017, based on 111 million weighted average shares outstanding. Please review the tables in today’s press release for additional information regarding historical and forward-looking stock-based compensation and shares outstanding. Now, turning to the balance sheet and cash flow. We exited Q2 with $494 million in cash, cash equivalents, marketable security and restricted cash, which is up from $275 million at the end of Q1. The Q2 balance includes IPO net proceeds of $233 million as the offering became effective in Q1 but closed in Q2. Operating cash flow for the second quarter was negative $22.8 million, this compares to a negative operating cash flow of $28.5 million in the year ago period. Capital expenditures were $1.8 million in the quarter. It is important to note that the year-to-date operating cash flow was negative $18 million, a significant improvement from $52 million in the first half of fiscal year 2017 and a measurable leverage in our business model. Total deferred revenue was $231 million at the end of the second quarter, up 39% year-over-year. Short-term deferred revenue was $194 million, up 38% year-over-year and up 4% sequentially, consistent with historical patterns. I will conclude by providing initial guidance for fiscal Q3 and updated guidance for the full year fiscal 2018. We expect Q3 total revenue to be between $90 million and $92 million, representing 34% to 37% growth compared to Q3 of last year with subscription software revenue in the range of $74 million to $76 million, up approximately 40% to 44% year-over-year. Non-GAAP net loss per share is projected to be $0.25 to $0.23, based on approximately 138 million weighted average shares outstanding. For fiscal year 2018, we expect total revenue to be between $355 million and $360 million, representing 36% to 38% growth; for subscription software revenue in the range of $290 million to $295 million, up approximately 45% to 47% year-over-year. We expect services revenue as a percent of total revenues to continue to drift downward due to the more rapid growth of subscription software revenue together with increased traction of our partner ecosystem. We project non-GAAP net loss per share of $0.95 to $0.93 based on approximately 133 million weighted average shares outstanding. We expect operating cash flow for the year to be negative $65 million to $60 million or roughly negative 17% of revenue. This is a significant improvement over last year, when operating cash flow was roughly negative 45% of revenue. We anticipate capital expenditures for the year to be $15 million, driven by leasehold improvements. We are very pleased with the progress we are making with our business model. We continue to be successful in acquiring and growing large customers and the benefits of our land and expand model are evident in our improving margins and cash flow. With that, I’ll turn it over to Tom for some concluding remarks.

[Tom Reilly:]

Thank you, Jim. As I stated early in the call, data is now the most valuable resource on the planet, a raw data, data that hasn’t been refined as insights, offers minimal business value. Our modern platform for machine learning analytics provides large enterprises the capabilities they require to refine their data and to act on the insights derived from it; in other words, to become data-driven. We saw the importance of these capabilities years ago. That is why the focus of our R&D investments and M&A for the past few years has been the machine learning realm. It is also why we’ve invested so heavily in the cloud, empowering our customers to extract value from data, no matter where it resides, in the public cloud, in private clouds or in any combination they choose. These investments reflect the dynamics of our market and the increasing opportunity set, as well as where we believe we have the natural competitive advantage. It is still remarkably early in the development of the market, but it’s continually expanding in terms of the volume and diversity of data types and use cases as the world becomes increasingly connected. We are well-positioned for the future and to extend our category leadership. The innovative work we’ve done with the open source community and then the development of our unique proprietary software geared towards the needs of large enterprises is delivering immense business value across multiple industries. The momentum in our newly introduced cloud platform as a service offering, Altus, is exciting to see. Our growth, driven in large part by machine learning analytic use cases and use cases and the early success of Cloudera Data Science Workbench is encouraging. The increasing impact our partner ecosystems have in developing industry-specific applications is accelerating adoption of our platform. And finally, the acquisition of Fast Forward Labs announced today helps differentiate us from competition and positions us well for the future machine learning and applied AI. The team and I are very grateful to our customers, our developer community, our many partners and to our investors. A special welcome to the Fast Forward Labs team. Thank you all. Devin, operator, we can take some questions. So, let’s please move to the Q&A portion of the call.

[Operator:]

[Operator Instructions] Your first question comes from the line of Sanjit Singh with Morgan Stanley. Please go ahead. Your line is open.

[Sanjit Singh:]

Thanks for taking the question and congrats to the team on a very nice Q2. I have two questions. First on the net new customer outperformance this year, 45 net new Global 8000 customers. That’s typically the type of net new adds we would see in Q4, so very strong result there. So, my question is, where did you see the strength? Was this a function of perhaps IBM exiting the market, that driving some customers to you guys or is it a function of adoption of Altus? What was sort of driving the strength in Q2, which seemed like kind of like a Q4 type result?

[Tom Reilly:]

Sanjit, this is Tom. So, thank you for that. We continually increase our focus on going after the Global 8000. It’s how we have aligned our sales force; it’s how we drive our marketing; it’s how we work with our partners in identifying industry-specific solutions. And I think that focus has really benefited us in capturing and we have executed very well. I think our competitors have made some missteps and that created some opportunities where we gained some more wins. But, we take great pride in just our focus. All of our R&D is going after the needs of large enterprises in these hybrid and multi-cloud environments and that is differentiating us. We also -- and I just -- Sanjit, also, we are pleased with our international growth. So, our international percentage of business grew quarter-over-quarter and we continue to see it one of our fastest growing markets.

[Sanjit Singh:]

That’s very helpful context. And my second question is on margins, so maybe this one is for Jim. Margins are up again really nicely in Q2, up over 2,200 basis points. If you can update us in terms of the efficiency of how you guys are acquiring new customers and expanding the existing base, maybe just want to get a little bit granularity, you can give us a sense of what percentage of the sales force is productive today versus this time last year. Any sort of detail you can give on sort of the productivity of the sales force and the efficiency acquiring customers would be helpful. Thanks.

[Jim Frankola:]

Yes. What drives our sales efficiency in Q2 and the trends over time are really the growth of our customers. As we displayed in the road show, once our customers get north of $0.5 million in size, we start throwing out positive cash flow; once they get over $1 million in size, we are at our long-term model with respect to sales cost. So, what you saw in the quarter is an increasing number of our large customers getting north of $1 million that drives increasing sales efficiencies as we continue to sell and expand. So, it’s that traction with large customers and expansion rate that drive sales efficiencies.

[Operator:]

Your next question comes from the line of Mark Murphy with JP Morgan. Please go ahead. Your line is open.

[Mark Murphy:]

Yes. Thank you very much. And I’ll add my congratulations on a great quarter. So, Mike, you had mentioned Azure as an opportunity. I’m wondering, is the usage mix within public cloud shifting at all, as you look across AWS, Azure and GCP. And can you just comment on where your presence is growing the fastest today?

[Mike Olson:]

I think if you look -- by the way, thank you, Mark. I think if you look at the secular results for the cloud vendor, generally analysts interpretation of the numbers, Azure is growing very quickly; it appears they’re gaining share right now. Certainly, they are a serious vendor among our enterprise clients. Microsoft has had a tremendous focus on large enterprises for some time. The Microsoft field is appropriately goaled and they’re going after those opportunities. We’ve had some very good collaborative wins with the Microsoft field. That said, Amazon has still an enormous presence in the market, much of the largest and also a very strong partner. So, while Microsoft has made great progress, it’s still a big market with lots of folks growing very quickly. Our ability to support all three of the hyper scale public cloud providers means that we’re able to tap the entire opportunity as well as the on-premises deployments. And as you know that has been our strategy from the beginning, participate in the full market; let customers take advantage of hybrid and multi-cloud; and avoid cloud vendor locking.

[Tom Reilly:]

Mark, this is Tom; I’d add one thing to Mike’s excellent comments. What our customers value, is multi-cloud capabilities and portability. We had a very exciting win in Q2, that was a net new logo and a 7-figure win in the cloud. And we were able to help them move to the cloud quickly early [ph] because they knew that they had portability in the future and can move quicker to pick one of the cloud providers.

[Mark Murphy:]

Thank you for that. So, Tom, on that note, net new customer, which is being a seven-figure win, I think that that’s a bit -- that sort of eclipses your normal new business ASP. And so, going back to the strength of the Global 8000 customer adds in the quarter, could you just comment on how that cohort fuels overall in terms of the landing ASPs and just the future growth potential of that cohort?

[Jim Frankola:]

Yes. This is Jim. So, our average landing ARR is about 50,000, it has the net way for several years. The landing of the Global 8000 is closer to $100,000 a year. So, with performance like what we had in this quarter, we were landing near that $100,000 mark, slightly higher than the long run average for the Global 8000.

[Mark Murphy:]

Okay. So that actually up-ticked. And then, the final one is, could you just comment on the IBM-Hortonworks agreement. Many of us are curious as to what extent is IBM belling out of the Hadoop market? And with the industry seeming to be consolidating around fewer players, do you think that that is going to help the pricing environment for Cloudera?

[Tom Reilly:]

Yes. Mark, this is Tom. I’ll answer that. I have -- a few things I’ll share on the recently announced IBM-Hortonworks partnership. First, this is not a new partnership. We’ve been competing against this partnership for several years, formerly was under the open data platform initiative where they’re collaborating and we’ve been competing against them successfully. When we compete against IBM, we’re competing against IBM Watson. And what Watson is now doing is they’re merely positioning Hortonworks as the key cloud storage and reserving most of the high value, the analytic workloads and machine learnings for the Watson family, and that’s where we compete against those machine learning analytic workloads. And I don’t know if you follow the news, but they clearly haven’t been doing very well as of late. So, we are pretty pleased with that because instead of competing against IBM, we now compete against two firms trying to put together the integrated solution. Finally, on consolidation. The market has been consolidating; we’ve seen it happen over the last few years. This is another further consolidation and we think it’s very good for the industry and the surviving players. In particular, this consolidation is forcing IBM’s prior customers to go through a migration, has opened up some opportunities for us to re-compete. It will benefit our competitor, Hortonworks in some of those migrations, but we think there’s a number of re-compete that as an opportunity for us.

[Operator:]

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

[Karl Keirstead:]

Thank you. I’ve got two, maybe the first for Tom or Mike. Especially with the Fast Forward acquisition, it feels like you’re putting a greater emphasis on this call, just my feeling on the machine learning opportunity. And I just want to take a step back and make sure I understand your strategy around ML. I guess, maybe it’s too simple to think of it as two potential strategies. One is that Cloudera remains, call it data-driven where you might help your customers write ML algorithms that better leverage data hosted in the Cloudera platform, or Cloudera itself ends up building those machine learning apps or algos that become part of your own IP that you sell to clients. I just want to make sure I understand the approach you’re making. And then my follow-up question, which I’ll give now to Jim, is, Jim, on long-term DR up $10 million sequentially, bigger than normal jump, maybe you could explain that.

[Mike Olson:]

Hey, Karl. It’s Mike. I will take the first half of that. Good to hear your voice, thanks for calling in. Absolutely true that the Fast Forward Labs acquisition is a significant secular strategic bet for us on machine learning and applied artificial intelligence. We see those use cases driving serious adoption and expansion among our clients, and we think that accelerates over time among large enterprises all across the region. You may need to go on mute, by the way, Karl. There’s a little bit of cross talk. But, Fast Forward Labs is not the only bet that we made there of course. We acquired Sense about two years ago. Cloudera Data Science Workbench in the market is a fantastic complement to the skills that FFL team brings in, lets us deliver the software applications development environment and the platform paired with the expertise and deep insights that the FFL team brains. We don’t view ourselves as competing in the algo market. There’s a lot of great work going on in places like Google, and Facebook and elsewhere, and we get to build on their open source releases in order to deliver that to large enterprises. I think I’ve covered all of the points. I’ll let Jim pick up Q2.

[Jim Frankola:]

We saw a healthy growth in terms of total imputed billings, long-term deferred revenue, short-term differed revenue. The $10 million increase in long-term deferred revenue was really driven by mostly one sale and it was a two-year transaction where the customer wanted to prepay both years. So, we didn’t have to offer any incentive to make that happen. It was a natural selling notion based on how that customer like to buy.

[Operator:]

Your next question comes from the line of Walter Pritchard with Citi. Please go ahead. Your line is open.

[Walter Pritchard:]

Just a question on Fast Forward Labs, if you could give us any sort of financial, number of people, any size in terms of deal, it sounds like it’s relatively small. I just want to make sure we have that.

[Jim Frankola:]

Fast Forward Labs is not material in terms of employee count or revenue today. It’ll be a modest amount of services revenue in the short run; and in the long run, we really see a lot of leverage on the subscription line, in terms of mating their offerings with our offerings and really driving growth in the space.

[Mike Olson:]

If I can just add a little bit there, Walter; this is Mike of course. Strategically, this is pretty big deal. This is a very, very rare skill set. Hilary Mason and her team are world class. And developing the expertise that large enterprises require is going to be both hugely valuable and very difficult. And we think we have done a good job. Our category leadership position lets us see who the best companies in the industry are, who is getting traction and engage with them. And I’m super pleased. So, while Jim is correct on all the details, we believe in this one for the long-term for sure.

[Walter Pritchard:]

And then, just a question on -- you talked about it in the past three drivers, the sort of customer insights, the IoT and the security. Any trends emerged this quarter with the robust new customer acquisition where it split and any difference versus what has been in the past?

[Tom Reilly:]

Yes. Walter, this is Tom. Traditionally, our strongest verticals have been in financial services, telecommunications, public sector. We are seeing increasing adoption in IoT’s cases. And these are in healthcare, in oil and gas, and in the energy industry. And we are seeing a significant uptick there and in its exciting area and we are seeing great success with some key partners also there.

[Walter Pritchard:]

Great, thank you.

[Tom Reilly:]

Walter, I have to mention one thing on that. I was just thinking on my own notes here. One of the things that’s exciting about the IoT’s cases, is the increasing adoption of Apache Kudu, which is a new data store we released for IoT analytics designed for time series data. It’s the only one on the planet and is getting great traction and it’s a differentiator for us in that space.

[Operator:]

Your next question comes from the line of Michael Turits with Raymond James. Please go ahead. Your line is open.

[Michael Turits:]

Two questions, one, first on Cloud. I don’t know if you have commented on that percentage of workload mix, which is 20ish percent range and how Altus has contributed or not contributed to that? And I have second question.

[Tom Reilly:]

So, I’ll take that first one just now on cloud. Our cloud mix is growing in conjunction with our overall business. So, we see customers growing on-prem and moving workloads to cloud. Today, roughly 20% of our customers are leveraging our cloud capabilities and 20% of all workloads are running in the cloud. And it’s that last half that I find actually more exciting, because first off, 20% of workloads is against, all the workloads we have in perpetuity, many of which eight years ago started out in the data center. So, we already have 20% of them there in the cloud. And increasingly, even all our on-prem customers are growing, yet the cloud business is growing with it. So, we’re pretty excited about the amount of workloads there.

[Michael Turits:]

And Altus…

[Tom Reilly:]

And finally, just one last thing on that, Michael I’d share. Altus is opening up the new class of workloads that can’t be done on-premise, so these more transient, elastic workloads, we want to spin up and spin down. And secondly, the announcements that Mike will be sharing at Strata Data this year are going to facilitate and even accelerate the movement of workloads between both the cloud and on-premise environments.

[Michael Turits:]

I have another question, I guess for Jim. Sanjit has asked a question earlier about the sales productivity and your comment was that it was getting customers at over 1 million ARR that was driving the sales force productivity. I’d like to ask it the other way around and then try to understand if you’re doing something in particular and/or something different to drive customers up to that level, because that -- obviously that’s the goal, that’s what’s crucial, that’s where it really starts to get the leverage.

[Tom Reilly:]

Michael, this is Tom. I’ll take a first half of that and Jim can add on. We’re doing a lot of things differently. We analyze -- we use big data ourselves, understand what drives expansions. We know which partners have the greatest impact, we know which applications drive the greatest use in key verticals. We train and go after those use cases. We have resources that support our sales force whether they are recall business value consultants or subject matter experts and customer success managers that engage with our largest customers to accelerate their deployments. And the beauty of going after large enterprises is as data grows and use cases grows, our relationships gross. And not having seen lifetime value, every one of these investments makes great sense. And as customers grow, each incremental project becomes more profitable and better margins for us.

[Jim Frankola:]

And I don’t have much to add. Just I’ll reemphasize, one of the big things that we have been focusing on at Cloudera is Cloudera on Cloudera, using our own technology. So, we have an internal data hub that is approaching a petabyte in size. We bringing all sorts of internal and external information that gives us insight in terms of what drives customer expansion. We use the information literally to understand our cost of sales at an opportunity level. Therefore, it allows us to focus our energies in the right place, and where there are deficiencies in our business, try to modify them pretty quickly.

[Operator:]

Your next question comes from the line of Brad Reback with Stifel. Please go ahead. Your line is open.

[Brad Reback:]

Jim, obviously a nice bounce back on the billings metric, and I know you don’t guide to that and try to get us away from that. That being said, given the second half of the year coming up here in the really strong G 8000 bookings you have, how should we think about the seasonality, some of the puts and takes for the back half as it relates to sort of customer adds and billings? Thanks.

[Jim Frankola:]

Yes. So, in terms of customer adds, our target for this year was 120 net new G8K logos. Clearly, with being half way there through the first half, puts us on track to certainly achieve it. And internally where our objective is to obviously beat that number. So, we feel very pleased in terms of our ability to add new Global 8000 customers. With that said, they start small and it takes some time for them to get spun up. So, I don’t see a big impact on revenue in this fiscal year from the strong performance of adding new logos. Did you have a part two to that question?

[Brad Reback:]

No, no, and on the billings, given how strong they were here, should we expect a bit of a low and then accelerate back in 4Q?

[Jim Frankola:]

Yes. And once again, I don’t want to go through the sermon on billings. In any given quarter, they can go up and down. What I would say is if you were to look at our revenue growth for the year, you would sort of expect that the trailing 12 months billings or year-over-year increases in deferred revenue would track to that sort of revenue guidance. So, we will have seasonal slowness in terms of Q3, like we do every single year relative to quite frankly a very strong Q4.

[Operator:]

[Operator Instructions] Your next question comes from the line of Greg McDowell with JMP Securities. Please go ahead. Your line is open.

[Greg McDowell:]

Great. Thank you very much. First one for Tom and then one follow-up for Jim. Tom, I was wondering if you could talk a little about the international expansion opportunity and maybe any geographical commentary on countries that did particularly well in Q2 and where you are seeing a lot of opportunities for expansion. Thanks.

[Tom Reilly:]

Greg, thank you. We are excited about the international opportunity. So, first off, just on a percentage basis. It grew as a percentage of our business. It grew from 26% last quarter to 28% this quarter. It is growing at roughly 50% year-over-year. And part of our success there is from our plant-the-flag strategy. For those of you who don’t know it. We expanded internationally. We made aggressive move internationally two plus years ago. And we went to countries; our plant-the-flag strategy was to win one of the largest banks, win one of the largest telcos, and penetrate the government in these countries we expanded into. If you do that, you can move into all the other verticals. Having done that and we are close to the 30 countries where we have planted our flag, that allows us to grow in those markets. And I do not have in front of me Greg any specific country where we’ve seen the success. The international across the boarders do pretty well for us.

[Mike Olson:]

And just to be specific, the 30 countries where we are planted our flags are where we have a physical presence in that country. We have sold our software in more than 68 countries around the world.

[Greg McDowell:]

That’s helpful. Thanks. And Jim, one quick follow-up for you. Certainly thematically, one of the themes is margin expansion. And I was just wondering if you could expand a little bit on specifically the subscription gross margin. I mean, at 85% it’s really not that far away from your long-term model. And as you pointed out, it’s 200 bps higher than the year ago quarter. So, I was just hoping if you could talk a little bit about where that 200 bps in subscription gross margin expansion came from and sort of what we should think about for the back half of the year. Thanks.

[Jim Frankola:]

So, the source of that is couple of pieces, one is leverage. So, our support organization years ago decided to build an infrastructure that scales to really literally billions of dollars of revenue. We use machine learning technologies, our own technologies to proactively diagnose customer problems before they occur, creates a ticket on their behalf and then contact them and say if you change this setting, it will prevent the cluster from failing. Clearly, when you do that, not only does it drive customer satisfaction but it’s a much lower cost way to solve customer problems. So, we use machine learning. As our customers move to the cloud, that is a more standardized environment, so we see efficiencies with the move to the cloud, and we’ve built an infrastructure in support that scales. So, as revenue increases, as customers increase, we have to add relatively fewer people. So that’s the model. We expect to continue to see improvements in our support margins over time. Quite frankly, Q2 was a surprisingly good quarter, we’ll be relatively flattish in terms of support margins or subscription margins for the back half of the year compared to Q2.

[Operator:]

There are no further questions at this time. I’ll turn the call back to the presenters.

[Tom Reilly:]

Well, thank you everyone. This is Tom Reilly, just in closing, I want to thank you all for joining us this past hour to learn about our Q2 results. We’re very pleased with our results and enjoyed this conversation. I want to extend a great amount of gratitude to our employees, our partners, the development community that we’re working with. Welcome to our Cloudera Fast Forward Lab team. And thank you to all our investors, we appreciate the call. Good bye now. Thanks everybody.

[Operator:]

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