Cloudera, Inc.
Q1 2019 Earnings Call Transcript

Published: 7 Jun 2018

[Operator:]

Good afternoon. My name is Cheryl and I will be your conference operator today. Welcome to the Cloudera, First Quarter Fiscal 2019 Quarterly Results Conference Call. All participants’ lines have been placed in a 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, Vice President, Corporate Development and Investor Relations. Kevin, you may begin your conference.

[Kevin Cook:]

Thank you, Cheryl. Good afternoon and welcome to Cloudera’s first quarter fiscal 2019 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 more fully detailed under the caption ‘Risk Factors’ in our annual report on Form 10-K and our other filings with the SEC. 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 provide a non-GAAP weighted average share count. These non-GAAP measures are not intended to be considered in isolation from, a substitute for, or superior to our GAAP results and we encourage you to consider all measures when analyzing Cloudera’s performance. Additionally, our commentary today and the guidance we provide are under existing accounting standard ASC605. For complete information regarding our non-GAAP financial information, the most directly comparable GAAP measures and a quantitative reconciliation of those figures, please refer to today’s Press Release regarding our first quarter fiscal 2019 results. The Press Release has also been furnished to the SEC as part of a Form 8-K. In addition, please note that the date of this conference call is June 6, 2018 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, let me turn the call over to Tom Reilly.

[Tom Reilly:]

Hello everyone. Thank you for joining us to discuss our first quarter fiscal 2019 results. As I will review shortly, in Q1 we executed the initial elements of our transition plan to position Cloudera to capture a larger portion of the market. I am very pleased with the performance of our team given the significant changes we initiated in late Q4 and early Q1. This multi-quarter transition is well underway and proceeding as expected. Let me quickly share our Q1 results and provide a more detailed update on the rationale, actions, and progress concerning our plans. Total revenue for the quarter was $103 million, representing a year-over-year growth of 29%. Subscription software revenue grew 33% year-over-year. Our net expansion rate was 132%, and we generated operating cash flow of $24 million in the first quarter. For several years, we have enabled large global organizations to collect and manage vast amounts of data in various forms at scale. Our offerings help enterprises in in three ways; first, for them to grow with greater insights into their customers; second, given the ability to connect their products and services via the internet-of-things to compete more effectively; and third, for them to protect their businesses in an environment of increasing digital risk. In many respects, we have led this market. We continue to mature our innovative technology into a platform that is highly performing and operates in a hybrid and multi-cloud world. As a result, today we have hundreds of large enterprise customers managing many petabytes of structured and unstructured data on premises and in the cloud. We are proud of these accomplishments, but not satisfied. We must continue to lead this market and now align with the next market evolution helping our customers get unprecedented insights out of data using techniques such as machine learning, artificial intelligence, self serve analytics, and stream processing. On a related note, because these technologies are all very compute intensive and elastic in nature, they particularly benefit from public cloud infrastructure and are moving rapidly to the cloud. Against this backdrop, we are making investments and business decisions required to capture more of the highest value opportunities. As we discussed in our year-end call and in our Investor and Financial Analyst Day in April, we have initiated a transition as the market advances and our understanding of customer behavior and use-case adoption is refined. The rapidly connecting world is driving every enterprise in every industry to go through a digital transformation in order to rank [ph] competitive in the modern era. Data is the foundation of digital transformation. A completely new architecture and set of technologies are required for enterprises to leverage and gain meaningful insights from data. To position Cloudera for this latest market evolution and long-term success, we are making transformational changes in primarily two forms. The first is to shift our technology and innovation focus to the needs of organizations that are striving to get insights out of data versus solely managing data. Truly gaining insights requires modern techniques such as machine learning and AI, combined with traditional analytics. The power is in exploiting both methods, this orientation means ensuring that our offerings are effective, easy and cost competitive in the public cloud as these compute intensive workloads benefit from the elasticity or public cloud infrastructure. In support of this, organizationally we have put in place three new general managers who are driving our investment plans in each of these areas. These leaders have completed the build out of their business units and have aligned the company behind each of their objectives, and I’ll step through them. First, we aim to lead the adoption of machine learning and AI at our largest customers. With the recent announcement of Cloudera Data Science Workbench 1.4, we now have more than 150 large enterprises that have adopted Cloudera as their development environment for machine learning and AI applications. This past quarter, more than 20 large enterprises selected Cloudera as their ML/AI development platform. Let me explain why winning these decisions is so important for our long-term success. The ML/AI market is early in its formation. There are no clear market definitions, industry standards, or best practices. In addition to the direct sale of our machine learning offerings, because of the data intensive and compute intensive nature of machine learning and AI, these workloads will naturally drive substantial future expansions on our underlying platform. As data scientists become more productive, our economics, of course finally improves, thus our objective to lead our customers with product innovation, research, and advisory work. In the second area, we are also making great strides in our objective to disrupt the analytics market. Although analytics encompasses some of the more traditional workloads, our engineers have been busy innovating to deliver data warehouse capabilities at dramatically lower costs and better performance, including our newly announced cloud based data warehouse, Cloudera Altus Analytic DB. With more than 20 new analytics customers gained in Q1, our analytic offerings now are being used at more than 850 accounts. And third, we are capitalizing on cloud adoption by large enterprises. We have substantially expanded Cloudera Altus, our family of platform as a service offering. In addition to the recent announcement of Altus Analytic DB, we have made generally available Altus data engineering on Microsoft Azure. We have also released the beta of Altus Shared Data experience or SDX providing unified data context for workloads running in the cloud. Approximately 26% of our customer base is now using our public cloud offerings, many in hybrid and multi-cloud deployments. In Q1, the number of customers running Cloudera in the cloud increased 55% year-over-year. Our innovation in these three high growth areas is being recognized by customers and I cannot be happier with the leadership being demonstrated by our general managers and the alignment of our organization behind them. We are executing on a multi-quarter transition. It will take time for the bookings from these initiatives to become evident, but every day we are making progress. Shifting to the topic of bookings and selling. The secondary transformation is in our go-to-market strategy. Here we are very hyper focused on landing those enterprises with the greatest propensity to adopt multiple use cases and expand consumption less software over time. Also, in support of our technology and innovation focus, we are increasing our selling and marketing efforts to a line of business executives. This is where decisions are being made from machine learning use cases, analytics and cloud adoption. Three areas of update here

[Jim Frankola:]

Hello, everyone. Our Q1 results reflect the anticipated impact of Cloudera’s go-to-market changes. We expect it to take a few quarters to execute the transition that Tom described and for bookings to benefit from these initiatives. Consistent with the outlook that we’ve shared, our net expansion rate was a bit lower in Q1. It is noteworthy that we exhibited strong financial controls and achieved positive operating cash flow for the quarter. Subscription software revenue was $86 million, an increase of 33% year-over-year. This represented 84% revenue, up from 81% in Q1 of fiscal ’18. In total revenue was $103 million for the first quarter, representing 29% growth over the year ago period. For Q1 our net expansion rate was 132%. Recall that net expansion rate factors retention, expansion and churn on a dollar basis. Based on feedback that we received from our investor and financial analyst day, we plan to share more about our land and expand business model and the measures that matter most in analyzing and managing our business. As Tom highlighted, we now have 539 customers with annual recurring software revenue in excess of $100,000. This measure best reflects our ability to both acquire target customers and advance customers along a journey toward attractive unit economics. For those interested in a progression of this measure over time, we have posted account by quarter in the investor materials on our website. As I review the remainder of the income statement, note that unless otherwise stated all references to expenses and operating results are on an on GAAP basis. Historical non-GAAP results are reconciled to GAAP results in the press release issued earlier today. In Q1, subscription gross margin was 85%, up from 84% a year ago. Services gross margin for the quarter was 10% versus 11% a year ago. Total gross margin for Q1 was 73% compared to 70% last year. Turning to operating expenses, sales and marketing expense was $54 million for the first quarter of 52% of revenue. This compares to 62% of revenue in a year ago period. Research and development was $34 million for the first quarter or 33% of revenue down from 35% a year ago. G&A was $12 million for the first quarter of 12% of revenue. This was up from 11% of revenue last year due to increased cost associated with operating as a public company. Overall operating loss was $24 million in Q1, representing a negative operating margin of 24%. This was an improvement of more than 14 percentage points compared to the year ago period. Loss per share was $0.17 in the first quarter based on 147 million weighted average shares outstanding, compared to a loss per share of $0.27 in the first quarter of fiscal ’18. Please review the financial statement tables in today’s press release for additional information regarding historical and forward looking stock based compensation expense and shares outstanding. Now turning to the balance sheet and cash flow, we exited Q1 with $487 million in cash, cash equivalents, marketable securities and restricted cash, which is up from $26 million from the end of fiscal 2018. Operating cash flow for the first quarter was positive $24 million driven by strong collections and continued improvement in operating efficiencies. This compares the positive operating cash flow of $5 million in the year ago period. This progress reflects the dynamics of the Cloudera business model with high customer acquisition costs, offset by much higher customer lifetime value. Capital expenditures were $4 million in the quarter. Total differed revenue was $279 million at the end of the first quarter, up 31% year-over-year, short term differed revenue was $247 million up 32% year-over-year. I will conclude with guidance. Our guidance for fiscal year 2019 is unchanged. Initial guidance for fiscal Q2 is as follows; we expect Q2 total revenues to be between $107 million and $108 million, representing approximately 20% growth over Q2 of last year, with subscription software revenue in the range of $90 million to $91 million up approximately 22% year-over-year. Loss per share is projected to be $0.15 to $0.13 based on approximately $150 million weighted average shares outstanding. With that, I’ll turn it back to Tom.

[Tom Reilly:]

Thank you, Jim. At Cloudera we believe in digital transformation and interim power for large enterprises. Data, machine learning, artificial intelligence, analytics and cloud, all play important roles in this transformation and our solutions are elemental to these high growth areas. Our customer base requires an enterprise grade platform that operates at scale with on premises in hybrid deployments or increasingly in multi cloud environments. Equally important to our commercial success is enforcing discipline and selling to our fine target market, engaging the line of business buyers as drivers of digital transformation in their businesses. Simply, these efforts are designed to lower our customer acquisition costs and increase our net expansion rates. I am proud of all that Cloudera’s accomplished in the short amount of time and I am impressed by the energy displayed by our team as we embrace change to make Cloudera a stronger company. The digital transformation is happening. We are intent to making the right investments to capture our share of this market for the benefit of all Cloudera stakeholders. The team and I remain grateful to our customers, our employees, our developer community, our partners and of course to our investors. Thank you all. As a reminder, we are joined by Mike Olson, our Co-Founder and Chief Strategy Officer for Q&A. Operator, let’s begin the Q&A portion of the call. Thank you.

[Operator:]

[Operator Instructions] Our first question comes from the line of Kash Rangan of Bank of America/Merrill Lynch. Please go ahead. Your line is open.

[Jacqueline Cheong:]

Hi, this is actually Jacqueline on for Kash, thanks for taking my question. My first question is, Oracle recently acquired DataScience. Does that make them more competitive against Cloudera?

[Mike Olson:]

Hi, this is Mike speaking, and I’ll take that question. No, we don’t think so. Oracle made a good buy with the DataScience.com team. I think Oracle is fleshing out its broad business software portfolio and applying DataScience techniques to traditional relational data with this addition, but look our modern platform for machine learning analytics optimized for the cloud is designed for much larger scale and much broader range of use cases than a traditional database. As you may know, Oracle is a good partner delivering the Cloudera platform as part of its Oracle big data appliance and that may help drive some additional appliance consumption.

[Jacqueline Cheong:]

Yeah, actually this is Tom, just jumping to Mike’s comments. We’re excited to see that. It reflects what we knew all along that machine learning is going to be a high growth area. That is now validated by Oracle. Their offering will be very good in Oracle environments. We intend to work in both hybrid, multi-public cloud environments in kind of broader underlying capabilities.

[Jacqueline Cheong:]

Got it, thank you so much. And how are you doing in terms of win rates against competitors and how is the win rate trending?

[Tom Reilly:]

Yeah, so actually our win rates with our traditional competitors, let me capture that as all the legacy guys with you know legacy data warehouses or MPTs as well as the on-premise competitors that we traditionally are competing with, our win rates are very strong and improving. We are increasingly seeing more wins against the traditional players. We are competing more often and increasingly in the cloud against the public cloud house offerings. Here, our win rates are not as strong, but we’re improving them and our focus with the new general manager, our machine learning, our investments – I’m sorry, general management in cloud, our investments in Altus, our specialists in the field gives us great confidence that we will compete effectively in the cloud as well.

[Jacqueline Cheong:]

Alright, thank you so much.

[Operator:]

Your next question comes from the line of Sanjit Singh of Morgan Stanley. Please go ahead. Your line is open.

[Sanjit Singh:]

Thank you for taking my questions and nice to see that the transitions kind of got off to a solid start. I have two questions, maybe one for Jim and then may be a follow up for you Tom. Jim, on the quarter, I think the net expansion rate came in fully – pretty solidly above where we were expecting, so I wanted to get your view on how should we expect the net expansion rate to trend over the balance of the year that’s sort of in line with the guidance that you laid out last quarter? And from a bookings perspective, any sort of commentary you can provide for Q1? How those bookings metrics -- booking performance came through this quarter?

[Jim Frankola:]

Yeah, well I’ll touch on bookings first. We don’t comment on bookings within the quarter. We’ll talk about the overall health of the business. With respect to the net expansion rate, there really isn’t much new news from what we disclosed 60 days ago on our conference call or even seven weeks ago in our Analyst Day, so when you look at the revenue projections for the year, we are guiding to 24% software revenue growth. In a typical year, you might get six points of growth give or take from new customers that have joined over the course of the year. That means that we’ll get about 18%, 19% revenue growth associated with existing customers for the year. So that would imply a net expansion rate at the low end of our historical range of 120 to 150. So that guidance of 120 to 150 is the long term net expansion rate that we expect for the business, and this year given the transitions that are going on with the field, we expect to be operating at the low end of that range.

[Sanjit Singh:]

Very helpful, and then maybe Tom for you. When I hear the message coming out of your introductory comments is that the company is really focused on the AI machine learning use cases as well as streaming analytics, which to me means cloud. And so as we go through the transition, are you thinking about you know from incentives or pulling in the levers of the business to really accelerate that adoption in the cloud where it’s sort of 26% today. Is now the right time to really be aggressive in terms of -- I am trying to get your customers over to the cloud as fast as possible given that you’re going through this multi-quarter transition anyway?

[Tom Reilly:]

The simple answer is yes Sanjit. That is why you know we put in place three new general managers to bring focus and emphasis in these high growth areas, machine learning and the analytics, and the cloud. Our strategy in cloud is to capitalize on our customers’ desire to take advantage of public cloud. That did not exist just two years ago, three years ago in our target market large enterprises. We now see that happening and I think our timing is very strong to help large enterprises capitalize on their desire to take advantage of public cloud. So that’s why we put in place our general managers, we put in place dedicated specialists around understanding the public cloud environment, and we have been investing in Altus, our platform as a service which is basically our competitive strategic weapon in the public cloud.

[Mike Olson:]

Hey Sanjit, this is Mike. I want to just pile on very briefly. While it’s absolutely true that machine learning and AI and the public cloud is a big deal, it’s been a big deal for our existing customers on premises for some time. We believe that our hybrid and multi-cloud strategy allows large enterprises to roll these capabilities out where they want it. I’ll point out, machine learning is in general for large enterprises a new technique. We are investing in DataScience, where [inaudible] team because we believe that winning the hearts and minds of our large enterprise DataScience and analyst clients early is critical. That will set us up to win substantial expansions as they roll those workloads out on premises into cloud or move them around among those places over the long term.

[Sanjit Singh:]

I appreciate that Mike, and maybe one really quick follow-up as it relates to the data warehousing use case, which has been I think a story of strength for the company. In terms of where that parts of the internet take place, do you see data warehousing use cases moving to the cloud as well?

[Mike Olson:]

Oh, we absolutely do. We see especially as customers consider some of the end of life products they might have invested in the past, say Natesa [ph]. They think about replatforming those systems. They are looking not nearly for a new platform to run them on, but they are thinking about doing away with the hardware investment altogether and moving those to the cloud. So we view that one in particular as a big opportunity for us.

[Sanjit Singh:]

Great, thank you very much.

[Operator:]

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

[Mark Murphy:]

Yes, thank you very much for taking my questions. So Jim, I guess I’m trying to compare back and forth between the net expansion rate is 132% in the quarter and then subscription software growth rate is 33% or just under 33%. Is that 1% spread? Is that essentially the – that’s all of the new logo business that you had pulled in in the last 12 months?

[Jim Frankola:]

There is one account that we exclude from the net expansion rate and that’s Intel. They are a related party and you know acute and caged, you can see how much revenue they have. So in any given quarter there might be 1% or 2% noise due to the Intel growth dynamics being different than everyone else. The second thing is the one 132 net expansion rate is an arithmetic average of the last four quarters net expansion rates. So you have some math that may yield a slight difference, but yes, what you have going on there is the net expansion rate plus some revenue from new, plus some of the dynamics on intel and a four quarter simple average versus a complex average.

[Mark Murphy:]

Okay, and then I think what you had said in a prior answer, I think you said in a typical ear you might get six points of growth from the new accounts right, and I realize there is a little bit of a swing factor, but essentially – I mean are you essentially trying to get – so the 1%-ish, 1% or 2% kind of contribution that is coming in from all the sales and marketing investments that go into new logos and what you get out of them; you know how that converts to revenue in the first year. You want to take that 1% to 2% back up to about 6%. Do you think that that is in the cards you know, let’s say if you know many quarters down the road is your transition is successful, giving it gets back to that kind of a spread.

[Jim Frankola:]

Yeah, so I mean if you look at the number of new logos that we add and average values of the new logos, you will find that in any given period we will have $20 million to $25 million of revenue associated with accounts that we picked up over the past year, so that’s where that 5%, 6% of total mix comes from. I don’t see that number radically changing over time. It’s the nature of the Atlantic spend model, customers to start small. What we’re looking to do is focus on the customer expansion pattern where we talked about the Cloudera journey, where we are investing and moving customers from that landing at less than $100,000 into the phases above that, going from a single use case, to multiple use cases to being an enterprise platform and that focus is on the net expansion rate. So to come back, I do think that we will see 5% or 6% of our revenue this year, some new and that won’t be much of a change from historical patterns

[Mark Murphy:]

Okay. And as well, did you just close the Global 8000 customer account exiting Q1?

[Jim Frankola:]

No. So consistent with what we discussed at Analyst Day, the Global 8000, e\we’ve tightened up our target market, so we’re using our own data and our knowledge of who is most likely to both buy and expand. We have an internal list of targeted customers that is much tighter than that now. It’s about 5000 enterprises give or take and that list changes dynamically as we update our models. So from a focus standpoint, what we’re going to focus on is the number of accounts over $100,000. We actually go some feedback in Analyst Day that that was a pretty good metric to show the customers that are really sticky. So Tom has alluded to it, but to recap, our churn rate for customers under $100,000 is roughly 20%. The churn rate for customers over $100,000 is in the mid single digits. So it’s an important milestone in our customer journey and we’re focusing our customer account on those customers over $100,000.

[Mark Murphy:]

Yeah, okay. So in terms of customer accounts now, how are you handling customer accounts from this point going forward? Are you going to give us a number once a year rather than quarterly or is it we’re not going to get a customer account?

[Jim Frankola:]

We will give you the number of customers that are north of $100,000. That – if you look at the slides we shared at Analyst Day, those customers represent 92% of our revenue. So if you’re trying to understand the meat of our business is the over $100,000 customer accounts. That’s what we’re going to share quarterly and the yes, annually we will provide supplemental metrics on total customer accounts G2K, whatever other supplemental metrics we think will add transparency to our business model and allow you to better understand that journey. Just like seven weeks ago we actually broke the customer accounts up into five different segments to try to show you how customers move through their expansion phases.

[Mark Murphy:]

Okay, got it, understood. And the last thing Mike, I was wondering if you could just update us on the percentage of Cloudera workloads on public cloud. I think the last summary you gave us a number as 20%. I think that included Altus. I guess I am just wondering, do you know the number that would be specifically that the pubic cloud percentage, because I think we are trying to triangulate on just what your market share would be on-prem versus in the public cloud. Thank you?

[Tom Reilly:]

Hey Mark, this is Tom. Let me jump in that, closer to the numbers on what’s happen there. So 26% of our customers are taking advantage of public cloud. That is up 55% over the same period a year ago. That’s a mix of both, using the public cloud as infrastructure service and our recent release of Altus Platform as a service, although we don’t break that out. And these figures that we report are basically us monitoring our software and the use of our software in the cloud, so it gives us visibility. As our customers move workloads to the public cloud we can see that visibility happening.

[Jim Frankola:]

The only thing that I would add Mark is that with the beta release of Altus Analytic Database on Azure joining the beta release on, much on Amazon web services rather, alongside the Altus data engineering offering GA on those platforms now as well, we’ve got a richer suite of capabilities and we think two of it play better together than either playing it solo. So we think the combination of those offerings is promising that ought to help us win more share there as well.

[Mark Murphy:]

Thank you.

[Operator:]

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

[Karl Keirstead:]

Okay great, I’ve got a couple. Maybe to start with Jim. Jim you mentioned you intended to reaffirm all of your fiscal ’19 guidance metrics. I didn’t explicitly hear you reaffirm your total DR and short term DR growth; maybe you did and I missed it, but do you mind just reaffirming that that plus 21 on total DR and plus 23 on short term DR are still your targets?

[Jim Frankola:]

Yes, so we don’t guide to billings or in effect to differed revenue. What I did last quarter was there so much movement in the numbers, the overall numbers, I wanted to make sure that as everyone built out their models, they had a better idea of what differed revenue was doing and for those just focused on billings, could help them with that one. We are in the middle of this transition, so we don’t guide the billings and I’m not going to reaffirm one way or the others the numbers that we shared last quarter on differed revenue.

[Karl Keirstead:]

Yep, okay great. And then maybe Tom, you mentioned that you’d like to improve your win rates against the house offerings of the cloud vendors. I presume you were referring to services like EMR and Redshift on AWS and maybe big query on GCP. I was just wondering if you could summarize; maybe this is partly a question for Mike as well. What are the functionality gaps that those house offerings have that Cloudera can sort of take advantage of and move up the stack and improve your win rates over time.

[Tom Reilly:]

Yeah, this is where I get really excited. So and I’ll – I had a customer sales call, a large financial bank yesterday, the CIO in the whole executive team in Silicon Valley for their tech road show and we meet with them every year. Last year I think it was before we weren’t discussing cloud, it’s all they want to discuss this time, and our pitch [inaudible] right, you want to take advantage of your data center and take advantage of public cloud with a hybrid offering. Yes, that’s required. So you need all those enterprise features that you have available today in your data center to be the same in the public cloud. Yes, do you know which public cloud you want to use? No. Do you want a multi cloud offering? Yes. So you want that portability? Are you then shifting against the private cloud in the future? Yes. I go okay, you have a few choices when it comes down to that. Amazon is not going to offer you on premise capabilities, nor are they going to run on GCP or Azure. The same thing for Google, and Microsoft while they might offer you on-prem, are not going to give you the capabilities on Azure and GCP and many of these players are lacking the enterprise securities features. When we are competing the cloud we have so many advantages. Our number one disadvantage is awareness of our capabilities and that’s what we are ramping up with our general manager machine learning, our marketing teams to create awareness, and we think will compete very effectively.

[Karl Keirstead:]

Got it. Okay, that’s helpful Tom, thanks. And if I could finish with a little bit of a technology questions for Mike. Mike, when I go to Cloudera’s website I don’t even see the world Hadoop anymore. When I listen to this earnings call, I don’t hear it anymore, and I’m just wondering whether the pivot that Tom laid out at the beginning of the call where you are moving to more ML centric workloads and cloud, I’m just wondering does that sort of motivate you to pivot even faster awry from those core Hadoop elements to either more Cloudera proprietary IP or perhaps different open source software, whether these shifts Tom talked about are almost forcing an accelerated shift away from those core Hadoop roots? Thank you.

[Mike Olson:]

No, thank you Karl, it’s a percepted question and its one that we’ve been taking with folk about a bunch. Look, we are in no way ashamed of Hadoop. It is still a core foundation element of our platform. But those projects, HCF, [inaudible] scale our storage system, not reduce the scale of our processing engine, they were all we had 10 years ago when we stated the company. Today we’ve got a rich suite of analytic engines and power for our distributed query and spark for receiving processing and model training and so on. We’ve got a rich collection of storage technologies, not just HDFS but on Amazon S3 native storage, on Microsoft ADLS native storage, even IoT native storage for workloads that demand that in the Apache Kudu project. So it’s just a much more interesting platform than before. What all of that technology shares is the use of the same design and architectural principals that Hadoop pioneered, scaled out, distributed, shared market architecture. You can now produce your data, you can empower your data, you can spark your data, it’s the same data. But you can get our governance, security compliance via our share data experience, SDX across all those workloads. So you know who ran queries, who trained models, who prepped in. It’s just a way more interesting platform. Hadoop is important, but it’s not where the action is these days, still in the platform but there is a lot more interest in these newer capabilities.

[Karl Keirstead:]

Okay, great insight. Thank you all very much, that’s helpful.

[Operator:]

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

[Michael Turits:]

Hi, I’d like to – thanks guys for taking my questions. I would like to come back to the question, the fact that you said win rates were a little bit lower than cloud. Maybe you specified, but just make it clear, was that in general or was that on analytics and data warehousing, or was it more on the data engineering side where you might be competing the last that you produced for example?

[Tom Reilly:]

The comment is more in general. We track every compete and then we you know evaluate you know win loss no decision type things. So we are tacking our cloud win rates, you know relative to our on-prem traditional win rates, so that an all in Michael not one area than the other.

[Mike Olson:]

Michael, this is Michael and I want to add just one thing. When we win a workload on Azure, when we win a workload on Google or Amazon, the cloud vendor wins as well. So the compute that we drive, the storage that we drive as part of those workloads wind up paying substantial revenue to those vendors, and the strength of our partnerships at Amazon, at Microsoft for example is because we are able to help them drive consumption of their cloud platform. So yeah, we compete on a point basis say with the MR on Amazon, but we are not going after just the EMR workloads. We are going after customers who have a broad range of processing analytic requirements and one, governs security and regulatory compliance across all of them who need the ability to move those workloads from their data centers into the cloud and then back again later for whatever business reason that might make a difference. We want to compete more often in the cloud, we want to see many more deals in the cloud, we want to win our fair share.

[Michael Turits:]

And then I guess I got you Mike, I think maybe for Mike and for Tom. Tom had mentioned, I have to go back and check the numbers, but you said a certain number of customers that may put their AI machine learning platform. Does that mean that those are the number of customs that are actually taking data science work bench and if not may be you could talk about that attach rate and how you are actually describing or defining that platform that you are standardizing on?

[Tom Reilly:]

Yes, so when we talk about the standardizing on our platform, let me understand the building blocks of this. You need data and so data links where data is at. Apache Spark is the defective kind of compute engine for machine learning and model training. We are the first to commercialize Apache Spark, but we are not capturing all just those customers doing that. When we talk about our machine learning and AI development environment, we are talking about Cloudera Data Science Workbench and Fast Forward Labs. And what Cloudera Data Science Workbench is a way to make data scientist more productive, by giving them secure well governed access to the data link, bigger data sets than they ever had before, to automate what they do as a data scientist from expiration to moving into production and then we have our Fast Forward Labs, which is our research that we make available to them, to almost make more productive.

[Mike Olson:]

I would add just one additional point to that Michael. We have had customers doing machine learning for many years. So big banks and insurance companies were doing fraud detection and any money laundering workloads on our platform by training up machine learning models way back in the table which now produces spark. We’ve made it much easier to do that with our acquisition of sense.io and the release of our data science workbench platform that’s interested to accelerate our ability to on-board those workloads. Two quarters back when we announced the Fast Forward Labs acquisition I pointed out that is intended to let us be much more advisory conclusive and supportive of our enterprise clients who want to us our platform for machine learning. Machine learning drives platform consumption, getting those early workloads, getting folks using our development environment, gives us a solid platform for growing our revenue on a platform as well as on the on the [inaudible].

[Michael Turits:]

Okay, thanks guys.

[Tom Reilly:]

Thank you, Michael.

[Operator:]

Your next question comes from the line of Tyler Radke of Citi. Please go ahead your line is open.

[Tyler Radke:]

Hey, thanks for taking my questions; I like the Pizza Hut example. Question for Tom, you mentioned in your prepared remarks that as you are looking to kind of hiring a cloud sales leader and hopefully to improve the win rates, you are looking to the messaging of having Cloudera be cost effect and easy to use. I’m just curious if you were to put your finger on you know may the reason why win rates are down. How much of it kind of comes down to costs and when you say cost effective, is this essentially you have to match EMR or Microsoft on kind on their like for like Hadoop service. I just want to interrupt what that comment was referring to and how you are thinking about the drives of cloud win rates?

[Tom Reilly:]

Alright, thank you Tyler. Thanks for calling out Pizza Hut. I was challenged to use your tag line, no one out pizza’s the hut, and I’m sure Pizza Hut is very happy with that. So first off, we are not hiring a new cloud sales leader. We put in place a new cloud general manger who drives our product roadmap, who understands the completion, who’s responsible for enabling our field and builds out a business case of where and how we are going to move workloads to the pubic cloud. But we are putting in place cloud field specialist, who understand the nuances of – the dynamic nature of the cloud infrastructure. So one of the things, when we compete in the cloud we are not loosing on price. We are very price competitive in the cloud and quite frankly we have a premium that we were able to get in the cloud because of our SDX capabilities and the new cog guys do not have any of the SDX capabilities, which means the data, the securities and the meditative management, the governance and the multi cloud capability. And for that slight premium to have no cloud lock in is an easy sell for us. When I talk about competitive win rates, our competitive win rates against the traditional guys are going up. We have no trouble competing with the traditional data warehouses, data marts NPTs like Greenplum and Azteca the Verticus. We have no problem competing with our traditional competitive, you know called MapR, Hortonworks, the Hortonworks plus IBM partnership. All that we are very, very – we are doing extremely well. Our cloud win rates are not as high as those traditional win rates, but we think our focus in our capabilities to take it directly to the cloud guys and to take our customers to the cloud quickly, we’ll see our win rates improve there. So I didn’t mean to say that they were decaling. I just wanted to share that they weren’t – our win rates there are not as high as the traditional guys, because we haven’t been competing there long.

[Mike Olson:]

Tyler, this is Mike. I want to make just one last point and that is that not withstanding some on the margins, point competitive, point to point competitive dynamic with all of the cloud vendors. We’ve got excellent partnerships with all of them as well. When we bring these enterprise workloads, the need data governance, compliance, regulatory support and so on into the cloud, we deliver those capabilities and allow them to consumer more cloud storage, more cloud compete. So we’ve got excellent relationships with the leadership at the Big 3 cloud vendors and its driven by our ability to bring these enterprise workloads on to those clouds that need governance, compliance regulatory support and so on and then drive much more storage, computer consumption and other service consumption in the cloud than they would have taking without cloud.

[Tyler Radke:]

Great thanks and a follow-up for Jim. I think last question you talked about bookings performance returning, reaccelerating, maybe in Q3 or Q4 of fiscal ’19. Just curious if you are still expecting that?

[Jim Frankola:]

Yes, so as Tom discussed we are right in the middle of the transaction that we are making across the field in the business, so we are looking forward to getting beyond the period of peak disruption which I think is right about now and expect that in the back half of the year we will start seeing performance return back to historical levels.

[Tyler Radke:]

Thank you.

[Operator:]

Your next question comes from the line of Chad Bennett of Craig-Hallum. Please go ahead your line is open.

[Chad Bennett:]

Great, thanks for taking my questions. So maybe I’ll try this at the different angle on kind of the whole ML and AI and analytics push, and up the stack push you guys are going after. So at your investor event you talked about the data science workbench I think being the fastest growing or fastest adopted product first 12 months out of the gate that you’ve even seen. So if I lump data science workbench and ML and AI and Fast Forward Labs all in one bucket and if we look at your customer base the way you split it up. So you have over 500 customers at $100,000 or better in AAR and then north of 60 customers over a million. What is the penetration rate in those buckets for your data science workbench, Fast Forward Labs, AI ML capabilities today?

[Tom Reilly:]

So Chad, I don’t have that exact analysis in front of me, what’s the actual penetration rate. However what we are seeing is our largest customer wanting to adopt us for their ML AI platform, and what’s exciting about that is when we sell CBSW, we are basically bringing data scientists to large data sets and that allows them to start exploring and developing new use cases. There is a lagging effect when those new use cases go into product. The exciting part is as we get those data scientists more productive, that’s going to drive the underlying growth and reveal itself in the expansion side of our business, and this is the investment we are making; getting out to the line of businesses, talking to these data scientists, bringing them to data links and the data hubs that we have been building in our customers for the last few years, getting them productive and then seeing that expansion rate. And we don’t want to give up that real-estate in the line of business to other tools or technologies, so it’s why we are making that emphasis right now.

[Mike Olson:]

Chad, the only point I would add is that when you get to a million plus with Cloudera, you are not a data science or an analytic database customer, you are data science and an analytic data base customer. In general, our large customers are using all of the capabilities of our platform and a rich collection of different workloads, by and large big means very broad as well.

[Chad Bennett:]

Got it, okay, that makes sense. And then from Tom, maybe from a go-to-market standpoint, again as you are focused more up the stack and towards AI and ML workloads in applications. Maybe you’ve touched on this in the past, but does the type of seller you need in the field have to chance relative to how you sold before and if so kind of how are we addressing that or where are we in addressing that. Thanks?

[Tom Reilly:]

Yeah Chad, so this is all part of the transition. It’s not the type of seller, because that’s someone individual, it’s the type of selling does need to change and we are work with our filed in chaining the selling. So if you followed us, we are in Phase III of our journey. The first Phase, we were selling into IT and when we transition to Phase II we are elevate the selling to the CIO and we trained our sales force and a higher value proposition to get the CIO and sell our data, we made our transition. Now we need to help our field sell to a line of business executives, much more around business solutions and how machine learning and AI can deliver these transformative applications, we are training them and we’ve identified the solutions, they have the value propositions, and we’ve got the training materials, we’ve got the specialist, we’ve invested in industry subject matter experts and we are helping our sales force make that transition. But as we bring our new sellers, we look for more application solution sellers and infrastructure as we make that transition as well.

[Chad Bennett:]

Got it, thanks.

[Operator:]

Your next question comes from the line of Greg McDowell of JMP Securities. Please go ahead your line is open.

[Greg McDowell:]

Great, thank you very much. Two questions, first I wanted to ask about the new metric that customers over 100 K. I was actually encouraged by that metric just 38% year-over-year growth. It looks like on a sequential basis 38 customers flipped over to over 100 K per year. And so I guess the question is for Jim. As we sort of reorient our models to looking at the metric versus total customers or GAK customers, you know maybe what are some of the puts and takes on a go forward basis that we should think about you know, should it be it the 20 to 40 range or was there something specific about Q4 to Q1 that made a difference in those customers flipping over and then I have one follow-up.

[Jim Frankola:]

Right, let try to get to it quickly. So first you mentioned both 38 and 38%. To be precise its 38 customers. If you want to look at the history, the slide deck on the website, we’ll give you the last four quarters of sequential numbers. Regarding the numbers each quarter, it’s going to vary. Right now we are thinking somewhere between 100 and 150 a year fits within the business model. It’s sort of an allocate to our target last year was 125 or so GAK. So that is where the business model is built. You will see some variability in each quarter and then the part which I think I heard about the business model, I’d encourage you to think about our business in two pieces; you have customers less than a $100,000. Last year there was about 8% of revenue. I think over time that will go down lightly, a few basis points each quarter, and then you can build your models on how many customers graduate from that less than 100,000 to the over 100,000 category and then by definition if they are over $100,000 their starting point of ARR will probably be between $100,000 and $150,000. But specifically, there is really nothing special seasonally about the Q4, Q1 dynamic.

[Tom Reilly:]

Greg, you have other questions.

[Greg McDowell:]

Yeah, just one real quick follow-up. I mean the cash flow was certainly a highlight. I was just wondering if could expand on that a little bit on why Q1 cash flow, etcetera?

[Jim Frankola:]

Yeah, so Q1 cash flow is always very strong because of the seasonal nature of our business. So most of our deal, 35% to 40% of our bookings are done in Q4. We typically collect the cash in Q1. We model collection of the cash both in Q1 and Q2. So we never want to miss our number, because some of our larger customers decided to pay us late by a couple of weeks. So what happened in Q1 is the normal seasonality, coupled with really good collections, so everyone who needed to pay did pay. And then on top of that you have the efficiencies. We are getting more efficient each year. The Cloudera customer journey where as customers grow, our unit cost economics increases, all added to the positive cash flow in the quarter.

[Greg McDowell:]

Great. Thank you.

[Tom Reilly:]

Right operator, even though we are over the hour, we are fine taking a couple more questions if there are some out there.

[Operator:]

Our next question comes from the line of Abhey Lamba of Mizuho Securities. Please go ahead, your line is open.

[Abhey Lamba:]

Yeah, thank you. Thanks for taking my questions guys. Tom you mentioned Altus a couple of times; in what type of work loads are you seeing greater traction off this offering and who do you see as a competition in that area?

[Tom Reilly:]

Yeah, so Altus, predominantly has been in data engineering, and in data engineering we compete directly against Amazon EMR, that’s where we have seen the greatest competition. But now that’s we’ve introduced Altus ADB, analytic database and data engineering we are not only – we call that multi function but we are also multi platform running across the cloud providers, we expand our competition. So on Amazon we compete against Red Shift and EMR. So we’ve opened up that footprint. The next area we expect to eventually go into in data science, opening up more of the foot print and one of our strategies is to be not only multi function, but also multi cloud and we do that through our SDX technology and hybrid. And so we think with every release we get increasingly competitive and we open up more workloads in the cloud that we can address.

[Mike Olson:]

Abhey if I can add one point, this is Mike Olson. In the quarter we also introduced for the first time our Altus shared data experience offering and that allows us to provide consistent governance compliance regulatory support across all of those cloud workloads that Tom described. We think it will help us win a larger share of large enterprise, where those capabilities matter, where data governance, compliance, regulatory support and so on make a difference.

[Abhey Lamba:]

Got it. Thank you, very helpful. Jim, you mentioned about the $100,000 plus customer mark, which we understand is important. Two variables in that are the size of the landing customers and how quickly they ramp up to the $100,000 mark. Can you talk a little bit about how those two factors are going to – performing for you guys over the last few quarters. That’s it from me.

[Jim Frankola:]

The landing mark and the expansion rate really hasn’t change very much. So historically customers have landed an average of $70,000 a year give or take. That dynamic didn’t change in Q1 and then the expansion rate from that first use case and second case has been constant with what we’ve seen in the past.

[Abhey Lamba:]

Thank you.

[Operator:]

Your last question comes from the line of Rishi Jaluria of D.A. Davidson. Please go ahead your line is open.

[Rishi Jaluria:]

Hi guys, thanks for taking my question and squeezing me in the call. Tom, in your prepared remarks you mentioned the desire to develop a channel partner to maybe service the customers outside of our target market. I know obviously it’s very early, but can you tell us little more about the strategy, your thoughts here and maybe how you see this play out and then I have a housekeeping question for Jim.

[Tom Reilly:]

Yeah Rishi, so one of the exciting things about our technology is, it is applicable to companies of all sizes. And there are many companies that can benefit from our technology. The way we are constructing today on our go-to-market though with the selling cost and the need to have expansions, we can’t address all the market with our direct sale force, and so this where we are now putting in place a channel that can more cost effectively address a broader market on our behalf. So that channel we have so many inbound leads and opportunities that are kind of outside our current target market of these 5,000 enterprises. So we are putting in place the ability to pass leads to them, to label and train them to build our own qualification process, so we make sure we are passing good opportunities to them, to provide you know support and training for the broader channel. With that early, and we have aspects of channels, we just haven’t had the real good discipline traditionally to use our resources only in our target market and you know have a channel go after the rest of it. Incoming calls we’ll have more detail here, but it’s one of the things when I’m talking to many of these candidates that we bring on board to be our field leader, you know they are experienced in building out these kind of channels.

[Rishi Jaluria:]

Got it, that’s helpful and Jim, just kind of from a housekeeping perspective. I know there has been touched on earlier in the call. I just want to make sure I fully understand. So you disclosed this new metric of customers running [inaudible] Asher and GCS and it’s at 26% now. I know you mentioned in the past of 20% of Cloudera customers using Cloudera in the cloud. Can you just help me bridge the delta between you know why these metrics have a decent gap. That’s it from my end, thanks.

[Jim Frankola:]

We’ve been disclosing the number of customers running in the cloud for several quarters now. So to be very precise, what that number is for customers who report diagnostic information to us, it is 26% of them who are running in the cloud. If you actually go in our website we have a slide 22 which shows you the progression. So, for example in Q1 of last year, 22% of our customers were running in the cloud. So if you go the website you should get all the historically information you need on this subject.

[Rishi Jaluria:]

Got it, thanks.

[Tom Reilly:]

Alright Rishi, any other questions Rishi before we wrap-up.

[Operator:]

We have no further audio questions.

[Tom Reilly:]

Alright, thank you operator. Thanks all for staying long with us today. We wanted to keep our prepared remarks short, we wanted to address your questions and we are happy many of you stayed with us long. We do appreciate you taking the time to understand not only our exciting market, but our strategy to tackle and win this market in the long run. We are making some exciting changes, and my thanks to the Cloudera team and all our customers the might be listening here for what has been as an exciting journey. Thank you all. We look forward to talking to you in the quarter.

[Operator:]

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