Category: Cloud Software

Zoom Video: Unique Billing Cycle and WFH Trend

Posted on by io-fund

This report is a 2-for-1 deal with Beth and Bradley both providing an analysis. Please reference Bradley’s Deep Dive on Financials Below.

This one has been especially challenging in terms of price action. Below, I tell you why we continue to hold the stock and added to it after the earnings report. If the market wants to give me a 15 forward P/S on Zoom, I’ll take it.

Growth is “slowing” because we are lapping extraordinary quarters. Zoom’s situation is very different from a company that put up 60%, then 50%, then 40%. I would call that slowing growth while l would call Zoom’s situation “tough comps.” There is an important difference.

When analysts “downgrade” a company yet set the price target comfortably higher than where the stock is trading at, then it’s meaningless because the analyst will be right no matter what happens. If you’re an institutional analyst, finding a way to be right no matter what happens with Zoom is probably a smart idea. The reason is that Zoom is very complicated to predict as management is offering very limited visibility into next year and because Q4 and Q3 are seasonal low quarters due to a unique billing cycle. We discuss the unique billing cycle in detail below.

The 350% revenue growth is a very hard comp to clear because consumers piled into the app unexpectedly. This has placed immense pressure on Zoom’s enterprise segment to carry the growth. Zoom is an enterprise company and the management had no intentions of being popular with consumers. Even now, the company does nothing to grow this segment other than to offer a free and lower priced tier. Zoom’s competition is Teams — not FaceTime.

One of the main reasons we want to continue holding Zoom is that hybrid work-from-home is an important trend for our portfolio. Asana’s growth is participating in this trend and Monday.com is also participating in the productivity tools category with work-from-home tailwinds. When we were down 40% in Asana, the portfolio manager Knox asked about my conviction and I said “we need to have more than Zoom for work-from-home – productivity tools will be winners this year.” The chances this trend wouldn’t carry Asana was low. Now, I’m reiterating “we don’t want to give up on the leader in work-from-home because the trend is not done yet.” On a side note, we will likely revisit Asana OR we will look at Monday.com if these companies get into a buy zone.

According to Gartner, 51% of knowledge workers will be working remotely by the end of 2021 up from 27% of knowledge workers in 2019. Looking forward, Gartner expects that 31% of all workers in the global workforce will be a mix of remote and hybrid with the United States at 53% of its workforce – in other words, not only knowledge workers. The senior research director who worked on the report stated, “Through 2024, organizations will be forced to bring forward digital business transformation plans by at least five years. Those plans will have to adapt to a post-COVID-19 world that involves permanently higher adoption of remote work and digital touchpoints.”

There are 3.3. billion workers in the world, which works out to about 1 billion remote workers.

Here's what is important to consider. On one hand, you could say that Zoom has 50% of the TAM already at more than 500 million users. However, those were many free accounts in the online segment. Instead, it’s important to consider that Zoom has substantial brand awareness yet only has 20% of the Global Fortune 2000.

Regarding productivity tools, Gartner reports 80% of workers are using collaboration tools for work, up from roughly half in 2019. Here’s the main statistic we think adds to our bull case: “Specifically, the use of meeting solutions surged during the pandemic. While workers globally reported that they spent, on average, 63% of their meeting time in-person in 2019, that number dropped to 33% by 2021 as more meetings took place over audio and video-enabled meeting solutions. The shift away from in-person meetings is expected to continue. Gartner predicts that by 2024, in-person meetings will drop from 60% of enterprise meetings to 25%, driven by remote work and changing workforce demographics.”remote work and changing workforce demographics.”

The good news is that Zoom is an enterprise product and always will be so this statistic directly applies (“enterprise meetings”). The consumer or online segment has distracted the market from the company’s core enterprise focus. Even today, Zoom is not attempting to expand on the consumer side or capture any market share here yet Wall Street has deeply discounted based on the drop-off in this segment. I discussed why this reminds me of when the market was deeply discounting Nvidia for fall-off in crypto mining in 2018 when I openly and consistently said crypto mining is not Nvidia’s thesis – rather the story is AI acceleration in the data center segment. Nvidia struggled to keep up with tough comps in Q4 2018 after crypto mining unexpectedly drove record revenue.

Zoom must execute on the enterprise side but there’s no reason in the recent earnings report to think they won’t. Meanwhile, the market is concerned over the wrong part of the story. Let’s talk about Q3 and Q4 specifically.

Why Q4 is Lower

An important factor as to why Zoom has reported lower third quarter (35%) and also low fourth quarter revenue guidance (19%) is because enterprise revenue is billed in Q1 and deferred revenue and billings are lower as the year continues.

So, how did Zoom put up its biggest quarters during Covid in Q3 and Q4? Well, it’s because consumers were piling in and paying monthly. This places Zoom in a predicament because enterprise is where the growth is coming from (and should be coming from) but the billing cycle means enterprise revenue is very weak in the second half at the very point in time that Zoom has high comps to clear.

The analysts covering the stock point towards lower deferred revenue growth as a concern, yet this is also front half-weighted.

“Turning to the balance sheet. Deferred revenue at the end of the period was one point two billion dollars, up thirty nine percent year over year from eight fifty five million dollars, and slightly up quarter over quarter. Looking at Q4, we expect the year-over-year growth rate in deferred revenue to be in the mid twenty. This is driven by the cyclical decline in the average remaining term of our annual customer contracts, which are front-half weighted.”This is driven by the cyclical decline in the average remaining term of our annual customer contracts, which are front-half weighted.”

There was a question from a financial analyst who covers Zoom and yet was not clear on this point. I’ve included the transcript below. I think it’s important to put into context what is contributing to the slower Q4 growth. Candidly, I find it strange that the analyst had to ask again as it’s pretty clear what management is saying. The last analysis I/O Fund published discussed this here when we said: “Please also note, that Zoom has what’s called “front-weighted seasonality” which means contracts renew more in the first half of the year than the second half of the year. This is technically a headwind to Q3 and Q4 although that was already taken into account with the guide.”

Here's the earnings call transcript:

Kyle Keirstead, UBS

“39:21 Okay, Great. Maybe Kelly, metrics like deferred revenues and RPO are certainly not the most important to watch with Zoom, but they can be indicative of changes in the business, so it's still important to keep an eye on them. And you made some color about DR and RPO next quarter that I'd love if you could elaborate. I think on DR you mentioned that it'll grow mid-twenties due to a cyclical decline in average remaining term of annual contracts. I'm not sure I totally understand what that means. So I'd love to ask for a clarification. And then likely as well on RPO, you mentioned that we would see a shift back to long term plans. I'm wondering if you could elaborate on that as well. Thanks so much.”

“40:05 Yes. So for deferred revenue, there's two things to remember, which is the seasonality trend of our renewal is that Q1 is the largest quarter for renewals and Q4 is the lowest. So, in terms of new deferred coming on to the books, Q4 is the lowest quarter because of that, as well as the fact that Q1 is the largest quarter when deferred gets out of the balance sheet, but they are annual contracts, by the time you get to Q4 most of that has already been amortized and recognized. There is only twenty five percent of it in theory about left when you come into the quarter. So the combination of the fact that anything added in Q1 is almost fully amortized and will get refilled and renewed back in Q1. And the fact that Q4 is the lowest renewal quarter, those two things are what's driving this trend of renewals. — Sorry, of deferred, which I know is probably counter intuitive to any other company that you see because of the seasonality that we have.”

Karl Keirstead

41:25 Yes. And so the fact that DR growth would slow to mid-twenties is due to what?

Kelly Steckelberg

41:30 It's due to the fact that Q4 is our lowest renewal period as well as all those annual renewals that came on in Q1, which is the biggest quarter are now almost fully amortized and recognized.

Kelly Steckelberg

41:49 And then this has a strong impact on billings and RPO as well, because the same thing like they are adding to the building of the collections are happening earlier in the quarter and the remaining term is being amortized throughout the year, so there is — it's the short amount of contract left during Q4.

The goal of my analysis is not to sugarcoat the slowing growth in the consumer or online segment that is billed monthly. That growth is slowing – no argument here. Rather it’s to help put into context that the 19% growth is not reflective of enterprise growth. Zoom is and always will be an enterprise story. In fact, the company is so ambitious at the enterprise-level that its goal is to disrupt traditional telecom with cloud communications.

Let’s Talk About the Enterprise Segment

Zoom is returning to an enterprise story with strong growth in customers that spend over $100K. The growth in this segment is higher than pre-pandemic levels at 94% year-over-year. This is on a high base, as well. The law of large numbers states it’s much harder to grow 94% YoY on a base of 1289 customers (2021) than to grow 86% on a base of about 350 customers (2019). The acceleration here is impressive if we remove 2020 as an anomaly and on top of the strong 2020 base.

When you separate the segment of under 10 employees, we can see the effects Covid had on the company with the current quarter being the highest hurdle to clear at 485% growth in the year-ago quarter although Q4 is not much easier to clear in terms of comps with 470% growth. To be honest, the fact the growth isn’t negative in this segment is a miracle. It seems preposterous that any consumer would be getting on Zoom for the first time 18 months into the pandemic – meaning negative growth would be logical. Of course, the growth is probably small teams creating accounts. Don’t forget that any churn in free accounts like K-12 don’t affect revenue growth.

Notably, we are going through a hard stretch for enterprise account growth in terms of comps with 156% growth and 160% growth to clear from the year-ago quarter of Q4 and Q1. The last two quarters Zoom has done an excellent job of maintaining and pacing growth here. I’m expecting Q4 to be lower in enterprise growth while hoping Q1 will resume strength again here.

What was Zoom’s valuation when it was fully understood to be an enterprise story? At its lowest point, it was at 30 P/S and at its highest point it was at 60 P/S in 2019. Once we lap the consumer growth and clear it out, which is weighing on Zoom’s enterprise story, then we should see these valuations again.

The I/O Fund thinks Zoom is oversold at these levels.

Bradley also pointed out on the forum that enterprise is showing strength in long-term deferred revenue, which grew 30% year-over-year compared to 26% growth in the year-ago period. This could be a return-to-normal after concessions were made during Covid (Datadog also moving in this direction), yet it shows strength to lengthen a contract period. He does a deep dive on the financials below.

The one thing that bothers me about the Zoom earnings report this quarter is the Zoom Phone Acceleration slide disappeared as did the numbers for account growth over $1 million. This could indicate the company is not disclosing the growth rates because they were weaker than expected. This is what we got last quarter that was missing from this quarter’s presentation:

Does Zoom Have a Catalyst on the Horizon?

The catalyst for Zoom remains the transition to hybrid and remote work. What makes a market is demand and Gartner predicts strong demand through 2024. Zoom Phone also remains a catalyst with one analyst on the call pointing towards the addressable market of 400 million business phones on legacy technology. AR/VR is a catalyst as Zoom will likely release an avatar and other augmented features. You likely saw that Facebook “Meta” is now integrated with Teams. There are technically integrations already with Zoom and Meta, as well, and Facebook worked with Zoom on Portal. As you know, I don’t think Facebook is actually leader in this space and Zoom could easily acquire a startup for avatars or AR/VR features. Hybrid events is another catalyst that we’ve covered in the past on our LTBH webinar.

Bringing video to the contact center as the video engagement center is not something I would shrug off although it does require more time to build a solid solution. Zoom is also spending its cash to encourage developers to build on its platform, which is a tried-and-true approach to innovation.

Where this Leaves Zoom Investors

There is certainly some suspense here as there is no visibility into Q1 at this time. Q4 tells us essentially nothing about how Q1 will perform. Again, this is partly due to the unique billing cycle and partly due to unusually high comps this year. Management is not willing to discuss guidance more than a quarter out. The combination of tough comps and seasonally low Q3 and low Q4 has beat up the price quite a bit. I/O Fund is willing to wait another quarter as the guidance for Q1 will start to show us what post-Covid Zoom truly looks like.

Deep Dive into Financials

By Bradley Cipriano

Zoom’s Q3 sales increased 35% YoY to $1.050 billion, which came in ahead of the Street’s estimate by $31 million (3%). Q3 also marked the 14th consecutive quarter that sales increased on a sequential basis. It is impressive that Zoom has been able to continue to grow sales every quarter even after its blockbuster 2020 results. Looking forward, management raised their guidance and now expects that total FY2022 sales will increase by ~54% YoY to $4.080 billion at the mid-point, which also implies another quarter of sequential growth.

Management also provided guidance for bookings, which is a key metric used by investors to gauge the sustainability of future topline growth. On the call, CFO Kelly Steckelberg stated that the company expects deferred revenue to increase around “mid-twenty” percent YoY in Q4. This implies a bookings growth rate of just 7%, which seems low, but is due to tough comps as bookings had increased 320% YoY in Q2 FY2021. Furthermore, the company’s bookings have become more seasonal and are now front-loaded to the beginning of the year. As a result, Q4 bookings will be relatively depressed while Q1 FY2023 bookings will be more robust. Nonetheless, the relatively low bookings guide may have spooked investors.

The soft bookings guide was offset with strong trends in RPO and net deferred revenue. RPO represents contracted sales that have yet to be fulfilled and can be used as a proxy for forward growth. RPO increased 51% YoY to $2.5 billion, while RPO to be completed in the NTM increased 39% YoY to $1.6 billion. Stated differently, long-term RPO increased 80% YoY to $821 million, which highlights Zoom’s strength with enterprise customers. Enterprise customers signing long-term deals is a favorable trend as it showcases their commitment to Zoom’s products. We can also see this in deferred revenue trends, as long-term deferred revenue increased 30% YoY, the fastest pace of growth since Q2 2020.

However, despite the strength in enterprise, small customer accounts do represent a headwind to growth in the near term. CFO Steckelberg explained on the Q3 call that small/online accounts represent a headwind that has been incorporated into the Q4 guide. She added that online churn in Q3 performed better than they had initially expected at the beginning of the year, but that online/small accounts are more impacted by the holidays than enterprise customers, leading to temporary increases in churn. This churn should reverse in FY2023, leading to stronger growth in future quarters. Furthermore, small accounts fell YoY from 38% of total sales to 34% of total sales in Q3, highlighting that this customer cohort is not as significant as enterprise customer strength.

Even with these temporary churn headwinds, forward looking metrics remain strong. For example, the growth in NTM RPO was also strong and grew 39% YoY and NTM RPO represented 38% of next twelve-month sales, up 751 bps YoY. The increase in NTM RPO as a percentage of forward sales signals that Zoom has more contractual support for future sales, which improves the quality of forward growth (Zoom is more likely to meet or exceed its sales targets).

Trends in deferred revenue also highlight the quality of recently reported sales. Net deferred revenue (which is total deferred revenue less accounts receivables) increased 41% YoY to $808 million, which was faster than the 35% YoY increase in sales. Looking forward, net deferred revenue represents 27% of NTM sales, which is up 309 bps YoY. The increase in net deferred revenue provides balance sheet support for future sales, which improves the quality of forward sales growth. So, while bookings may be slowing, the quality of the company’s forward sales is improving. In our opinion, analysts are likely being conservative with their forward sales estimates.

Continuing down the income statement, gross margin increased 750 bps YoY to 74%, while non-GAAP gross margin increased 774 bps YoY to 76%. Non-GAAP R&D and S&M expense margin increased 320 bps and 444 bps YoY to 6.4% and 22.6%, respectively, while non-GAAP G&A expense declined 163 bps YoY to 7.8%. It is great to see that management has kept G&A under control despite the surge in sales during the last two years. Following these trends, non-GAAP operating margin increased 173 bps YoY to 39.1%, and non-GAAP EPS also increased 12% YoY to $1.11, which beat by $0.02.

Finally, cashflows also remained robust during the year. In the LTM, free cashflow increased 60% YoY to $1.7 billion, which followed a 1,019% YoY increase in the prior year quarter. Relative to TTM sales, TTM FCF margin fell 1,063 bps YoY to 42%, but this remained well above the pre-covid levels of 17% (in Q3 FY20). Zoom’s valuation also does not appear to correctly reflect the company’s strong cashflows. As shown below, Zoom’s EV/FCF metric is well below other SaaS peers, yet Zoom is growing nearly 2x as fast as the peer median.

In all, Zoom beat top and bottom -line estimates and raised its sales guide for FY2022. However, trends in bookings may have spooked investors as they are expected to grow just 7% YoY next quarter, which could signal that sales may slow down in FY2023. However, this is offset with a rise in both contractual and balance sheet support for future sales as NTM RPO and net deferred revenue increased YoY relative to forward sales estimates. This increase in support for future sales improves the quality of forward estimates and suggests that sales estimates are conservative. Furthermore, gross and operating margins improved YoY while cashflows remained robust and increased YoY despite tough comps. Zoom remains a high-quality company with strong growth and cashflows and also appears to be undervalued relative to other SaaS companies.

I/O Fund’s Overview of 7 Cloud Stocks for Q3 Earnings – December Edition

Posted on by io-fund

I/O Fund is covering the preview for the second part of earnings for cloud stocks. It includes seven of the leading cloud security, productivity tools and data analytics companies.

We covered the first round of cloud earnings at the beginning of November.

We now cover:

  • Zscaler Inc
  • CrowdStrike Holdings Inc
  • Elastic N.V
  • Snowflake Inc
  • Okta Inc
  • DocuSign Inc
  • Asana Inc

These earnings previews help our readers keep track of changes in trends and where to focus for new opportunities. It also helps to hear what analysts are saying about key companies prior to earnings reports.

We noticed that cloud companies with solid stock performance on the run-up to the results beat estimates. For example, Cloudflare stock rose 67% a month before our coverage and the company had a blowout result. We identified in this analysis that the company is adding numerous customers and, also, the trend continued in its third-quarter results.

To better understand recent valuations across cloud stocks and how the sector is positioned, please refer to our analyst Bradley Cipriano’s analysis, “I/O Fund Q3 2021 Cloud Stock Earnings Preview – December Edition”.

 

Zscaler – Earnings on November 30

Source: YCharts and Earnings Reports

Zscaler Inc has recently rescheduled the release of its results a day earlier as peer companies are releasing on December 1st. The consensus revenue estimates suggest a 49% YoY growth and are slightly higher than the management’s revenue guidance of $210M to $212M. Zscaler is up around 140% in the past year and has been outperforming cybersecurity peers.

Source: YCharts

Mizuho analyst Gregg Moskowitz raised the firm's price target to $385 from $320 and has a buy rating on the company. The analyst says software valuations have "continued their ascent in recent weeks" and that he's raising price targets to reflect recent appreciation in comp multiples.

BTIG analyst Gray Powell has a buy rating and raised the firm's price target to $401 from $324. The analyst states that his discussion with an industry expert and his checks over the last few weeks indicate a positive spending environment across the majority of categories in the space. Powell adds that the expert described the Zscaler business as one that continues to accelerate, following "strong" demand trends observed for the company in October.

Sign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereClick here

Daiwa analyst Stephen Bersey initiated coverage of Zscaler with a neutral rating and a $266 price target. The analyst says the stock's trading multiple is near a level that he believes is appropriate. While Zscaler's recent sales growth results have been well above many of its peers, a 28x sales multiple more than accounts for its strong top-line growth and earnings potential.

Please note, I/O Fund is objectively reporting what the Street is saying. We covered Zscaler previously below:

Tech Growth Earnings Review for Q3 2020 – Part 3

CrowdStrike – Earnings on December 1

Source: YCharts and Earnings Reports

CrowdStrike’s revenue accelerated 70% in the 2Q to $337.7M and subscription revenue increased by 71% YoY to $315.8M. It added a net 1,660 subscription customers, raising the total to 13,080 subscription customers. Recently, it announced new security products to expand its reach in extended detection technology (XDR).

DA Davidson analyst Rudy Kessinger initiated coverage of CrowdStrike with a buy rating and a $320 price target. The analyst is positive on the company's "superior" cloud-native technology that has significant network effects driving sustainable competitive advantages, along with its large and expanding total addressable market. Kessinger further cites CrowdStrike's multiple drivers to sustain high rates of growth and its "significant operating margin expansion" that is likely over the next several years.

Morgan Stanley analyst Hamza Fodderwala has undertaken coverage on the stock with an underweight rating and a price target of $247. He said in a research note that CrowdStrike has benefitted from the shift toward digitalization and remote work over the past two years and gained a leading position in the area of what’s called endpoint detection and response (EDR) security.

However, Fodderwala said that checks within the security industry "indicate CrowdStrike's early leadership position is now increasingly challenged by more competitive next-gen EDR alternatives." Fodderwala said that competitors have come in and undercut CrowdStrike's prices by at least 15% to 20%, and that "this competitive dynamic will make sustaining [CrowdStrike's] current pace of share gains more difficult" through 2022 as working from home becomes commonplace.

Read our previous analyses below:

Nasdaq100 Levels to Watch for the Next Leg Higher

Tech Growth Earnings Review for Q3 2020 – Part 3

Momentum is on CrowdStrike’s Side: Will it Last?

Elastic – Earnings on December 1

Source: YCharts and Earnings Reports

Elastic N.V’s revenue grew by 50% in the last quarter and Elastic Cloud revenues increased 89% YoY to $61.5M (accounts for about 32% of total revenue). The company had over 16,000 subscription customers at the end of Q1. However, growth is expected to slow down in the next quarter. Management’s revenue guidance is between $193M to $195M, representing a YoY growth of 34% at the mid-point.

Source: Investor Presentation

Barclays analyst Raimo Lenschow raised the firm's price target on Elastic to $200 from $185 and kept an overweight rating on shares. In a research note, Lenschow informs investors that over the next few months, investors will move to 2023, their new base year for valuations. For software, "with its high growth rates, this move is important as valuation levels often see a meaningful step down," says the analyst.

Oppenheimer analyst Ittai Kidron has an overweight rating and a price target of $185. The analyst notes Elastic reported a "strong" Q1 well ahead of consensus, reflecting broad-based demand across search, observability, and security; continued SaaS momentum; strong customer adds; and steady expansion metrics.

Read our previous analysis on the stock here: Tech Growth Earnings Review for Q3 2020 – Part 3

 

Snowflake  – Earnings on December 1

Source: YCharts and Earnings Reports

The company’s revenue growth has been solid. During 2Q, total revenue accelerated 104% YoY and product revenue accelerated by 103% YoY to $255M. The remaining performance obligation (RPO) grew to $1.5B at the end of the second quarter. It also reported adjusted free cash flow for the third consecutive quarter.

For the next quarter, revenue is expected to decelerate slightly and management has given product revenue guidance in the range of $280M to $285M.

Source: Investor Presentation

Credit Suisse analyst Phil Winslow initiated coverage of Snowflake with an outperform rating and a price target of $455. Winslow views Snowflake as a true pioneer in cloud-native data analytics and believes the company will play an increasingly important role across the entire data value chain– telling investors, in a research note, that Snowflake is helping drive strong new customer acquisition, robust customer expansion, and attractive unit economics that can be sustained longer than the market appreciates.

Sign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereClick here

Rosenblatt analyst Blair Abernethy downgraded the stock from a buy rating to a neutral rating. At the same time, he raised the price target to $370 from $300 and believes most near-term gains are already priced into the stock.

Read our previous analyses:

Snowflake: IPO In-depth Analysis

Podcast: My favorite picks for 2021, Zoom Video, and IPOs/SPACs

Analyzing the IPO Glut of 2020: Snowflake, AirBnB, DoorDash and Roblox

Okta – Earnings on December 1

Source: YCharts and Earnings Reports

The company’s revenue in the 2Q grew by 57% YoY to $315.5M. On a standalone basis, Okta revenue grew by 39% YoY and it was the first quarter that included Auth0 revenues. The company’s TTM Net Retention rate has been quite stable and, in the most recent quarter, it came at 124%.

Source: Investor Presentation

Morgan Stanley analyst Hamza Fodderwala has updated the company to an overweight rating with a price target of $315. In his words, “After slower topline over the past year, an improving demand environment and more buy-in with developers should drive stronger growth and upside in estimates going forward.”

DA Davidson analyst Rudy Kessinger initiated coverage of Okta with a Buy rating and $315 price target. The analyst says Okta is a "best-of-breed" cloud workforce identity and access management provider that is still in the early innings" of growth. He sees sustainable 35%-plus growth and "compelling" margin expansion through fiscal 2026 for the company.

Read our past analyses on the company:

Podcast with Motley Fool: I’m Bullish on These Trends for 2021

Okta Earnings: More to Squeeze From Valuation?

DocuSign – Earnings on December 2

Source: YCharts and Earnings Reports

DocuSign’s revenue in the 2Q increased by 50% YoY to $511.8M. The international business grew by 71% YoY to $114M. The company’s Net Dollar Retention rates have improved in the past few quarters, and, for the most recent quarter, it was 124%. The management anticipates revenue of $526M to $532M in the 3Q.

Source: Investor Presentation

Needham analyst Scott Berg raised the firm's price target on DocuSign to $340 from $275 and keeps a Buy rating on the shares. The company reported a "strong" Q2 with "typical" upside to revenue and profitability. He further adds that while DocuSign's sales metrics and growth decelerated sequentially, this was at a much slower rate than the Street was anticipating.

Asana – Earnings on December 2

Source: YCharts and Earnings Reports

This company’s revenue growth in the 2Q was strong as it grew 72% YoY and 11% QoQ. It added 7,000 net paying customers, exceeding 107,000 in total. Management has raised full-year revenue guidance to $357M-$359M, representing a YoY growth of 57% to 58%, up from previous guidance of $336M to $340M.

Piper Sandler analyst Brent Bracelin raised the firm's price target on Asana to $140 from $85 and kept an overweight rating on the shares. The analyst says multiple third-party data inputs across domain traffic, job postings, and application downloads give him an upward bias to street estimates of 59% growth for Q3 and 33% growth next year. While the stock's risk/reward is less favorable after the 345% year-to-date run, Asana remains a "compelling high margin and high growth model that is still in the nascent stages of adoption with fewer than 2 million paid users.”

Jefferies analyst Brent Thill downgraded Asana to Hold from Buy with a price target of $135, up from $115. The analyst cites valuation for the downgrade, with shares up 348% year-to-date. He continues to view Asana as a "differentiated solution for work management in a large and growing market" but says the valuation is full at current levels. Thill looks to get constructive at a "more reasonable valuation."

You can read our previous analysis here: Asana Setup (5/20/21) – up 85% in a month

I/O Fund is comprised of a team of analysts who share their research publicly as they build a portfolio of 30 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here. clicking here or sign up for our free newsletter here.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

I/O Fund Q3 2021 Cloud Stock Earnings Preview – December Edition

Posted on by io-fund

Tech earnings season is long and extends over six weeks. We are finally nearing the end of Q3 earnings season as the last round of cloud companies are expected to report in early December. The I/O Fund had previously highlighted Six Cloud Stocks to Watch During Q3 Earnings, all of which have since reported Q3 results.

One of the notable performers we highlighted was Bill.com, which reported an 11% topline beat during the quarter. I/O Fund analyst Bradley Cipriano discussed the company’s strong Q3 results in a short video presentation here.   

In the analysis that follows, we provide an update on the cloud category and review cloud stocks that have yet to report Q3 earnings. We also discuss key metrics that investors should be aware of heading into the final weeks of Q3 earnings season.

Cloud Stocks: Top 10 EV/FWD Revenue Multiples

Below is a table of cloud stocks that have yet to report Q3 results, ranked by their EV/FWD sales multiples. Snowflake has the richest multiple out of the 26 remaining cloud stocks set to report in the next few weeks. As we mentioned in our initial Q3 Cloud Earnings Overview, Snowflake is benefitting from increasing rates of data consumption, a trend that will likely continue into the future.

Somewhat cheaper than Snowflake but still sporting a premium multiple are Asana, Zscaler, and MongoDB. Asana most recently grew 72% YoY, an acceleration from the 61% and 57% YoY growth rate in Q2 and Q1, respectively. Zscaler sales grew over 55% for three consecutive quarters and sales are expected to grow 50% in the upcoming quarter. MongoDB has reported an acceleration in sales for three consecutive quarters, and the most recent 44% YoY growth was the fastest pace of growth since Q1 2020. These strong growth trends help illustrate why these firms have premium valuations.

Cloud Stocks: Top 10 Three-Month Forward YoY Growth Rates

Below is a chart of forward sales growth expectations.

Out of the remaining cloud stocks that must report Q3 earnings, Snowflake and Kingsoft are expected to grow the fastest. Snowflake is expected to grow sales 92% YoY as the company continues to benefit from rising rates of data consumption.

Chinese cloud infrastructure company, Kingsoft, is also expected to grow sales strongly in Q3 as they quickly scale their operations.

Sign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereClick here

Other noteworthy mentions are CrowdStrike, Okta, and Zscaler, all of which have exposure to cyber security, a sector that has seen outsized growth recently. These three cyber security firms are expected to grow sales ~50% YoY heading into Q3 earnings, highlighting the overall strength in the cyber security market.

Top 10 Weekly Share Price Movements

Below is a table of the weekly change in share price for our universe of cloud stocks (week ended 11/19). Zscaler is a notable stand out and increased 6% during the week. It is up 85% YTD. Out of the 26 cloud stocks that have yet to report Q3 earnings, Zscaler and Snowflake were the only stocks that advanced last week.

Top 10 Changes in Sales Growth Estimates – Last 90 Days

The table below ranks cloud companies that have yet to report Q3 earnings by their topline revisions over the last 90 days. An increase in topline revisions signals that the Street believes that the company will grow faster than initially believed.

Smartsheet (SMAR) has had the largest topline revision, as the company recently increased their Q3 sales guidance from 40% YoY growth to 46% YoY growth, citing a robust demand environment for its platform.

Zscaler also had its topline revisions increase 5% over the last 90 days, above other cyber security players such as CrowdStrike and Okta. This increase in expectations signals that Zscaler is likely expected to outperform its peers in the near term.

 

Update on Top 5 EV/Fwd Revenue Multiples:

Overall stats:

  • Overall Cloud forward median:    15x
  • Top 5 Cloud forward median:       69x
  • Overall Cloud forward average:  22x

OVERVIEW OF EV/FWD SALES:

As shown below, the median and average cloud EV/Fwd revenue multiple has trended up throughout the year. Around June, the average multiple had started to increase faster than the median, and this bifurcation accelerated during Q3 earnings.

The average is being driven higher by premium valued cloud stocks (shown above). Since cloud has increasingly proven to be a sector where the leader ‘wins most’, this bifurcating trend may very well continue into the future.  

 

TOP 5 HIGH-RANKING EV/FWD SALES:

In the chart below, we can more clearly see the large dispersion in cloud valuations, as the top 5 premium valued cloud stocks have had their EV/Fwd sales multiples rapidly expand through Q3 earnings. Investors likely continue to believe that cloud is a “winner gets most” market, where the market leader captures the majority of the addressable market. This dynamic helps explain why the top 5 valued cloud stocks have grown their multiples much faster than the median.

EV TO FWD SALES – Growth Buckets:

We can further dissect the changes in cloud valuations by breaking up the group into high growth (>30% growth), mid growth (>15% and <30%), and low growth (<15%). The below chart shows that higher growth cloud stocks receive a higher multiple from the Street. Furthermore, high growth stocks used to be valued more richly back in Q4 2020 but have since seen their valuations normalize to a lower multiple. If Q3 cloud earnings come in strong, then the market may push valuations back up to their historic highs.  

WHO DELIVERS SUPERIOR EV TO FWD SALES?

The below chart provides a more holistic view of the remaining cloud stocks that have yet to report Q3 results, sorted by their EV to Fwd revenue multiples.

As highlighted in the above tables, Snowflake (SNOW) has the highest valuation of the group and its multiple is more than 600% higher than the cloud median of 15x.

Growth Adjusted EV/Fwd Revenue (EV/Fwd Rev/Fwd Growth)

The last chart (below) is based on EV to FWD sales but also takes into account forward growth expectations.

By scaling valuation relative to forward growth, we can more clearly see which companies are cheapest, based on their expected growth rate. A low value in the chart below means that a company is cheap relative to growth.

For example, Snowflake can be considered cheaper than Asana once we consider its strong growth rate expected next quarter.

Kingsoft (KC) is evaluated as the cheapest; given its robust growth rate and low valuation, the company has very low margins, which warrants a cheaper valuation.

 

CLOUD OUTLOOK

Finally, the last table we will be discussing includes aggregate cloud operating metrics.

The below table shows that cloud is performing strongly as the median forward growth rate is above 20%, while gross margins are high at over 70%. The median cloud company is also FCF positive with a 3% FCF margin.

 

Strong growth and positive cashflows signal that the cloud category is healthy and performing well. I/O Fund expects this strength to progress going forward.

Find out which cloud stocks I/O Fund will be watching, heading into the final weeks of Q3 earnings, in analyst Royston Roche’s piece, “I/O Fund’s Q3 Earnings Preview of Cloud Stocks -December Edition.”

I/O Fund is comprised of a team of analysts who share their research publicly as they build a portfolio of 30 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here. clicking here or sign up for our free newsletter here.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

Datadog Deep Dive: Rare Pure Play with Cloud IaaS Tailwinds

Posted on by io-fund

Datadog is a company that quietly appears every three months with earnings results that say: “Remember me?” We are looking to increase allocation to this LTBH position as this is a rare leader in the migration to the cloud and the observability that is required across increasingly complex architectures. If you want a simple thesis that you can share with your friends and family, it’s this: Datadog lets us directly participate in the growth of AWS, Azure and Google Cloud through a pureplay that cross-sells better than almost any other cloud company.

Product Overview:

Datadog’s management team was very early to address the issue of silos in a cloud-native environment. As systems moved from on-premise to the public cloud to include virtualized machines and containers, the number of applications to monitor grew. Virtualized machines create more data from many more applications. The next iteration of the cloud, which was containers, exponentially grew the number of applications. Now that there are serverless architectures where every function needs to be tracked individually – which means the complexity has grown yet again.

Here's a picture of what I mean:

 

Datadog is a company that solves the complexity associated with the cloud as the products are able to observe and monitor any environment no matter how large the tech stack scales.

The second thing to understand about Datadog is that it’s not only cloud native but it also works well in a multi-cloud environment. This means Datadog is downstream from Azure, AWS and Google Cloud – no matter who a customer goes with and at what percentages for the deployment. The fact that companies prefer to work with more than one cloud vendor is actually a driving force for Datadog as it’s observability and security products can scale across any deployment a customer chooses and is flexible if the customer makes changes down the line.

The trend of multi-cloud and hybrid cloud is only going to accelerate from here which we covered in detail in our Big Data and Analytics analysis. It’s worth a read if you haven’t read it yet.

The company uses the word “standardization” to describe how the multi-cloud trend is a main driver for Datadog. We covered this in our last analysis but it bears repeating here as to why multi-cloud and hybrid cloud are important drivers for Datadog and how standardization plays a key role.

Standardizing means interoperability between various cloud environments and integrated interfaces. This is especially important with multi-cloud or hybrid cloud where companies have more than one environment. This is becoming the new normal to prevent vendor lock-in. The word standardization/ standardize was mentioned 20 times on the Q2 Earnings Call, highlighting its importance to Datadog’s story going forward. If corporations continue to standardize on Datadog’s platform, then the company will continue to capture market share.

Here’s a quote from our previous analysis:

Since dealing with multiple cloud vendors quickly becomes cumbersome, there is a natural tendency to standardize in tech, especially with software. Moreover, cloud applications need to communicate, so having everything on one platform can make detecting and resolving issues less complex and costly. We believe that we are on the cusp of this standardization trend with cloud software vendors, with Datadog leading the way. We believe that Datadog is best positioned to benefit from both the rise in cloud usage and the standardization of cloud software.”Moreover, cloud applications need to communicate, so having everything on one platform can make detecting and resolving issues less complex and costly. We believe that we are on the cusp of this standardization trend with cloud software vendors, with Datadog leading the way. We believe that Datadog is best positioned to benefit from both the rise in cloud usage and the standardization of cloud software.”

Datadog eliminates the need to work with many different vendors and pulls the entire DevOpsSec stack into one platform. This not only breaks down silos in terms of the observability framework yet also breaks down silos within the company.

Infrastructure Monitoring

At the point that companies migrate to the cloud from on-premise servers, how they monitor their infrastructure fundamentally changes. On-premise servers have fixed IP addresses and there are static servers and virtualized machines. Once you move to the cloud, this changes as servers are spun-up in the cloud and are not on-site and components are hosted across many regions.

At the start, Datadog helped monitor the hardware in cloud-native environments, the operating systems, and the application servers. Infrastructure monitoring is essential if there is a problem with the functionality of a cloud-native company on the back-end. It offers tools, such as CPU utilization, to determine if there’s sufficient processing capacity, memory utilization to determine if there’s memory capacity, and storage use which indicates the amount of disk that the host is using to store files and other content.

The goal of infrastructure monitoring is to prevent or troubleshoot performance issues and to lower costs. We’ve covered in the Big Data and ML analysis here the costs associated with cloud environments and why this is coming under pressure with more companies choosing hybrid architectures, including a mix of cloud and on-premise servers.

Datadog set out to disrupt on-premise solutions that monitored servers and virtualized machines. This is called “host-centric.” The primary issues with former infrastructure monitoring tools are that they do not scale for the cloud and it creates silos between departments. In the cloud, infrastructure monitoring uses an API for cloud-based metrics. Datadog’s products also remove the need for Secure Shell, or SSH, to log onto remote servers. As architectures evolved to serverless, legacy monitoring tools were even more outdated as there isn’t a server to run the code and install a monitoring agent.

One key thing about Datadog is the company allows for metadata to be tagged on backend components for better monitoring. These tags inform alerts and visualization tools. The company tags both the zones and the applications. Unified tagging limits the need for reconfiguration as a company scales. This is one of Datadog’s core competencies and their unique method approach to tagging is what they launched with in 2010. This aggregates and contextualizes the data no matter where the data comes from.

Another main selling point to many of Datadog’s features is a unified platform rather than many disparate tools or vendors. This is how Datadog has disrupted its competitors and crept into larger addressable markets. The unified platform works across all environments – on-premise, hybrid and cloud – and spans infrastructure monitoring, application monitoring, log management, observability and now security. By being so strong in the area of observability, Datadog can knock down its competitors by cross-selling 13 products from the key critical piece in the stack, which is monitoring and observability. With 450 integrations, Datadog leaves little reason to leave the platform and the dashboard for other tools.

The unified platform for complex architectures is also partly why Datadog is able to lead its competitors in standardization. The dashboard also offers AI to help customers move through the dashboard by recommending the next monitoring step. Here’s a direct quote from an analyst on the call that sums up Datadog’s positioning:

“Congrats on the solid quarter as well for me. But Oli, you’re already bigger than all your near-term or nearest competitors growing faster than all of them by a couple of magnitude. You talked about enterprise standardization trend that led to your largest deal in the company’s history.”

Application Performance Monitoring

As discussed, the number of applications that need monitoring began to exponentially grow with virtualized machines and containers. Infrastructure monitoring is incomplete in these architectures without application performance monitoring to assure applications and websites run as expected with optimal speeds across mobile platforms, cloud-native infrastructures, virtualized and containerized servers. Distributed application environments can cause numerous bottlenecks and it can be challenging to figure where the bottleneck is coming from. Meanwhile, slow speeds can cause customer drop-off.  

APM also assures that the application is performing as it should and backend processes are executing as they should, including transaction processing, and detects bug or errors in the application code.

APM performs the following functions: 

  • Digital user experience monitoring: determines if there are errors or downtime that could lead to a loss of revenue 
  • Transaction profiling: analyzes the transaction flow to isolate the cause 
  • Code-level diagnostics: According to DZone, 43% of application performance issues come from code. Diagnostics help to identify the line of code or query causing the issue. 
  • Deep-dive analysis: Looks beyond code at the server and application infrastructure for problems such as insufficient memory or long wait times 
  • Infrastructure monitoring: similar to deep drive analysis, ideally infrastructure monitoring is part of the APM package to monitor slow network connections or virtualization bottlenecks.

Datadog’s APM also comes with network performance monitoring to verify if the network is slowing down traffic or if there is a low connectivity issue. The 360-degree view of infrastructure, applications and networks helps diagnose issues more quickly and with more accuracy.

According to Gartner, the number of applications monitored with APM tools has increased from 5% in 2018 to 20% in 2021. Machine learning is also used to forecast usage patterns and to detect anomalies outside of manual alerts.

Observability

Where observability differs from APM is that it monitors external data across metrics, events, logging and tracing (MELT). It’s called observability because it provides visibility as the issue is occurring and ideally before there is a performance issue.

Observability tools work with telemetry data, which is this combination of logs, metrics and traces. Metrics are numerical measurements, such a transactions per second. Events are individual actions. Logs are application-specific structured and unstructured data. Tracing tracks how many requests flow through a system. This is achieved through APIs, such as the Tracer API or the Metric API.

An observability framework allows you to work with telemetry data with fast retrieval and good visualization. In this specific area, Datadog competes yet is also compatible with the open-source framework called OpenTelemetry. You could also argue the project erodes some of Datadog’s moat as it reduces vendor lock-in but it’s the end-to-end tools that draws customers to Datadog rather than only the telemetry data. We covered this here in Q2.

Because Datadog is an end-to-end tool, it can be compatible with OpenTelemetry by allowing the open-sourced SDK to connect to the platform for telemetry data. The company also supports other open-source projects under the OpenTelemetry umbrella, such as OpenTracing, OpenCensus and OpenMetrics. This has created a standard set of APIs and libraries for observability and allows for the telemetry data to be easily migrated between vendors. Datadog has contributed to the project with its auto-instrumentation libraries.

Kubernetes and the rise of microservice-based architectures increase application reliability and efficiency; however, developers need the ability to monitor these architectures. Microservices benefit from Observability as it helps understand how microservices communicate. This keeps track of metadata for performance purposes and also distributed traces or requests. Observability allows for a more holistic picture so developers can connect data to monitoring tools and solve issues quickly.

Datadog has a new product that offers observability before code goes to production called CI Visibility. The launch of the CI Visibility product follows the acquisition of Undefined Labs. Datadog talks about “shifting left” which means moving more into the development phase prior to production.

Continuous integration and continuous delivery (CI/CD) provide a shared repository of code for an automated build process with regular intervals. This helps speed up development by deploying smaller batches of code. In data science machine learning models, projects are based on code and also the data used to train the model. The CI/CD data pipelines help to deliver machine learning models and this is another opportunity for Datadog’s observability tools to serve a growing demand.

Security Platform

Datadog’s core product is observability and security is an additional catalyst (or an accelerant). Datadog’s positioning with observability puts the products into the right place in the tech stack for threat detection. Cloud environments have an increased attack surface across infrastructure, containers and applications. As teams seek simplified operations, there are more third-party managed services being deployed which reduces visibility. Datadog offers a few security products to allow teams to detect real-time threats to applications and infrastructure, track compliance posture, and also workload security across infrastructure or workloads, such as Kubernetes clusters. With security monitoring, engineering teams have end-to-end analytics coverage from a unified dashboard. This increases time to resolution and also means you can find threats buried deep in the architecture.

As we covered in our previous write-up, the Sqreen acquisition helps Datadog take advantage of the trend towards microservices and Kubernetes rather than monolithic architectures. Generally speaking, Kubernetes can introduce vulnerable clusters due to default configurations. In the past, demonstrations at BlackHat, the annual security conference held in Las Vegas, have exploited features in Kubernetes default attack surface rather than bugs. Sqreen specializes in protecting code-level risks across distributed applications by protecting application logic. Sqreen’s main goal is to deliver security solutions to developers and the operations teams, as well, i.e., to “democratize” and emphasize security testing and implementation during the development process, often called DevSecOps. These are the two main points on this acquisition – more market share across security for microservices and more stakeholders at a company who can buy and deploy Datadog products outside of the security team.

The breakdown between developers, operations and security called DevSecOps is a transition that Datadog plans to capture similar to how the company captured DevOps. Applications and infrastructure security is new to Datadog yet management has hinted towards it becoming as big as the observability market, which is at $38 billion in 2021.

Datadog’s Financials

Datadog accelerated revenue growth during a year of tough covid comps. This shows remarkable product strength. The company’s revenue is up 75% year-over-year to $270 million, an acceleration from 66.81% last quarter, and 61.35% revenue growth in the year-ago quarter. The revenue comfortably beat estimates by 10% and was up 16% QoQ.

The company has an adjusted operating margin of 16% and adjusted EPS of $0.13. The company also had free cash flow of $57.1 million which is an increase from last quarter’s $52 million. This proves the company can grow the top line and invest heavily in R&D but not at the expense of the bottom line. The company has $1.5 billion in cash and cash equivalents.

The company issued guidance of $291 million in revenue, or 52.3% growth in the fourth quarter and EPS of $0.11. For the full year, the company is guiding for $994 million, at the midpoint, and adjusted EPS of $0.39-$0.40. According to the company, usage is down for them seasonally in Q4 as employees and businesses take holiday breaks.

It’s the underlying key metrics on customer growth that help forecast strength for Datadog as we move into 2022. The company has 17,500 total customers of which 1,800 have a ARR of $100K or more, up 66%. These accounts make up 80% of ARR, so growth in the <$100K segment is key. The other key driver of growth for Datadog is the cross-selling of products. The company is unusually strong here with 77% of customers using two or more products, up from 71% a year ago. The number of customers who use four or more products is at 31%, up from 20% a year-ago. The company also stated that net dollar retention rate is above 130 for the 17th consecutive quarter.

Annual recurring revenue helps gauge what level of revenue a company is expecting. According to management, “We also had a record quarter of ARR adds, including record ARR adds in all of our major products. And we saw strong growth across geographical regions, with all regions accelerated on a year-over-year basis compared to Q2.”

Although billings contract terms have fluctuated due to Covid with shorter terms in 2020 that are slowly returning to a more normal length. This helped drive Billings growth of 98% year-over-year. Increased contract duration to annual and multi-year partly contributed to remaining performance obligations (RPO) growth of 127%. On a more normalized basis, the company mentioned current RPO growth was closer to 100%. Revenue still remains the primary way to value Datadog, however, this under-the-hood growth certainly helps understand the strength of the company and how customers view the products as we move into 2022.

The company is investing “significantly in R&D” and plans to spend on travel and conferences in the coming year. The R&D expenses were up 80% in Q3 which management explained by saying, “It’s important to go fast when scaling those teams because there’s quite a bit of a lead time between the time when you hire engineers and the time when you get new products on the other hand. I’ve mentioned in other calls like maybe hiring now is a good predictor of output two years from now on the engineering side. So we should get started. That’s why we’re doing it.”

Notably, we like companies that invest in their engineering teams. Datadog points towards pricing power and cross-selling as to why they’re able to invest heavily in R&D and still remain profitable.

Conclusion:

As someone had said on the forum following the stellar earnings report: “Who let the Dog out?!”

To be literal, it’s AWS, Azure and Google Cloud that let the dog out. Our simplified thesis as we rounded the corner into tough Q2 covid comps was specifically, “If the tech giants are communicating that cloud infrastructure-as-a-service is one of the most critical markets in the future, then who are we to argue with this by not investing in the leader across cloud monitoring products?”

Observability is not exactly the most conversational topic, but hopefully it’s understood that architectures are becoming more complex in terms of monitoring and observability. I’m also hoping it’s clear from this analysis that Datadog has additional tailwinds from the trend towards hybrid and multi-cloud. Lastly, the management has not only executed before, during, and after Covid, yet has also grown its product suite to leverage its key positioning at the observability layer. Many companies will begin here and remain with Datadog for other products.

Valuation is high at 43X forward P/S. We rarely buy above 50 forward P/S and much prefer under 40. However, you’ll get buy alerts as we go along to help communicate when the risk/reward looks favorable as we continue to build this position.

Update On Affirm and Palantir Q3 2021

Posted on by io-fund

Affirm’s Q1 Results and Exclusive Agreement with Amazon

Affirm reported strong Q1 FY2022 results that beat on the topline as sales grew 55% YoY to $269 million. Management also raised its guide for FY2022 sales to grow 42% YoY to $1.2 billion, up from its prior guide of 35% YoY growth. On top of the strong growth, Affirm announced an exclusive agreement with Amazon to be the only BNPL payment option on the e-commerce platform for at least the next two years, just in time for the holidays. This exclusive agreement is not yet included in management’s FY2022 sales guide.

The Amazon partnership was initially announced in August but was made exclusive in November. The exclusive agreement with Amazon follows partnerships with Shopify, Walmart and Target, all since June 2021 and before the holiday shopping season ramps. As shown below, Affirm has exposure to >60% of total retail e-commerce market share following these partnerships.

However, these partnerships do have a cost. In exchange for the exclusive agreement, Amazon is receiving up to 15 million warrants of Affirm equity with a strike price of $100. While this is a hefty price to pay, it does create a mutual interest in Affirm’s success on Amazon’s platform. For example, Amazon will conduct its own marketing to encourage the conversion and adoption of the Affirm program.

The terms of the agreement highlighted a few different marketing strategies that Amazon may use to promote BNPL to its customers, such as promoting BNPL to Prime members when they use credit cards; include cashback for Prime members that use BNPL; email Prime members about Affirm’s BNPL offering; and even use packing tape to promote the program (AFRM 8k, 11/10/2021). The exclusive agreement has Amazon working to encourage adoption of Affirm’s payment methods, because if Affirm succeeds, Amazon will also benefit. Considering that Affirm wants to quickly scale, this was a huge win for the company.

In the chart below, we can clearly see the benefits that these partnerships have on Affirm’s growth. Active merchants surged nearly 1,500% YoY to 102,200. Active merchant growth is important, because it is the primary driver of consumer growth. Merchant growth is a forward looking metric that supports sales growth in the future. Importantly, the rapid rise in active merchants shown below was driven by the Shopify agreement signed in June 2021, implying that merchant growth will likely continue to ramp following the Amazon agreement discussed above. 

Affirm’s Q1 FY2022 Financial Results

Following the rapid growth in active merchants, Affirm’s topline growth also came in strong. Q1 sales increased 55% YoY to $269 million, which beat estimates by $20 million. Network fees, which are fees paid by merchants, increased 13% YoY to $112 million and interest income and gains on sale of loans increased 116% and 89% YoY to $117 million and $31 million, respectively. To be complete, servicing revenue increased 132% YoY to $10 million.

Gross merchandise volume (GMV) increased 83% YoY to $2.7 billion, and this growth flowed into loans, as loans held for investment increased 62% YoY to $2.1 billion. However, expenses also rose, driven in part by stock based compensation (SBC) from the recent IPO and a change in estimates. Q1 net loss was -$307 million, and excluding $87 million in SBC following the IPO and $142 million due to changes acquisition related expenses, adjusted net loss was $78 million, or -$0.29/share, slightly ahead of estimates at -$0.30.

Management also raised their guide for the year. The midpoint of its GMV guide was raised 5% to $13.3 billion for the year, while the mid-point of its FY2022 sales guide was also raised 5% to $1.2 billion, implying a 42% YoY growth rate. Adjusted operated loss is guided to be -13% of revenues, slightly higher than the initial -12% guide.

Importantly, management’s guide is somewhat conservative as it does not include any contribution from the exclusive Amazon agreement discussed above (however the dilution from the warrants is included in the EPS guide). Once Affirm has gathered sufficient data from the program, they will incorporate that into their guide going forward. Based on management’s current guide and Affirm’s stock price, Affirm trades at ~35x P/S.

Finally, the company’s credit metrics appear healthy. Provisions for loan losses increased 133% YoY to $64 million, which was skewed by a low base period due to provision releases in the prior year quarter. The rise in provisions drove allowance for loan losses up 24% YoY to $152 million, or 7% of total loans. The rise in allowance for loan losses provides a ‘safety net’ in case defaults begin to rise in the future. As shown below, Affirm’s allowance for loan losses is near its historical average of ~9% of total loans.

Affirm’s reserves for loan losses has trended up with the company’s rapid growth, which provides downside protection from rising defaults. As Affirm’s credit risk model is proven overtime, the company’s reserve for loan losses may decline relative to loan growth, which would fuel earnings growth in the future.

The company’s recent partnerships with major online retailers such as Shopify and Amazon, positions the company well for strong growth going forward. The company’s credit metrics appear healthy and growth should continue to be robust as we enter the holiday shopping season.

 

Update on Palantir

Palantir reported Q3 results on 11/9/21 and sales grew 36% YoY to $392 million which beat topline estimates by $5 million. Commercial sales accelerated to 37% YoY growth in Q3, up from 28%, 19% and 4% YoY growth rates in Q2, Q1 and Q4 2020, respectively, while government sales increased 33% YoY to $218 million.

On the call, Palantir COO Shyam Sankar explained that the company’s commercial offerings have been robust and that the Foundry tool (primarily used in commercial offerings) has benefited from three key trends: 1) defense industrial 2) automotive and mobility and 3) healthcare. Specifically, defense and healthcare are benefitting from increased spending while automotive and mobility are benefitting from the ramp in EVs and the large amounts of data that this secular trend is creating.

Continuing down the income statement, adjusted gross margin was 82%, up from 81% in the prior year quarter. Q3 operating margin was a slight loss of 1% while adjusted operating profit margin was 30%, its 4th consecutive quarter at or above 30%. Adjusted EBITDA increased 59% YoY to $119 million and adjusted EBITDA margin increased YoY from 26% to 30%. Non-GAAP earnings were $0.04, which met the consensus estimate.

Adjusted earnings exclude large amounts of SBC, but SBC has materially declined and was down 78% YoY to $184 million during the most recent quarter. The normalization of Palantir’s high SBC is due to the outsized levels from last year following its IPO, and a continued normalization in this trend should benefit shareholders going forward as dilution slows.

Looking ahead, management guided for Q4 sales to increase 30% YoY to $418 million, which was 4% higher than initial estimates.  For the full year 2021, sales are expected to grow 40% YoY to $1.5 billion, 2% higher than initially expected. Management also raised their adjusted FCF guide to be in excess of $400 million, up from the prior guide of $300 million. The company continues to expect long-term topline growth of 30% or more through 2025.

While Palantir largely came in as expected, there were some concerns with Palantir’s results. For instance, sales growth slowed relative to the prior two quarters. Furthermore, cashflows from customers was lumpy, as deferred revenue and customer deposits decreased relative to sales growth. However, this was offset with a sharp rise in backlog, as RPO to be completed in the next twelve months increased 111% YoY to $393 million, while bookings increased 56% YoY to $510 million. The outsized growth in NTM RPO and bookings relative to sales suggests that there is ample support for future sales growth.  

Palantir has also made a series of investments that could further help fuel topline growth going forward. The company invests in commercial customers that gives Palantir exposure to their success if they benefit from Palantir’s tools. As shown below, the company has invested $153 million in commercial partnerships YTD, with a maximum potential revenue from these contracts of $640 million.

Investments in commercial customers is similar to what Amazon has done with Affirm (discussed above), as Palantir gets exposure to companies that can materially benefit from its tools. While Palantir has a robust toolset that can transform data into actionable insights, it takes time for commercial customers to find uses for the products. These investment agreements can help accelerate the time it takes for commercial customers to realize the strength in Palantir’s services. These investments are not without risks, however, because if the company fails then Palantir will be required to write off the investments, impacting earnings.

Looking forward, Palantir’s guide appears reasonable as it has amble support from backlog and bookings to continue to grow 30%+. The company’s commercial segment has been robust, which has been aided by the company’s investments in commercial customers. While growth slightly slowed relative to prior periods, if government spending begins to ramp, then Palantir’s sales growth will likely reaccelerate in the future.  

New LTBH Position: Stars are Aligning for Palantir

Posted on by io-fund

A few notes before we look at Palantir:

  • We are looking to close Atomera and will look to get back in when the timing is better. We feel the message in the earnings call is that during supply shortages, things are moving slowly for Atomera. Meanwhile, small caps are looking like they could break out (follow Knox on the forum and on webinars) and we prefer to put our money elsewhere for now. Atomera could still move but we have to make these decisions to keep the portfolio at a reasonable size.
  • There’s a chance we re-enter Vuzix as small caps are starting to break out. Knox might be seeing a setup he wants to take and the company had some good quarters in the past. Note: this is likely good news for many small caps, not only the ones we own.
  • Confluent is a strong company. We stepped aside until the lockup expires as a matter of discipline. Look for us to put MongoDB in the LTBH portfolio and Confluent at some point, as well. We are encouraged by cloud results so far this earnings season (so far, so good).

Palantir Analysis:

We break down Palantir’s product below and we believe the Apollo layer is especially interesting, competitively speaking. We also point out that government contracts will likely boost the company’s revenue in the near term. The Obama Administration used Palantir for many government projects and we believe the Biden Administration is a tailwind for the company in the near-term. With a company that is two-thirds deal value from the government, this piece cannot be ignored.

The company has as many risks as opportunities. We go through those risks below, mainly the price of the product, the unusually high stock based compensation, widespread ethical concerns (for 10+ years), and more agile AI/ML competitors sprouting up to compete for commercialized accounts. Due to these risks, we may not hold for the 3-5 year time frame that we typically target, rather are entering as a LTBH for the 6-month to 1-year tailwinds that we are expecting from increased government spending. This is distinguished from momentum positions that are often higher beta and/or moving in price. In this case, we think Palantir being off 50% from its all-time high of 39.00 does not reflect the current tailwinds from the government segment. We are encouraged by the commercial growth, as well, but it’s the government spending in the near-term that could cause a material change to the story. The upcoming earnings report will tell us more.

Palantir has two platforms: Gotham and Foundry. There is a layer between the two platforms and applications called Apollo. The Apollo layer is where innovation has been rapidly occurring and helps contribute to Palantir’s competitive edge (more on this below).

Gotham and Foundry create a unified data set for actionable insights across industries such as manufacturing, product development, and customer experience. The data that Palantir gets is from the customer database although the company may use other data sets for government customers, such as scraping social media or other publicly available information on the web. The traditional deployment includes hosting Palantir’s servers in a customer’s data center.

The difference between Palantir and competitors, such as Tableau, Alteryx or Cloudera is that Palantir is able to answer questions a model cannot answer. Traditional business intelligence companies require a complete data set whereas Palantir is able to tackle situations where there is not a complete data set.

Palantir Gotham was the company’s first platform, built for government operatives in defense and intelligence sectors. The platform enables users to identify patterns hidden deep within datasets using semantic, temporal, geospatial and full-text analysis.

The Graph product allows data to be seen as nodes and edges to visualize and plot characteristics in a logical manner.

Map brings geospatial capabilities to track geo-located objects and events and to create heatmaps for the density of the objects.

Object Explorer is powered by the Horizon in-memory database, which competes with Apache Spark for letting users query billions of objects. The data provides further analysis for Map and Graph data.

Browser: This enables search queries for investigations and surfaces information, runs relevant searches, displays key data points and answers analytical questions.

Palantir Foundry is the commercial offering and has four layers of tooling: Foundry Core, Data Foundation, Ontology and Workflows. 

This four-step process does the following:

  1. brings volumes of data into one place,
  2. transforms the data into a format that analysts can work with and enables validation in a number of programming languages
  3. the “ontology layer” allows datasets to be turned into real-world concepts
  4. workflows is where it all comes together in an integrated environment for object exploration, point-and-click top down analysis, code authoring, time series analysis, data science and application development. When a user has a question, it answers it using all layers and tools available.

Palantir describes Gotham and Foundry as the “ability to construct a model of the real world from countless data points.” Unlike a SQL database, natural language is used to query data and return results in real-time rather than through strings.

Apollo is the Linchpin:

The company has a third platform or layer called Apollo and also Apollo for Edge AI. This product provides continuous delivery and an automated configuration layer that allows Foundry and Gotham to work across all cloud environments and also in places where there is little to no connectivity. On top of Palantir being able to form conclusions from incomplete data sets, the company can also deploy its platform and applications anywhere.

Palantir’s marketing team says Apollo “goes where no SaaS has gone before” because it allows what is done on-premise to also run on multi-cloud SaaS with code that is deployed across all environments rather than written for a specific environment. The orchestration allows for on-hardware AI models to consume real-time data from sensors, radio, geo-data and time series data. Where bandwidth is not an issue, the company transmits all raw inputs and enriched metadata from models. Where there are constraints, the platform transmits meta-data only which can reduce bitrate by 20X. At times, a simulated environment can be created with Palantir’s Edge AI from historical data to help train AI models. The simulated environment is then deployed at the edge. With Apollo, Palantir’s centralized operations team is capable of 41,000 updates per week at no additional cost.

Apollo Edge AI links together satellites to lower latency for the AI-enabled decision chain by orchestrating up to 237 satellites in what the company is calling a “meta-constellation.” This meta-constellation optimizes hundreds of orbital sensors and AI models to power Palantir’s models. One example they provide is tracking submarines that pose a threat to the U.S. and its allies. In this case, submarines are being tracked on a granular level in areas where there is no bandwidth available. These are the kinds of obstacles that Palantir overcomes while being independent of one cloud environment, such as AWS or Azure.

Financials:

Palantir is growing its annual revenue of roughly $1 billion by 50% for estimates of $1.5 billion in 2021. This looks like it will be accomplished with the last two quarters at revenue growth of 49% year-over-year growth. Current estimates for Palantir in the upcoming quarter are at $386.53 million, or growth of 33.5%. To me, these estimates seem low considering the commercial growth the company has been posting. On top of revenue > $1 billion and growth > 40%, Palantir is free cash flow positive with a 13% adjusted free cash flow margin and adjusted EPS of $0.04.

The main metric for Palantir is commercial revenue, which has accelerated nicely over the past few quarters. In the last quarter, commercial revenue grew 90% year-over-year. The company is also adding commercial customers faster than overall customers at 32% compared to 13% for total customers. In the quarter ending in March, the company reported revenue growth of 72% year-over-year. This was slightly lower than government revenue growth of 83% YoY.

The company also grew total contract value booked from $337 million to $925 million, although this is a mix of both government and commercial contracts. According to the fine print, the maximum potential revenue from commercial contracts is $348 million and an additional $195 million in commercial contracts that are subject to negotiation and approval.

The deal value also increased 63% to $3.4 billion, however, of this $428 million comes from commercial contracts and $195 million comes from commercial contracts currently under negotiation. Therefore, the majority of the deal value increase came from the government.

For FY 2021, the company plans to double its adjusted free cash flow in the upcoming quarter from $150 million to $300 million.

Stock Based Compensation:

Palantir has some of the highest rates of SBC in the cloud universe. Over the last twelve months, SBC was 114% of sales, which is well above the peer median of ~18%. The issuance of SBC is dilutive to shareholders and can weigh on the share price in the near term. However, Palantir recently completed its IPO, which is typically a period of outsized stock-based compensation.

Looking forward, Palantir’s rate of SBC will likely normalize to a more sustainable rate, which will lessen the impact of dilution and should benefit shareholders going forward.

A key benefit of high SBC is that employees become owners in the company and have a vested interest in the company’s success, which can even help reduce turnover and improve productivity. The biggest concern Bradley sees with high rates of SBC is if the SBC is repurchased via stock buybacks but is still excluded from adjusted EBITDA and earnings. This accounting trick can cosmetically improve the presentation of profitability by excluding payroll expense from non-GAAP metrics. However, Palantir does not appear to be playing these games, as it has not repurchased any stock during the year.

Catalysts:

Some real-world uses for Palantir include Hershey’s using the software for global food distribution and to correlate weather patterns with snack consumption. Chase Bank and other financial firms have used Palantir’s data analysis to identify troubled properties and ensure employees are not committing fraud (and in turn, the management team was actually spied on instead).

Pharmaceutical companies use Palantir to expedite the development of new drugs – this being a substantial use case during Covid and partly why Palantir’s revenue has accelerated. In the last earnings report, Palantir discussed companies leveraging Palantir’s software for R&D and manufacturing to accelerate development. The software helps health care data be shared to share trial data.

Utility companies use Palantir to monitor equipment, such as to monitor equipment in mining shafts or for grid management and safety. The powerlines from PG&E in California created wildfires and experience ongoing power outages during heavy winds. PG&E partnered with Palantir in early 2021 to help assess where the most danger is for power shutoffs and for wildfire risk assessments.

Climate change initiatives coming from the government will also be a tailwind for Palantir as the company’s software is used to help companies de-carbonize and achieve low carbon footprints. The more spent here, the more Palantir will see additional tailwinds.

DataRobot is a popular company used for unifying data for AI and they are partnered with Palantir to help forecast demand.

As stated, Palantir was first hired by Obama for border patrol with the New York Times reporting “Palantir’s technology was used extensively by the Obama Administration.” It is not clear as to whether the Trump Administration used Palantir or if the agencies, such as the FBI and CIA did during the years the Trump Administration was in Office. In other words, I am not sure if Palantir is bipartisan or not but my understanding is the company saw more government contracts during Obama and now Biden. We also saw Biden place a former Palantir advisor as the director of national intelligence. The DNC is headquartered in Denver and the company recently moved to this city. Alex Karp attempted to state sensational reasons for this move, which I called out as simply creating headlines. I believe the move was strategic for Palantir to be closer to the money.

Risks:

The closest competitor for Palantir is Semantic AI, which supplies graph-based analytical platforms to the DoD and other government agencies. There will likely be more competitors in the near future as the AI/ML market is built out. For instance, there is energy-specific software such as Stem that uses AI software to optimize energy resources and battery usage by using algorithms to issue forecasts that then work across the grid, batteries and solar for optimal output. Stem claims to have taken over 100 energy storage systems that were previously managed by competitors and is also used across Big Tech, such as Facebook, Amazon, Apple and Home Depot. In this case, one could argue Stem serves the commercial market whereas Palantir is more suited for larger utility companies due to its government-sized solution. Essentially, the risk is that Palantir could be “too much product” for commercialized companies that prefer a simple solution. You can read our analysis regarding Stem here.

Tiberius is a database used for administering the Covid vaccine. USA Today reported complaints from a few healthcare agencies that Tiberius was often wrong and did not improve results compared to their own in-house databases.

Palantir greatly centralizes datasets and AI/ML — which is a risk. You’ve likely read my analysis on the Blockchain is Going to Eat the Internet and why decentralization is important. Using Palantir for defense is one thing, but now that Palantir is beginning to move into other industries, the blurring of the lines as to where the government ends and the free market begins is problematic with a company like Palantir. Palantir’s greater loyalty will be with the government (it’s biggest customer) yet their software is now inside company databases. The United States tends to prefer a separation across government bodies whether it’s church/state or state/federal or judicial/executive/legislative, when possible. What Palantir is proposing is that a heavily government-funded company be the middleman.

What affect could this have? Already, Palantir has been used to hunt down illegal immigrants and to enter their homes for arrests. This article is worth a read for more information. Uber has been in a string of never-ending lawsuits over the independent contractor/employee debates – another human rights issue that a tech company faced. These lawsuits threaten Uber’s business model, and even after getting the measure on a ballot which passed in California, the class action lawsuits are still ongoing after the State of California decided to sue the company. We predicted this would be troublesome long-term for Uber as part of our bear thesis at the time of IPO. As Palantir moves outside the government, I expect we could see some States and non-profits fight the company on the use of its software. There is a history of non-profits, such as Amnesty International, calling out Palantir on how the software is used in terms of targeting specific individuals. The bigger Palantir gets, the more the public and critics will see how powerful (and invasive) the software can be. As of now, Palantir has chosen to target illegal immigrants who can’t bring a class action lawsuit – hence non-profits stepping in. If the company were to target United States citizens, I would fully expect lawsuits to pop up.

To help illustrate, the week Palantir went public, Hootsuite stated the company would terminate its ICE contract due to disagreements within the company. The CEO of Hootsuite tweeted: “We typically do not make public facing statements about specific customers or contracts. However, due to the attention around this particular case we can confirm that Hootsuite has decided not to do business with the U.S. Immigration and Customs Enforcement.” Tech companies often see employees engage in protests when a company contracts with the government on AI-driven war missions and privacy issues.

In the past, Google ended a contract with the Pentagon when employees protested using AI for lethal purposes. Karp became controversial and challenged Google on this decision, saying it was a “loser” position. Palantir could become subject to competing for talent with companies that are more privacy-compliant or viewed as being more ethical. Here’s an example about how they describe their hiring process: “We spend time thinking about exactly what gamma radiation your incoming Bruce Banner needs to turn into the Incredible Hulk. And then we irradiate them.” The hyperbolic description of using “gamma radiation” is likely just the stock-based compensation.

Conclusion:

The stars (and satellites) are aligning for Palantir, and with government spending, it has the ingredients to become a stock market darling if the revenue accelerates. The product is often framed as captivating and the company will likely sell Wall Street on commercial growth. Regardless, the ethical issues can mire the company long-term, and at its core, Palantir is still a government contractor. We are more likely to be 3-10 year bulls for decentralized blockchain companies that handle data in privacy compliant ways over a heavily centralized company. However, for the sake of the current tailwinds, we have entered the stock and added it to our LTBH portfolio.

I/O Fund – Bill.com Reports Another Blockbuster Quarter

Posted on by io-fund

In the video below, I quickly go over Bill.com’s Q1 FY2022 results, which pushed the company’s stock to a new all-time high. Bill.com’s business model is bifurcated between subscription sales and transaction fee revenues, both of which accelerated in the most recent quarter. In fact, transaction revenues have exploded and grew over 300% YoY!

The company’s balance sheet is also clean as Bill.com operates an asset light business model. Cash is over $1 billion and the majority of assets on the balance sheet relate to cash held for clients. With sales accelerating and a strong cash balance, Bill.com appears poised for strong growth going forward. Watch the video below to find out more!

Sign up for I/O Fund's free newsletter with gains of up to 1100% -Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% -Click hereClick here

I/O Fund’s Cloud Q3 2021 Earnings Overview

Posted on by io-fund

In the analysis below, we give a brief overview of our universe of cloud stocks and discuss key metrics that investors should be aware of heading into Q3 earnings.

Cloud Stocks: Top 10 EV/FWD Revenue Multiples

Below is a table of cloud stocks ranked by their EV/FWD sales multiples, along with their most recent YoY growth rate, gross and free cashflow (FCF) margins. Cloud has been a strong category for growth recently, which has rewarded the top performers with premium multiples

Cloudflare (NET) has the highest EV/FWD sales multiple in our universe of cloud stocks. The company has made some announcements around object storage costs recently, which could be impactful for the company going forward.

Snowflake is right behind Cloudflare at a 91x EV/FWD Revenue multiple. Snowflake grew sales over 100% in Q2, and its net revenue retention rate was 169% during the quarter, highlighting the company’s success in capturing market share. Management attributed the strong results to increased customer data consumption, a trend that will likely continue into the future.

Cloud Stocks: Top 10 Three-month Forward YoY Growth Rates

Looking forward, Bill.com (BILL) and Snowflake are expected to be the fastest growing cloud stocks in our universe. BILL’s expected growth rate is skewed by its recent acquisition of Divvy, and excluding the acquisition, organic growth is expected to be ~60% next quarter. Snowflake is expected to continue to report strong growth of 92%, similar to the 104% growth it reported in the most recent quarter. As mentioned above, Snowflake is benefitting from a secular tailwinds as enterprises increase their data consumption.

Top 10 Weekly Share Price Movements

In the table below, we ranked the cloud stocks that saw the largest one week increase in their share price. Shopify (SHOP) has been a top performer this past week, as the stock rebounded after a slight sell-off following its Q3 results. Microsoft (MSFT) also reported last week and the market reacted by increasing its market cap to $2.5T, surpassing Apple as the most valuable company in the world.

Sign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereClick here

The I/O Fund has covered Microsoft in detail since 2018 when Beth explained Microsoft’s hybrid strategy when she boldly stated that Azure could overtake AWS on cloud IaaS. Microsoft’s hybrid cloud approach has allowed the company to outperform its peers and positions Microsoft well to continue to take share in the hyper-growth cloud market.

Top 10 Changes in sales growth estimates – last 90 days

The table below ranks the cloud companies that have had the largest revisions to their forward topline growth expectations over the last 90 days. As mentioned above, Bill.com (BILL) recently completed a series of acquisitions which contributed to an outsized increase in its sales expectations. Similarly, Qualtrics (XM) recently completed its acquisition of Clarabridge, which has led to an upward adjustment in its growth rate. Datadog’s (DDOG) estimates have increased 9% over the last 90 days and its stock price has also increased nearly 50% over the same time period. The market is likely pricing in strong growth for the company as Datadog continues to lead in the cloud observability category.

Update on EV/Fwd revenue multiples:Update on EV/Fwd revenue multiples:

Overall stats:

  • Overall Cloud forward median:  16x
  • Top 5 Cloud forward median:  65x
  • Overall Cloud forward average:  22x

EV/FWD SALES:

As shown below, the median and average cloud EV/Fwd revenue multiple has trended up throughout the year. The average multiple has started to increase faster than the median, as the top valued cloud companies have experienced a sharp rise in their multiples in recent months.

Top 5 EV/FWD SALES:

In the chart below, we can more clearly see the large dispersion in cloud valuations, as the top 5 premium valued cloud stocks have had their EV/Fwd sales multiples rapidly expand since May 2021 and are now at new highs. The cloud category is often considered to a be a “winner gets most” market, where the market leader captures the majority of the addressable market. This dynamic helps explain why the top 5 valued cloud stocks have grown their multiples much faster than the median.

EV TO FWD SALES Growth Buckets:

We can further dissect the changes in cloud valuations by breaking up the group into high growth (>30% growth), mid growth (>15% and <30%) and low growth (<15%). The below chart shows that higher growth cloud stocks receive a higher multiple from the Street. Furthermore, high growth stocks used to be valued more richly back in Q4 2020 but have since seen their valuations normalize to a lower multiple. If Q3 cloud earnings come in strong, then the market may push valuations back up to their historic highs.

Top 30 EV TO FWD SALES:

The below chart provides a more holistic view of the top 30 valued cloud stocks based on EV to Fwd revenue estimates. Cloudflare (NET) and Snowflake (SNOW) have the highest valuations of the group and are valued more than 500% higher than the cloud median of 15x. As mentioned above, NET and SNOW are benefitting from trends that are expected to continue to result in robust growth going forward, such as cloud storage costs and data consumption. 

Growth adjusted EV/Fwd Revenue (EV/Fwd Rev/Fwd Growth):

The last chart is based on EV to FWD sales but also takes into account forward growth expectations. By scaling valuation relative to forward growth, we can more clearly see which companies are cheapest relative to forward growth. A low value in the chart below means that a company is cheap relative to growth. For example, SNOW dropped from being one of the most expensive stocks to being valued closer to the median once we take into account its strong growth expected next quarter.

Finally, the last table we will be discussing includes aggregate cloud operating metrics. The below table shows that cloud is performing strongly as the median forward growth rate is above 20%, while gross margins are high at over 70%. The median cloud company is also FCF positive with a 6% FCF margin.

Strong growth and positive cashflows signal that the cloud category is healthy and performing well. The I/O Fund expects this strength to continue going forward. Find out which the Street has been saying about cloud stocks heading into earnings. “Overview of 6 Cloud Stocks for Q3 Earnings”

The I/O Fund is a team of analysts that share their research publicly as they build a portfolio of 30 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here.premium service by clicking here or sign up for our free newsletter here.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

I/O Fund’s Overview 6 Cloud Stocks for Q3 Earnings

Posted on by io-fund

Cloud stocks continue to do well in the market as these companies are growing very fast. This quarter we chose Cloudflare, Datadog, Dropbox, Bill.com, Five9, and RingCentral with some already reporting today and some reporting soon.

To understand valuations across cloud stocks and how the sector is positioned, please refer to our analysis “I/O Fund’s Cloud Q3 2021 Earnings Overview”

Cloudflare Inc – Earnings on November 04

Cloudflare’s Q3 sales grew 51% YoY to $172 million, which beat the consensus estimate of $166 million by 4%. The company also expects Q4 sales to grow 47% YoY to $185 million, which is 5% higher than the Street’s initial forecast of $176 million.

Source: Earnings report and YCharts

Cloudflare’s revenue grew from $85M in 2016 to $431M in the year 2020, a compounded annual growth rate of 50% during the period. In the second quarter revenue grew 53% YoY to $152M, it was primarily helped by the strong growth in paying customers. At the end of the second quarter, it had 126,735 paying customers (+32% YoY) and it also witnessed a significant addition of large customers. This growth continued into Q3 as Cloudflare beat topline estimates by 4% after reporting strong YoY sales growth of 51% during the quarter.

Going into earning, Jefferies analyst Brent Thill had downgraded the company to a hold rating from a buy with a price target of $195. The analyst is concerned of the valuation after the strong share gains. However, he continues to view Cloudflare as the "most disruptive cyber vendor with strong fundamentals," he is of the view that “the company has the richest multiple in his coverage universe at 56 times enterprise value to consensus 2023 revenue estimates” and he "would look to get more constructive at a more reasonable valuation."

Needham analyst Alex Henderson has said that the company’s move into email security as a positive. He says “just one more example of why Cloudflare will become a major company.”

Please note, the I/O Fund is objectively reporting what the Street is saying. We covered Cloudflare previously here: Pinterest and Snap Show V-Shaped Recovery; Cloudflare Guns for Zero-Trust

Datadog Inc reports on November 04th

Datadog reported that Q3 sales grew 75% YoY to $270 million, which bested the consensus estimate of $248 million by 9%. The company expects Q4 sales to grow 64% YoY to $291 million, which is 10% higher than initial expectations.

Source: Earnings report and YCharts

In the prior quarter of Q2, Datadog reported strong second quarter results. It beat the analyst’s revenue estimates by $21M and the adjusted earnings by $0.06. The company had also raised the full-year revenue guidance to $938M-$944M, up from the previous guidance of $880M-$890M. Datadog continued this momentum and reported a 9% top line beat during Q3 and guided Q4 sales 10% higher than initially expected.

It also witnessed strong growth of large customers (annual recurring revenue of over $100,000) as they grew to 1,610 from 1,015 from the same period last year in Q2. This quarter, large customers grew to 1,800, up 66% from 1,082 in the prior year quarter.

RBC Capital analyst Matthew Hedberg has raised the company’s price target to $176 from $154 and has kept the Sector Perform rating on the shares. The analyst expects the company to report "strong" Q3 results with upside, building off last quarter's acceleration. The analyst adds that he expects Datadog to continue to benefit from continued traction in multi-module sales, strong new customer adds, and favorable cloud adoption trends.

Our previous analysis on the company:

Podcast with Motley Fool: Big Tech Plus the 1 Stock I’d Buy Right Now

Tech Growth Earnings Review for Q3 2020 – Part 2

Video: Our Stock Picking Strategy

Dropbox Inc reports on November 04th

Dropbox reported Q3 sales of $550 million, which grew 13% YoY and came in 1% higher than the consensus estimate of $545 million. The company’s outlook for Q4 forecasted sales to grow 12% YoY to $563 million, 2% higher than the Street’s initial estimate of $553 million.

Source: Earnings report and YCharts

The company’s revenue growth is not very strong when compared to other cloud stocks. However, the company has got good free cash flow and it’s profitable. In the last quarter, the management has raised the full-year revenue guidance to $2.136B-$2.142B from $2.118B-$2.130B. It aims to generate annual free cash flow of $1B by the year 2024. The management revenue guidance for the third quarter is $543M-$546M, which represents a growth of 12% YoY at the mid-point.

Sign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereClick here

Jefferies analyst Brent Thill has a price target of $40 and a buy rating on the stock. He believes that the company’s increased full year guidance is still conservative.

Bill.com Holdings Inc reports on November 04th

Bill’s Q1 FY22 sales were $116 million, which beat the consensus estimate by 11% and represented a 166% YoY growth rate (organic growth was 78% YoY). Bill.com guided for Q2 sales to grow 141% YoY to $131 million, which was 12% higher than initial estimates.

Source: Earnings report and YCharts

The consensus analyst’s revenue estimates are strong for the next quarter. However, we cannot compare to the previous periods as the results will include Divvy. It completed the acquisition of the spend management solutions provider Divvy, on June 01, 2021, and the 4Q results included Divvy results. The stock has been one of the best performers in the sector. However, it would be interesting to watch how the company faces competition from other players and justifies its valuation. Bill.com reported Q1 FY2022 sales that beat top line estimates by 11% and guided next quarter sales well above consensus estimates.

Jefferies analyst Samad Samana had a buy rating going into earnings and a price target of $350. The analyst anticipates organic core revenue growth to "decelerate modestly" against a tougher comp, but his 60% growth outlook is still "very healthy". Bill.com should be a "core" long-term growth holding, with the stock offering "solid upside" based on his potentially "conservative" assumptions.

Deutsche Bank analyst Bryan Keane initiated coverage of Bill.com with a Buy rating and $360 price target. He believes that “BILL is uniquely positioned in the market due to its end-to-end offering, including accounts payables (AP) and accounts receivables (AR) automation as well as electronic payment offerings like virtual cards, instant transfers and cross-border FX. He further states “We see potential for ~70% Y/Y core organic growth in 1Q22 and ~57% Y/Y for FY22 compared to guidance of ~60% Y/Y and ~45% Y/Y driven by new customers, higher engagement, and increasing take rates from mix shift with reported growth reaching as high as +124% Y/Y in FY22 including Divvy and Invoice2go.”

Five9 Inc reports on November 08th

Source: Earnings report and YCharts

The consensus analyst’s revenue growth is slower than the second quarter and also from the previous year. The company did not have an earnings call in the last quarter due to the pending merger transaction and the next call would have more details about growth prospects as a standalone company.

Analysts have been positive after the Zoom-Five9 deal failed to materialize. Barclays upgraded FIVN to Overweight, saying the deal's breakdown refocuses the investment case back on fundamentals. And “We don’t think lack of a deal hurts Five9’s positioning with enterprise customers."

Evercore has an overweight rating on the stock and in the words of analyst Peter Levine, "firing on all cylinders, the pending acquisition was not a distraction, partner contributions remain strong, and the numbers released in the proxy are a fair representation of the current trends in the business."

Sign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereSign up for I/O Fund's free newsletter with gains of up to 1100% – Click hereClick here

Jefferies analyst has a $180 price target and a hold rating. His checks throughout Q3 suggested demand remains solid across both UCaaS and CCaaS, he thinks Five9 "has a tough setup" given that management not providing guidance last quarter has resulted in "a wider than normal estimate dispersion." Management's 10-year financial plan in their merger proxy raised buyside expectations, but he does not expect the company to guide to the proxy levels, which may disappoint some investors.

We have covered Five9 stock in our premium site in the past.

Please find our semiconductor earnings preview here.

RingCentral Inc reports on November 09th

Source: Earnings report and YCharts

RingCentral has been showing steady growth. The management had raised the full-year revenue guidance to $1.539B to $1.545B, which represents a growth of 30% to 31%, which is up from the prior guidance of $1.5B to $1.51B. The third quarter revenue guidance is in the range of $390.5M to $393.5M.

Source: Earnings Slides  

Jefferies analyst Samad Samana has a buy rating on the stock with a price target of $360. His checks throughout Q3 suggested demand remains solid across both UCaaS and CCaaS, which he thinks should translate into solid Q3 results.

Barclays analyst Ryan MacWilliams initiated coverage of RingCentral (RNG) with an Overweight rating and $350 price target. “RingCentral shares are attractive and RingCentral Office remains the most applicable as well as marketable solution for mid-market enterprise customers, even though Zoom Phone (ZM) and Microsoft (MSFT) Teams adoption has unfairly changed investor perception of the stock, leading to a disconnect in valuation to the company's recent quarterly performance.” 

The I/O Fund is a team of analysts that share their research publicly as they build a portfolio of 30 stocks. Our team has record results for a retail Fund and we also have four-digit gains on some of our free newsletter coverage. You can learn more about our premium service by clicking here or sign up for our free newsletter here.by clicking here or sign up for our free newsletter here.

Disclaimer: This is not financial advice. Please consult with your financial advisor in regards to any stocks you buy.

Big Data, Analytics (and ML): Microtrend Deep Dive

Posted on by io-fund

There are three important trends we weave together in this report to draw conclusions around potential winners in big data and analytics. We’ve recently covered MongoDB, Elastic, and we’ve discussed Confluent. What do they all have in common and why are these companies important right now? That’s what we aim to answer in this write-up.

Before we go into where we are with big data and analytics right now, I’ll quickly touch on cloud IaaS and especially why hybrid and multi-cloud are leading this space and why investors should not be concerned with tech giants that offer competing products in the data and analytics space.

When we talk about Big Data, the main driver is machine learning, which is performed through supervised learning with the use of historical data, or unsupervised learning, with clustering models and associations to identify rules. There is also reinforcement learning that is trained from feedback. Here are the three trends we are going to weave together to form a full picture of Big Data and Analytics.

  1. The migration to the cloud — but more specifically multi-cloud and hybrid
  2. Why multi-cloud drives demand for best-of-breed, i.e., generally speaking, we do not need to be overly concerned when tech giants that offer competing products
  3. How Apache Spark helped catalyze the AI/ML market with efficient data processing
  4. How we plan to invest right now given that #2 and #3 are prepping the market for us

Hybrid and Multi-Cloud are Driving the Cloud IaaS Market:

For cloud IaaS, we don’t want to only focus on CAGR but also the budget allocation that cloud IaaS is capturing. According to IDC, the IaaS market will reach $112.9 billion at a CAGR of 11.3% through 2025 and will account for 66.1% of total compute and storage infrastructure spend. Two-thirds of IaaS spend is on the public cloud.

To compare, the on-premise market (i.e., not hybrid) will grow at 0.3% CAGR for a total of $57.9 billion. According to the most recent Denodo survey, hybrid cloud drove 35% of the workloads worldwide. Private cloud expanded from 16.6% of workloads to 24% percent of workloads worldwide. Meanwhile, the public cloud had flat growth.

Hybrid cloud is a mix of public and private clouds or a mix of cloud and on-premise. Enterprise companies that choose hybrid deployments are motivated to not share intellectual property or data with a vendor, known as data residency, plus other security implications that come with storing data on another company’s servers. Other companies find moving to the cloud to be time and resource-intensive and prefer to keep some workloads on the servers they own.

Recently, a report came out that repatriation, or moving some workloads back to on-premise, has resulted in quite a bit of cost savings for companies like Dropbox, Crowdstrike and Zscaler, who use hybrid approaches. The report is quite surprising as the conclusion is that $100 billion to $500 billion in market value is lost on cloud deployments in terms of margins. One use case that is detailed is Dropbox, a company that reported savings of $75 million in two years after repatriation, which in turn, helped the company’s gross margins increase from 33% to 67%. Meanwhile, companies like Asana and Datadog spend about 60% of their revenue towards committed cloud spend. This report, among others, shows why hybrid is likely to be the chosen deployment for many enterprises into the near future.

We had previously formed a Microsoft thesis in 2018 based on the trend towards hybrid cloud and why a focus on a hybrid strategy for governments and enterprises was important to Azure’s growth rate. Microsoft is especially well suited to serve the hybrid market because of the company’s deep roots with on-premise enterprise software. When the I/O Fund first covered hybrid cloud as a major driver of cloud IaaS in 2018, Amazon’s AWS did not even have a publicly available hybrid product. The company later publicly released Outposts in 2019 to compete with Azure. If you want more information about how these two compete on hybrid on-prem deployments specifically, the in-depth analysis I published in the past is found on Seeking Alpha and also Forbes.

Multi-cloud refers to using more than one cloud provider, which is usually done to avoid vendor lock-in and to choose best-of-breed products. It also helps to avoid downtime should one cloud provider go down or become overwhelmed with demand.

Source: Statista

Multi-cloud is the dominant strategy today and is used by 80% to 90% of organizations. In 2019, Gartner stated 81% of respondents were using two or more cloud providers. The top reason was to avoid vendor lock-in by the “megavendors.” Therefore, this is why investors should not be concerned with tech giants offering competing products in the data and analytics space. The far majority of companies are taking strides to avoid vendor lock-in as multi-cloud technically requires more work yet increases agility and flexibility. The end result is these companies will use best-in-breed products.

According to IBM, 98% of companies plan to use multiple hybrid clouds and 85% operate in multi-cloud environment. There is substantial evidence that organizations are preferring a mix of cloud providers. Most importantly to our thesis and this particular analysis, only 40% use management tools and/or have implemented DevOps practices. The migration to the cloud was happening slowly over time and this migration is under-served in terms of management tools, data and analytics. This would be a sufficient tailwind on its own yet we also have the additional tailwind of data-intensive industries that are moving into machine learning. 

The motivation behind cloud IaaS growth and especially hybrid and multi-cloud growth is partially driven by the need for analytics, and also newer trends, such as stream processing. Stream processing is a continuous stream of events that is processed in real-time as it’s received. This allows applications to respond to events as they occur. It combines real-time analytics, inferencing and monitoring to achieve things like optimizing transportation routes, understanding traffic patterns, anomaly detection in cyber security, making real-time predictions powered by machine learning, and even location-based advertising.

In terms of architecture, we’ve covered how microservices and containers are also driving the multi-cloud trend as microservices often span multiple clouds. You can find this write-up here on Forbes and also here on Medium, where we discussed a background on Google Cloud and how the company was the first to automate orchestration across containers. This write-up provides a great overview of where the major cloud IaaS providers are today and where they might go next strategically speaking.

Big Data and Analytics will Explode because of AI/ML Applications

There is an oft-quoted statistic that 90% of the world’s data was created in the last two years – and this stat is from 2018. The world produces 44 zetabytes of data across the digital universe as of 2020 and there is expected to be 200+ zetabytes of data in cloud storage by 2025. Each zettabyte has 21 zeroes or is 1,000 bytes to the 7th power. By these estimates, we can expect to see up to 5X growth specifically in data centers. Statista places the number at 181 zetabytes by 2025 up from 64.2 zettabytes in 2020.

In regards to data integration in the cloud, this spans from data lakes, to ETL pipelines, cloud data warehouses and object storage. Data fabrics and data virtualization is key to both hybrid and multi-cloud strategies.

Here's how Datadog’s CEO describes what is going on in terms of big data in the most recent earnings call: “it's almost a given that there will need to be a different way of charging for capturing some of the value provided to customers that can't just be attached to the straight volumes of data that are being exchanged because those volume of data are exploding exponentially while our customers' revenues are not going to explode exponentially.”

Generating the data is not the issue (clearly), and distributed data storage has been largely solved with Hadoop. I think it’s worth going through what Hadoop is and how it came about, and then we can look at how Apache Spark helped accelerate data processing, including for Machine Learning. Notably, most open-source projects are not “easy” and this is why companies do well that simplify how to work with Apache Spark and other frameworks, like Kafka.

Background on Hadoop and Data Storage:

Hadoop became instrumental in helping companies store large amounts of structured data, semi-structured and unstructured data through distributed storage and compute. The result was that data storage became cheap enough to retain any/all data that was generated rather than only the essential data due to its distributed file system. The distributed file system was designed to store and process billions of search engine pages across thousands of nodes. The project was created in 2006 by a team of engineers at Yahoo, who had worked previously on a search engine in the early 2000s with the goal of indexing 1 billion pages.

You can think of search engines as some of the first projects that needed to utilize Big Data. The original search engine project “Nutch was limited to 20-to-40 node clusters, and for this amount of data, more clusters were needed. At Yahoo, the team separated the distributed computing parts from Nutch and renamed the project Hadoop, which successfully worked on thousands of nodes. Parallelism was key for the data processing model as Yahoo’s algorithm would need to be run on multiple nodes at the same time and it had to scale linearly. It was then released in 2008 as an open-source project with up to 4000 nodes with distributed capacity with contributors such as Facebook and LinkedIN.

Distributed systems and parallel computing didn’t begin with Yahoo, of course, it began with Google. The paper “MapReduce: Simplified Data Processing on Large Clusters” is considered a defining moment in how programming models handled large data sets. MapReduce was a key moment because it was specifically designed to handle Big Data in terabytes and petabytes due to its framework for parallel computation using a key-value pair.

By 2012, Hadoop’s clusters were up to 42,000 nodes and the number of contributors had reached nearly 1500. Apache Hive is a ETL and data warehouse tool that uses SQL, but Hadoop can manage and process large volumes of data that are structured, unstructured or semi-structured data depending on the database that is chosen. Therefore, you can use many tools with Hadoop, such as Spark.

Background on Apache Spark and Data Processing for Machine Learning:

In 2014, Apache Spark was released which took over the MapReduce model primarily because of its speed. By working with data in-memory, the parallel processing framework can push queries 100X faster and on-disk queries run 10X faster. After the extract, transform and load the data (ETL) process, with Spark you can run a training algorithm on the same in-memory data. This helps Spark reach peak performance over competitors for ETL and relational queries, but also for machine learning. Spark’s goal was to become (and now remain) the general platform for distributed programmers where many specialized systems have one interface and one system to install and manage. Apache Spark also reduces code volume by using APIs for Scala, Java and Python. The framework offers a unified API for fault-tolerant stream processing, which reduces the number of APIs to learn. Spark ML and SparkML are the two APIs that are offered for machine learning pipelines.

Hadoop helped solve some of the data storage issues and reduced the cost for expensive storage and compute. Therefore, the next issue is who can work with these databases and can this be simplified. Apache Spark simplifies who can work with the framework by supporting libraries, which can be executed to interact with data shared across many libraries. The data processing engine is extremely fast because it processes and keeps the data in-memory without reading or writing to disk. This has resulted in Apache Spark becoming popular for machine learning and AI applications with the support of Apache’s very large community of contributors.

Overview of Public Companies in the Big Data and Analytics Space

Databricks and Snowflake:

I’m starting with Databricks and Snowflake simply because we discussed Apache Spark in this analysis. The founders of Apache Spark are from Berkeley and later went onto become the founders of Databricks. We covered this company in-depth on our Snowflake analysis because we feel this is Snowflake’s strongest competitor (i.e., not traditional SQL warehouses or Big Tech). Databricks is not public right now but plans to go public soon.

Here is a summary of the explanation we published in April as to how these two companies compare:

The major difference between Snowflake and Databricks from a customer standpoint is that Snowflake is laser-focused on the public cloud/cloud native while Databricks is differentiated in that it can build information pipelines across silos, including on-premise and hybrid architectures. As we know from this analysis, hybrid is key moving forward.

Snowflake's main value proposition is to reduce the time required to prep and monitor data so that a customer does not need to manage software or hardware. Even if a team has the technical skills, they may not want to spend the time required for Databricks, which is perhaps one reason why Snowflake is reporting decent growth in the Fortune 500 and other key accounts.

The architecture of a data lakehouse allows for business intelligence and machine learning through a more open paradigm. The idea is to combine the best of data warehouses and data lakes to span unstructured and semi-structured data while keeping costs low. By combining both, teams can move faster and without duplicating the data. This is a key benefit to Databricks DeltaLake, and this is especially important for data analytics and machine learning. With that said, Databricks is more advanced and expert-level.

I want to point out that Snowflake is very clear as to why it's done well – which is that it handles migrations to the public cloud from legacy on-premise systems better than the competitors. Snowflake's priority is to compete with other SQL databases right now, although the company will need to eventually compete with Databricks. Management has discussed rolling out support for unstructured data, for instance, but no timeline has been set.

Looking longer-term, what Snowflake needs to answer is how will it compete with Databricks on machine learning? Databricks is superior here for ML as it’s built on top of Apache Spark and supports Spark, Python, Scala and also SQL. This was discussed in the thread on the forum here.

The forum thread points out that Databricks is more complex to upload the data, monitor and manage, but there are benefits to going through this hassle. One of the primary benefits is support for Python and Scala, which are programming languages for machine learning. For now, you must use an outside vendor or tool as connectors or integrations in order to support these programming languages and libraries with Snowflake. It’s also worth mentioning that Databricks is cheaper for processing a lot of data at petabyte scale.

Growth is the great equalizer when comparing products and my preliminary understanding is that Snowflake is growing much faster than Databricks and expects to continue to outpace the competitor. I will need to look into Databrick’s financials and see an earnings report or two to determine more about the competitor’s sustained growth rate.

What I find to be very intriguing is what Snowflake will do to compete on ML. This gap in product capability is not lost on the Snowflake team and management. Being laser-focused on the public cloud/cloud-native lends itself well for Snowflake to compete here theoretically, yet its laser-focus on SQL is getting in the way strategically speaking. The company is aware of this and plans to roll out support for unstructured data.

We have two strong products here yet the valuation on Snowflake is stretched and I imagine Databricks will be, too. It’s rare to see a company sustain higher than a 40 or a 50 forward P/S for an extended period of time. Right now, Snowflake is at a 79 forward P/S.

MongoDB:

Big Data applications require a flexible data model, which NoSQL supports. MongoDB is a database that can handle unstructured and semi-structured data, whereas SQL competitors require data to be structured and stored in tables. The predefined schema of the relational database is correlated due to common characteristics. SQL is well-supported as the original database management type yet NoSQL is also reaching critical mass.

The reason NoSQL has risen in popularity is because as data grows, there are more data types to work with outside of Excel spreadsheets/CSV or tabular structures. MongoDB and its competitors are a good choice for Big Data because NoSQL databases can process unpredictable and unstructured data. The most popular types of NoSQL databases include graph, key-value pairs, columnar and document.

Moving forward, we think NoSQL is going to take more market share, simply because it saves steps when dealing with Big Data as the unstructured data does not need to be converted and this is preferred for some machine learning models. This is why NoSQL is used by companies that generate the most data, like Amazon, Facebook, LinkedIN and Google. The extra bonus is that the JSON documents in NoSQL databases can be prepared for machine learning. Because you do not have to define a schema, this allows data to be directly loaded from any new source without changing lines of code. SQL is used in training machine learning models with most of this data coming from on-premise servers. Therefore, the migration to the cloud and various types of data that are generated is also helpful for companies like MongoDB in growing market share. This is because the cloud produces various forms of data.

MongoDB has a query language and secondary indexes for specific values to filter, sort and aggregate data. The leading NoSQL database also allows for the storage and retrieval of trained models as JSON documents. In this case, you can query MongoDB to pull up a previous model.

In the multi-cloud trend, MongoDB is a leader here as the company was the first cloud database to run applications simultaneously on all major cloud providers. The multi-cloud clusters allow developers to deploy applications across multiple cloud providers without having to manage the complexity. In addition, the technical team at MongoDB maintains that you can forego Hadoop and Spark, which requires complex functions and logic, and instead rely on Tensorflow.js, MongoDB and a browser for the same level of machine learning but with less complexity. In an example, a MongoDB representative was able to write a ML program with 88 lines of code. With that said, NoSQL requires more expertise than the universal language of SQL.

The takeaway is that Big Data companies prefer NoSQL for many reasons, and we think in the era of ML and AI, that more companies will lean towards having similar requirements as Big Data companies. This isn’t to say that SQL isn’t alive and well due to the sheer amount of support for structured data across various database systems. Financial transactions for instance fit well into SQL. This is not a “SQL will die” discussion, instead it’s a “NoSQL may see a bigger market thanks to big data and the sheer amounts of unstructured and semi-structured data that will continue to grow” discussion.

Although the SQL and NoSQL debate has lingered for some time with SQL being the leading database today, requirements may change and we think MDB is positioned well for this shift.                                                                                                                                                                                

Also, refer to the fact that MongoDB is fifth in terms of database market share yet is tied for first place for most wanted database skills among software developers. Notably, MySQL and Oracle are the top database systems globally yet MySQL is fifth in terms of most wanted database skills. The demand for talent is typically an important indicator of where we are now and where the puck is going.

You can read more about MongoDB here in our deep dive research report including more details on Atlas.

Confluent:

The founding team of Apache Kafka worked at LInkedIN before leaving to start Confluent. Apache Kafka is used by thousands of companies for message streaming, such as LinkedIN, where a publish/subscribe model allows applications to share and create data in a serverless and microservices architecture. What Kafka solved for is the ingestion of events data in real-time and with low latency.

At the time that Kafka was developed, LinkedIN was ingesting 1 billion events a day. The company is now ingesting 1 trillion per day. Kafka does this through a log that writes messages to a topic and is able to retain messages for a long time. Kafka is also used in stream processing by parallelizing the pipelines. Kafka Streams were built to increase simplicity while retaining the same amount of performance as a Spark streaming job.

As with Spark and other open-source projects, there is a marketplace for making the frameworks easier to use. Confluent Kafka opens up the amount of data that can be integrated, for example, to combine transactional data (orders, inventory) with sentiment-driven data (likes, page clicks). This helps with predictive analytics and also machine learning because the “data flow” allows for algorithms to work as they are intended to. This is what is meant by the title slide of the S-1 filing “Set Data in Motion.” In order for data to be in motion, Confluent’s platform connects data from many different sources.

The end result for Confluent is that the company allows large amounts of data to be moved very quickly. This is needed for machine learning algorithms that are very data hungry. Kafka can be paired with Apache Spark and Apache Samza to route data and then load it into ElasticSearch, for instance, so it’s a bridge (or a nervous system according to Confluent’s marketing department).

The goal of Confluent is to reduce operational complexity. In the case of Kafka Streams, this is done by not requiring a cluster to be spun up, offering a single framework for streams of events, and reducing the number of pieces in a stream architecture. Confluent Cloud is growing rapidly at 200% year-over-year, primarily driven by event streaming.

Please note, that Confluent is on a partial lockup schedule. The partial lockup dates are 15% on the day of the IPO (June 24th), 25% on the second day of trading (August 09th) after the Q2 earnings, with the remaining at the earlier of the second day of trading after Q3 earnings and 181 days of the IPO.My note: Already up to 40% of the shares have already been released by the eligible employees. The full lock-up expiry is between November and December..

Elastic:

Elastic is a best-of-breed search company that has other benefits, as well. Elasticsearch is the core product that allows for the searching, storing and analyzing of data. This allows developers to build search features that pair Uber passengers with drivers, recommend grocery items on Instacart based on your history, match online data profiles for Tinder, or log events for Fitbit at a rate of 250,000 logs per second. In addition to searching and storing data, Logstash and Beats are ingestion tools to ingest data from applications and to query external systems. Kibana is an open-source tool for visualizing the data. We’ve covered Elastic Stack in more detail here.

Since 2018, the Elastic License has been free and open source with paid proprietary features. As Bradley detailed in this write-up, Amazon began to profit from Elastic’s open-source software and did not contribute back. According to Elastic, over 90% of new downloads choose Elastic’s License. As of January 2021, the company dual-licensed Elasticsearch and Kibana under SSPL or “Server Side Public License,” which requires Amazon or any others to publish modifications and the entirety of their source code. We think the multi-cloud trend is one reason that Elastic has been able to overcome Amazon as the primary driver is to avoid vendor lock-in. Notably, Elastic is cloud neutral so it does not rely on any specific external services for machine learning like AWS’s OpenSearch. Basically, this goes back to the points we made about multi-cloud earlier in this analysis.

We also discussed Elastic’s move into XDR is important because security is a primary concern for those who are on multi-cloud deployments. The SIEM and XDR space is not without its competitors yet it could be Elastic’s combination of already having ingestion tools for thousands of applications and sensors that lends itself well to monitoring and detection. SIEM is security, information and event management while XDR stands for extended, detection and response (XDR). SIEM was first used as a compliance product and often works alongside endpoint and network security products in order to offer a narrower yet deeper set of activity. This last piece has become critical over time. For Elastic’s product, XDR builds on the SIEM and EDR (endpoints) combination for more accuracy and applies machine learning models to detect anomalies.

Where there is data, there will be new opportunities for growth as the AI/ML landscape goes from nascent to mature (i.e. not all uses cases have arrived for big data and analytics companies). Due to Elastic being essentially a pretrained model for extracting keywords and synonyms and “term co-occurrences”, it lends itself well to natural language processing (NLP). With Elastic, terms can be filtered by significance and offer out-of-the box shortcuts to Python with its REST API. Cognitive search is a new form of search that uses AI to improve search queries and to extract information from multiple data sets. Cognitive search can combine a traditional search engine with NLP to extract more useful information since keyword search is limited in the variety of data that can be searched. Cognitive search uses machine learning algorithms for its greatly improved search results and will be a $6 billion market by 2025. We think it's impressive that Elastic was named a Leader in the Gartner Magic Quadrant for cognitive search in the first year it was added as a new entrant, blowing past Microsoft, AWS and even Google.

Conclusion:

I wanted to cover Big Data and Analytics broadly and horizontally rather than vertically by company because it paints a better picture of what we are positioning for and why. It’s easy to get lost in the jargon when discussing companies individually especially with technical companies like these. But what really separates each of them? We think the side-by-side comparison can be more conducive at times when setting up a microtrend.

We had a few goals with this analysis that I hope we accomplished:

  • Bring to your attention this trend (and the common thread) and pull-out names from the general “cloud” list to discuss why they may have a unique catalyst. There will be many winners in this space and we are limited in terms of number of positions we can enter. It’s easy to get caught up in “stock picks” yet we also want to offer you microtrends to help inform your individual portfolio decisions.
  • We think big data and analytics from best-of-breed companies could become a solid post-covid cloud play due to the sheer number of companies that migrated to the cloud yet have multi-cloud and hybrid deployments
  • Third, I want to make sure and elaborate on where the MongoDB, Confluent and Elastic positions are coming from that the I/O Fund recently entered. We offer deep dives on companies but we also want to anchor our readers with the underlying microtrends that we are investing in. For instance, Snowflake is a great choice, yet the valuation is high and that range above 50 has not treated us well in the past (i.e., personal choice). Perhaps for your investment profile, you prefer Snowflake right now, etc.

This is a big space and it’d be impossible for me to cover everything but we pulled out the critical pieces. We think it’s important to simplify the key drivers of a microtrend and illustrate the ways that specific companies are serving the trend. You can expect to see MongoDB and perhaps Confluent added to the LTBH portfolio as the thesis should take about 3-5 years to fully play out. The main thing to know is this means we will have to remove a name or two from the current LTBH portfolio. We will keep you in the loop as we weigh these decisions.