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.
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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.
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.
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.
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.”
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.
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.
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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."
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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.
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.
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.
The migration to the cloud — but more specifically multi-cloud and hybrid
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
How Apache Spark helped catalyze the AI/ML market with efficient data processing
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.
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.
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 forand 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.
We discussed on the forum that we like the Elastic setup and we want to add that we also like MongoDB in terms of both fundamentals and technicals. Below, we revisit MongoDB, a company that we have owned in the past, and our coverage of Atlas, a product that we expanded on in July of 2019.
In addition to these two, we have been eyeing Confluent (CFLT) a recent IPO. We will cover this company soon yet want to caution our readers as to the likelihood of a company holding its IPO opening price after the lock-up expires. This is very rare even for quality companies. Therefore, if we enter a company prior to lock-up, we sometimes have to exit and re-enter again due to the nature of IPOs.
MongoDB is officially an Atlas company with 56% of revenue coming from this product. The CEO ended the call by saying that aren’t many businesses growing at 80% with a run rate of $0.5 billion. He is talking specifically about Atlas. In fact, Atlas is mentioned on the earnings call 90 times (!)
Here’s a quote from the call:
And I think you’ll see us continue to invest aggressively in Atlas, because every customer that we know, even the customers who are predominantly on-premise, they know that the benefit of using MongoDB is that they can start on-prem, but they have a very seamless path to the cloud. There’s no forklift upgrade. There’s no rewrite of the application code. It’s just a very seamless migration path. And so there’s different customers based on the regulatory environment they’re in, compliance reasons, sometimes even cultural reasons. They may be moving more slowly. But every customer has a very clear migration path to the cloud and we believe the ultimate destination will be Atlas.you’ll see us continue to invest aggressively in Atlas, because every customer that we know, even the customers who are predominantly on-premise, they know that the benefit of using MongoDB is that they can start on-prem, but they have a very seamless path to the cloud. There’s no forklift upgrade. There’s no rewrite of the application code. It’s just a very seamless migration path. And so there’s different customers based on the regulatory environment they’re in, compliance reasons, sometimes even cultural reasons. They may be moving more slowly. But every customer has a very clear migration path to the cloud and we believe the ultimate destination will be Atlas.
Atlas is the MongoDB product that allows the flexibility and scale of a document database with the automation of the cloud. The company took the well-loved NoSQL database that put MongoDB on the map and allowed companies to leverage NoSQL in the cloud and connect pipes to companies like Snowflake for structured and semi-structured data analysis. MongoDB has done well because its platform is nearly universal in terms of training and software developer experience. The fact that MongoDB is a highly requested skill across software developers is not a moat, per se, but it’s helped the company remain defensible. You’ll see here that MongoDB is the top-ranking document store with a comfortable lead in terms of score and is also in the top 5 database systems worldwide. Of the top 5, it’s the top-ranking NoSQL database.
The global NoSQL market was worth $4.9 billion in 2020 and will be worth $29.6 billion In 2026. This is equal to the SQL database market. With Atlas, developers can leverage any cloud infrastructure company and also leverage best-of-breed data analytics, when needed, such as Snowflake. Essentially, MongoDB is integrated with every major player and this has helped the company do well in a multi-cloud environment. Elastic is also a NoSQL database yet is primarily a search engine, and therefore, superior in terms of search with better tokenizers and analyzers that result in a more advanced search.
Here is why the market is excited about MongoDB as we’ve seen Atlas re-accelerate two quarters in a row.
A few things that could drive the more growth in the future is the ease-of-use features the company launched recently. The first is Atlas Serverless which allows a company to add compute and storage during traffic spikes or scale back during low usage periods. MongoDB will charge for usage and will maintain the servers for scaling compared to SQL which tends to charge on an hourly basis. This can help Atlas’ growth because it now competes with serverless databases like Google’s Firestone and expands the customer base to include those who can’t or don’t want to pay for dedicated Atlas clusters. It also allows for more integrations with serverless app platforms. As MongoDB put it in the earnings call, “We expect Serverless to drive more customer demand, because getting started on and using Atlas just became even easier.”
The release of MongoDB 5.0 in July includes Live Resharding, which simplifies the process of splitting a database into smaller pieces for horizontal scale. MongoDB now handles the data redistribution and backend synchronization of moving the data to the appropriate shards. The company also allows for time series data to sequence data in order of time. Of the 5.0 updates, the Versioned API release was the most requested change, which allows developers to update or change an API without breaking the client integration.
The 5.0 release followed the 4.0 release which improved the Atomicity, Consistency, Isolation and Durability (ACID) which is a set of properties offered in SQL databases that helps to make accurate transactions. Previously, NoSQL databases prioritized speed by complying with ACID on a single-document level. With the 4.0 release, MongoDB can compete with SQL on multi-document ACID transactions, which puts the company in a stronger position for e-commerce companies and also enterprises. This was an important release because it combined the best of both worlds, which is the speed of NoSQL with the transactional accuracy of SQL.
The main takeaway from MongoDB’s fast iterations of 4.0 and 5.0 is that more enterprises can use MongoDB because it’s bridging the gap with the benefits of SQL and serves both on-prem and cloud by allowing for a seamless transition if/when the enterprise is ready. Here’s a quote as to how Atlas has evolved since its launch:
Atlas has clearly become a mission critical platform. In the early days, there were probably people were, obviously, being a new service and people didn’t know what to expect. You saw more dev and test workloads, perhaps, peripheral or Tier 3 workloads moving on to Atlas. But as people got more and more experience with Atlas, as we added more enterprise features to Atlas, people became increasingly more comfortable and now we’re seeing, very, very large and demanding applications move to Atlas, even from some of the more conservative mainstream organizations out there. So what we’re really seeing now is enterprise adoption of Atlas at scale.as we added more enterprise features to Atlas, people became increasingly more comfortable and now we’re seeing, very, very large and demanding applications move to Atlas, even from some of the more conservative mainstream organizations out there. So what we’re really seeing now is enterprise adoption of Atlas at scale.
Notably, the recent FedRAMP approval could also be a catalyst for MongoDB as the company is able to serve local and federal governments now.
Multi-cloud is another driver, which we will expand on with in a cloud report for next week so that our members can have a more holistic view of why this trend is critical to have exposure to. On a similar note as multi-cloud, our past Atlas coverage focused on MongoDB’s ability to stave off competitors who had cloned its product, such as Amazon’s Document DB. At the time, the market was concerned MongoDB would lose substantial share to AWS. We thought that was unlikely as during the OSCON conference Amazon had stated that Atlas was the segment winner and that Atlas growth had continued after the AWS DocumentDB release. During that time period, I was particularly fond of this comment by the CEO, which exudes confidence: “Imitation is the sincerest form of flattery, so it’s not surprising that Amazon would try to capitalize on the popularity and momentum of MongoDB. However, developers are savvy enough to distinguish between the real thing and a poor imitation.” Dev Ittycheria, MongoDB’s CEO
Notably, MongoDB is fully valued at 38 forward P/S and won’t rank on cash efficiency in terms of the cloud category. Similar to Snowflake, competing with tech giants costs money and we can see this reflected in the sales and marketing costs with both Snowflake and MongoDB in the upper range for this cost.
Financials:
By Bradley Cipriano
MongoDB reported strong Q2 FY2022 results on 9/2/21 which beat both on the top and bottom-line. The 20%+ move in the stock price coupled with a strong surge in volume following the results suggests that there was a shift in the narrative. If so, MongoDB could be nearing an inflection point, where sales will reaccelerate and grow faster than usual, therefore, attracting a premium multiple.
While sales growth recently accelerated (growing 44% YOY, the fastest pace of growth since Q1 FY2021), the company’s core product, MongoDB Atlas, grew much faster at 83% YoY. This also represented the second quarter in a row where MongoDB Atlas sales accelerated on a YoY basis. Specifically, MongoDB Atlas sales increased 83% YoY, which represented the fastest pace of growth in the last six quarters. As shown in the chart above, MongoDB’s Atlas revenue growth rate has been trending up in recent quarters, suggesting that MongoDB is nearing an inflection point in its growth rate. Atlas accounts for 56% of MongoDB’s total sales and the net sequential dollar increase doubled in the recent quarter from $8 million in revenue to $18 million. Seasonally, Q2 tends to be stronger than Q1, adding to this increase.
Another key indicator that MongoDB’s business is doing well is the increase in downloads of MongoDB’s free basic products. The company utilizes an open-source distribution model, where users can download the basic version of MongoDB’s products for free. Once these users become familiar with the products and integrate them into their work flow, then they often convert to paying customers.
MongoDB reported that downloads in the LTM increased 50% YoY to 75 million, bringing the cumulative downloads of MongoDB to over 200 million. Moreover, the 75 million downloads over the last 12 months was greater than the cumulative downloads in the first 11 years of MongoDB’s existence. Since downloads often result in paying customers, the surge in downloads is a leading indicator of future sales. We can see that MongoDB is taking advantage of the surge in downloads, as the company has ramped its expenditures on sales and marketing expense to convert these new users into customers.
Customer metrics have also accelerated, as customers with ARR over $100k grew to 1,126, up 37% YoY and above the 36% and 30% YoY growth rates in Q1 FY2022 and Q4 FY2021, respectively. Total customer count grew 44% YoY to 29,000+ customers, while Atlas customer increased 46% YoY to 27,500+ customers. It is great to see that customers are driving growth (+44%), rather than an increase in price. Since raising prices is ultimately an unsustainable trend, we prefer to see growth driven by volume (customer count) rather than price.
Another key trend to monitor going forward is international growth, especially growth in Asia. In February 2021, MongoDB announced a global partnership with Tencent Cloud. The company also has a partnership with Alibaba Cloud. These partnerships have benefitted MongoDB, as sales to Asia increased 84% YoY to $20 million. Asia accounted for just 10% of sales in the most recent quarter, which means that there is plenty of runway left to capture share in the Asian market.
In summary, MongoDB appears to be nearing an inflection point as the company’s core product, MongoDB Atlas, has accelerated for two consecutive quarters. This acceleration may continue, as downloads have also greatly increased and downloads are a leading indicator of future sales since free users usually convert into paying customers. There were more downloads in the last twelve months than in the prior 11 years, suggesting that growth will remain robust going forward. MongoDB has also ramped its investments in sales and marketing to convert free users into paying customers, which should help support a further acceleration in sales going forward. Lastly, the company has started ramping in Asia with key cloud partnerships, with a long runway of sales available in the APAC key market.
We are seeing nearly the exact same names in the Top 10 list for forward growth after the Q1 earnings reports. The good news is we picked strong companies and we didn’t abandon them when they were called Covid stocks. This is what you want although you won’t get the drama that comes with SPACs or small caps.
We continue to like Zoom, Shopify and Datadog. Of those Zoom has the most room in terms of 1-year and 2-year forward estimates as it’s ranked quite low due to the uncertainty following its banner year last year. If we can get some revisions on those estimates with another quarter of strong reporting, then we could see the company return to all-time highs.
Right now, Zoom is ranked number #6 on current year growth at 51% and is ranked #34 for 1-year forward growth at 20% and then ranked #39 on 2-year forward growth at 17%. That’s quite the gap between current year and 1-year forward on a company that’s reported strong for many years. If the company clears Q2, the uncertainty should start to clear up. You know where we stand – winners keep winning and this product has exceptional product-market fit. We’ve covered this very in-depth on the site across many reports.
Regarding Shopify, I had said that the immense distribution that comes from reaching roughly 4.4 billion social media users across many sites, including TikTok, should not be overlooked in the noise. The combination of a strong product cracking open the pinata on this kind of distribution is what we want in our portfolio for the LTBH positions. Here’s an excerpt from the May update:
“The reason we want to increase our position in Shopify throughout the year is fairly straight forward – Shopify is now reaching billions of consumers through social media. The distribution potential of these partnerships reminds me of an avalanche trigger as Shopify will reach billions with Facebook and Tik Tok and hundreds of millions with Pinterest. Now, they only need to build out the Fulfillment Center and focus on improving their own app; although borrowing these mega size audiences is probably the fastest path to growth for our purposes.”
Datadog is a position that lets us participate in the cloud IaaS growth of Azure and AWS and Google Cloud but through a pureplay. We reviewed this company post-Covid here and also on the 1-Hour LTBH Webinar Update last month.
Twilio’s story hasn’t fully come together yet but we like the Signal acquisition very much. In an effort to get in front of the market, we held a 1- hour LTBH webinar on this company as we like to highlight stocks where the story is not fully known yet our conviction is high.
We’ve also covered Crowdstrike and entered/exited this stock. There is plenty of coverage on Cloudflare on our forum although we have not officially covered this stock. We’ve passed on Palantir due to low commercial account growth. We front run many stocks (technically, we are front running Twilio on the pivot), however, transitioning from government contracts to commercial accounts is tricky in the tech industry. This is because the product was developed and the team built with guaranteed sales and moving into a more “only the strong survive” environment, is a different skillset. We continue to monitor this company.
Notably, we are also pleased that Asana is doing well. It’s the top performing cloud stock this year, up 127% year-to-date with our position up about 100%. I can’t claim credit for this as all of the credit goes to Knox’s technical chops. Atlassian guided for negative growth sequentially and this is being revised upward quite a bit right now with some 60-day revisions up as much as 35%. However, at the roughly 21% growth that management guided for, we like Asana better for now.
Spotlight on Okta and the Auth0 Acquisition
One name that is starting to pop up in my Q1 post-earnings scans is Okta. Okta is a stock we’ve covered in the past yet shied away from during budget constraints in Covid. You can access our prior research here.
Okta
Okta gets an honorable mention for moving back into the top 10 list for both the 1-year and the 2-year forward revenue estimates. In fact, right now it’s estimated to be a percentage point higher than Crowdstrike on the forward estimates. This isn’t organic as it’s due to the Auth0 acquisition, which we discuss in detail below.
Okta: Product Summary
As mentioned, we’ve covered Okta with an in-depth analysis published last year. I’d like to review a few key points from that analysis before we talk about Auth0.
In the previous analysis, we discussed the importance of IAM systems as it allows for the administration of user access across an enterprise and also ensures compliance. This is critical because 60% of data breaches are caused by an organization’s own employees. By having one digital identity for employees and customers, a company can easily modify and monitor a digital identity to allow access to the appropriate assets and in the right context.
IAM became more complicated once employees began to use their own devices and as companies transitioned to the cloud. This is because there was no longer a perimeter. Today there are on-site employees, off-site contractors, hybrid cloud environments, software-as-a-service applications, bring-your-own-device users, UNIX, Windows, Mac, iOS, Android – and soon there will be billions of machine-to-machine connections (internet of things) communicating through APIs.
Okta is an independent IAM provider that allows customers to integrate with any application or scalable platform. Because Okta is best-in-breed, the company can win over Chief Security Officers (CISOs) that want flexibility and who want to avoid vendor lock-in (i.e., Microsoft). IAM allows access to critical assets, ad not only are switching costs high but CISOs will want a vendor that lets them sleep well at night.
The solution Workforce Identity comprises the majority of the business and simplifies the way an organization’s employees, contractors and partners connect to applications and data from any device (as discussed above). You can think of these as internal employee uses. New Products from Okta include FastPass, which allows for password-less login across multiple devices.
The Customer Identity Cloud enables organizations to transform their own customer’s experience making use of API-level access and seamless customer experiences. This is more external. Dynamic Scale helps enterprises handle traffic bursts up to 500,000 authentications per minute.
Here are the six technologies that IAM comprehensively covers:
API security: Allows for single sign-on (SSO) access for B2B ecommerce and API integrations.
Customer identity and access management (CIAM) enables organizations to capture and manage customer identity and profile data
Identity Analytics (IA) creates risk profiles for user behaviors and manages risk profiles.
Identity-as-a-Service (IDaaS) provides single-sign on and identity management as a software service
Identity Management Governance (IMG): Helps to minimize risk of data breaches and improves end user productivity
Risk-based authentication (RBA): Allow for variation of single-sign on and two-factor authentication
Auth0 is in the Identity-as-a-Service space (IDaaS) and offers an identity platform suite that supports single sign-on (SSO) through a centralized authentication server. To illustrate, you use single sign-on when you use the same username and password for the I/O Fund website as the I/O Fund forum. It allows you to be authenticated securely through an API.
The company is able to detect password compromises in real-time by checking against a database of hundreds of millions of breached credentials. The compromised user is then notified by email or text and Auth0 can restrict access until the password is reset. The API authentications are integrated with Microsoft Azure, Facebook, Twitter, WordPress, GitHub and Paypal.
Although there are many competitors in the startup scene, Auth0 can claim it’s prevented millions of malicious attempts with up to 1 billion transactions every day and 4 billion logins per month.
The dashboard for administrators offers control over user account provisioning and deletion, and offers full visibility into history and logs. Auth0 also offers personalized user targeting that enables control over features like social logins and multi-factor authentication. There is also automation through rule builders.
Auth0 and Okta are competitors in the customer identity space and are typically both evaluated by customers. The result will be better pricing power and a stronger product when going up against Microsoft on IAM. Okta’s primary source of revenue has been Workforce Identity. Auth0 acquisition will help strengthen the Customer Identity segment and will diversify Okta across both markets for IAM.
Here's how the two work together. What’s being illustrated is that the Workplace Identity often leads to a cross-sell on Customer Identity with lifetime spend of $17 million.
The last private valuation for Auth0 was $1.9 billion when the company raised a $120 million round. The round was led by Salesforce Ventures likely for the Customer 360 product that Salesforce has, which enables a universal identity and first-party data collection through the sign-on process. From there, audiences can be segmented and personalized experiences can be created (similar to our Twilio discussion on Segment). It would make sense that Okta acquired Auth0 to prevent Salesforce from competing with Okta.
Auth0 is a developer-centric company, similar to Twilio. The company has won over developers with its easy-to-use drop-in identity management solution for authentication APIs. The issue with Okta not being developer-centric and competing more at the Microsoft level is that developers prefer to work with companies that offer more support for the SMB-level. The Auth0 acquisition helps with this quite a bit. Okta is more of a sales-driven culture for the enterprise than a developer-centric focus.
Okta has stated they’d like to court developers for advanced use cases, such as the use of biometrics for authentication. Not only is the use of biometrics very complex but it needs to be properly implemented by developers. The top-down approach Okta uses is not well suited for this, yet the bottoms-up approach from Auth0 is well suited.
Financials
Okta is a $1 billion run rate company with the most recent quarter posting $251 million in revenue. This represents an increase of 37% year-over-year. The remaining performance obligations (RPO) was $1.89 billion, for an increase of 52%, with current RPO expected to be recognized this year up 45% compared to the year-ago quarter. Most bullish analysis will focus on RPO growth.
The adjusted earnings the company reported was EPS of ($0.10) compared to ($0.06) in the year-ago quarter. The bearish side to Okta is the ongoing lack of profitability. The company’s losses are increasing in terms of percentage of revenue on a GAAP basis from 36% to 29% of total revenue. On an adjusted basis, the operating losses were 6% of total revenue, a slight improvement from 7% last year.
These losses steepen with the Auth0 acquisition with adjusted EPS for next quarter of ($0.36) to ($0.35) and for fiscal year 2022 of ($1.16) to ($1.13). Forward guidance includes the Auth0 acquisition with total revenue of $295 million to $297 million, or 47% to 48% year-over-year. Last fiscal year, the adjusted EPS was $0.11
To be fair, the free cash flow margin is at 21% and this has improved. The company has $2.5 billion in cash and cash equivalents. This helps the company to satisfy the Rule of 40, which helps to sift through the many key metrics in the cloud and SaaS vertical to establish what companies have a healthy top line combined with a healthy bottom line. There is a great write-up here from Scale Ventures who has specialized in cloud startups for twenty years. They discuss why this is an important rule for public companies. Here’s an excerpt:
The Rule of 40 states that, at scale, a company's revenue growth rate plus profitability margin should be equal to or greater than 40%. SaaS management teams are often driving towards either rapid growth or increased profitability, and the Rule of 40 has become a construct for framing the balance of these two phenomena. Given that increased investment (whether from external or internal sources) is usually required to drive growth, rapid expansion and strong profitability are usually at odds with each other, and finding the right mix between the two can be tricky. a company's revenue growth rate plus profitability margin should be equal to or greater than 40%. SaaS management teams are often driving towards either rapid growth or increased profitability, and the Rule of 40 has become a construct for framing the balance of these two phenomena. Given that increased investment (whether from external or internal sources) is usually required to drive growth, rapid expansion and strong profitability are usually at odds with each other, and finding the right mix between the two can be tricky.
One of the more interesting slides from Okta’s Auth0 Investor Presentation is the chart showing the 2018 Cohort’s Contribution Margin:
My only concern with the above chart is that Okta has been in business for about twelve years, and therefore, there should be at least ten cohorts with high contribution margins yet the company is still unprofitable.
The company is forecasting a minimum of 35% growth each year through 2026 for revenue of $4 billion. The key drivers will be Customer Identity segment with Auth0, growth in the enterprise customer base, expanding partnerships and international expansion.
We will be keeping Okta on our radar for any re-acceleration in revenue or increased forward guidance as the 35% minimum growth is a solid baseline.
With Auth0, the company is now guiding for fiscal year growth of 45% to 47% year-over-year. There was some criticism from an analyst on the earnings call because Okta did not break up the organic growth in the guidance. The losses are expected to be in the range of adjusted EPS ($1.16) to ($1.13).
Addressable Markets and Valuations
Auth0 was valued at $1.9 billion last July and Okta is paying $6.5 billion, or a 350% increase. The all-stock deal dilutes shareholders by 20%. Notably, Auth0 will be issued shares at $276.21
Okta is known for being downgraded due to valuation concerns. Despite the company having average performance during the 2020 due to Covid, it’s still in the top 10 on forward P/S. By average performance, the 40% range was overshadowed by many other cloud stocks seeing outsized performance. The digital transformation did not show up for Okta in a big way.
Sometimes you can squeeze out a 40 forward P/S but that doesn’t leave too much room in Okta’s current valuation. We will need to see more post-acquisition as we don’t want to front run this right now. If it was in the 20s, we likely would bite.
In the most recent earnings report, Okta stated the identity’s market addressable market was at $80 billion. If we break this down, we find the identity access management (IAM) market was at $12.3 billion in 2020 and will reach $24.1 billion by 2025 for a CAGR of 14.5% during the forecast period of 2021 to 2025. There is another forecast of 13.2% CAGR for IAM between 2018 and 2026 from $9.5 billion to $24.76 billion.
According to Okta in the most recent earnings call, customer identity TAM is $30 billion.
Of the key markets, health care is expected to be the fastest growing market driven by the need to prevent unauthorized users from accessing patient information. Healthcare organizations experience 5 times more attacks than financial institutions.
Asia Pacific is the region expected to drive the most growth with North America holding the largest share.
Conclusion:
Okta is firmly back on radar. What we want to see from this company is increased guidance following the Auth0 acquisition. The baseline of 35% forward growth is an excellent baseline to work from as any increase from here will help the stock quite a bit. There is strategic value to diversification and cross-selling in your customer base. For Okta, the acquisition adds developers plus strengthens their fastest growing segment (customer identity).
For now, the company get honorable mention, and if we see the right set-up, we will take it, but only if we see the right setup. Taking the number two position on 1-year and 2-year forward is a key reason as to why Okta is back on our radar. To be candid, the bottom line is a bit ugly for a company this age and at these growth levels (i.e., not hyper growth), so let’s see if the cross-selling improves this.
If you want to see Knox’s recent thoughts on the market, please click here. He wrote out a long explanation on the forum as to what he’s seeing and correlates this to inter-market analysis, including money flow, breadth and sector rotations.
Below, I discuss TWLO, DDOG, MGNI and ROKU. We review what was pertinent from the earnings reports. Our thesis has not changed on these 4 companies.
Also, I have a LTBH webinar planned for next Monday to go over the IDFA changes from Apple with a highlight on Magnite and also Roku. We will briefly touch base on all ad-tech stocks we own and IDFA but this is mainly a CTV ads webinar from the product perspective. I’ll send instructions on the LTBH webinar mid-week.
Last but not least, if you have not transitioned over to the new website io-fund.com, please do so soon. You will need to set a new password. The Beth.Technology password will not work on the new site. You must also use the same email address you signed up with. We are redirecting the URLs on Beth.Technology this week in anticipation of our forum launching next week. Our old site will be archived and new content will not be published starting 5/13. Thank you! J
Twilio:
We recently had our second LTBH webinar on Twilio. I thought it was important to highlight this company for the important pivot taking place. In the webinar, we stressed the first-party customer data platform and why this was an important strategic approach for a company that has PII from phone numbers in its core product and PII from emails from the SendGrid acquisition. The vehicle to maximize Twilio’s position is Segment, and the company is showing us very clearly the future for by separating R&D into three departments and placing the former CEO of Segment in charge of two of those departments.
The earnings call also communicated the importance of Segment with management stating two-thirds of their sales calls centered around this product. There was an analyst on the call who nearly verbatim discussed what we talked about on our webinar. I find management’s response encouraging as to the accuracy of our thesis (and, I guess good to know that Alex Zukin shares in this exact thesis).
Alex ZukinAlex Zukin
That makes perfect sense. And then another again kind of big picture question, if you think about the rise of IDFA, the demise of – potential demise of third party cookies, it's our thesis that we're entering the world where the notion of CDP for first-party data is going to rapidly accelerate in strategic performance.
You guys mentioned – I think George you mentioned that Segment is now in two thirds or was in two thirds of your customer conversations. I guess a couple of angles around this question. Is this something – is this future world something you contemplated when making that acquisition? Are you, you know just now reaping even greater amount of strategic benefit? Just talk to us about how you think about segments in this new world, both integrated with the rest of your solutions as part of the platform, but also on a stand-alone basis with respect to Strategic impact to all these things.
Jeff LawsonJeff Lawson
This is Jeff. I'll answer, unless George, you want to?
George HuGeorge Hu
Go ahead, Jeff. I'll chime in.
Jeff LawsonJeff Lawson
Well, I'll give my point of view and I'll let George give his point of view. You know collaboration is the answer and is harder in this virtual world.
My point of view is yes, you know we did think about the importance of first party data and how every company is having to become great digital marketers and great digital executors, and you can't necessarily rely on some of the, let's say, sloppier ways of acquiring and re-engaging your customers when you've got a lot of third party data floating around that. So we did believe – we do believe that the CDP market in and of itself as a standalone becomes ever more important to companies, not just because of the plurality of systems you have to figure out how to make sense of, but also because outside their walls it's getting more complex to actually target and reach your own customers.So it becomes even more important that once you meet a customer, so there's your marketing and they buy something or whatever it is, you do a really good job of continually engaging them, because going back out to kind of reacquire that customer is getting harder and harder and harder. And so companies have to treat their existing customers incredibly well, and those relationships are getting even more valuable. And then you add in all the value of – and then integrating that and creating that journey that's going to achieve that using Twilio's customer engagement cloud, that is the next level of benefit on top of the core CDP.
Twilio grew revenue 62% year-over-year for $590 million and guided for $596 million next quarter, or 49% year-over-year at the midpoint. This represents a 4% raise above consensus estimates of $579 million, according to FactSet.
Adjusted EPS came in at $0.05, or $0.15 ahead of estimates. Active customer accounts totaled 235,000 at the end of the Q1 compared to 190,000 in 2020, representing 24% growth YoY. Dollar-based net expansion rate came in at 133% for the quarter compared to an organic DBNER of 135% in Q1 of 2020. Gross margins were 55% for the quarter and the company recorded a -2% free cash flow margin.
The blemish on the report was Twilio’s forward EPS as the company guided for adjusted losses of $0.14 per share compared to analyst expectations of adjusted losses of 4 cents per share. We posted on the forum that this does not concern us as the company had planned investments that did not materialize in 2020 due to Covid. These investments are focused on enterprise sales, flex and new growth products, plus core systems and infrastructure. Twilio management expects these investments to generate losses in the short term, but in the long term it will allow the company to grow at elevated levels.
Datadog allows us exposure to the market that AWS, Azure and Google Cloud participates in but with a pureplay. 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?
The company capitalizes on the trend that vendor-specific is becoming unpopular due to issues that vendor lock-in creates. On the flip side, the company competes with open-source options, such as OpenTelemetry.
Here is what the company stated as to why customers choose Datadog in light of many competitors: “We lean into open-source format and libraries to instrument obligations for a very long time. And we support a large number of them. The way we see the problem is not like what matters is not with technology we use to get from here to there. What matters is to solve the end-to-end problem for our customers. And to make it as easy as possible for them to just plug us in and everything just work everything to show that we don’t get our mess, a gigantic mess with all these different technologies and applications and clouds, everything else. We turn that into something that the understanding is well ordered, without any effort.”What matters is to solve the end-to-end problem for our customers. And to make it as easy as possible for them to just plug us in and everything just work everything to show that we don’t get our mess, a gigantic mess with all these different technologies and applications and clouds, everything else. We turn that into something that the understanding is well ordered, without any effort.”
Datadog deserves an updated LTBH report as the product has evolved since we last covered the company with the acquisition of Sqreen. Keep an eye out for this after we get through cloud earnings.
I had said on a Motley Fool podcast in February that we faced a unique environment for cloud stocks this year with a tight pack of cloud stocks guiding between 20-30% and then another tight pack guiding between 30-40% on forward growth. Only Snowflake and Kingsoft Cloud were guiding higher than 50%. We provided a chart here. This is unusual as cloud guidance usually tells us our leaders in advance. Tougher comps from last year require cloud companies to show endurance and prove that any growth last year was not a pull forward from the one-time event of Covid.
You can view my explanation of cloud valuations going into 2021 here at minute 2:15 – YouTube linkYouTube link
What we want to see are cloud companies breaking through the ceiling of 40% growth. That is exactly what Datadog did this quarter and also provided >40% guidance for next quarter and full-year guidance, as well.
Notably, the tone on the earnings call was that their guidance is conservative in light of many unknowns. I can’t guarantee this but I’m hoping to see Datadog come in above guidance in the future, per comments like this: “Now, some notes on our guidance, while usage growth was strong in Q1, when providing guidance as usual, we use more conservative assumptions.”, we use more conservative assumptions.”
The company grew revenue 51% YoY to $198.5M, representing a 6% beat above consensus estimates. Management attributed the revenue beat in Q1 to stronger than expected usage growth from existing customers. On the bottom line, EPS came in at $0.06, topping consensus estimates by $0.03. The company logged a record EBITDA total of $24M in the quarter and free cash flow of $44M (22% FCF Margin).
Customers with $100K+ ARR totaled 1,437 at the end of Q1, representing growth of 50% YoY. These customers generate over 75% of Datadog’s ARR.
Additionally, Datadog announced that 75% of its customers are using two or more products at the end of Q1. This is up from 63% in Q1 of 2020.
For Q2, Datadog guided for $212M of revenue, or 51% year-over-year at the midpoint, beating the consensus estimate by 8%. The company is expecting $0.03 of EPS and $10M of operating income in Q2.
For the FY21, DDOG raised revenue guidance to $885M, or 47% year-over-year at the midpoint, and 6% above consensus estimates. The company is expecting EPS of $0.15 and operating income of $50M for the full year.
We laid out our thoughts here on Magnite and our conviction and thesis remains the same. We go over why Magnite’s Q1 report came in weaker than expected and why we aren’t concerned as management has provided enough statements Q2’s guidance being stronger than expected. We take short-term misses as long as guidance remains strong and the story is intact.
Per my post on the forum, I do believe some of the weakness we saw in ad-tech today is due to IDFA changes from the April 30th iOS update. There was a report from Flurry, as reported by Mashable, over the weekend that stated “only 4 percent of iOS users in the United States let apps track them.” Here’s the full post from Flurry. I believe this partly caused the weakness today in TTD, MGNI, Unity plus other ad-tech companies as there is a lot of confusion in regards to IDFA.
On one hand, we have companies like Unity saying it’ll impact low single digits for their revenue, and on the other hand we see sensational comments from mobile analysts that this is an Apocalypse and “Book of Revelation” stuff
I’ve been covering the IDFA specifically since October of 2019 after attending Advertising Week and I followed up again in 2020 with free version here. I also covered Facebook’s tracking behaviors in-depth for public investors around Q1 2018, when I criticized the company for not talking about Audience Network in their earnings calls (the IDFA threatens Facebook’s Audience Network the most).
With that said, I don’t think information is easily accessible to public investors on this topic, and meanwhile, iOS 14.5 rolled out at the end of April. Therefore, seeing the reaction to Magnite and The Trade Desk today, Citi’s downgrade, and Flurry’s report, I think it makes sense to have our next LTBH webinar on the IDFA this Monday with a primary focus on Magnite and Roku but we will touch on other ad-tech stocks we own too (Unity, Snap, Pinterest, etcetera).
The summary of my thoughts can be found in the links above if you want the information before Monday. Similar to the tide of all boats, I believe we will see the supply side come out better than the demand side – but that’s my personal opinion and the way that we’ve structured I/O Fund with our positions. I’ll present the information from a product perspective and you can make your own conclusions when we review this on Monday.
Although I don’t think it will be Apocalypse, I do believe it will affect the ad industry enough that we should do the next LTBH webinar on this topic. We will dive deeper into Magnite and Roku, as well.
Magnite’s Earnings:
I had said that Magnite is not the “shiniest company to analyze if you’re a financial analyst” and this earnings report validated that statement. There have been two acquisitions and a major rebranding, so what we really have is really three companies reporting earnings: Telaria, Rubicon and SpotX.
Magnite reported revenue growth of 67%, up 18% on a pro-forma basis. CTV revenue was up 32% on a pro-forma basis or $12 million. Compare this to last quarter’s report which was 69% revenue growth, up 20% on a pro-forma basis, with CTV revenue up 53% on a pro-forma basis, or $15.4 million. Therefore, Q1 was meaningfully weaker than Q4 on CTV (more on this below).
The company was profitable on an adjusted basis at $0.03 EPS compared to a loss of $0.06 EPS in the year-ago quarter.
SpotX results showed considerable strength on CTV with overall revenue excluding traffic acquisition costs of $31.2 million. CTV revenue was at $19.7 million, up 70% year-over-year.
Management is guiding for revenue of $94 million with CTV revenue of $32 million, at the midpoint. This represents 90% growth if the company had closed the acquisition on SpotX on April 1st rather than April 30th. The company raised its long-term revenue targets from 20% to 25% and had raised long-term adjusted EBITDA targets to 30% to 35% in the last quarter.
This comment here provides color for the weaker-than-expected CTV revenue:
Yes, so I think, March was a bit of a disappointment for us at Magnite. I think if you look at the combined company going forward, you're just going to have a greater line of CTV products that each kind of address a different sliver of the marketplace. We talked a bit about the SpotX managed service business, which was able to extract linear dollars into CTV capability that we did not build out at Magnite, but saw as something incredibly attractive in its products, along with a few other products. But as we said, severe acceleration in Q2 for Magnite's business, and if you look at the two combined, you're 90% plus growth range for Q2. So, so all is well there.which was able to extract linear dollars into CTV capability that we did not build out at Magnite, but saw as something incredibly attractive in its products, along with a few other products. But as we said, severe acceleration in Q2 for Magnite's business, and if you look at the two combined, you're 90% plus growth range for Q2. So, so all is well there.
Another analyst also asked about March, which management provided this answer:
Suffice to say, Magnite is growing in terms of — its back to where we always thought it would be and then some. So, I think that this isn't a case of — in q2, particularly SpotX coming in and saving the show, if you will, I think both are growing exceptionally well. And any kind of slowdown that we witness in Magnite in March has been more than made up for, but David, do you have any more color to bring to that?its back to where we always thought it would be and then some. So, I think that this isn't a case of — in q2, particularly SpotX coming in and saving the show, if you will, I think both are growing exceptionally well. And any kind of slowdown that we witness in Magnite in March has been more than made up for, but David, do you have any more color to bring to that?
And there was yet another question about the weaker guidance in March. Management stressed how early in the cycle the Connected TV market is and how some inventory is still being sold direct versus programmatic.
So, I think that there's in any kind of nascent marketplace and CTV is certainly nascent … I would say that Q2 is behaving what in excess of what we would have thought going into it, and that Q1 was strong going in, and then had a weaker March. And, again, probably a handful of reasons there, but nothing systemic or anything that takes the bloom off the rose in terms of our position in CTV or the attractiveness of that marketplace.
As I said, we are comfortable with short-term misses as long as the story is intact and guidance remains strong. There was also more to the earnings call in terms of IDFA, which we will unpack during the upcoming webinar on Monday.
I’ve written a library of research about this company from very early-on. If you want more information as to how we arrived here, I encourage you to read my analysis as it dates back to a time when the market doubted Roku and we withstood two 60% drawdowns.
On that note, Roku is the perfect example of how long it takes for a trend to play out. While many investors are conditioned for instant gratification following last year, we know that tech trends are a 3-5 year exit or longer. In the meantime, our job is to make sure a company is consistently reporting along the thesis we’ve laid out.
Here’s what I want to emphasize: the 3-5 year investment period for Roku begins this year. If someone were to learn about Roku for the first time today, I’d say they’re right on time. In fact, there is less risk now as Roku is a mature and consistent performer. As an analyst, I’m on cruise control with this stock as it’s been performing as we laid out nearly three years ago.
Rarely, do we get a full-stack opportunity that is centered in the middle of a future trend. It’s my belief that Apple’s IDFA deprecation will positively impact Roku – and I hope a few others we have picked out too.
That’s what my library of research answered through the past few years. We will touch on this in the upcoming webinar, as well. The simple answer is Roku delivers the targeting capabilities of mobile with the completion rates of Pay TV. This was outlined in May of 2018.
“For example, according to Nielsen in March, ratings, linear TV ratings for adults 18 to 24 was down 22%. Q1 TV ad spending was down 11% and according to Media Radar. Meanwhile, we doubled, monetized video ad impressions on the platform, ad spending by major agency holding companies with Roku more than doubled. We saw strength really up and down the ad business.”linear TV ratings for adults 18 to 24 was down 22%. Q1 TV ad spending was down 11% and according to Media Radar. Meanwhile, we doubled, monetized video ad impressions on the platform, ad spending by major agency holding companies with Roku more than doubled. We saw strength really up and down the ad business.”
Since my coverage began, Roku has become an even bigger force in the Connected TV ad space. OneView is Roku’s move into the demand side while The Roku Channel provides original content to optimize ad formats.
This sums up some of Roku’s strength competitively speaking:
I will say that the use of OneView to buy media on Roku, whether that's media we're selling, for example, a video ad that runs in The Roku Channel or an ad bought from a publisher on Roku through one year. That segment is growing even faster because, of course, we have data and identity and optimization capabilities to help them do that better than were they to buy through a third-party DSP.we have data and identity and optimization capabilities to help them do that better than were they to buy through a third-party DSP.
And also here …
“The second part of your question was about volume and CPMs. Our product remains a premium product. If anything, we've added, better data, better targeting, better measurement, newer ad products over time. And I think that, that bodes well for continuing to be able to command premium CPMs, but I will also call out to the earlier question from Ralph that streaming is increasingly also a performance media.”we've added, better data, better targeting, better measurement, newer ad products over time. And I think that, that bodes well for continuing to be able to command premium CPMs, but I will also call out to the earlier question from Ralph that streaming is increasingly also a performance media.”
Roku also recently acquired Nielsen’s advanced video advertising business and is expected to close in Q2 2021. The automatic content recognition and dynamic ad insertion will help Roku show different ads to different households based on Nielsen data.
We’ve written quite a bit on Roku and I hesitate to spend more time on the company when we have other stocks we are forming a thesis on and/or need a reiteration of our conviction. However, that should not be confused for lack of conviction by any means as Roku has received my highest conviction for some time and continues to.
Here’s a clip we created of me explaining Roku in October of last year – view on YouTube here.
Roku delivered excellent Q1 results on May 6th led by strong growth in advertising and the expansion of content distribution partnerships. Total revenue grew 79% YoY to $574.2M, representing a 17% beat above consensus estimates.
The growth was led by platform revenue, which increased 101% YoY to $466.5M. Gross profit rose 132% YoY to $326.8M while operating income came in at $75.8M after negative operating income $55.2M in the year-ago quarter.
Roku also announced positive EBITDA of $125.9M in Q1 from a loss of $16.3M in the year-ago quarter. Roku added 2.4M active accounts in Q1 to reach 53.6M in total, representing 35% growth YoY.
Streaming Hours increased 49% YoY to 18.3 billion, while average revenue per user (ARPU) grew 32% YoY to $32.14.
For Q2, Roku management is guiding for $615M of revenue at the midpoint (73% YoY growth), representing a 13% raise above consensus estimates. The company is also guiding for total gross profit to rise 104% YoY to $300M and EBITDA of $65M after recording negative EBITDA of $3M in Q2 ’20.
This article was originally published on Forbes on Oct 29, 2020,11:49pm EDTForbes on Oct 29, 2020,11:49pm EDT
Before breaking out the earnings reports from the high-growth universe, here are the results from Big Tech earnings today. Each company beat on both the top and bottom lines. Other than Alphabet, they are all trading down after-hours following these results as the market digests the magnitude of the beats, and in Apple's case, the lack of guidance.
Snap:
Snap reported Q3 results on October 20th, beating both the top and bottom lines. The ongoing recovery of advertising budgets helped to boost Snap's revenue growth to 52% YoY in Q3, which now sits just below the 58% pre-COVID growth rate the company recorded during Q1.
Notably, the reacceleration that Snapchat reported is the highest Q3 growth rates since 2017. According to management, some of the user growth highlights from this quarter include Lens Studio, which saw creative applications to use AR as a way to try-on products from brands including Sally Hansen for nail polish and Champs for sneakers.
Other product features released contributing to this quarter's beat include Brand Profiles, Minis, Places on the Map, Dynamic Ads, Bidded AR Lenses, Dynamic Lenses, Camera Kit, Snap ML Lenses including the Anime Lense.
The company also attributes the growth to linear TV and sports being featured on the social media platform at a time when content is seeing a surge.
Here is what the company said about Dynamic Ads and AR Ads on the earnings call:
For example, last quarter we launched Dynamic Ads globally, which combine product catalogs with our optimization capabilities to reward advertisers who invest in our platform with ROI at scale, and we are already seeing strong adoption rates from Retail, CPG, Restaurant, and Gaming verticals, among others.
While Dynamic Ads recommend items to Snapchatters based on their interests, AR try-on takes this a step further and allows Snapchatters to visualize the item in real life. For example, Clearly, an eyewear retailer, leveraged our sponsored AR Lenses to enable our community to try on different pairs of glasses, which resulted in 33 seconds of average playtime and a 5.3% share rate. Clearly was able to drive a full-funnel impact for their brand, achieving a 7-point lift in brand awareness and a 5-point lift in brand consideration while also driving a 46% lift in unique page viewers on their site and a 3.3% lift in purchases.
Daily active users rose 18% to 249M, topping the consensus of 243M. For user base demographics, Snapchat reaches over 90% of Gen Z and 75% of Gen Z and Millennials in the United States, the UK and France. This is one reason the company believes its augmented reality platform is seeing early success with brands as this demographic is more likely to engage with AR advertisements. Snapchat also has a gaming platform with new releases every quarter.
The majority of Snap’s growth came from the Rest of World category, at 43% growth. North America grew 7% and Europe by 10%. Meanwhile, North America and Europe carried the majority of the revenue growth at 56% year-over-year and 49% year-over-year, respectively.
Snap also recorded its most successful quarter ever in terms of monetizing its user base with a global ARPU of $2.73, coming in well ahead of the $2.23 consensus estimate.
Even though the company did not offer guidance for Q4 due to COVID uncertainties, SNAP stock surged over 20% following the results. Kids being schooled virtually, especially college-aged, is likely contributing to the company’s record Q3 usage and monetization.
Pinterest:
Pinterest rose with Snap following Q3 results as investors anticipated a similar recovery in ad spend for the social media company. The company delivered outstanding Q3 results that easily cleared consensus expectations.
Total revenue rose 58% YoY in Q3 with 49% growth in the US and 145% growth internationally. Monthly active users jumped 37% overall to 442M and ARPU rose 15% (US +31% and international +66%) to $1.03.
Perhaps most impressive was management’s 60% YoY growth guidance for Q4:
Additionally, we expect our business to maintain its momentum in Q4, with revenue growing around 60% year-over-year.
And then finally, this brand safety concept, especially post-July and the boycotts that we saw, I would imagine that we're seeing a sustained benefit just due to the election season. But I think it's a secular trend where advertisers want to be around positivity as they build their brands, and that that's contributing to our growth as well. That's what we're hearing.
Management did state there is a level of uncertainty with this guidance due to Covid and tailwinds the company saw from being “brand safe” during the election (i.e. attracting ad spend typically given to Facebook).
Here is what the company said when asked if the beat came from factors inherent to the product or due to the macro conditions of ad spend being thin in Q2.
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Yes, I mean, Ross, it's really hard to parse. I mean, I would love to be able to disaggregate that and say, we're getting X amounts from the technology investments we've made. We're getting Y amounts on demand returning from a macro perspective, or insights give us a certain amount and the brand safety equates to the remainder, in reality, it's the combination of all the above. Ads are working. I think we went through this a little bit on Brian's question, but making it easier for especially medium-sized advertisers to on-board and automate spending their budgets effectively against their desired online conversion and sales objectives has been a big driver for us …. [some parts omitted here for brevity]
So it's a mix of product and technology, macro recovery, the insights that we're able to deliver, and the brand safety and positivity that Pinterest uniquely brings and the world of social media.
Twilio:
Twilio pre-announced Q3 revenue would come in ahead of previously issued guidance from the company of $401 million to $406 million, with analyst consensus at $404M. Expectations were already high going into the earnings report and Twilio went on to beat revenue estimates by 10% for revenue of $448 million and growth of 52% year-over-year. This was the largest beat by dollar in Twilio’s history, as referenced by analyst Khozema on the earnings call.
Twilio also handily beat on earnings at $0.04 EPS compared to analyst consensus of -$0.03 EPS.
For Q4, Twilio expects revenue of $450M-$455M (37% YoY growth) vs. consensus of $432.1M. The net retention rate came in at 137% for TWLO in Q3. The guidance the company provided for earnings next quarter did not match expectations with an operating loss ranging between $10 to $15 million.
Twilio is on an expansion streak fueled by acquisitions. The company completed the acquisition of SendGrid in early 2019, launched the Flex platform, and has now acquired Segment to “enable developers and companies to unify customer data from every touchpoint.” The guidance provided does not include Segment which is expected to close in the current quarter and will modestly impact the top and bottom line.
On the earnings call, the company highlighted the importance of health care with Twilio’s products:
In healthcare, the innovative solutions that have been built on top of Twilio to address the COVID-19 crisis, provide an opportunity for the industry to advance the use of technology to better deliver outcomes for patients and create tools that fit seamlessly within a physician's workflow. This has always been the vision, but the coronavirus crisis highlighted the urgency, immediacy, and magnitude of that need.
Most importantly, CEO Jeff Lawson and the management does not see these trends slowing down with a vaccine or return-to-normal and specifically addressed this:
The other thing I would just point out, though, is that some of the acceleration that we've seen, for example, in healthcare and education, e-commerce, but we also think that those use cases are going to be pretty resilient. I don't think they're going to be ephemeral at all. In fact, I think we see a lot more opportunity in some of those industries. And so I think that's going to provide ongoing tailwind over the medium-term as well.
You can access the Investors Day presentation here where the company guided for 30% growth over the next 4 years.
Shopify:
Shopify announced outstanding Q3 results, with revenue growth of 96% year-over-year and Gross Merchandise Volume growth of 109%. The revenue number came in 18% above consensus estimates while GMV was 13% above forecasts.
The company announced subscription revenue grew 48% during the quarter, merchant revenue rose 132%, and monthly recurring revenue grew 47%. Non-GAAP EPS of $1.13 came in well ahead of estimates calling for $0.50, and operating margin increased to 17.6% vs. an 8.7% consensus. This compares to an adjusted loss of $0.29 EPS.
Shopify gave away a 90-day free trial with this cohort transitioning from a free trial to paid merchants in Q3, which had a “double cohort effect” on merchant revenue growth of 132%. The company does not expect the Q4 demand for subscriptions on a year-over-year MRR growth rate to match Q3. This note was addressed by Amy Shapero, CFO, in the earnings call:
So, I want to just highlight that we did have a record quarter in Q3 for merchant growth due to the double cohort effect that I talked about in my opening remarks. But I think it's really important to emphasize that even excluding the 90-day free trial as who converted in Q3, we still would have seen an acceleration in our merchant growth over pre-COVID levels, which tells you that more merchants are coming to the platform with this shift to online commerce and COVID.
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The free trial was addressed again here as to how the key metrics compare to the 14-day trial with lower conversions but higher retention:
So, the new store creations in Q2 were the new stores coming on the platform associated with the 90-day free trial. So, we were not able to count them as merchants in Q2. We saw many of them convert to paying merchants in Q3. The conversion rates that we've seen on the 90-day free trials is slightly lower than cohorts historically on 14 day free trials, but we think that's okay, because they're more intentional when they convert because they've had a longer time period. The data that we have in the three months in some of the earliest 90-day free trial cohorts and converted suggests that those merchants that have a higher retention than 14-day free trial. As we know, many of them coming online in Q2 were established businesses looking for a multi-channel platform. And so we believe that those 90-day free trials will be more sticky than the 14-day free trials cohorts historically.The conversion rates that we've seen on the 90-day free trials is slightly lower than cohorts historically on 14 day free trials, but we think that's okay, because they're more intentional when they convert because they've had a longer time period. The data that we have in the three months in some of the earliest 90-day free trial cohorts and converted suggests that those merchants that have a higher retention than 14-day free trial. As we know, many of them coming online in Q2 were established businesses looking for a multi-channel platform. And so we believe that those 90-day free trials will be more sticky than the 14-day free trials cohorts historically.
Notably, Shopify incredible B2B brand power with philanthropic efforts to support Black entrepreneurship with $130 million dedicated to supporting businesses with diverse ownership. The company also launched a Tiktok channel that allows merchants to market their products using TikTok for Business. The collaboration allows for in-feed video ads to expand their paid and organic reach.
Microsoft announced FQ1 2021 results on October 27th, outperforming on headline metrics led by strong Commercial Cloud and Azure growth. EPS of $1.82 came in ahead of estimates of $1.54 EPS while 12.4% YoY revenue growth represents a 4% beat above consensus.
Intelligent Cloud revenue of $12.99B was well ahead of the $12.73B consensus, while the 48% YoY growth in Azure was better than the expected 44% growth. Management issued a somewhat tepid outlook for FQ2, expecting weaker Consumer PC growth and intelligent cloud revenue in line with forecasts, along with stronger Processes and Business Productivity revenue.
The reason for the lower-than-expected guidance is due to softer business demand that will cut into Windows licensing revenue. We also saw commercial PCs crater 22% after support for Windows 7 ended and the coronavirus pandemic forced more people to work from home.
However, these are not the segments that would cause an investor to choose Microsoft as a portfolio holding. For the most part, the bull thesis centers around Azure and the line of horizontal products under the Azure infrastructure and PaaS umbrella: Azure Arc, Azure Synapse, Azure SQL Edge, Azure Machine Learning, Azure Space and Microsoft Cloud for Healthcare. Azure saw a slight acceleration of 1% this quarter. Gross margins on Commercial Cloud are an impressive 71% when including an accounting change on server equipment from two to four years.
Notably, when asked about the effects a decline in on-premise and transactional revenue could have on Microsoft, CEO Satya Nadella answered that the strategy for Microsoft is distributed computing with the public cloud and edge (and presumably these will make up for any decline seen from transitioning on-premise).
One is, the approach we have always taken is that distributed computing will remain distributed. So, the cloud and the edge is what will be the distributor fabric for applications. So, if you look at where our growth is coming from for the all-up number in Intelligent Cloud, it's coming from the infrastructure layer, the flexibility that we have around hybrid deployment, things like Azure Arc, a very differentiated. The same thing with data, that's one of the big future innovations, even in the last quarter was the ability to deploy, for example, Azure data in any cloud, including the edge.
The more interesting note came at the end of the earnings call by Brent Bracelin of Piper Sandler, who pointed out Azure had grown to 17% of revenue — larger than Windows – and up from 45% just three years ago, according to his model.
I wanted to follow up on Azure. This is a segment that’s grown now to 17% of revenue. I think, that’s up from 4% just three years ago. You talked about the number of petabyte-scale applications doubling. And from a size standpoint, it looks like in my model, Azure is bigger than the Windows business for the first time ever. My question really is around where are we at in the journey around Azure? How important is this to the Microsoft model? And ultimately, how big could it be looking out over the next three to five years?
This provided an important glimpse into Azure’s ongoing importance and the evolution of Microsoft.
It would be impossible to look at the AMD-Xilinx acquisition without doing an in-depth breakdown of FPGAs. The chips are powerful yet are challenging to program, and therefore, adoption has been slower than originally forecasted around 2016-2018.
Around the time that I began writing on Nvidia, I was also covering Xilinx. At the time, the company was very promising as Microsoft was adopting FPGAs and the chips were slated to be a front-runner for 5G networks. This began to change, however, when Nokia announced they were moving away from Intel-Altera’s FPGAs after the poor results discussed in Q3 2019.
Below, I compare GPUs with FPGAs and ASICs to help break down the potential strengths for the AMD-Xilinx acquisition, especially why a predominantly CPU-company would acquire a FPGA-company. We discuss the headwinds for FPGAs and Xilinx and how AMD could potentially alleviate these.
For our purposes, we will call these chips data accelerator chips or AI chips. As you know, GPUs, ASICs/SoCs and FPGAs have many other uses (gaming, PCs, smartphones, electronics), but as tech growth investors, we are mainly interested in modern data centers and cloud IaaS infrastructure that can handle the increase of networking bandwidth and optimization of AI workloads. By accelerating the computing platform, these chips can power machine learning, deep learning and high-performance computing workloads. The leading chips will have quite the addressable market to capture and we want to be there when this happens.
The data center accelerator market was forecast to grow 49.47% CAGR between 2018-2023 from $1.4 billion in 2017 to $21.2 billion in 2023. At the time of the forecast, FPGAs were forecast to be the leading chip in terms of growth but this has not materialized (we cover this below). According to a more recent report in May of 2020, the global accelerator market will reach $38.9 billion with GPUs growing at a compounded rate of 47.1%
The growing need for cloud resources is driving a healthy market for hyperscale data centers with an expected increase of 60% between 2016 and 2021. We are also seeing substantial investments in next-generation data centers due to the pandemic, such as Alibaba’s announcement to invest RMB 200 billion in core technologies and future-oriented data centers.
When it comes to processors, the question is which chip will answer the demand. This is a question that has not been fully answered yet although GPUs are almost universal for general AI due to the ease of development for software engineers, ASICs are gaining popularity with Google and others who seek application-specific advantages, and FPGAs are continually forecast to lead the growth but sees roadblocks in the learning curve for hardware configuration.
The competition between FPGAs and ASICs could also be alleviated by combining an Arm-based processor with an FPGA. The Zynq-7000 SoC allows for dedicated hardware blocs to split-up non-critical tasks from critical tasks. I imagine AMD fully comprehends the strengths and weaknesses of FPGAs and is set to solve the developer adoption uptake should the acquisition go through.
Overview of GPUs, FPGAs and ASICs:
General Definitions:
GPU: graphics processing unit with a highly parallel structure compared to CPUs. When training deep learning neural networks, GPUs are up to 250 times faster than CPUs. When compared to FPGAs and ASICs, GPUs continue to lead due to the learning curve for software developers and not requiring changes to existing code. Notably, GPUs originated as graphics cards used in gaming but now lead in general AI processors. Nvidia is the major GPU player.
FPGA: field-programmable gate arrays that can be programmed electronically “in the field” post-manufacturing after the chip leaves the foundry. The chip is made up of configurable logic blocks and programmable interconnects that allow for the chips to be reprogrammed. FPGAs are preferred for prototyping or for instances where the design may evolve. Designing with FPGAs are low cost but can become expensive over time. Intel and Xilinx are the major FPGA players.
A few points to note:
• FPGAs increase real-time inference compared to CPUs and reduce latency compared to GPUs.
• FPGAs chips are also cheaper and also faster to bring to market than ASICs (although this is not an advantage for high production volumes — more below including a visualization of this).
• FPGAs are unique from ASICs and GPUs due to being customizable post-manufacturing. The “fieldprogrammable” piece is unique to FPGAs.
• There are new products being released all of the time that aim to get an advantage between ASICs and FPGAs, but for the most part, these two are similar in latency and somewhat similar in power efficiency except ASICs technically lead here due to being application-specific. The design needs will often determine the decision between ASICs and FPGAs and the production volume. Notably, FPGAs will often be used for prototyping before switching to ASICs.
• The drawback to FPGAs is the complexity in programming as software engineers are not as familiar with hardware-specific languages.
ASIC: application-specific integrated circuit customized for a specific application. If an ASIC has more than one processor core and/or combines various computer components, then it’s considered an SoC. You’ll hear these words used interchangeably (ASIC/SoC) on earnings calls.
ASICs are preferred for large production volumes as the cost for design can be in the millions of dollars but then averages out over time.
• Google is the perfect example of a company that uses ASICs as the company has many servers dedicated to solving specific problems.
• These chips, including Google’s TPU, can be designed for maximum efficiency by shifting the optimization and resource assignments to the CPU with the TPU/ASIC acting as a coprocessor for vertical instructions.
• Some companies may find ASICs to be too rigid and fixed. As of recently, Microsoft for example has preferred FPGAs as there is more flexibility in the design.
Expanding on these Definitions:
For most design purposes, FPGAs (Xilinx) are considered superior to GPUs (Nvidia) when it comes to power efficiency. They offer a higher amount of on-chip cache memory to help reduce the bottlenecks from external memory, and are flexible enough to be reconfigured for various data types, such as binary, ternary, and custom data types, whereas GPUs must be modified at the vendor level. With that said, Nvidia will likely leverage Mellanox to speed up GPUs and close the gap on latency performance with FPGAs and ASICs.
GPUs are programmed at the foundry and are restricted by Single Instruction Multiple Thread (SIMT), which provides an advantage over CPUs, but can also result in lower performance efficiency when enough parallels are not found for the workload.
Despite FPGAs resulting in faster high-performance computing, they are harder to program due to hardware circuit configurations compared to GPUs for machine learning, which require less engineering resources due to being programmed through software. FPGAs are generally run with high-level languages such as VHDL or Verilog. GPUs are also more cost efficient.
ASICs rival FPGAs on efficiency and power (and often beats FPGAs in these areas for specific workloads) and this is one reason why we’ve seen ASICs become more popular in recent years. The difference between these two is reconfigurability. This is a major advantage for end applications and workloads as the chip can be programmed “in the field” after it’s left the foundry. As discussed, the reconfigurability is what the acronym FPGA stands for – Field-programmable gate array. You can program the chip to be a microprocessor, graphics card or encryption unit.
ASICS are Application-specific Integrated Circuits and are designed to be application-specific for one purpose only. The circuitry cannot be changed because it is made up of permanent circuitry. You use ASICs every day in your smartphone, laptop and television.
ASICs have high “non-recurring engineering” costs (NRE) and are more expensive at the onset.
However, FPGAs come at an increased cost after a certain time period and have limited analog functionality. Therefore, FPGAs actually cost more overall because the cost of ASICs becomes lower with higher production.
FPGAs have limited analog functionality, such as Bluetooth and WiFi. This is why ASICs are the chosen chip for electronic devices. “Low power” is also a major advantage to ASICs which makes the chips ideal for specific battery-operated devices.
Here is a picture I provided in Marvell’s PDF. What this picture is showing is that it costs millions to begin with ASICs and less than $5000 to begin with FPGAs. However, over time, the cost of FPGAs exceeds that of ASICs.
FPGAs are used more for prototyping due to the reconfigurability and due to ASICs requiring more during the design process. ASICs take months longer to implement due to the manufacturing cycle, and as mentioned above, cost quite a bit at the onset. The R&D cycle for ASICs can become problematic when companies are competing neck-to-neck for market share.
FPGAs in Real Use Cases
We want to be clear that we are ultimately bullish on the AMD-Xilinx acquisition as we believe AMD has what it takes to bridge FPGAs. In the use cases below, you will see there are some mixed results with FPGAs competitively which is likely leading to Xilinx looking at an offer.
The Nvidia-Mellanox-Arm combination is a looming threat, as well, and if AMD can make FPGAs more accessible, then this will provide AMD with critical market share in data center acceleration/AI chips without having to compete with Nvidia head-on with GPUs.
Xilinx’s Segments
ABC: Automotive, Broadcast and Consumer
AIT: Aerospace and Defense, Industrial, Test and Measurement
DCG: Data Center Group
ISM: Industrial, Scientific & Medical
TME: Test, Measurement & Emulation
WWG: Wired and Wireless Group
Aerospace/Defense and Automotive:
Xilinx leads in aerospace/defense and automotive. These are industries where FPGAs have a clear advantage.
In May, Xilinx announced the first 20-nanometer (nm) space-grade FPGA to deliver machine-learning for space applications. This allows satellites to update in real-time, deliver video-on-demand, and perform compute “onthe-fly” to process complex algorithms.
Although autonomous driving is very new and the market is wide open, FPGAs beat GPUs in many regards for this application. This is likely part of Intel’s motivation in buying Altera. This space is constantly evolving but here is a recent quote from a product manager in the field, “Autonomous vehicles rely a great deal on machine learning, and every new vehicle in every new situation may contribute to the shared knowledge base,” said Tobias Welp, product manager at OneSpin Solutions.
FPGAs offer flexibility for many applications because both the hardware and the software can be reprogrammed. Reprogramming FPGAs when knowledge or algorithms are enhanced has the potential to keep autonomous driving in a state of continuous improvement.”
But there are tradeoffs. Verification in this case becomes a continuous process.“Every time the design changes, the full verification suite (static, formal, and simulation) must be run,” Welp said. “Formal equivalence checking also must be run to ensure that the FPGAs have no implementation errors, security vulnerabilities, or lurking hardware Trojans. Finally, the reprogrammed FPGAs must be extensively validated on test vehicles before updates are sent to the field.”
In regards to ASICs and how AVs are in constant flux, here is what Welp stated:
“When we started in this space and we were talking to automotive customers a year ago, everybody was going straight to Level 4 and Level 5 autonomous,” said Geoff Tate, CEO ofFlex Logix. “They were all going to do their own custom chips. They were all looking to license IP for inference acceleration. That’s changed dramatically. I don’t know of anybody who’s looking to do an ASIC in the automotive space right now. Everybody that was telling us they’re going do their own chips has changed to buying off-the-shelf chips, and almost all the major car companies are focused more on driver assist.”
Therefore, we see that FPGAs have a serious shot here at being the chosen chip for AV development.
Wired and Wireless Group:
Xilinx saw a significant slowdown in the wired and wireless group in the previous quarter due to supplying Huawei but some of this growth has returned. The Nokia-Intel FPGA flop in November 2019 has hurt the prospects for using FPGAs in 5G with Nokia turning more towards ASICs/Marvell.
Originally, Nokia stated that FPGAs seemed like the best choice because 5G standards were not developed yet and the flexibility was key. However, as we’ve illustrated in this analysis and covered in the Marvell PDF, ASICs cost less over time and this is becoming a priority for Nokia to protect their bottom line on the already-expensive 5G infrastructure overhaul. In an earnings call, Nokia’s CEO lamented that FPGAs were more costly than anticipated.
So, what’s happening right now is when he moved to 5G, we chose FPGA-based products. They give you flexibility, they give you time to market advantage, but then they’re expensive. And so, what we’re doing is, we’re moving to equity SoC-based products, which we’ll progressively start shipping during 2020. -Q3 2019 earnings call
Like I said earlier, I mean, the System on Chip strategy has been put into motion already a while ago, diversified our supplier base. We are increasing the investments purely because we want to increase even more the SoC penetration in our products and continue that. And of course, we know how to do System on Chip. Yes, we started with FPGA with 5G because it’s gave us that time to market catch-up advantage, but we do that in much of our portfolio with FP4 and PSE-3. So, we’re just replicating that in mobile. –Q3 2019 earnings call
And Tal, on the question regarding to System on Chip, so we are transitioning from FPGA to System on Chip and this is the metric that will give you an update and this is that we got to 10% of the 5G product by ReefShark System on Chip portfolio. We started ramping up volumes and that will get to 35% by the end of this year or greater than 35% and then 70% by the end of ‘21 and then this whole thing will be complete about 100% in 2022. -Q4 2019 earnings call, when an analyst asked for an update in moving from FPGA to SoC.
Despite Nokia’s decision, FPGA-proponents for 5G will argue that these chips are ideal for network infrastructure to prevent vendor lock-in and for futureproofing the network due to the ability to reprogram. In this way, FPGAs could reduce long-term operating expenses and reduce total cost of ownership.
You can access a full list of Xilinx’s segments here and how the company serves each of these markets, including Industrial, Medical and Video Processing.
High-Performance Supercomputing
FPGAs and high-performance computing (HPC) is an important part of data center acceleration that can benefit from easier programming options. This is because FPGAs are known to be pliable when involving interconnects and this is valuable for supercomputers.
This will not replace GPUs rather it will serve applications with heavy computations. FPGAs can optimize purposebuilt architecture and there is a high probability we will see the supercomputers of the future powered by a combination of CPUs/GPUs and FPGAs. As of now, Nvidia is the undisputed leader in the data center. In fact, as of May 2019, Nvidia was employed in 97.4% of cloud IaaS compute instance types with dedicated accelerators with combined Xilinx and Intel at 1.6%.
Liftr Insights shows a slightly better picture for FPGAs at about 5% for Xilinx and a little under 5% for Intel across Alibaba, AWS and Azure in March of 2020. The analysis firm puts Nvidia at 86% in this study.
In 2015, Intel acquired Altera in an all-cash transaction worth $16.7 billion. Altera was second to Xilinx as a leading provider of FPGAs. This acquisition occurred during the years that FPGAs were favored for data center growth over its counterparts yet Intel has not been able to penetrate data centers with this acquisition as originally estimated. This could be for two reasons: (1) FPGAs are more advanced and will rise in popularity after general AI is exhausted and more complexity is required by the market (2) ASICs are superior to FPGAs and are meeting the market demand with customizable that was once assumed would be met by field-programmable.
In 2018, Microsoft announced it would be phasing out Intel-Altera FPGAs for over half its servers in favor of Xilinx’s processors. This was confirmed again in October of 2019 by Microsoft at a Xilinx conference although no official update for two years now. At the time, I guessed this move by Microsoft was due to AI engineers preferring Xilinx over Intel, which I still believe to be the case when FPGAs are being considered.
It should be noted that AMD is already solid in the supercomputer category with Epyc CPUs powering many of the supercomputers in the top 500 list. Here’s a great write-up from Moor Insights on AMD’s partnership with HP’s supercomputer manufacturer Cray. The partnership is expected to launch the second Exascale system in the United States costing over $600 million. The Frontier Supercomputer is expected to put AMD on the map for AI accelerators and as a competitor to Nvidia.
Constantly Evolving:
Xilinx’s SDAccel IDE has attempted to provide software developers the same experience no matter the cloud provider (AWS, Alibaba, etc). The goal was to copy Nvidia’s CUDA platform to enable a larger ecosystem. The tool platform is called “Vitis” and is designed to provide accessibility for hardware developers and software developers. The first release of SDAccel supported deep learning frameworks, such as Caffe, MXNet and TensorFlow through Python APIs.
AMD backed Xilinx around this time for the Alveo model launch for machine learning, which was the first supported environment on Xilinx’s SDAccel IDE. Alveo was dubbed “the world’s fastest data center and accelerator cards” to increase real-time inference throughput by 20X compared to CPUs and 3X compared to GPUs. AMD offers Radeon Instinct accelerator cards built on Vega 7nm GPUs.
Xilinx also launched the “Versal” advanced computing acceleration platform. This is a fully softwareprogrammable heterogeneous compute platform that improves performance 20X over current FPGAs and 100X over CPUs (per Xilinx’s white paper). The SoC-like chips combine CPU cores, programmable logic and ASIC elements. This was around the time that Xilinx stopped referring to itself as a FPGA company and instead as a platform company with a focus on “whole application acceleration.”
The Versal series includes AI engines in the device series, such as Versal AI Edge, Versal AI Core and Versal AI RF. Xilinx aims to not only accelerate the AI portion of the task but to combine AI engines with DSP engines and also adaptive engines to accelerate the entire task, such as beamforming for 5G radar wireless communications or for smart controllers for storage systems in data centers.
Financials:
Advanced Micro Devices reported Q2 results on July 28th, beating comfortably on both the top and bottom lines. Revenue came in at $1.93B (+26% YoY), representing a beat of $70M above consensus estimates. Management attributed the revenue growth primarily to higher Computing and Graphics segment revenue.
Non-GAAP EPS grew 333% YoY to $0.18 per share in the quarter. Gross margin increased 3 percentage points YoY to 44%, primarily driven by Ryzen™ and EPYC™ processor sales. For the 3rd quarter and FY, management is calling for an acceleration of revenue growth to 42% YoY and 32% for FY 2020.
Xilinx reported Fiscal Q1 results on July 30th, reporting a slight miss on the top line and a slight beat on the bottom line. Revenue decelerated 14% YoY to just under $727M, missing consensus estimates by about $1M. Non-GAAP EPS decelerated 33% YoY to $0.65 per share, beating the consensus by half a cent. The company made no adjustments to its outlook and expects to record $755M in revenue in its next quarter.
At a listed acquisition price of $30B, Xilinx would be valued at 10x sales. Xilinx has 244.3M shares outstanding and the company is projected to deliver $3.54 in EPS in FY 2022, meaning they are on pace for $864M in net income in 2022.
At an acquisition price of $30B, AMD would need to issue 361M shares in an all-stock deal for Xilinx. In 2022, AMD is projected to deliver $2.20 in EPS. With $1.17B shares outstanding, the company is on pace for approximately $2.6B in net income for FY 2022.
In order for the deal to be accretive for AMD, the Xilinx business has to generate approximately $800M in 2022 annual net income. The Xilinx standalone business is projected to generate $864M in annual profits in 2022.
If the acquisition price exceeds $30B, an all-stock deal may become dilutive. At an acquisition price of $35B, AMD would need to issue 422M shares to acquire Xilinx. This would require the Xilinx business to generate $1B in 2022 net income for the deal to be accretive.
Conclusion:
On a technical level, workloads like machine learning, AI and 5G can benefit from a chip that is field-programmable and bridges the gap with customized chips that take too long to bring-to-market. Xilinx’s FPGAs allow algorithms to be adjusted for critical technologies and R&D processes.
This space is constantly evolving. FPGAs also have SOC-chips that Xilinx calls “all programmable SOCs.” Hard-silicon processor cores are being combined with FPGAs to compete with ASICs. In this case, an ARM Cortex A9 and Xilinx Zynq become dedicated hardware blocs to split up non-critical tasks from tasks requiring high-speed acceleration.
The question is can AMD do this for Xilinx versus Nvidia in key markets:
When it comes to efficiency, AMD is an unstoppable powerhouse. There are leaks that the 7nm Milan release will achieve a higher clock rate with performance increases of 10-20% between generations. This is virtually unheard of.
Lisa Su brought AMD from a $3 billion market cap to a $100 billion market cap in 5 years. As of now, we see Xilinx spread across too many segments and lacking focus. If AMD can popularize a platform for Xilinx/FPGAs that competes with CUDA and chooses the segments where FPGAs have the most promise, then we could see FPGAs finally live up to their true potential.
AMD under Lisa Su as CEO has risen 2000% over the past 5 years
I believe there will be quite a few negative opinions about AMD’s move with Xilinx. Analysts will say Nvidia has the undisputed throne, there is no overlap with AMD-Xilinx, that the acquisition is too expensive and dilutes shareholder value and that AMD does not have enough successful acquisitions under its belt to gamble on this combination.
Others may not see why AMD would acquire Xilinx, but I’ve been waiting for something to happen with FPGAs and this very well could be it. We had discussed AMD innovating past Intel prior to this happening in July. Our premium members were pretty happy about that call. On a similar note, I’ve been tracking Xilinx closely, waiting for a breakthrough of some sort.
Of course, I am a mega Nvidia bull and this will not change. This will not be a winner-takes-all market, rather a market that compounds quickly for the top handful of companies. There are a few CEOs I won’t be against and Lisa Su is one of them.
Grab some popcorn because it’s going to get pretty exciting between 2022-2025 as Su and Huang dual it out. My prediction is they will both take enough market share for my premium subs to remember these calls as some of my very best.
This article was originally published on Forbes on Sep 11, 2020,03:02am EDTForbes on Sep 11, 2020,03:02am EDT
Snowflake is the most anticipated IPO of the year. Investors should decide in advance how much they are willing to pay as Snowflake will test the upper limits of what it means to have a stretched valuation. Heck, the company has even inspired value-legend Warren Buffet to change his thesis and invest in an IPO prior to profitability (!)
Perhaps because the company delivered sky-high revenue growth last fiscal year of 173% and 121% in the most recent quarter with a record-breaking net retention rate of 158% — which is the highest of any public cloud company at time of listing.
David Marlin
These industry-leading numbers are due to the company disrupting the data warehousing market with a superior cloud data platform that delivers across key differentiators (we review this below). Despite Snowflake demonstrating excellent product-market fit, clear competitive advantages, and strong management — no company is perfect. We go over a few key risks that investors should keep in mind as the bidding becomes fierce on opening day.
Snowflake Financials
Snowflake has strong financials for a tech IPO, yet it’s important to remember the product has been available for only six years and tech growth is typically strongest in the early days. The company delivered 173% growth in the fiscal year ending January 31, growing from $96.7 million to $264.7 million with gross profit margins of 56.2%.
These gross margins are below what cloud companies are capable of yet improved in the most recent period. Revenue grew 133% year-over-year in the first six months of fiscal 2021 ending in July, growing from $104 million to $242 million with improving gross profit margins of 61.5%.
In the most recent quarter, the company reported growth of 121%. Here, we already see the effects of age within a short time period as Snowflake settles from 173% growth to 133% growth and now to 121% growth. This is not a negative by any means (triple-digit growth is to be celebrated) but keep in perspective it’s age when comparing Snowflake to any high-growth cloud SaaS peers.
David Marlin
The bottom line has been varied depending on what period you look at. The losses doubled from fiscal year 2019 with net losses of $178 million increasing to net losses of $348.5 million in fiscal year 2020.
More recently in the first six months of fiscal 2021, the net losses were flat period-over-period at $177.2 million compared to losses of $171.3 million. This could be an encouraging sign or it could be Snowflake tightening the belt temporarily for the public offering before returning to the original pace of worsening losses. There is not enough history to know if the more encouraging flat rate of losses is sustainable. Adjusted EPS was negative $1.63 in the fiscal year ending in January compared to negative adjusted EPS of $0.72 in the first half of fiscal 2021.
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Net retention rate for Snowflake is a record 158% — the highest of any company when going public. However, it’s important to remember that net retention rate lowers over time as customers become harder to retain long-term (I cover net retention rates more in-depth here).
The company was founded in 2012 yet the product came out of stealth mode in 2014. When considering the product launch, Snowflake is a very young company of only six years old.
David Marlin
You can see evidence of how net retention is affected by number of years in Snowflake’s S-1 filing as the company had a rate of 223% in the first half of 2019 compared to 158% period-over-period. Annually, the company lost 11 percentage points in net retention rate from 180% to 169%.
SNOWFLAKE S-1 FILING
Regardless, Snowflake has impressive numbers. Perhaps the most impressive key metric in the S-1 filing is the growth in the percentage of customers with product revenue greater than $1 million. This has grown considerably from 14% in fiscal year 2019 to 41% in fiscal year 2020. There is evidence high-end accounts are continuing to grow with the first six months of 2020 at 56% compared to 22% in the year-ago period.
The new CEO, Frank Slootman, clearly knows how to make a company attractive to investors. Not only did the company quicky tighten its belt in regard to net losses, the company also doubled customers from 1,547 to 3,117 over the past twelve months. This includes 7 of the Fortune 10 and 146 of the Fortune 500.
Cash used in operating activities decreased from $110 million to $45.3 million in the first six months of fiscal 2021. The company has cash and investments of $591 million and no debt.
As outlined in the S-1, IDC places the addressable market for Analytics Data Management and Integration Platforms and Business Intelligence and Analytics Tools at $56 billion in 2020 and $84 billion in 2023.
In an effort to narrow this addressable market, I dug up a few more sources. According to MarketsandMarkets, the addressable market for Data Warehouse-as-a-Service is much smaller at $1.2 billion in 2018 and set to grow to $3.4 billion by 2023 at a CAGR of 23.8%. P&S Intelligence reports a similar CAGR of 29.2%, estimating the Data Warehouse-as-a-Service market to reach $23.8 billion by 2030. When combining on-premise, Allied Market Research places the data warehousing market at $34.7 billion by 2025.
You’ll find larger addressable markets in tech but the weight Snowflake brings to the category is considerable.
Snowflake’s former CEO, Bob Muglia, grew the company from 80 customers in 2015 to 1000 customers in early 2018 when he was replaced by Frank Slootman. The change likely happened due to pressure from private investors who want a grand slam exit (and looks like they’ll be getting just that).
Slootman is known for resuscitating Data Domain from nearly running out of money in 2003 to an acquisition in 2009 after the company “grew to sell more than all its competitors combined.” This was detailed in a book that Slootman wrote called: “TAPE SUCKS: Inside Data Domain, A Silicon Valley Growth Story.” Three years later, Slootman took over the CEO role of ServiceNow between 2011 to 2017 and grew the company from $75 million in annual revenue to $1.5 billion. This was achieved by diversifying the product beyond the IT department.
For many investors, management is a key factor in deciding to invest or not. Here, Snowflake fires on yet another cylinder.
Product:
Snowflake’s decoupled architecture allows for compute and storage to scale separately with the storage provided from any cloud provider the customer chooses. By processing queries using massively parallel processing (MPP), where each node in the cluster stores a portion of the data set locally, the virtual warehouses can access the storage layer independently so as not to compete for compute power. With the competitors, such as Redshift, where compute and storage are coupled, more time is spent reconfiguring the cluster.
Snowflake calls this offering a virtual data warehouse where workloads share the same data but can run independently. This is crucial because Snowflake’s competitors combine compute and storage and require customers to size and pay based on the largest workload.
Data warehouses are centralized data repositories that collect and store information across many sources that are both internal and external. The raw data is ingested into the data warehouse and processed to answer queries. To ingest data, warehouses follow the ETL process, which is: (1) Extract the data from the internal or external database or file, (2) Transform by cleaning and preparing the data to fit the schema and constraints of the data warehouse and (3) Load into the data warehouse. The ETL method helps to organize the data into a relational format. Notably, Snowflake supports both ETL and ELT, which allows for data transformation during or after loading.
One key product differentiator is that Snowflake is not built on Hadoop, rather the company usesa new SQL database engine with cloud-optimized architecture. Overall, this translates to faster queries and also reduces costs by scaling up or down for both capacity and performance. This also allows the shift to the cloud while still honoring traditional relational database tools. Just like cloud infrastructure does not require you to hold server space for peak times year-round, a cloud data warehouse does not require you to plan, acquire or manage resources for peak data demand (i.e. elasticity).
The need for resources could change by either increasing or decreasing (scaling up or down). Customers that have a need for storage but less of a need for CPU computations do not have to pay up front and can shrink the environment dynamically. Users either pay for terabytes or are billed on a per-second basis for computations. Notably, Snowflake charges by execution-based usage and is not a cloud SaaS-company that charges by subscription.
Snowflake has a multi-cluster architecture which is unique from single cluster databases. The multi-cluster approach allows the clusters to access the same underlying data yet to run independently. This allows for heavy queries and operations to run very quickly and with fewer errors because the queries are not accessing the same data warehouse.
Queries are made with standard SQL, for analytics, and integrates with R and Python programming languages. The company delivers the ability to handle all incongruent data types in a single data warehouse. Because the data is accessible through SQL, there is widespread developer uptake as it’s the most common database language.
Snowflake supports both structured data and semi-structured data. As machine-generated data grows to include applications, sensors and mobile devices, Snowflake allows semi-structure data to be handled without preparation or schema definitions. The result is handling JSON, Avro, ORC, Parquet or XML data as if it were relational and structured.
Snowflake uses a compressed columnar database. Columnar databases are optimized for the fast retrieval of columns of data and is used for analytic data queries. Other features include centralized metadata management that is stored in a single-key value store that allows cloning of tables and databases. Security is baked into the platform to where Snowflake automatically encrypts all data to the point where unencrypted data is not even allowed. There is third-party certification and validation for security standards like HIPAA.
Beyond the value proposition of separating storage from compute for speed, and also scaling up or down to reduce costs, the third takeaway is that Snowflake is also much easier for customers to use as it’s designed to remove the role of a database administrator for monitoring and/or to tune query performance.
The end goal of choosing Snowflake is that you load data, run queries, and do little else – which is an immense value proposition due to the amount of time wasted prepping, balancing, tuning and monitoring traditional data warehouses originally built for on-premise.
Snowflake is capitalizing on the multi-cloud trend and growing rapidly with customers who want a choice in public cloud provider despite the cloud giants having their own data warehouse systems, such as Amazon Redshift, Azure Synapse and Google Big Query.
Generally speaking, Big Query is a closer competitor as Google’s offering also separates storage and compute. The differences between BigQuery and Snowflake include pricing structure where Snowflake is a time-based pricing model where users are charged for execution time and BigQuery is a query-based pricing model, where users are charged for the amount of data returned from the queries. BigQuery has a serverless feature that makes it easier to begin using the data warehouse a the serverless feature removes the need for manual scaling and performance tuning. Dremel is the query engine for BigQuery.
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When it comes to deciding between BigQuery and Snowflake, it can come down to what you do with the database due to pricing structure differences. For instance, Snowflake is a better choice for concurrent users and business intelligence. It’s also a great choice for data-as-a-service, where you might give client access to your data in the form of analytics. BigQuery is perhaps a better choice for ad hoc reporting, where you have occasional complex reports on a quarterly basis or recommendation models and machine learning that require high idle time. Again, these examples are mainly due to pricing structure.
Despite BigQuery having a strong following with nearly twice the number of companies as Snowflake and growing around 40%, it tested slower than Snowflake in field tests performed by GigaOm in 2019. Vendor lock-in from BigQuery is also undesirable as companies may prefer AWS or Azure and/or more interoperability or best-in-breed solutions – we can see this in the growing trend of multi-cloud. AWS Redshift has the biggest market presence but growth is nearly flat at 6.5% and AWS is the leading partner for Snowflake.
Here's a great write-up from the Hashmap Engineering and Technology Blog that points out why implementing optimized row columnar (ORC) format data loads is ideal for either Snowflake or Amazon Redshift due to the ORC file format. Again, ultimately the choice in which system you use comes down to the individual needs for implementation although Snowflake is designed to be a competitor in nearly every case.
There’s a great write-up from analyst David Vellante that discusses how Snowflake competes with cloud native database giants. His analysis discusses survey responses from CIOs and IT buyers with Snowflake having a lead over the tech giants in spending intentions. The Enterprise Technology Research study he highlights showed 80% of AWS accounts plan to spend more on Snowflake in 2020 relative to 2019 with 35% adding Snowflake as new compared to 12% adding Redshift as new. In Azure, 78% plan to spend more on Snowflake with 41% adding new. On Google Cloud, 80% plan to increase spending on Snowflake. We can see the people have spoken.
A few risks …
Due to Snowflake’s product strengths, the public cloud providers offer Snowflake while at the same time being in competition. The main risk being discussed is that public cloud providers have competing databases, but in reality, the risk may be pricing pressure over time. Snowflake has a great top line; however, the bottom line is affected by its partnership with the competitors. Plus, tech giants can greatly undercut Snowflake on pricing. Therefore, margins may be an inherent issue.
The company pays quite a bit for sales and marketing, which is typical for a company going public as this strengthens the top line yet could make it hard to balance this growth with profitability in the future. (But hey, if Berkshire doesn’t care, why should we!)
In the S-1 filing, it was noted that Salesforce will buy $250 million in stock in a private placement. This could be a risk if Salesforce becomes too intertwined with Snowflake as it’s best possible growth will be achieved by stating neutral, in my opinion. This involvement is something to monitor.
As stated, Berkshire Hathaway is also intending to purchase $250 million in shares in a private placement plus an additional $300 million from an unnamed stockholder in a secondary transaction. As Business Insider pointed out, this involvement from Berkshire is “rarer than a unicorn” and will be viewed as a strength by both institutions and retailers.
There could be risk in Snowflake being cloud-native only and not offering hybrid or on-premise. This can limit the customer pool as enterprises prefer hybrid options. Perhaps the bigger picture for Snowflake’s strength will be leveraging artificial intelligence in applications and business intelligence, and in this case, a hybrid and on-premise offering won’t be as necessary.
Valuation
Snowflake’s amended filing on September 8th shows the company will be priced at $75 to $85 per share with a valuation between $20.9 billion and $23.7 billion. This would raise $2.7 billion. The last private valuation for Snowflake was $12.5 billion when the company raised a Series G for $479 million.
When we look at various scenarios, we see Snowflake hitting 40 forward price-to-sales in the $30 billion valuation range.
Snowflake IPO Valuation Table – BETH KINDIG
Snowflake is not profitable while Shopify, Zoom Video and Datadog are profitable with some showing accelerating revenue. These three have commanded above a 40 forward price-to-sales in perfect conditions only. The majority of their trading history has been beneath a 30 forward price-to-sales. Being profitable should come with a premium yet Snowflake will likely inch its way into this valuation range without demonstrating profitability.
Snowflake's IPO opening price will test the upper limits of high growth valuations. – BETH KINDIG
When we look at Zoom Video, Crowdstrike and Datadog, we see these three traded at or beneath their opening IPO price many times in the year following IPO. Crowdstrike saw roughly a 50% drawdown from its opening price.
Snowflake's IPO opening price may not sustain if history is any indication – BETH KINDIG
Therefore, if Snowflake trades at a 30 forward price-to-sales and sustains this valuation, it will be the first high growth company with negative earnings to do so. Even those with positive earnings growth have only traded above this valuation for a brief period over the last three months.
A better strategy would be not pay over this amount and count on history rhyming. At NTM revenue of $750 million, that means Snowflake would have to open at the price listed in the prospectus in order to remain within a reasonable $25 billion valuation (“reasonable” being used loosely here as only a few companies have traded at this valuation in the most ideal conditions/tech market and these comparables were profitable).
Conclusion:
When you were a child, your parents probably asked, “are you going to jump off a bridge if everyone else does?” The goal of the question was to get you to think for yourself in the face of peer pressure.
In this situation, the question that should be asked is, “are you going to invest in a company with triple-digit growth, clear product differentiation, key metrics that prove product-market fit and gravity-defying management … if Berkshire does?” The answer is probably “yes.”
The issue is that we aren’t Berkshire or Salesforce so we will probably overpay. Therefore, the biggest risk of all is how much alpha will be left in the first year of trading by the time retailers are offered the crumbs.
I’ve participated in IPOs out the gate and the only ones that have paid off were under-hyped (Roku). Those that were over-hyped, such as Zoom Video and Crowdstrike, either retreated back to their opening price or saw up to a 50% haircut from the opening price.
I did not participate in either of these over-hyped IPOs but I did snag Zoom Video later in January of 2020. I was able to put that money to use elsewhere while waiting for the lock-up period to expire and the right entry in the low $60s nearly 9 months after Zoom Video had listed.
Even as a Snowflake enthusiast. I may back-off after 30 forward price-to-sales (and most certainly at 40 forward P/S) as I’m confident I can find many great tech companies that are less hyped while I wait it out. We will always see periods of indiscriminate selling across high-growth and I don’t think Snowflake will escape those rotations.
Please note: The I/O Fund conducts research and draws conclusions for the Fund’s positions. We then share that information with our readers. This is not a guarantee of a stock’s performance. Please consult your personal financial advisor before buying any stock in the companies mentioned in this analysis.
Our goal is to catch Micron for the 2021 rebound which is likely delayed a year from the anticipated 2020 rebound. This rebound should occur when high-end smartphones are released again and the automotive market comes back to help drive demand in embedded DRAM. Mobile and automotive are the hardest hit segments in 2020. Data center segment remains strong.
Due to the cyclical nature of memory and storage, Micron is likely to become a 1-2 year holding rather than a permanent buy-and-hold.
Product:
Micron is the only company in the world with a portfolio of DRAM, NAND, and 3D XPoint technologies. X100 is the fastest storage device in the world. The company has also entered into a new 3D XPoint wafer sale agreement with Intel that replaces the previous agreements.
In the most recent fiscal year, DRAM comprised two-thirds of Micron’s revenue and NAND one-third of revenue.
NAND memory saves data even when the power is removed, such as when a cell phone is turned off. DRAM only saves memory when a device has power but is much faster than NAND and lasts longer. Beyond mobile devices, NAND is found in traffic lights, digital advertising panels/displays, and anything with artificial intelligence that needs to store data.
As covered in the Lam Research report, NAND has been around since the 1980s but got a much-needed boost from 3D NAND, which stacks vertical chips. Historically, Micron focused on DRAM for PCs and servers an expanded into NAND over the past ten years.
One risk to Micron is the thin moat as competitors Samsung and SK Hynix outpace Micron in total memory/storage shipments. With little differentiation, these companies have pricing wars with Samsung generally considered the industry leader. Toshiba and Western Digital (SanDisk) are also competitors.
This is one reason Micron continues to invest in R&D in products such as 128-layer 3D NAND, 3D XPoint and also 1Z-nanometer DRAM.
“The Memory Guy” Jim Handy has a great write-up describing how Micron has improved its profitability in the DRAM market. His analysis points towards Micron holding a leadership position in 1Znm production over Samsung and Hynix. The new DRAM was introduced at CES and is geared towards the server and hyperscale markets.
One of the bull cases for Micron right now is DRAM and NAND pricing, which is high due to low inventory and previous capex cuts. There is low supply right now regardless of contracting demand. Prior to Covid-19, the market believed pricing had bottomed in 2019.
Micron is one of the most volatile semiconductor stocks with lows around $10 in 2016 and highs around $60 in 2018. Regarding valuation, the stock is trading at double its current PE ratio as 2019 and similar forward PE ratio as 2019. The issue here is any data center strength may not be able to offset the weakness in the mobile and automotive segment.
Historically, buying Micron at a price-to-book value of 1 has done well. The stock is currently trading at a price-tobook of 1.439.
Micron Financials:
In the most recent quarter ending in February, Micron’s revenue beat estimates yet fell 18% year-over-year to $4.80 billion. Revenue was down 7% from $5.14 billion quarter-over-quarter. TTM revenue was $19.6 billion with non-GAAP net income of $2.9 billion, or $2.54 EPS.
DRAM sales were down 11% sequentially and NAND sales were up 6% sequentially. DRAM was impacted by flat sales prices and lower bit shipments.
Earnings were also down YoY with Micron reporting GAAP net income of $405 million, or $0.36 EPS, compared to $1.62 billion, or $1.42 EPS in the year-ago quarter and $0.45 EPS last quarter. Non-GAAP income of $517 million or $0.45 per share beat estimates by $0.08 compared to $1.71 EPS.
Capital expenditures were $1.94 billion in Q2 2020. Management expects FY 2020 capex to be $7 to $8 billion. For fiscal Q2 ending in February, the company had cash and investments of $8.12 billion with a net cash position of $2.7 billion. The company has about $5 billion in long term debt. Recently, Micron drew on a $2.5 billion revolver to have cash on hand.
Margins are decreasing with gross margins of 28% in Q2 2020 compared to 49% in Q2 2019. Operating margins were at 9.2% in the most recent quarter compared to 33.5% in the year-ago quarter.
The median forecast for FY 2020 ending in August is $20.11 billion, down 14.7% year-over-year.
The median forecast for FY 2021 is $24.49 billion, up 21.74% year-over-year. Forward estimates for EPS of $4.90 for FY 2021 will represent an increase of 124% YoY.
QLC SSD bit shipments rose 60% sequentially in the 2Q FY2020. The company expects QLC SSD to grow in the 2H 2020.
The company began to deliver LP5 mobile DRAM products to customers including Xiaomi, which is using LP5 in its 5G-capable Mi smartphones in 8GB and 12GM configurations.
In the graphics market, GDDR6 bit shipments increased more than 40% q-o-q. In the new gaming consoles the company will deploy SSD’s in place of hard drives for the first time.
Effects of Covid-19:
Micron is more exposed than other semiconductors to consumer spending.
About 15% of Micron’s revenue comes from China, where there was weaker sell-through of consumer electronics and factory shutdowns in the fiscal second quarter ending in February. According to the most recent earnings call, some of this was offset by stronger data center demand due to increased gaming, e-commerce, and remote-work. Management expects this trend to continue globally.
Due to Covid-19, Micron expects to see lower demand for smartphones, consumer electronics, and automobiles than prior expectations. Anticipating changes to customer demand, Micron is moving supply from smartphones to service the strength in the data center markets for both DRAM and SSDs.
Some equipment companies have also indicated delays in equipment deliveries due to the impact of various government actions to combat COVID-19.
The Malaysian government issued lockdown orders on March 16 and Micron closed the manufacturing plants in Muar and Penang. Later, the Malaysian government declared semiconductor production as essential and after a few days the production resumed on a limited basis. In the earnings call, the company stated it’s using its global supply chain to mitigate production impact.
For the most part, analysts are cutting their forecasts for Micron, primarily due to Covid-19. Goldman Sachs, Piper Sandler, KeyBanc and Morgan Stanley have all lowered price targets.
Revenue Segments & Addressable Market:
Micron’s business composition is 64% DRAM, 32% NAND and 4% 3D XPoint memory.
Micron has four business units, which are reportable segments:
• Compute and Networking Business Unit (CNBU) — 41%
• Mobile Business Unit (MBU) — 26%,
• Storage Business Unit (SBU) — 18%
• Embedded Business Unit (EBU) — 15%
Micron has the following revenue segments. According to recent earnings reports from various semiconductor companies, mobile and automotive are exposed.
• Mobile — 25%
• Client and Graphics — 20%
• Enterprise and Cloud Server — 20%
• SSDs and other storage — 15%
• Automotive, Industrial and Consumer — 15%
Country 2019 Revenue in US$ Mil %
United States 12,451 53
Mainland China excl Hong Kong 3595 15
Taiwan 2,703 12
Hong Kong 1,614 7
Other Asia Pacific 1,032 4
Japan 958 4
Other 1,053 4
23,406 100
One of Micron’s strongest selling points is the addressable market of $83 billion for DRAM and $99 billion for 3D NAND by 2025. This is a combined addressable market of $182 billion.
Source: Micron Presentation
Future catalysts for NAND and DRAM include artificial intelligence and autonomous vehicles requiring data storage and memory capacities. In the long-term, the management believes it will benefit from secular growth in the industrial IoT market as 5G rolls out. Current markets include the data center and internet of things in addition to PCs and mobile smartphones
According to TrendForce, YMTC, a new competitor located in Wuhan, China, is set to compete with 128L products by the end of the year.
Technical Analysis
The above chart is a look at the weekly price pattern of Micron (MU). The larger the trend, the more important it is to the direction of the price. Since 2009, Micron has been trading within a leading diagonal pattern. This is a 5wave pattern that tracks along a trend channel (in gray). Each of the larger degree 5 waves (in red) are comprised of 3-waves (in blue).
According to this pattern, we are in the larger degree 4th wave (in red). Within this wave, we have completed the A and B wave. Therefore, we are in the middle of the final C-wave down. I will target the lower end of the trend channel, which we have not touched. There are a cluster of Fibonacci price levels around the trend channel between $34-$22.
The weekly RSI is also confirming that we are not yet in a renewed uptrend for MU. Until the RSI can break above the downward sloping trend line as well as break above 60, the momentum suggests the current uptrend off the March lows is a corrective move in a larger degree trend, which is pointing down.
It would be rare to see this larger degree pattern not follow the current trend. However, if price can break above the $61 level, which is confirmed by the weekly RSI, I will look at that level as a bullish move and a targeted entry to ride the new bull market in MU.
The daily chart shows this trend unfolding in real time. The uptrend’s structure off the March lows is overlapping and symmetrical. It further suggests weakness. This is also confirmed by the internals.
The volume is slowing down at current levels, suggesting that the participation at current prices is weakening. The Accumulation/Distribution line suggests that the smart money has not been buying into this uptrend, and in fact using it to unload shares. The MACD histogram and the MFI are showing notable weakness below the price as well.
All of this together further supports a topping pattern that is unfolding. If price can break below $41, this will confirm the target entries below.
