Stock Idea: Alteryx (NYSE:AYX) is leading the data driven digital transformation revolution
Part 3 of 6 part series
Check out Part 1: Cloud Platform Provider Stock Analysis Series
Check out Part 2: Why Fastly Could Be a Multi Bagger Over the Next Few Years
In this six part series I analyze what I think are the five best cloud platform stocks within the investing theme of building blocks for digital transformation. I will post my evaluation of each one every few weeks, culminating in my selection of the best of the bunch. Follow me on Twitter @OwlWealthy to read my analysis when it's fresh off the press.
The amount of data being generated in our society today is mind bending.
In 2010, Google CEO (at the time) Eric Schmidt famously put this in perspective:
Every two days now we create as much information as we did from the dawn of civilization up until 2003
That was in 2010!
How far have we come in the last 10 years? In the last two years alone, astonishingly 90% of the world’s data has been created (Source: IORG). And, the exponential growth of data is only expected to get steeper as more people come online, social tools get further adopted, more things get connected to the Internet, more of the human genome gets sequenced, and more cat videos get uploaded to YouTube. IDC forecasts that there will be a 10 fold increase in the amount of data on the planet by 2025.
Data is now considered to be one of the most valuable assets on the planet. This shouldn't come as a surprise as you think about the millions of use cases in which data is being used strategically by organizations today. Data is sold to marketers so they can better target advertising and sell more products. Data generated from sensors is used by operators to track machine performance, do predictive maintenance and to optimize delivery routes. Data is used by human resources to better track employee performance and to recruit top talent. Data is being used by companies to develop their next generation of products and services. Banks use data to identify fraudulent transactions. Pharmaceutical companies are using data and analytics to aid in the discovery of new life saving drugs and to accelerate their time to market with these new drugs. Data is used by health care practitioners to dramatically improve health outcomes and to drive down the costs of care.
The number of use cases for mining data for insights and driving action is endless. And, proper use of data and analytics is very impactful. One example is how UPS uses data. UPS feeds data into its ORION platform to determine the most efficient routes for its drivers dynamically. In the United States alone, the company estimates that the system will reduce the number of miles its vehicles travel each year by 100 million, saving more than $300 million annually (Source: The Wall Street Journal, February 16, 2015).
The ability to harness data is a competitive differentiator that is driving growth for companies that are leading the way. In a McKinsey study they state:
Analytics capabilities have become a differentiating factor in industry competition, as leading players use data and analytics to grow revenue, to enter or even create new markets, to change the nature of their relationship with customers, and to increase organizational efficiencies. Organizations that are lagging behind will need to adapt quickly before the gap grows wider.
The business case for companies to invest in data analytics is very compelling. Research has found that investing in data and analytics capabilities has high returns. Organizations can use data analytics capabilities to achieve productivity gains of 6 to 8 percent, which translates into returns roughly doubling their investment within a decade. This is a higher rate of return than other recent technologies have yielded, surpassing even the computer investment cycle in the 1980s (Source: Jacques Bughin, “Ten lessons learned from big data analytics,” Journal of Applied Marketing Analytics).
Despite the significant opportunity with data and analytics, much is still going uncaptured. McKinsey in their 2016 Age of Analytics study found most companies are capturing only a fraction of the potential value from data and analytics.
If there is so much opportunity and proven success stories in the area of data analytics, why has value capture of it proven elusive?
Human capital is one of the biggest barriers standing in the way of realizing the full potential of data and analytics. There is a major shortage of the necessary data science talent that analyzes data with increasingly sophisticated techniques to derive insights. The demand for data scientists is exceeding the availability of people to fill this crucial role within organizations. In a McKinsey & Company survey, approximately half of executives across geographies and industries reported greater difficulty recruiting analytical talent than filling any other kind of role. Forty percent say retention is also an issue.
What if organizations enabled more knowledge workers to become "good enough" data scientists thru tech? This is where Alteryx (NYSE:AYX) comes in. They provide the platform and code free approach for anyone in an organization with curiosity to build analytic models that help them make better decisions.
Let's take a deeper look at Alteryx, using the evaluation criteria I outlined in my article about the cloud platform providers powering digital transformation and investing gains.
Alteryx describes themselves as an analytic process automation platform. Huh? Sounds impressive. The cache that comes with calling yourself a platform can sometimes cause tech companies to throw this moniker around prematurely. In the case of Alteryx they have all the makings of a platform, including 260+ analytics, data science and process automation building blocks that organizations can use to drive impactful business outcomes. Before I'm ready to bestow Alteryx with the achievement of platform status, I need to see a larger base of customers that have truly built their business on top of Alteryx's platform.
There are some very cool examples of customers building on top of Alteryx that I believe represent the future for Alteryx. Beyond the current market perception that Alteryx is just a data analytics vendor. The Alteryx analytics platform is used by banks to do derivatives modeling, by airlines to hedge fuel, by retailers to optimize hyperlocal merchandising, and by football teams to do sentiment analysis in the stadium during the game.
Take Alteryx customer Brookson, a UK based financial services company. They used Alteryx to implement a complex algorithm that automates the financial and tax advice they provide to clients. The Alteryx built algorithm generates targeted SMS messages and emails to provide customers with relevant advice. Because of the automation of the bookkeeping process through Alteryx, their accountants spend the majority of their time engaging directly with customers rather than back office activities.
Coca Cola is another example of a customer building something meaningful on top of Alteryx. Among the many use cases of Alteryx at Coca Cola, they have used it for automating inventory management. In the past, Coke product managers walked the aisles in stores with scanners to check inventory levels and to place restocking orders. Coke's new system built on Alteryx integrates automatic scanners which see when a product is taken off the shelf. The scanners are integrated with the store checkout system and report inventory changes in real time so Coke can restock the shelves on the next delivery truck. The real-time data is picked up by a bot, which sends the information to delivery trucks in distribution centers. The system orders the delivery trucks to remain in the distribution center until the right mixes of products have been loaded for delivery.
These are powerful examples of companies building new business models or automating processes on the Alteryx platform. I want to see more large customers implement sophisticated Artificial Intelligence powered by Alteryx at scale for transformative impact before I say they have a mature platform.
Alteryx recently stole a page out of the playbooks of SalesForce (creator of cloud-based sales automation category) and Netflix (creator of streaming media category) and unveiled a new data and analytics category called Automated Process Automation (APA). Alteryx defines APA as the technology that allows anyone in an organization to easily share data, automate tedious and complex processes, and turn data into results.
Why is Alteryx trying to define it's own software category? They felt it was important to educate the market about the problem of using analytic point solutions - one for data cataloging, one for data preparation and separate tools for each sub-category of business intelligence, data science, and machine learning tools. Of course, Alteryx positions themselves as the ultimate end-to-end platform that covers off all of these important stages of the data analytics lifecycle. The other unstated reason for launching a new software category altogether is the reality that companies who create a new category typically capture 76% of the total category market capitalization (Source: Play Bigger: How Pirates, Dreamers, and Innovators Create and Dominate Markets, 2016, Chris Lochhead). Time will tell if this move is industry foresight or marketing trickery. The case for APA as a true category is compelling to me.
Alteryx estimates their total addressable market (TAM) at $50B, made up of IT and Line of Business (LoB) spend on analytics software. Objectively, their $24B estimate of LoB spend potential is an Alteryx estimate so you will have to take it with a grain of salt. It is 100% fair to say LoBs are spending budget on analytics so let's not quibble over the amount.
Alteryx reached FY 2019 full year revenue of $418 million, representing 0.8% of the TAM. A promising sign that there is a lot of market left to claim.
Here is a good breakdown of how Alteryx sees the market opportunity.
In a nutshell:
No other vendor provides an all-in-one analytics solution that covers the range of analytical needs, from simple cataloging and preparation, to descriptive, predictive and prescriptive analytics.
There is a market opportunity to get the limited in supply data scientist, analyst and knowledge workers onto the same platform so they can collaborate on strategic analytic use cases. This has the potential to transform how organizations do data science in the same way that spreadsheets transformed how organizations worked on manual ledgers decades ago.
There are a number of encouraging signs of Alteryx's product superiority, including analyst validation and customer/user enthusiasm for the product.
In Gartner's 2020 Magic Quadrant for Data Science and Machine Learning platforms, Alteryx was anointed a leader, pulling ahead of all competition in terms of their ability to execute.
Customers love Alteryx. Just take a look at some of the quotes presented at their 2019 investor day.
Alteryx was recognized as a Gartner Peer Insights Customers’ Choice for Data Science and Machine Learning Platforms.
Customer love is also shown in the amount they are willing to pay for Alteryx, in the $10,000 range. A good sign of product strength is the premium they can charge.
There is good reason for analysts and customers to show love for Alteryx:
Its no code approach, via a drag and drop user interface, positions Alteryx as self-service data analytics leader. This was further strengthened with the release of its assisted modeling product that walks analysts through the process of building machine learning models without writing a line of code.
Alteryx differentiates itself amongst the big vendors (SAS, IBM) in the advanced and predictive analytics space by combining self-service data preparation, visualization capabilities and advanced analytics capabilities within its platform.
Alteryx enables non-data scientists to create data driven, automated business processes. This means using data models built on Alteryx to speed business outcomes and trigger daily actions that don't need any manual intervention. Alteryx does this by automating business outcomes directly into applications, bots that take action, and into AI systems and mobile apps. This can get pretty powerful. Imagine a product manager building a customer demand forecasting model based on machine learning methods built with Alteryx, which then triggers the automated order of hundreds of components from a myriad of suppliers when inventory levels dip below a pre-set threshold. Think of the time savings and reductions in inventory levels (i.e., cost savings). This is all possible today with Alteryx.
Where I do see some weakness in the Alteryx platform is the lack of pre-canned solutions for common business problems. While Alteryx publishes starter kits and solution templates, customers have noted limitations in the lack of packaged solutions.
Alteryx has built an engaged online community of users that regularly share knowledge and answer questions. The Alteryx Community allows users to gain valuable insights from one another, collaborate and share their experiences and ideas, and innovate on the Alteryx platform.
While Alteryx counts many analysts, data scientists and statisticians as big fans, there doesn't appear to be a lot of developers taking much notice. Aside from a developer guide on their community site (not a mention on their company site), developers do not appear to be a major audience focus for Alteryx. What I'm not clear on is whether that is because there is not much of a developer opportunity for Alteryx or because they are focused on analysts and data scientists at the expense of developers. It's likely a little bit of both. I'm sure there are major tech overhauls necessary to the Alteryx platform for them to be able to offer developers Machine Learning (ML) services that scale. All the major cloud titans (AWS, Azure, Google Cloud)offer machine learning cloud services for developers to use as part of the apps they are building. There is clearly a large and growing market for developer targeted ML services.
There are some low hanging fruit for Alteryx to be able to expand their footprint in existing accounts. Alteryx's expensive licensing has stalled penetration within current customer organizations. Alteryx’s high price concerns some customers when open-source and lower-cost options exist. This could be limiting broader use of Alteryx across organizations. Alteryx is known for their lack of willingness to provide volume discounts to large customers, which could also be limiting broader use of the platform within organizations. Alteryx is pursuing an account expansion strategy mainly through improvements in ease of use. Recent comments from their CFO confirm this focus:
"We also are going to continue heavy innovation around ease of use. That is a big pillar of our development efforts. Our goal is not just to make the platform more sophisticated, but to make it just drop-dead easy for data workers who may not have as much comfort and experience working with data... And then how do you take data analysts and move them up the curve to behave like data scientists? And so you’re just going to see us continue to press on sophistication of the platform along with ease of use.”
Alteryx has also confirmed plans recently to build a web based designer client, expanding their installed base beyond their current Windows only designer client. This should open up more desktops in organizations as sockets to sell into.
Their land and expand playbook is nothing new but good to see Alteryx doubling down in some key areas. A re-thinking of their licensing model could accelerate their account penetration efforts.
Where I see true optionality, a second or third act for Alteryx, is for them to take a page out of other digital transformation cloud platform providers like Fastly and Twilio:
Developer focus: There is a new generation of cloud based AI software platforms that are being used to develop applications that incorporate various AI techniques into production applications. With the automated data input and data preparation capabilities of Alteryx, it would seem like they have an opportunity to offer a pretty powerful AI development platform that incorporates real time data sources for better decision making. This would likely be a big shift for Alteryx, in terms of serving the developer audience in a deep manner. While I'm not suggesting a dramatic pivot in this direction, I do wonder if Alteryx could be doing more to evolve into an AI platform for developers. Alteryx would have to properly monetize, which would entail being more flexible with their licensing models. Both Fastly and Twilio have driven massive growth off of their intense developer focus.
Packaged solutions: With a million plus different use cases Alteryx can be applied to, it can be hard for customers to imagine all of the possibilities. Alteryx is in a unique position to observe the most common customer use cases for their solution. Where they spot a large addressable market, Alteryx could build a packaged solution to solve a common problem. Communications-as-a-Service provider Twilio observed many of their customers were using Twilio services to build custom call center applications. They have since turned this insight into a major new contact center platform called Flex that they launched last year. I don't know what the right product is for Alteryx to build next. Maybe it's a predictive analytics platform for next best actions. Organizations are investing heavily in customer personalization technology and a highly accurate next best action model could be appealing for this scenario. There is likely opportunity for Alteryx to productize (and further monetize) repeatable analytics based use cases.
As with many public companies, I think Alteryx could do a better job in terms of clarifying the optionality in their business. Public companies are rightfully concerned about giving away trade secrets, but investors need to understand the whole story. There is a balance in appeasing investors and not hurting competitiveness. For its lack of disclosing a compelling optionality story I have to ding Alteryx some marks.
The future is looking bright as the CFO recently shared that more innovation is coming:
“Part of what you’re going to see from us under this new defined category of APA is significant innovation over the next 12 to 18 months. More so than you’ve probably seen from us previously at all. And that innovation is going to come from a couple different directions. You kind of touched on it with AI, ML, just call it advanced data science. You’re going to see quite a bit of innovation coming out of the Feature Labs acquisition that will be part and parcel of the APA platform.
Alteryx is a $11.3 billion company today (as of July 29th). As a mid sized company, the stock has had a good ride the last couple of years but I do see meaningful value upside. This next year may be tough as companies cut spending due to the COVID induced recession, but data analytics is a strategic priority for CIOs and I expect accelerated growth once the worst is behind us. If Alteryx can continue to provide new and innovative analytic capabilities, and further flatten the data analytics learning curve, I see a lot of growth ahead for investors willing to take on the risk.
I’d like to see better Glassdoor ratings for Alteryx. CEO Dean Stoecker has a 85% rating from employees on Glassdoor. Only 65% of current or former employees would recommend Alteryx to a friend. The company also lacks diversity at the senior leadership level. While the CEO is quite visionary, overall leadership ratings are lower than I'd like to see.
Alteryx excels in some performance areas. Existing customers are spending more with them (net expansion rate), they have strong growth and their margins are incredible.
My Bottom Line
I have owned Alteryx for a few months now. I'm not ready to issue a recommendation. I will provide a formal recommendation (or not) once I complete my analysis of the other cloud platform providers I'm covering in this six part series. I want to compare Alteryx against some of the top tech growth stocks available. Up next is Pager Duty. Stay tuned.
Follow me on Twitter @OwlWealthy to read my analysis when it's fresh off the press.