Description

Alteryx

Thesis

·         The coronavirus is having a short-term impact on Alteryx and we think the market doesn’t understand the company’s growth prospects and industry structure.

·         Coronavirus is impacting Alteryx due to 1) manual person to person sales process where a salesperson is required to close a deal, which is currently acting as a bottleneck and limiting growth; 2) customers in travel and retail affected by coronavirus are cutting back on seats; these customers also typically pay higher prices due to geospatial features; and 3) slowdown in new contracts and decline in average duration results in flat reported revenue growth due to 606 accounting (despite ARR up +30%).

·         Demand for data analytics is increasing dramatically as more business processes are using data to make decisions. Accenture CEO Julie Sweet recently said on CNBC that 90% of the worlds data was created in the last 2 years and only 1% of that data has been analyzed.

·         Alteryx is the dominant provider of self-service data analytics software for data analysts (line of business users who lack the programming/technical skills of data scientists). The product delivers high utility relative to price due to significant time savings for customers (weeks to days and hours to minutes).

·         Concerns around Snowflake are overblown. The profiling, cataloging, governance, and all the practices an IT department (“IT”) puts around the process of getting data into a data warehouse (cloud or on-prem) ends up slowing everything down to the point where the warehouse has limited utility. The volume of data and quantity of data sources is growing too quickly for IT to keep up and without Alteryx data analysts would become increasingly reliant on decreasing IT talent.

·         Our survey work and conversations with customers lead us to believe 1-2% of G2000 employees will be an Alteryx user by 2030, which results in $5-10 billion in revenues and $25 and $50 per share of earnings power. At the current price of $100, this forecast results in a 20-30% IRR over the next 10 years.

Business

·         Alteryx is a self-service, no-code/low-code analytics software platform that allows data analysts to quickly and easily discover, access, prepare, blend, and analyze data from a multitude of sources, then deploy and share analytics at scale.

·         Key capabilities of the platform include:

o   Data profiling: empowers data analysts to independently assess the health and quality of a dataset prior to building analytic models. For many data analysts, assessing data quality often requires help from statisticians or data scientists, which delays model development and decision-making process. Automated data profiling accelerates data preparation and enables data analysts to maintain control of the entire analytic process.

o   Data preparation and blending: platform provides the ability to easily connect, clean, transform, and filter data significantly faster than traditional analytic tools. Data analysts can easily blend structured, unstructured, and semi-structured data sources without complex programming requirements. Once a workflow is assembled, the platform automates the analytic process and can be rerun in seconds.

o   Advanced analytics: data analysts can leverage a wide range of code-free tools within the platform to create analytic models ranging from basic to highly complex, including those used to understand data relative to spatial criteria or more advanced tools use to apply statistical algorithms for predictive analysis. Data scientists can also incorporate R and Python models using the platform’s code-friendly tools to bring more advanced analytic modeling into workflows.

o   Visualytics: the platform has visual, interactive charting and reporting to enable more insights throughout the entire analytic process. Visualytics interactive charts and reports can be published for broader consumption and collaboration across an organization.

·         Data analysts typically spend 80% of their time on collecting, cleaning, and preparing data, leaving only 20% of their time for discovering business patterns and insights. The Alteryx platform utilizes an intuitive drag-and-drop interface that eliminates the need to write code and reduces tedious, time-consuming tasks, which significantly reduces the 80% of time it takes data analysts to prepare data for analysis.

Industry Structure

·         On-prem data warehouses:

o   Data warehouses require heavy IT involvement because they are highly fragile; data in a warehouse is highly structured and in order to add new data sources IT needs confirm data quality (so warehouse doesn’t crash) and build out logic so the data is in the right format and location.

o   ETL tools like Informatica involves IT due to deep technical knowledge required. These ETL tools were designed from an IT perspective to be highly resilient with strong governance, but in order to achieve that the tools sacrifice productivity and ease of use. In addition to lengthy ETL process, IT involvement also creates a bottleneck as they typically have a backlog of projects and it can take weeks or months for them to add datasets or make changes to a warehouse.

o   As a result, data warehouses often fail because the profiling, cataloging, governance, and all the practices an IT puts around the process of getting data into the warehouse ends up slowing everything down to the point where it is not able to stay current and therefore has limited utility.

o   Data warehouses can typically answer 3/4 of data analysts queries, but volume of data and quantity of data sources is growing too quickly for IT to keep up and therefore Alteryx is needed for 1/4 of queriers that need to be answered quickly.

o   Centralizing data management in the hands of the IT lacks the ability to scale; Alteryx decentralizes and democratizes data analysis so analysts can answer critical questions quickly.

o   IT is also increasingly being viewed as a non-core cost center. Organizations are doing everything they can to reduce the cost of IT, which is resulting in decreasing IT talent.

·         Microsoft, Amazon, Google:

o   Tech megavendors view this market as being too small to be worth their time and effort. Typically, if a market is below $10 billion in annual revenue and the product is not a threat to their business, the megavendors will not be interested.

·         Tableau Prep:

o   Tableau has spent almost 5 years developing Tableau Prep and the product has maybe 5% of the functionality of Alteryx. We believe the product could be good enough for simple use cases, but this is a small segment of the market. We do not view Tableau Prep as a disruptor as their engine isn’t capable of scaling without completely redoing the product. Tableau also isn’t making money on the product and they are mostly focused on people not leaving their ecosystem as visualization gets commoditized.

·         DataRobot, SAS, Knime, Matillion, RapidMiner, Dataiku:

o   These products are built more for the data scientist and IT than the data analyst. To these companies it is more important to add more accuracy to their product than it is to make the software easier to use and increase user productivity. Alteryx’s core philosophy and main focus has always been about increasing the productivity of the data analyst by increasing simplicity and ease of use.

·         Snowflake:

o   Cloud data warehouses have significant advantages over on-prem data warehouses: scalability, faster implementation, lower costs, separation of compute and storage, pay for usage. However, Snowflake doesn’t change anything that is wrong with data warehouses. There’s nothing about Snowflake that makes it easier to add data to a warehouse and data is still getting created faster than IT can handle. Without Alteryx data analysts would be increasingly reliant on decreasing IT talent.

o   The goal of a single source of truth is aspirational and not realistic. People have been trying to put data in one place for decades; it was tried with Oracle, Teradata, SQL, etc. What ends up happening is even if data is put in one place, data still needs to be connected together. There is always a push toward consolidation, then it will fragment again with new data and new data analysts wanting to analyze data in different ways. Data is stored and shaped for specific reporting needs; data analysts are not thinking about how to store the data so that it is easily combined with other data sources an organization already has.

o   Conversations with Snowflake management and comments from Frank Slootman indicate that they do not have much interest in recreating Alteryx capabilities within Snowflake since Alteryx is viewed as a best-of-breed product by customers.

o   We also do not understand the long-term value of cloud data warehouses. On-prem data warehouses were created when companies realized that analyzing data directly from operational systems (ERP, CRM, etc) slowed or even crashed these systems. Therefore, data was duplicated in a data warehouse for analysis. However, in a world where operational systems move to the cloud with infinite compute and storage and you can query those systems without slowing or crashing, why do data warehouses need to exist?

Valuation

·         Our survey work and conversations with customers leads us to believe that Alteryx penetration within G2000 employees will be approximately 1-2% and these customers are currently paying approximately $3,750 per seat on average.

·         The G2000 is approximately 100 million employees; if Alteryx reached 1-2% penetration in 10 years and nominal prices stay at $3,750 per seat it would result in G2000 revenues of $3.75 to $7.5 billion. We then gross up this revenue estimate by 33% to account for the long tail of customers outside the G2000, which results in a total 2030 revenue forecast of $5-10 billion.

·         We estimate Alteryx will have operating margins of 50% in 2030 due to gross margins of 90%, research and development expenses of 15%, general and administrative of 10%, and sales and marketing expenses of 15%. Combining our revenue and margin estimates results in approximately $25-50 per share of earnings power in 2030.

·         At the current price of $100, this forecast results in a 20-30% IRR over the next 10 years.

Catalyst

Sales acceleration in 2H as hopefully covid comes to an end