In recent years the volume of data being generated and stored globally has exploded and as companies with advanced analytic capabilities tend to outperform their peers across a broad range of metrics, analytics is now a required competency. Firms must improve their analytic capabilities and access to advanced tools to remain competitive, which is leading to rapid growth in areas like database, search and analytic software.
Figure 1: Analytic Capabilities and Performance
(source: Bain)
The amount and diversity of data (type, format, and source location) are rapidly increasing and this is driving the need for efficient tools to create and maintain data pipelines which convert raw data into monetizable insights. While there are now a wide range of opensource tools available, like Python and R, which have powerful and flexible analytic capabilities they require some knowledge of programming and are more commonly used by people with a background in computer science or data science. Alteryx offers easy to use and intuitive self-service analytic software, which is targeted at business analysts not data scientists and primarily aims to replace tools like spreadsheets, not specialized data science software. While there may be some trade offs in capabilities like flexibility this is more than made up for by its ease of use by non-specialists, allowing access to data insights to be democratized.
Traditional tools have a number of weaknesses for use as self-service analytics tools by business analysts, including:
Figure 2: Percentage of Organizations Using Data Analysis Tools
(source: HBR)
Self-service data preparation using spreadsheet software remains common today with approximately 8% of employees using spreadsheets for self-service analytics. There are an estimated 21 million advanced spreadsheet users worldwide, who on average spend 26 hours per week working on spreadsheets. These spreadsheets were not designed for modern big data requirements and as a result are inefficient, causing an estimated $60 billion of lost productivity in the U.S. every year by advanced spreadsheet users.
Table 1: Estimated Cost of Inefficient Use of Spreadsheets
(source: Created by author using data from Alteryx)
The global market for big data and analytics software is large and growing rapidly. IDC estimated the market size to be $49 billion in 2016 and projected it to grow at a rate of 10.5% annually through 2021. Within the big data and analytics software market Alteryx's software addresses the business intelligence and analytic tools, analytic data integration and spatial information analysis markets, which collectively represented approximately $19 billion in 2016 and were projected to grow at a rate of approximately 8.8% through 2021. Alteryx also estimates that there is an additional $10 billion opportunity their platform can address by replacing spreadsheets for advanced data preparation and analytics.
Alteryx (AYX) offers self-service data analytics software which is designed to improve the productivity of business analysts by bringing a fragmented data analytic pipeline into one service. The functionality of Alteryx's platform includes accessing various data sources, cleaning and preparing data, and performing a variety of analyses. The software aims to replace traditional tools by offering ease of use, speed, sophistication of analysis and an intuitive user interface with a visual workflow. Alteryx's ultimate goal is to make their platform as ubiquitous in the workplace as spreadsheets are today.
Alteryx promotes the following virtues of their platform:
The software can be licensed for use on a desktop or server, or it can be delivered through a hosted model. Subscription periods for the platform generally range from one to three years with fees typically billed annually in advance and revenue recognized ratably over the term of the contract. Revenue is also generated from professional services, including training and consulting.
Alteryx's platform includes:
Alteryx continues to achieve high revenue growth through increased customer numbers and expansion of revenue per customer. This revenue growth is yet to show significant signs of decline, indicating Alteryx still has significant room to grow before reaching market saturation. Alteryx's revenue growth rate is broadly in line with other SaaS companies which offer software related to data.
Figure 3: Alteryx Revenue
(source: Created by author using data from company reports)
Figure 4: Alteryx Revenue Growth
(source: Created by author using data from company reports)
Alteryx is pursuing a number of growth strategies including:
Figure 5: Alteryx Customers
(source: Created by author using data from Alteryx)
Alteryx employs a "land and expand" business model which aims to increase revenue per customer over time. Customers are often offered a free trial which is then followed by an initial subscription to the platform. Alteryx then try to increase use of the platform across departments, divisions, and geographies of the organization as the benefits of the platform are realized. This can be seen in the customer cohort data where revenue for each cohort expands significantly over time.
Figure 6: Annualized Subscription Revenue by Customer Cohort
(source: Alteryx)
Figure 7: Alteryx Net Expansion Rate
(source: Created by author using data from company reports)
Figure 8: Alteryx Revenue per Customer
(source: Created by author using data from company reports)
Alteryx's gross profit margin is high, even by the standards of enterprise software companies and this is likely to lead to high operating profit margins as Alteryx continues to scale. Alteryx's ability to achieve and maintain such high gross margins is indicative of a strong competitive position in the market.
Figure 9: Alteryx Gross Profit Margin
(source: Created by author using data from company reports)
Alteryx has exhibited significant operating leverage in the past and are likely to achieve consistently positive operating profits going forward. Based on Alteryx's gross profit margin typical operating expenses for enterprise software companies Alteryx is likely to eventually achieve an operating profit margin of above 30% which along with Alteryx's high growth rate support current valuation multiples.
Figure 10: Alteryx Operating Profit Margin
(source: Created by author using data from company reports)
Most of Alteryx's operating leverage is being achieved through reduced sales and marketing expenses relative to revenue. This trend is likely to continue as Alteryx continues to grow and build a stable base of subscription customers. As a subscription software provider Alteryx's sales and marketing expenses should be expected to be a large burden as the company establishes a market presence, particularly while the company is growing rapidly.
Figure 11: Alteryx Operating Expenses
(source: Created by author using data from company reports)
Not only is Alteryx likely to achieve a high level of profitability as the business scales, it is also likely to generate significant free cash flow. Alteryx's business has relatively low capital expenditure requirements and in recent years Alteryx has exhibited the ability to consistently generate free cash flow despite their current high growth rate.
Figure 12: Alteryx Free Cash Flow
(source: Created by author using data from Alteryx)
Alteryx faces competition from a range of companies including incumbents offering traditional tools, specialized self-service data analytics software providers, open source software and cloud storage providers who will look to continue expanding their offerings to provide customers with holistic solutions.
Alteryx has been assessed by Gartner in the Data Science and Machine Learning Platforms Magic Quadrant, although there is overlap with the Analytics and Business Intelligence Platforms Magic Quadrant. Leaders in business intelligence include Microsoft (MSFT), Tableau (CRM) and Qlik whilst leaders in machine learning include KNIME, RapidMiner and TIBCO.
Figure 13: Gartner Magic Quadrant for Analytics and Business Intelligence Platforms
(source: Sisense)
Gartner rates Alteryx relatively low on completeness of vision as they are not a standout vendor in terms of automation, deep learning or the Internet of Things and they do not appeal to expert data scientists. This is a reflection of Alteryx's niche strategy, offering an intuitive and easy to use platform for non-experts. While reduced functionality may limit adoption amongst data scientists, Alteryx's strategy has the potential to help them reach a much broader base of users who place greater value on ease of use.
Figure 14: Gartner Magic Quadrant for Data Science and Machine Learning Platforms
(source: Alteryx)
The Forrester Wave assessment of the business intelligence and machine learning software markets is broadly similar to the Gartner assessment. Alteryx was not included in the Predictive Analytics and Machine Learning Solutions assessment as it is a data blending tool with the ability to run R scripts and does not have native machine learning capability.
Traditional Tools
Traditional tools like spreadsheets are likely to remain common for more basic applications but as software requirements increase specialized tools are likely to be more widely adopted.
Self-Service Data Analytics
There are a wide range of self-service data analytics tools available which are designed for different applications. Visualization tools like Tableau and Spotfire are designed primarily to allow users to visually explore datasets. Specialized self-service data analytics software like SAS are designed to allow powerful and flexible quantitative and visual analysis of data sets but tend to be less intuitive and have a steeper learning curve.
Open Source Software
There are a wide range of open source tools available for data analytics including R, Python, Pytorch and TensorFlow. These tools are less intuitive and require a basic knowledge of programming which limits their widespread adoption. It is possible that these tools will be made easier to use over time increasing their adoption by general business analysts. It is also likely that a basic level of programming knowledge will become more common amongst knowledge workers leading to more widespread adoption.
Cloud Computing
The major cloud computing providers already offer an assortment of business intelligence and data analytics services and I expect these offerings to be expanded and more closely integrated with their cloud offerings in the future as these companies seek to offer customers holistic solutions. I believe Microsoft and Amazon (AMZN) are likely to be the most competitive in this area. Google (GOOG) is also likely to have a strong service offering but their weak enterprise DNA may continue to hold them back even if they have technically superior solutions.
For Alteryx to be successful as a specialized provider of self-service data analytic software it must offer customers a compelling value proposition where the ease of use and efficiency of the software justify the higher cost relative to traditional tools. Alteryx must also ensure that their software offers customers sufficient benefit to justify the cost and complexity of having an additional service provider when they could use a cloud computing vendor as a one stop provider of all services.
Despite Alteryx's high EV/S ratio I believe the company is significantly undervalued due to its strong prospects going forward. Both revenue and profit margins are likely to continue improving significantly in coming years which I believe will result in an increase in the share price despite the inevitable multiple contraction as growth slows. Based on a discounted cash flow I estimate an intrinsic value of $195 per share.
Disclosure: I am/we are long AYX. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.
Read more from the original source:
Alteryx: Intuitive Data Analytics Solution Driven By Industry Tailwinds - Seeking Alpha
