A year ago, college kids laid out the case for a Trump’s victory. And they did it with a mess of disparate data in just 20 hours, using a data prep platform by the little-known company that I’m recommending today.
Cabot Small Cap Confidential 221
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THE BIG IDEA
I remember a consulting project I did for an academic medical center a number of years ago. The center was growing revenue at around 10% a year, had 12 divisions (Radiology, Surgery, Women’s Health, etc.), and was on the cusp of generating $650 million in annual revenue.
Despite its size, association with a leading college of medicine, and depth of services offered, the business was disorganized. My job was to analyze the current state of affairs for each division (including growth potential, profitability and sustainability), integrate this analysis into one comprehensive report that provided a big picture view of what was going on, and identify opportunities.
I spent days working with division heads, talking to them about the nuances of their respective businesses, poring over their income statements and analyzing their core operating indicators. Early in the process, I found myself spending too much time manually moving data between excel spreadsheets.
As my computing power grew from one monitor, to two, to three, I realized that one of the organization’s biggest challenges was fragmented data. I could get income statements for each division, but to plot out their respective growth rates in one chart, I had to clean up each data file first, then manually merge them. The same was true for each division’s core operating indicators. To make matters worse, many data files only covered limited timeframes. To get a year’s worth of data, and forward-looking estimates, I had to merge multiple files for each division!
To be perfectly honest, the work stunk. All day long in excel, copy-move-paste, copy-move-paste. Double check. Oops, screwed that one up. Go back, fix it. Do it over.
Once I had it all organized, it only took a couple of days to do the actual analysis. Nice charts showed which divisions were growing the fastest (Surgery was the biggest, but Neurology was growing the fastest), generating the most profit (Radiology and Surgery were killing it), and costing the organization money (Children’s was operating deep in the red). And that was only the surface-level stuff, but it provided the foundation for the real value-added work.
But man, the data was a mess. It was fragmented, held by different managers, not in formats allowing efficient analysis, let alone continuous performance monitoring. Worse, there was no consistency between divisions with respect to management tools, process design and data communication.
Hundreds of hours of work showed that the organization had a data problem. It was all over the place. And it led to serious issues managing the business, directing investments and communicating with people at various levels within the academic medical center.
What’s the point of this story?
My experience is not unique. And it’s not unique to the health care industry. This same type of data-quality program is going on in organizations big and small all over the world, right now.
Research shows that analysts spend up to 80% of their time just collecting data from various sources, such as the web, PDF files, income statements, log files, text reports and other file formats, then getting it ready to analyze. I can vouch for that.
These guys are the modern-day hunters and gathers. And their time isn’t very well used organizing nuts and berries, when they should be teaching their clan how to catch a mastodon.
Think about how data is used by companies that sell consumer goods, like Nike, The North Face, Clorox, P&G and WD-40. Ultimately, they all want to sell the right products to the right people in the right locations.
What can data do to help?
A good data platform can help them collect and analyze data, then give powerful, actionable insights into consumer behavior. To do this, the software must continuously access consumer-generated data, including mobile payment apps, electronic coupons, point-of-sale logs, ecommerce user clickstreams and social media feeds. Then it needs to blend that consumer-driven data with an organization’s own data, which typically includes product-level information, inventory and supply chain data.
The next step is to blend and clean the data to uncover relationships, correlations and, ultimately, opportunities. Then managers can act on the insights to better achieve the goal: put the right products in front of the right people, at the right time.
In short, data is power—if you can work with it. Today’s Cabot Small-Cap Confidential candidate makes that possible.
THE COMPANY/PRODUCT
Datawatch (DWCH) is a $143 million market cap company that develops and sells self-service data preparation and visual data discovery software. “Self-service” means users are creating and analyzing the data without help from IT departments.
Customers are able to access, prepare, cleanse, blend and analyze disparate types of data, quickly and easily. The software pulls almost any type of data, including structured, unstructured and semi-structured data, from a wide variety of sources and formats, including ERP systems, reports, PDF files, excel files, web sites, point-of-sale terminals and real-time streaming data terminals.
Typical types of data being analyzed include sales reports, inventory, invoices, balance sheets, customer lists, equity trading logs, loan data, and more. In the end, analysts have visually rich analytical applications, which, in turn, are used to dynamically discover the key factors influencing their companies’ operations.
Datawatch’s ability to provide rapid data analysis, in a user-friendly manner, utilizing vast amounts of data from multiple, disparate sources, is what sets it apart in the broader business analytics (BI) industry.
As I mentioned earlier, industry research shows that analysts can spend up to 80% of their time just preparing data. Datawatch cuts that time down dramatically. As a result, analysts can dedicate more time to doing actual analysis, uncovering important business insights, and helping their organizations make better decisions.
The company’s software is used by 14,000 customers all over the world, and by more than 90 of the Fortune 100. It’s used extensively by financial services companies, where the client roster is over 2,000 deep.
Datawatch has been around for a while. It was founded in 1986, went public in 1999, and is based in Bedford, Massachusetts. It also has offices in New York, London, Stockholm, Singapore and Manila. While it does have international exposure (14% of sales in 2016), the majority of revenue comes from within the U.S. Renewal rates are consistently above 85%.
The company is coming out of a period of fairly significant change as it transitions to a subscription-based pricing model, retires legacy products, and rolls out next-generation data analytics solutions. Things appear to be going well, which is why we’re getting in now. Revenue is up 20% year-to-date. And I believe much of the work it’s doing internally is to pivot the business model and product lineup to make the company more attractive to potential acquirers.
A Crash Course in Big Data
Big Data can be broken down into a wide variety of formats, housed in different devices and databases, and moving at different speeds. It used to be that IT departments had authority over the large systems that housed all the data. But that’s changing. Now, companies are striving to become more agile. And that means putting data within reach of the people who can use it to make good operational decisions.
Still, it’s not like all data is easily accessible given that there are so many different systems collecting it.
At one extreme, we have structured data that’s well organized. The best example that comes to mind is a public company’s income statement, which likely resides in an enterprise resource planning (ERP) system from a provider like SAP (SAP). This data is pretty easy to search through and analyze.
On the other end of the spectrum is an ever-increasing pile of data that is not very well organized, called semi-structured or unstructured data. Most comes from outside of an organization, where the method of collection is beyond its control. Think of things like customer return data for a retailer, click-stream data for an online shopper, and trading activity for an online broker. A lot of this type of data is in reports in PDF, MS Word, MS Excel, HTML and various other formats. It has a lot of value. But only if it can be quickly accessed, manipulated and integrated with other data, such as structured income statement data.
Enter Datawatch. This is exactly what they do, and they do it better than anybody else with a platform they call Monarch.
The Platform
Datawatch’s products help users make the most of their data assets, regardless of what type of file that data is in, and where it is located. It solutions are designed to be relatively simple, intuitive and capable of automating repetitive data requests so people can focus on analysis, not data gathering.
It’s used in a wide variety of industries. Banks use it to connect to core systems like Fiserv, FIS, Oracle, SAP and Jack Henry. Credit unions use it do detect fraud and manage risk. Health care institutions use it to enhance patient outcomes and improve the quality of care. Manufacturers use it to improve productivity and operational efficiency. And retailers us it to gain a holistic view of their operations and seek competitive advantages.
Management believes the current product lineup has an addressable market of $24 billion, broken down roughly into traditional data prep ($3 billion, growing at 8%), self-service data prep ($1 billion, growing at 17%), streaming data analytics ($8 billion, growing at 35%), and Cloud Analytics-as-a-Service ($12 billion, growing at 38%). Its most established product (Monarch Complete) addresses the traditional and self-service data prep markets, while newer solutions target the larger and faster growing streaming data (Panopticon) and Cloud Analytics-as-a-Service (Monarch Swarm) markets.
This is the current product lineup.
Monarch Complete
Monarch Complete is a self-service data prep tool designed for single users and business units to explore, manipulate and blend data from different sources, then organize the data for operational processes or visual analytics. A user drags a file onto the Monarch canvas, drops it in, and the product automatically extracts data into analysis-ready rows and columns. Information can be pulled from structured, relational sources (like databases), or from unstructured and/or semi-structured sources, including PDF, XML, JSON, HTML, text, spool and ASCII files. The most recent new product release (late 2016) introduced support for new sources, including Google Analytics and Salesforce, new export integrations, including Microsoft Power BI, and features to analyze even larger data sets. Monarch Complete featured a major upgrade just over two years ago and is the main growth driver, with around 1,400 customers using over 12,000 seats.
Monarch Server
Monarch Server adds more multi-user functions to Monarch Complete. Models created within Monarch Complete on a desktop can be stored and shared on a centrally managed, on-premise server. With the Automator feature, users can cut down on repetitive tasks by designing and automating data prep requests, then automatically distributing the results to all approved people. The Report Mining Server (RMS) feature turns a company into a more dynamic business by opening up all the corporate data previously locked in a company’s content management system, static reports and business documents. RMS integrates with existing enterprise content management systems, including Datawatch Report Manager On-Demand, IBM Content Manager On-Demand, Microsoft SharePoint, Hyland OnBase and ASG Mobius ViewDirect.
Monarch Swarm
Monarch Swarm is the most recent addition to the Monarch family and was just released in Q2 2017. It is designed for enterprise-wide deployment. It helps business analysts, data scientists and even relative novices find, share and reuse prepared, managed data in a collaborative and easy way. Swarm is a web-based self-service data prep platform that combines data socialization, gamification, collaboration, data cataloging and governance features with attributes commonly found on social media platforms. Examples of such attributes include the ability to incorporate user ratings, recommendations, comments, discussions and popularity, all of which help users make better decisions about which data sets to use. The solution is available either on-premise, or in a private cloud. We don’t have any data yet on how the solution is selling, but management said Monarch Swarm was well received. Stay tuned!
Panopticon
Panopticon is a data visualization tool that lets users quickly find hidden patterns, outliers, data correlations (even from different data sets), spot problems and realize missed opportunities, without programming or scripting. The in-memory analytics engine integrates with data from almost any source, including message brokers and complex event-processing engines, and allows on-the-fly aggregations and intuitive navigation. It has a user-friendly drag-and-drop interface through which users set up a dashboard, complete with hierarchies and filters.
Panopticon has a number of specialized visualizations that are specifically designed to analyze streaming data, time series data and historical data. It can connect with push sources (which are subscribed to) and pull sources, including Tick Databases, CEP Engines, Message Buses, Web, Cube and Big Data. It’s used by 12 of the top 15 financial institutions, including Nasdaq, Citi, Fidelity, Citadel, Blackrock, Deutsche Bank, Credit Suisse and UBS. The latest release (late 2016) represented a giant leap forward to better support the market’s most demanding real-time visualization requirements. Management indicated on the Q3 earnings call that demand for Panopticon appears to be growing.
How Companies Succeed with Datawatch
Here are just a few examples of how Datawatch’s self-prep data tools bring data to life for its customers and help them manage their businesses more effectively.
MasterCard (MA) is a global financial company that connects consumers, financial institutions, merchants, governments and businesses in over 200 countries with the world’s fastest payments processing network. Despite its size, it had 13 people spending 40 to 80 hours per week manually reconciling mainframe reports, which required hand-keying 25 to 30 multi-page reports—each week! It turned to Datawatch Monarch to extract vital information locked in the mainframe reports, and had the data automatically delivered to a team of analysts for immediate use. The investment paid off within six months, and saved 40 to 80 hours of mindless labor per week.
KPMG is a global professional services firm that services clients in over 150 countries with audit, tax and advisory services. Its main objective is to help clients mitigate risks, and seize opportunities. It has a Healthcare and Life Sciences practice that helps providers analyze their revenue cycles, and become more efficient by offering point-in-time business snapshots that show how they are doing relative to operational objectives. Such analysis required manually gathering disorganized data from disparate systems (I’ve felt their pain!), and in different formats (CSV, Excel, PDF, text-based abstracts, etc.), then working with the healthcare organization’s IT department to complete the modeling work. To make matters worse, KPMG could only access 30 days of data. It took up to 12 weeks to complete a project. KPMG turned to Datawatch Monarch and was able to cut prep time by 58% and, without the help of IT, analyze up to one year’s worth of data. The end result was a much more valuable product at lower cost.
A Growing List of Strategic Partners Could Accelerate Profitable Growth
Datawatch has teamed up with leading companies to help customers around the world access user-friendly data. Partners include Tableau, Sage, Qlik, Dell, Microsoft, IBM, Cloudera, Kronos and Baker Tilly. In many cases, OEM partners embed Datawatch’s visual analytics capabilities into its own solutions for things like trading, risk and compliance. Examples include Dell Statistica, Thomson Reuters, Factset and Imagine Software. Management is working to expand these partnerships since they typically generate profitable growth, and the solutions sold are on a subscription basis. Here are a few examples of how these partnerships work.
In early 2016, Datawatch announced that IBM (IBM) would begin reselling Monarch as a self-service data prep solution for IBM Watson Analytics and Cognos Analytics. How might this be helpful? Let’s say you’re a Business Intelligence (BI) analyst for a global soft goods company, and you’re using Watson Analytics to study sales by country. You realize your data doesn’t include customer returns. That data is only available in a SAP database, which you don’t have access to. However, you can access a PDF of the returns through a URL for an online SAP business objects report. The report is 320 pages long. You can now use Monarch to pull data from the PDF report, clean it (for instance, change formatting conventions for things like order ID #s), and merge it in Watson Analytics with your sales by country data. Now you have a map of the world with country-level sales data, net of returns. And it only took moments to accomplish. Harley Davidson (HOG) was one of the first joint customers to sign up last year.
Kronos (KRO) is a leading provider of human capital management (HCM) software. When it lands new clients, it needs to bring their data into its cloud-based platform. Doing so often requires preparing and migrating data from both legacy on-premise systems and cloud-based systems. After a competitive bidding process, Kronos selected Datawatch as its partner to help onboard these customers faster, and with greater accuracy.
Microsoft (MSFT) PowerBI is a basic, cloud-based business analytics program that businesses use to analyze, visualize and share structured data to gain insights. It uses reports, charts, dashboards, etc. to help analysts with forecasting, predictive learning, machine learnings, etc. But the program isn’t capable of pulling unstructured data (such as from a PDF document). Datawatch can now do that for PowerBI users. Let’s say a user is analyzing loan data from a credit union, and the necessary data resides in two places: an SQL database and a PDF. Since PowerBI can access SQL data just fine, the user just needs Datawatch to pull, clean and merge the data from the PDF document.
What Does the Future Hold for Datawatch? Two Big Industry Trends to Watch
The self-prep and data visual data discovery markets are evolving quickly, and management says the industry is shifting from reliance on products, to reliance on platforms. This is consistent with what we’re seeing in the broader software industry as the cloud takes over.
Data visualization platforms are increasingly capable of acquiring, preparing, automating and managing a much wider variety of data sets from rapidly growing sources. The two most dramatic examples affecting Datawatch are the emergence of industrial data analysis from the Internet of Things (IoT), and expanded use of Enterprise Content Management (ECM) solutions.
Internet of Things (IoT)
The world now has billions of connected smart devices kicking out data on everything from the amount of water an apartment building uses, to your heartrate, to the temperature of a jet engine. The Industrial Internet Consortium says we’ll have over 50 billion devices by 2020, and these will change how businesses operate and how they view their customers. Datawatch sees immense opportunity in the use of data from the industrial internet. This is data from connected devices that helps monitor and manage manufacturing equipment, supply chains, warehouses, energy infrastructure and health care facilities.
Organizations will have access to tons of data from sensors that they’ll want to take in, prepare and blend, in real-time, with historical data. Then use the results to run a more agile and responsive organization that’s more efficient, more profitable, provides better customer service and assumes less risk. Datawatch sees its self-prep platform as capable of driving new partnerships with key IoT infrastructure providers.
Enterprise Content Management (ECM)
For decades, organizations have stored tons of unstructured and semi-structured data in their ECM systems, mainly because of compliance requirements. Think of things like health care records. Now, these organizations are realizing that this data can unlock insights into their business, and when combined with other data sources, drive better results in the future. The challenge is that these legacy ECM systems weren’t built to quickly find and retrieve user-defined data sets. And when the data is retrieved, it isn’t in a format that can be worked with. Datawatch’s software changes this. It can grab the data, put it into a format that allows the user to manipulate it, and then merge it with other valuable data sets.
The Business Model – The Times They Are a-Changin’
Datawatch develops and sells software through a direct sales force, distribution channel partners, and a global partner network of over 100 companies. Like most software companies, revenue consists of a mix of software licenses (50% of revenue in 2016), maintenance (46% of revenue) and professional services (4% of revenue).
There is one big change going on behind the scenes: Datawatch is slowly transitioning from selling perpetual licenses to subscription licenses. This is the same type of transition that Adobe did, that Microsoft is just completing, and that Oracle is in the relatively early stages of. The most important thing to know is that this subscription-based business model is becoming the norm, and it typically leads to more dependable revenue, higher recurring revenue, more rapid growth (it’s easier for a customer to say “yes” to additional products from the same company), and, importantly, tends to warrant a higher valuation from investors.
That said, it takes a few years to complete the transition, and during that process, the income statement becomes a little muddled and it looks like growth evaporates. The reason is that term licenses, which typically come with a maintenance contract, go away, and are replaced with lower cost subscription licenses (usually for one year). That sounds bad, but it’s not. Over the course of several years, the subscription-based model results in higher revenue per customer. And it facilitates a “land-and-expand” sales strategy, which simply means they sell you one core product, then work on you to buy more add-on solutions.
The bottom line is that the transition is a little messy, but Datawatch needed to do it. Not only to keep up with the times, but to make it a more attractive acquisition target. It’s trying to do it in a measured way so that it can grow profits simultaneously. Subscriptions currently make up roughly 20% of license sales. Management says it could go up to 80% quickly, but that would hurt the bottom line, which is a trade-off it’s not willing to make right now.
Datawatch began the transition in the second half of 2015 when it required all new and non-current maintenance customers that wanted to buy 10 seats or less of Monarch to buy subscription licenses. The company has a nice slide from their investor presentation (see image above) that shows the transition in terms of major milestones, as well as quarterly year-over-year revenue growth.
The Bottom Line
Datawatch had a breakthrough year in 2012 when it grew revenue by 45% (to $26 million). Then it grew by 17% and 16% in 2013 and 2014, respectively, before it became apparent that its business model was behind the times. The next year, 2015, was a step back. It took over accounts from a large distribution partner (which previously accounted for 15% of revenue), and began transitioning to a subscription model. Revenue fell by 14% (to $30 million).
While revenue was only up 1% in 2016 (to $31 million), the real heavy lifting was done. Growth returned in fiscal Q4 2016 (ended September 30), and has accelerated every quarter since. In the most recent quarter, Q3 2017 (ended June 30), revenue was up 23%. The trends suggest Datawatch is poised to grow revenue by 17% in 2017 (which just ended on September 30), and by around 13% in 2018.
Importantly, Datawatch was also profitable in the most recent quarter, when it delivered EPS of $0.02. That represents the first quarterly profit in a couple of years. While the company will probably lose $0.10 this year (largely due to severance costs), its trajectory suggests a profit of around $0.08 in 2018. Management has said it intends to maintain discipline to be profitable each year moving forward.
Through the first three fiscal quarters of 2017, total revenue is up 19%, license revenue is growing north of 35% and subscription revenue (reported as part of license revenue) is up over 85%. Subscription revenue now represents 20% of total license revenue, and is on pace to almost double this year. I expect a continued measure ramp in subscription revenue, which should account for 25% to 30% of license revenue by the end of 2018.
The company has generated positive cash from operations over the past three quarters ($3 million total), and is now sitting on $29.4 million in cash. Its average deal size in the last quarter was $39,000, up from $35,000 in the same quarter of 2016. It has revamped its approach to selling, and is breaking down “land” (new) deals, and “expand” deals (additional products sold to existing customers). In the last quarter, it had 233 land deals (up 18%) and 67 expand deals (up 131%).
Lastly, significant customer-related news from Q2 included the Navy Federal Credit Union (a long-time customer) upgrading to an enterprise-wide deployment of the newest Monarch platform, Credit Agricole rolling out Monarch to over 24 banks throughout France, and AquaQ (a provider of kdb+ analytic services to global capital markets organizations) partnering to provide advanced trading analytics to banks, brokers and fund managers to meet regulatory and compliance requirements.
RISK
Transition to Subscriptions: While the market appears to prefer subscription-based pricing models, Datawatch has said it needs to transition gradually so as not to disrupt its client base and burn through cash. So far, it appears the measured approach is working (the share price is up!). That said, it does make analysis of the sales trends a little more challenging, and extends the story line for longer than we probably want to talk about it. Expect the pros vs. cons of a gradual rollout to be a topic of debate on earnings conference calls.
New Product Introductions: Management says the bigger opportunities lie in markets where it is less established (data visualization, Cloud Analytics-as-a-Service). There is risk that the newer products intended to address these markets (Panopticon, Monarch Swarm) won’t catch on. That would diminish the long-term opportunity.
Microcap Company: Datawatch is a tiny company, generating less than $50 million in revenue a year. While its business has historically been stable, it wouldn’t take many clients leaving to rock the boat.
COMPETITION
Datawatch’s market is most broadly described as business analytics, and more specifically as the self-preparation data visualization market. As such, it competes with large software companies that sell traditional business intelligence products, including IBM, Microsoft, Oracle and SAP. It also competes with emerging companies selling data prep products, including Alteryx, Informatica, Paxata and Trifacta, companies focused on real-time visualization, including First Derivatives and Zoomdata. Finally, Datawatch competes with companies in the market with products that extract specific forms of data (machine data, data in content management systems, EDI, XBRL, HTML and PDFs), including Splunk and Actuate.
THE STOCK
Trading Volume: Datawatch has a market cap of $144 million and trades an average of 143,000 shares daily. That’s just under $1.7 million worth of stock trading hands each day. Our subscriber group can move this stock. Average in, and build a position over time. Heavy volume days are 500,000 shares or more, and that’s happened six times in the last six months.
Historical Price: The stock was at 5 back in 2011. As growth accelerated shares took off, and hit a high of 38.70 in late-2013. Then high hopes for acquired products failed to materialize, and it became clear the business needed to evolve toward the cloud and subscriptions. Shares gave back all of their gains, and hit a low of 3.1 in early 2016. That was the turn-around point, and it happened quickly. Buyers stepped in and shares rose to 8 in October as it appeared the transition to some subscription licenses was working. A short wobble late in the year knocked the stock down to 5.25, but it was back above 8 before long. Then a choppy uptrend began to form with shares trending mostly above their 50-day line, with the occasional sharp drop to the 200-day line. We saw a pattern of higher highs and higher lows emerge, and in late-July, DWCH hit a multi-year high near 12. A pullback to 9 in August opened the door to new investors, and since then, shares have been moving steadily higher and trading in a tight range.
We’re just now knocking on the door of that multi-year high near 12, and a breakthrough here suggests higher prices ahead.
Valuation and Projected Price Target: Shares of DWCH trade with a 2018 EV/Sales multiple of 2.8, based on estimated revenue of $40.2 million and a current enterprise value of $114 million (market cap of $144 million less $30 million in cash). By almost any measure, that’s cheap. It reflects the company’s small size, unknown story and uncertainty regarding the level of success it will achieve with new products, and the transition to subscriptions. A reasonable baseline is to assume 10% to 20% revenue growth, with similar EPS growth, which should be plenty to validate an EV/Sales multiple between near 4. That implies roughly 50% upside over the next year. Let’s start with an 18 price target.
Buy Range (next two months): Initially, I’d like to see buying in the 10.5 to 12.5 range. That extends down to the 50-day line (has held since mid-summer) and gives a little room above where we are here. If shares fall apart we could buy down to 9, where we should get some support. But we’ll take that as it comes. If we see continued strength and shares truly break out here, we’ll likely be buying up to around 14. But again, we’ll take that as it comes. For now, look to buy in the 10.5 to 12.5 range.
The Next Event: We should see Q4 earnings (through September 30) released in the beginning of November.
Datawatch (DWCH) Financials
Datawatch (DWCH 11.40) |
UPDATES ON CURRENT RECOMMENDATIONS
Due to the nature of the stocks recommended, it is to your advantage not to share these recommendations.
Strong Buy means the stock should be bought immediately and is expected to move sharply higher in the very near future.
Buy means accumulate shares at or around the current price.
Hold means just that; hold what you have. Don’t buy, or sell, shares.
Sell means the original reasons for buying the stock no longer apply, and I recommend exiting the position.
Sell a Half means it’s time to take partial profits. Sell half (or whatever portion feels right to you) to lock in a gain, and hold on to the rest until another ratings change is issued.
Small caps are still ripping higher and were up 1.7% this week. After last week’s 3.5% gain, the S&P 600 Index is now up 9% year-to-date, and is making progress catching up with the S&P 500 Index (which is up 14% so far this year).
I went through a few reasons why small caps are moving higher last week, so I won’t repeat myself today. The important thing to know is that the small-cap index has now broken out of its trading range in dramatic fashion. I suspect there is limited upside with the index from here in the short-term however, given that its forward P/E has now surged to 20 (it hasn’t been higher since 2002). That said, the move has certainly drawn attention to individual small-cap stocks that likely have more room to run after retreating in August.
Our portfolio bested the index this week by moving 2.6% higher, on average. It was led by LogMeIn (LOGM), up 8%, Tactile Systems (TCMD), up 6%, and AppFolio (APPF), up 6%. Details below.
Updates
AppFolio (APPF) was up again this week. That’s become a welcome pattern! There was no new news. Keeping at Buy. BUY.
Asure Software (ASUR) was up modestly this week after a big 18% move last week. I was happy to see that it’s made another acquisition just a few days after hiring a new CEO. This week, Asure announced it has acquired Associated Data Services (ADS), an Alabama-based regional HR and payroll services bureau that’s a current reseller of Asure’s HCM solution. Financial terms weren’t disclosed, but that’s not too important. What’s important is that the company is communicating to the market that its acquisition-led growth strategy remains intact. Keeping at Buy. BUY.
AxoGen (AXGN) pulled back a little this week after an SEC filing showed an early investor, EW Healthcare Partners, has filed to sell out of its position, which consists of up to 4.86 million shares. That represents roughly 14.5% of total shares outstanding, so it’s not insignificant. We don’t know when those shares will hit the market, but based on trading volume, they haven’t done so yet. I typically like to see this sort of thing since it spreads out the investor base and improves liquidity. I don’t read too much into why the seller has chosen to move on. In this case, I suspect the stock could be a little volatile as shares hit the market. They typically trade 200,000 to 300,000 shares per day, and 4.86 million is a relatively large number of fresh stock to sell. Keep this in mind over the coming days and weeks. I’ll keep an eye on it too. For now, keeping at Buy. BUY.
BioTelemetry (BEAT) was down a little yesterday but is still up 4% over the past week. It’s been helped by more positive coverage in the media after the short report pummeled it a few weeks back. Most notably, Jim Cramer came out in support of the stock, and had the CEO in studio for an interview this week. The video is available on Yahoo! Finance (Click here). It’s worth watching. Keeping at Buy. BUY.
Everbridge (EVBG) popped its head above resistance and took a quick peak at a 52-week high before ducking down again yesterday. We’ve enjoyed a very strong move from 23 to 27 over the last three weeks, so let’s not get too greedy here. I think it’ll break out, it just might take a little while for investors to gather up the courage to push it. The company announced a partnership last week with an integrated security company (G4S). And this week was back at it, announcing enhancements to its IT Alerting solution. The team has added Smart Analytics and ChatOps integrations. The main value-add of the features are to include more interactive collaboration options, and help management teams streamline their incident response processes. HOLD HALF.
LogMeIn (LOGM) was at a good technical level to buy last week (its 200-day line), and, true to form, it has jumped 8% higher since I last wrote. It helped that Robert Baird upgraded the stock and put a 130 price target on it on Tuesday too. Keep holding. HOLD HALF.
Primo Water (PRMW) continues to be the laggard in our portfolio, even though revenue is up 28%, 88% and 117% over the last three quarters, respectively. This year, revenue should be up by around 100%. Of course, that’s mostly because of the Glacier acquisition. And that came at significant cost as Primo’s long-term debt and capital leases jumped up to roughly $270 million after the deal closed. That’s led to around $5 million in quarterly interest expense, as well as non-recurring and acquisition-related expenses. In short, Primo has paid the price for the acquisition, but it hasn’t yet enjoyed the benefits, other than top-line growth.
Investors now want to see some of that debt get paid off, and see tangible benefits from the acquisition. On the last conference call, management said it was a peak quarter for cash usage, and that Primo would generate far more cash in the second half of the year. With the acquisition now one year behind us, we should start to see cleaner numbers and, hopefully, a clearer trend in Primo’s growth profile. The stock’s chart reflects all this messiness. And while I don’t like that it’s back to around 11 this week, I do think its ability to find support there is a positive sign. One more thing. I’ve studied Primo’s chart and, while the pattern isn’t perfect, it has shown a history of rallying for several months, then plateauing for several months. Check out the chart below. The blue lines don’t define the consolidation periods perfectly, but they do show the general pattern. Check out how the stock has been more-or-less trading sideways since the Glacier acquisition closed a year ago.
What I’ve been thinking is that, once the integration is behind us, the stock should get back to a more normal trading pattern as management steers the now much larger company back onto a track that generates more measured results. That assumes the acquisition wasn’t a colossal mistake of course! The bottom line is this was a big acquisition and it’s probably a bit of a one step back, two steps forward situation (hopefully). We’ve hung with Primo for the past year while it’s done nothing, and I’d like to give it more room to show us what it’s capable of. Keeping at Buy. BUY.
Q2 Holdings (QTWO) is back to hanging out around 41 after an intra-day pop to 43 on Monday. No new news. Keep holding. HOLD HALF.
Tactile Systems (TCMD) is following what’s now become a reliable pattern after a short attach; stock falls hard for a few days, stock stabilizes, stock begins to move higher within a week. It happened with MindBody (MB), with BioTelemetry (BEAT), and now with Tactile Systems. I’ve kept at Buy, and the 6% rally over the past week suggests that’s been the right call. BUY.
U.S. Concrete (USCR) has been active this week. First, it announced that it has snatched up Polaris Materials, which owns aggregate quarries (Orca Sand and Gravel, the Black Bear Project, and the Eagle Rock Quarry Project) in British Columbia and Vancouver Island. It supplies aggregate via two receiving terminals in Richmond, California and Long Beach, California. U.S. Concrete management says the acquisition will help roll out the same vertically integrated strategy that’s working in New York to the aggregate supply-constrained Californian market. The price paid was C$309 millioThe company also acquired two independently owned and operated ready-mixed concrete operations near San Francisco; Harbor Ready-Mix and A-1 Materials. In the deal it picks up two ready-mixed concrete batch plants, 23 mixer trucks, and the assets of L.C. Frey, a landscape materials business related to A-1’s operations. These acquisitions will help U.S. Concrete better serve the Peninsula and South Bay areas of San Francisco.
As if that’s not enough, the company also announced the acquisition of Action Supply, which supplies ready-mixed concrete to the Philadelphia metropolitan market for commercial and infrastructure construction projects. It’s close to Corbett Aggregates, which U.S. Concrete also owns. This acquisition expands its east coast operations into another metro-market that’s both stable and mature.
Investors like the news and shares of U.S. Concrete are up 3% since I last wrote. I’ve had the stock at Hold, but it’s now found support twice at 70 and is back above its 50-day line. It could have a tough time moving through resistance in the 80 to 84 range, but the trend is now strong enough to move back to Buy. BUY.
Please email me at tyler@cabotwealth.com with any questions or comments about any of our stocks, or anything else on your mind.
Next Cabot Small-Cap Confidential issue is scheduled for November 3, 2017
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