“It is difficult to think of a major industry that AI will not transform. This includes healthcare, education, transportation, retail, communications, and agriculture. There are surprisingly clear paths for AI to make a big difference in all of these industries.” – Andrew Ng

This month we’ve dissected Artificial Intelligence (AI) and its impact on three industries:

• • Retail
  • Healthcare
  • Education

We’re finishing our industry overviews with Finance. The potential applications of AI from the back to front office are astounding.

But it’s not the number of potential applications that makes us excited. AI has the power to transform the way finance companies operate. From acquiring customers to differentiating their products. Even how they gain competitive advantages over peers.

In doing so, AI will rewrite the definition of a dominant finance company. We’ll see how AI changes the meaning of assets, production, and scale through company examples.

This essay will answer three main questions:

1. 1. What new competitive advantages does AI bring?
   2. How will AI transform the finance industry?
   3. What are some examples of companies doing this today?

Why this matters: Tomorrow’s finance winners will look very different from the finance of old.

Why you should care: AI creates stronger competitive advantages than ever before. Tomorrow’s leaders will be the ones that recognize this shift. Adopt new technologies. Then forge deeper and wider moats around competitors.

But before we paint a picture of finance’s future, we must look at what it is today: an industry built on antiquated competitive advantages.

Note: Data presented in this essay comes from World Economic Forum

Today’s Competitive Advantages Are Tomorrow’s Value Trap

Before AI, finance companies created dominant competitive advantages via five levers:

• • Scale of assets: economies of scale provided cost advantages (think BlackRock)
  • Mass production: Standardized products and physical footprints (e.g., the most mutual fund offerings on the market)
  • Exclusivity of Relationships: Firms had access to special deal-flow due to size of assets
  • High Switching Costs: Keep customers because it was too difficult/expensive to switch finance providers
  • Dependence on Human Ingenuity: Achieve scale through adding workers and training labor

In an AI-first world, these levers depreciate and become legacy systems. This means investors looking for the above characteristics are looking at value traps. Finance companies of tomorrow won’t need large AUMs or cumbersome switching processes.

Investing in companies on such hypotheses will lead to suboptimal performance. Not because you picked the wrong investment. But because the companies themselves weren’t equipped to tackle an AI-first future.

If you get nothing else from this essay, get this. AI transforms each legacy advantage and changes the definition of a leading finance company.

It does this by altering each legacy system to fit an AI-first business model:

• • Scaled Assets → Scaled Data
  • Mass Production → Tailored Experiences
  • Exclusivity of Relationships → Optimizing & Matching
  • High Switching Costs → High Retention Benefits
  • Dependence on Human Ingenuity → Value of Augmented Performance

These new levers spawn defensible datasets and first-mover advantages.

This triggers a virtuous data cycle. Where better data leads to a better product. Which leads to more customers. Which brings in more (and better) data. The cycle repeats.

Let’s review our three most important levers in finance: scaled data, tailored experiences, and high retention benefits.

The Three Most Important Levers in AI Finance

Scaled Data

Traditionally, finance companies grew by amassing the highest AUM in the industry. Having the most money is great. It allows you to invest in R&D, hire more advisors/salespeople, and commands lower prices on investment products. BlackRock is the easiest example. The company has a mind-boggling $7.34 trillion in AUM as of 2019.

Asset levels of that size create positive feedback loops that attract even more capital. Yet a greater feedback loop exists in the world of data. BlackRock’s Aladdin is a testament to the power of Scaled Data.

According to an FT.com article, Aladdin is BlackRock’s “system [that] links investors to the markets, ensures portfolios hold the right assets and measures risk in the world’s stocks, bonds and derivatives, currencies and private equity.”

Aladdin’s asset levels are astounding. The article notes, “Today, $21.6tn sits on the platform from just a third of its 240 clients, according to public documents verified with the companies and first-hand accounts. That figure alone is equivalent to 10 percent of global stocks and bonds.”

This “central nervous system” creates new income streams for the company. Aladdin did $947M in revenue last year. BlackRock CEO Larry Fink wants BLK’s technology segment (almost 100% Aladdin) to generate 1/3rd of total revenue by 2022.

Scaled data is the first-movers paradise. Whoever amasses the most data can create the best products. That’s exactly what BlackRock is doing with Aladdin.

Having the largest dataset allows you to create new products and services others can’t match.

Tailored Experiences

Mass production made it easy for companies with large asset bases (like BlackRock) to generate high-margin revenue. Mass-produced products help spread total costs over a larger pool of units. This lowers the incremental cost to make each unit, thus raising margins.

But mass production robs larger companies of two important features: personalization and customization.

AI allows large companies to offer customized products once reserved for wide-eyed start-ups.

The World Economic Forum outlines this new AI-based personalized reality (emphasis mine):

“Advanced recommendation engines can be used to customize the features and price for each financial product, using data from a variety of sources, including customers, groups and third-party sources. Automated, sophisticated advice allows for a frequent, proactive and personalized service that is not economical under traditional human-based customer service models.”

Tomorrow’s leaders will create “self-driving” financial services/products. APIs and third-party platforms make this possible.

Citi does this with their new mobile app that allows customers to link third party accounts into one easy-to-read platform. Citi benefits by seeing how their customers spend money and where they can offer complementary products.

High Retention Benefits

Past financial models relied on high switching costs to keep customers. Businesses weren’t incentivized to create the best product for the customer. They wanted to make the hardest-to-switch product. This is a subtle yet powerful distinction.

Optimizing for switching costs opens the door for a more customer-centered competitor to take share. Think of TIKR/Koyfin and Bloomberg. People hate Bloomberg. In turn, they love TIKR because they almost get everything they need without the stress of Bloomberg’s UI and price.

Let’s invert that idea. Companies that optimize for superior customer outcomes faceless fierce competition.

How do finance companies achieve this? By using the first two levers (scaled data and tailored products).

Better customer data allows a company to provide customized and tailored products/services. This leads to customers spending more time and money on these products, birthing more data. This enables companies to create even more customized products and tailored services.

Customers won’t leave because they want to stay. Not because they can’t leave.

We discussed this in-depth in our post The Delta Model: How To Love Customers & Win Business. Here’s a snippet on how businesses can redefine the customer experience to lock-in high retention rates (emphasis mine):

“Brands with the best price, coolest product or most memorable marketing campaign might not have an advantage compared with those that exhibit emotional intelligence and communicate with care, honesty, and empathy, and build trust as a result. In times of crisis, people want to be seen and understood, and they are extremely sensitive to tone and motive. Are you reaching out to help them — or to sell them something? Does your outreach feel authentic and caring — or does it appear self-serving?

Here’s what we’ve learned so far:

• • AI will transform the competitive advantages of finance companies
  • Investors looking for legacy finance models are searching for value traps
  • The three most important levers in an AI-based finance industry are Scaled Data, Tailored Experiences, and High Retention Benefits.

The finance companies that leverage AI will create higher barriers to entry, stronger customer relationships, and higher switching costs through customer loyalty.

Next, we’ll look at how AI attempts to solve finance’s most pressing challenges.

The Scope of AI Opportunities     

According to the WEF, AI solutions fall on a spectrum from “Doing the same things, better” to “Doing something radically different”. There are five main strategies that occupy this spectrum:

1. 1. Leaner, faster operations
   2. Tailored products and advice
   3. Ubiquitous presence
   4. Smarter Decision-Making
   5. New Value Propositions

Let’s review each of these strategies.

Leaner, Faster Operations

Leaner, faster operations streamline mundane back-office tasks which reduce operating costs. Finance companies love this because costs matter in a commodity industry.

A great example of this is Freee (4478.JP). Freee provides back-office accounting software for small-medium sized businesses.

Another US-based company using this strategy is Bill.com (BILL). BILL provides software-as-a-service, cloud-based payments products, which allow users to automate accounts payable and accounts receivable transactions, as well as enable users to connect with their suppliers and/or customers to do business, manage cash flows, and enhance office efficiency.

Tailored Products & Advice

Like we mentioned earlier, Tailored Products & Advice are Finance companies’ weapons of choice. Consumers already enjoy personalized offerings from Facebook, Twitter, Google, and Spotify. It’s only a matter of time before they expect the same from their bank or investment manager.

One example of this in public markets is Cardlytics (CDLX). We own CDLX at Macro Ops. Why? They help credit card companies match their customers’ actual spending habits with discounts on items they already buy.

Tailored Products are perfect for large banks with a hand in every service (insurance, investments, loans, etc.). AI helps translate disparate data into actionable insights. This allows large corporations to create customized products by combining all product silos.

Ubiquitous Presence

Ubiquitous Presence is one of the most interesting AI finance opportunities. The WEF defines Ubiquitous Presence as (emphasis mine):

“A presence [that] allows institutions to tie directly with customers’ ongoing experiences without distracting from their end goals”

In other words, any service that’s a means to an end. Think physical credit cards. You swipe the card (the means) to get what you want (the ends).

Ubiquitous Presence admits that these processes are (for the most part) unconscious decisions. And soon we’ll stop caring what card we use as long as we get the product or service we want.

This creates exciting opportunities in the world of digital-first applications. More people are using apps like Mint, Personal Capital or Acorns to track their spending. Finance companies that integrate seamlessly with third-parties will gain more customers.

It also means that more people are completing transactions and processes online. Insurance claims are a great example. Companies like Lemonade (LMND) use AI to process and pay claims in seconds. And if the AI can’t figure it out, LMND has a team of human insurance agents waiting to help.

Ubiquitous Presence is also seen in Amazon’s newest venture, Amazon Go. Amazon Go is a revolutionary grocery store experience. There’s no check-out line. No cashiers. You don’t even have to pay at the store. Everything’s done through your Amazon account.

More companies will do this because the opportunity to capture customers at the point of ubiquity is enormous. What’s stopping Visa or Mastercard from partnering with leading omnichannel retailers?

Here’s what that reality could look like. You walk into a Best Buy or Bed Bath and Beyond. Find your item. And walk out because those stores partner with Visa’s omnichannel program.

Smarter Decision-Making

I heard Andrew Ng, a pioneer in AI, on a podcast (the name of which escapes me) discuss the most basic definition of machine learning. He said (and I’m paraphrasing). “Any time a machine, computer, or algorithm takes data as an input and tries to make predictions off that data. That is machine learning.”

Making smarter decisions is the entire point of machine learning. We wouldn’t risk creating a real-life Terminator if that wasn’t true, right?

There’s myriad ways to make smarter decisions in finance applications:

• • Insurance companies better manage risk parameters
  • Third-party data collection companies drive better insights
  • Trading departments leverage AI to reduce cost of trade execution

A good example of a company leveraging AI to help other companies make smarter decisions is Riwi Corp (RIW.TO). The company provides real-time, proprietary data from 220 countries around the world. Companies can purchase this data as a service and use it to make better decisions about trades, public policy, or product placement.

New Value Propositions

If semiconductors obey Moore’s Law, finance companies bow at the feet of arbitrage. It’s hard enough to compete in an industry dominated by lowest-cost providers and commoditized product offerings. But with AI, finance companies can rise against arbitrage. They can create new investment strategies from differentiated datasets.

In short, new value propositions will come from new ways of using either existing data or new data. Breakthroughs will happen at the insight and application layer.

We’re seeing this in the ETF space where companies create ultra-niche thematic ETFs or “smart beta” strategies.

Conclusion

The number of AI applications in finance is exhilarating. As an investor, I can’t wait to see what new business models and strategies come out of this industry breakthrough. AI will change what it means to be a dominant finance company.

The old model of growing assets, mass-producing products, and creating frustration-based switching costs are over. Scaled data will create tailored experiences for customers which will translate to high retention benefits and a virtuous competitive cycle.

If you liked this essay and want to learn more about AI and its impact on other industries, give these a read:

• • Great AI Companies: What They Are & How To Find Them
  • Where We’re Finding AI Investment Opportunities: Education
  • Pearson (PSON): From Overpriced Textbooks To Digital-First Platforms

Brandon Beylo

Student of value investing for over 13 years spending his time in small to micro-cap companies, spin-offs, SPACs and deep value liquidation situations.

Last week’s essay covered AI in Education and the myriad companies attacking the space. I ended the post by saying we’d cover AI in Finance this week. That changed when I looked closer at Pearson (PSON). The stock had everything we look for at Macro Ops:

• • Improving fundamentals
  • Long-term industry/secular tailwinds
  • Technical confirmation from the tape (price chart)

I sent this write-up to our Macro Ops Collective members over the weekend to prepare them for a potential buying opportunity this week. The opportunity is here and we wanted to share our thoughts with you as well as how we’re reading the tape.

But before we dive in I want to tell you about our premium service, the Macro Ops Collective.

The Collective is our full-kit soup-to-nuts service that provides research, theory, actionable trades and a killer community that consists of dedicated traders, investors, and fund managers from around the world. We’ve been told that there’s nothing else like it on the web.

If you’d like to tackle markets with our group (whom I should say has been having a great year in markets). Just click this link and sign up. And, as always, don’t hesitate to shoot me any Qs!

Alright, let’s get to our turnaround idea!

Pearson PLC (PSON): From Overpriced Textbooks To A Digital-First Platform

Pearson (PSON) built its legacy business selling overpriced textbooks to college students. Then Chegg came along and eliminated the need for those expensive hunks of paper. The company recognized this and is now leading an effort to turnaround the antiquated model.

Elevator Pitch: If it works, PSON will transform from a publisher of overpriced books to a fully digital learning platform powered by AI and massive amounts of student data. Mr. Market still values PSON like a textbook publisher as the stock trades <2x EV/Sales and <10x EV/EBIT.

The Problem: College Textbooks Are A Scam

College textbook prices are one of the largest scams in the US. Prices have risen a cool 1,216% since 1977. For context, healthcare prices increased 835%, housing increased 439% and inflation another 404%. The reason for the price increase is equally sinister. College professors mandate certain textbooks (usuall written by the professor) as prerequisites to pass a class. Don’t have a textbook? You won’t pass the course.

This forced students (or their parents) to pay whatever cost for that “required” textbook. Not to mention paying for the class itself.

And we can’t forget the classic “updated” textbooks. You know the ones with an extra chapter for brand new material? Or an updated version that rearranges pages and chapters. These tactics ensure publishers and professors get paid at the expense of students’ wallets.

The Workaround Solution: Re-used Books and E-Books

Students eventually adapted to the highway financial robbery. They bought used textbooks and shared notes on Chegg. This led publishers to introduce e-textbooks, which seemed like the logical thing to do. Yet the early e-books weren’t made for a digital-first world.

They were clunky with terrible user interfaces (UI). E-textbooks also Ied publishers to include one-time activation codes in physical textbooks. That meant students had to buy the physical copy of the book to get access to the online edition.

The one-time activation codes led to the same problem students encountered with “next edition” physical textbooks. You can’t resell a one-time activation code.

Against this backdrop it’s easy to see why Chegg’s (CHGG) stock price rocketed over 500% since IPO. CHGG has generated a 50% CAGR since IPO.

At the same time, traditional hard-copy publishers like Pearson experienced declining sales and lower stock prices. Check out the 10YR stock chart of PSON below. It’s ugly.

So now textbook publishers have a choice. They can either stick with the hard-copy book sales and one-time fees that rob students.

If they do that they don’t have to overhaul their business plan. They can keep the status quo. That puts them at risk for insolvency as companies like CHEGG take more market share over time.

The other option is to accept that the future of textbook/learning material is fully digital. While this seems like the smart choice, it’s a difficult one to make. Accepting that reality means entire organizations rearranged to optimize in a digital-first world.

Pearson Bites The Bullet

PSON accepted this reality in 2012 under the direction of then-CEO John Fallon. Fallon’s 2012 Shareholder Letter revealed insights into how PSON thought about the initial transformation.

The letter also puts things in perspective. By 2012 PSON generated nearly 50% of its revenue from digital sales. A turnaround of this magnitude for a company of PSON’s size takes time.

The company became the first education publisher to take a digital-first approach to textbook publishing and education content delivery.

Here’s a few of my favorite quotes from the letter (emphasis mine):

“We wanted to make a radical shift from traditional print products to digital and services businesses, and, for the first time in Pearson’s history, those now account for half of our revenues. We aimed to become a significant player in the world’s most dynamic education markets, and Pearson is now a meaningful education company in China, India, Brazil and Southern Africa.”

“This shift to digital is profoundly changing the business model for content: it means one-off sales will diminish while subscription sales, most bundled with services, will grow. That same shift to digital causes considerable change and consolidation in the retail channel, with a dramatic shift to online sales and different sales patterns for physical and digital formats.”

“We need to shift resources more quickly from textbook publishing activities, primarily in the developed world, where demand is flat or declining, so we can invest more quickly in our fast-growing digital and services businesses, with a special emphasis on emerging markets.”

Despite this commitment to digital transformation, the company’s struggled to grow revenue and profits. Check out the declines in each category since from 2012-2019:

• • Revenue: -$1B
  • Gross Margin: -$761M
  • Operating Profit: -$214M

There’s a few reasons to explain the decline in PSON’s operating metrics. First, the company continues to churn print sales to digital sales. Print sales commanded 80% of PSON’s business in 2010.

A legacy revenue base that high takes time to flow through the income statement. Second, digital sales look different than print sales. PSON could charge hundreds of dollars per physical textbook. Under a digital subscription-based platform PSON relies on textbook rentals, which they charge roughly $40/book. That’s a huge difference.

In other words, you wouldn’t see the business’ transformational success if you only look at the financials.

The company’s determined to see the transformation through. Less than two weeks ago PSON sold its Institute of Higher Education to Stellenbosch Graduate Institute and EXEO Capital. This sale fits with PSON’s mission to shift from physical, large-scale textbook delivery to digital-first platforms.

Eventually PSON needs to grow revenues, expand margins and increase profits to command higher market multiples.

Let’s see how they plan to do that.

The Pearson of Tomorrow: Digital First

PSON grew its digital sales from 20% of revenue in 2012 to 66% in 2019. The company expects its core US Higher Education Courseware (largest segment) to be ~100% digital by 2022. To catalyze the transition to fully digital, Pearson will rely on three product innovations:

• • Aida: The world’s first AI-enabled Calculus app
  • Pearson Learning Platform
  • Next Generation Pearson eText Textbooks

In fact, we can distill PSON’s future success down to its Pearson Learning Platform (PLP). The PLP acts like an operating system for students and teachers. A way to share educational content and learning material in a one-stop-shop environment.

It feels like a Netflix distribution model. Pearson provides the platform for viewers (students) to consume content (ebooks, interactive learning, etc.) on the platform. It also allows teachers and authors to revise texts and material on an “as needed” basis.

By slashing costs and controlling distribution, PSON’s model becomes a lot more attractive to students and investors.

This allows them to generate more data on students than any other learning company. How? Because they’re the largest publisher of education content on the planet. And that entire library (1,500+ titles) becomes digital and data-able (made-up word?). Over time, that creates a Data Dominance amongst students and universities that other companies can’t copy.

The idea of PSON as the “Netflix of Education” isn’t new. CIO Albert Hitchcock mentioned this in a2018 Forbes Interview. Here’s a snippet of the conversation (emphasis mine):

“The intention of the message was to have the viewpoint that we needed to move to a platform type of model where we have all our products, services, and capabilities that we deliver to our customers in a single ecosystem. A great deal has been written around this model at Pearson, and that is especially relevant as our company grew through acquisitions. Ours is a diverse business that is over 170 years old and has had many different types of companies under the umbrella of Pearson for many years.”

Hitchcock elaborates further, saying “It would be game-changing for not only Pearson, but for the entire industry if we could create that single platform, similar to Netflix, Spotify, and Amazon. This platform would be highly scalable, global in nature, high-quality, and a platform that could deliver all our experiences around the world to millions of learners.”

Let’s dive deeper into Pearson’s Learning Platform. Jack Graham, who ran UI/UX for Pearson’s REVEL product was tapped to help Pearson’s Learning Platform. He has an entire blog post about his journey with PSON. Here’s what we learned:

“We created a series of definition statements across the three main user roles expected to exist in the software. From these, we were able to derive focused “How might we?” questions to advance during ideation. Based on what we learned during the discovery process, we felt it would be a high priority to improve the experience for creating new courses and assigning material within them over past products.“

“In a marketplace where premium products are subject to disruption by free alternatives, proving the efficacy of their products was key to Pearson’s strategy. Equipping instructors with timely feedback on student mastery of learning objectives was one way of implementing this strategy.”

On one hand we can say PSON’s platform benefits from “Netflix-like” distribution capabilities. But there’s another competitive advantage at play: Name Recognition.

Like Moody’s or S&P for bond ratings, the Pearson name carries weight in the education community. As the world leader in education publications, the PSON name brings with it a sense of quality and dominance. When people think of college textbooks, they think of Pearson. This helps explain why PSON controls nearly 43% market share as of 2018.

Numbers To Narratives

PSON has an opportunity to take share as the go-to digital learning platform for the next generation of learners and educators. If they do, they’d rapidly transform their business from a lower-margin print business to a high-margin software company.

Let’s assume the company can turn things around and complete this transformation. What would their run-rate margins and cash flow look like? What type of multiple would the market pay for that type of company?

We can use Blackboard as a proxy for what a digital-first education company’s margins would look like. Blackboard generated ~21-25% operating margins. Let’s assume PSON gets to 25% OM by 2025. Revenues will take a hit this year (-10%), but should bounce back in 2021 forward. BY 2024 PSON would generate 4.27B in revenue.

The transition to a digital-first business brings with it higher margins. We’re assuming PSON grows GM % from the low-mid 50s to the low 60s. This gives us 2.65B in 2024 Gross Profit. At the same time, they’ll spend less in SG&A, decreasing from mid-40% to low 40%. That gets us to 22% operating margins by 2024, or 940M in OI, 779M in after-tax income and 800M in FCF.

This gives us 7.3B in PV of Terminal Value. Adding our cash flows gets us 9B in EV. Subtract net debt and we’re left with $12/share in shareholder value (100% upside).

If the company manages the turnaround, they’d increase Operating Cushion from 8% to 22% and sport a 17% FCF Growth Profile.

Risks

PSON is a turnaround with myriad risks. The company hired Andy Bird to replace John Fallon, indicating a bit of a CEO carousel. While rotating CEOs isn’t ideal, Andy Bird’s early stats show promise. 84% of employees approve of the new chief.

The company also sports a -2 NPS rating. We think this improves as the company transforms to a digital-first platform better able to serve customers. Glassdoor ratings show 60% of employees would recommend the company to a friend.

Finally, price is in a sustained downtrend. This makes it harder for the stock to turnaround as it faces overhead selling pressure.

Reading The Tape

There’s no hiding the fact that PSON is a turnaround story fraught with difficulty. Turnaround stories can frequently make new lows and end up on 52-week low lists. As such, we want to ensure we’re buying at an inflection point in the market.

PSON’s chart tells the story of a company whose shareholders are seeing the benefits of their transformation. The stock is breaking out of a 11-month symmetrical triangle reversal pattern above the 50MA.

We’re watching the 200MA as potential resistance but like the long-term Reward/Risk set-up if the stock closes above the chart pattern boundary line on the weekly timeframe.

Brandon Beylo

Student of value investing for over 13 years spending his time in small to micro-cap companies, spin-offs, SPACs and deep value liquidation situations.

“It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers… They would be able to converse with each other to sharpen their wits. At some stage, therefore, we should have to expect the machines to take control.” – Alan Turing

If that doesn’t smash your rose-colored glasses on Artificial Intelligence (or, AI) I don’t know what will. Given the dire (and logical) end-points we can assume about this incredible technology, why not make some money off of it before we’re removed from the equation!

There’s a number of ways to conceptualize what makes a great investment. One of these is to think about an investment opportunity as a function of the growth potential divided by the penetration rate. In this equation, the most attractive investment is one with high growth potential and a low current penetration rate.

And the best place to find these ideas is in the AI space. Like the stages of a company’s development, there are levels (or “Quadrants, as Deloitte highlights) to AI’s industry-specific applications.

These quadrants help us determine what stage an industry is in its AI adoption cycle and allows us to invest in the highest growth areas possible. In short, we can pinpoint specific industries at the cross-section of high growth and low penetration. Our Holy Grail sweet spot.

But before we dive into specific industries and investment ideas, we first need to understand what makes a great AI company.

This essay will cover three topics:

• • What Makes A Great AI Company?
  • What are the Four Quadrants of AI Application?
  • Which Industry/Quadrant Do We Want To Invest?

“Come with me if you want to live find awesome investment ideas.” – Terminator (or something like that)

What Makes A Great AI Company?

A great AI company takes internally generated data and uses it to provide a better service/product to its users. Which in turn grows its user base and creates more (and better) data. Which begets a better product. Etcetera.

Great AI companies are true beneficiaries of the first-mover advantage. At the end of the day, whoever has the best data wins. The best data trains the best model which produces the best product.

William Vorhies of Data Science Central calls this idea Data Dominance. Here’s his take on this idea (emphasis mine):

“To create a successful AI company you must create such a wide moat that no one can catch up unless they pay your price.  That moat is not about technology.  There are essentially no monopolies on deep learning technologies, only leaders that can quickly be copied.The secret to a wide moat in AI is to have a virtual monopoly on the data you are using to train.  In this case monopoly also means such a large lead in users and data volume that no one can reasonably catch up.”

But data for data’s sake isn’t enough. Great AI companies need the right type of data. Defensible data. In the case of AI, it’s the data that creates the economic moat, not the technology. In fact, Vorhies argues that the actual technology aspect of AI companies is purely a commodity.

So, we can reduce the formula for a great AI company into two factors:

1. 1. The company that has a monopoly on the right type of data
   2. The data is used to solve a specific (niche) problem

Armed with this framework, we scan areas where AI can create both a differentiated data set and solve a unique problem. This process will eliminate most industries from investment. Vorhies reminds us about Facebook and Google’s dominance in advertising, and Amazon’s moat on consumer spending (emphasis mine):

“For example, there’s no wide moat available in advertising.  Google dominates search-based advertising and Facebook dominates social media based advertising.  General e-commerce?  Can’t beat the lead that Amazon has in learning about our personal shopping desires.  These three industry giants clearly have defensible positions by virtue of their dominant data.”

Vorheis offered three examples in his blog post:

1. Blue River Technologies

Blue River Technologies has the largest collection of plant images in the world. This allows them to recommend the exact levels of water, nutrients and fertilizers for any plant at any stage of its lifecycle.

That type of data is extremely valuable. So much so that John Deere bought the company for $305M in 2017.

Blue River is a perfect example of defensible datasets and unique problems. Here’s a snippet from a Medium article on the acquisition (emphasis mine):

“Their robots actually sense, decide, act, and learn — millisecond by millisecond. Each year, Blue River robot systems collect all the data from crops over time and close the loop, ensuring they understand the impact of each management decision and optimizing for next year’s crop.”

2. Axon

Axon is an interesting case study. Many people know it by its former name, Taser. Yep, the company that makes the famous stun guns. Seeing a one-and-done sales cycle, Taser leveraged its massive database of video footage from its police body cams.

We’re talking a massive database: 4.9 Petabytes, or the equivalent of the entire Netflix 2016 catalogue.

Like Blue River, Axon’s database housed unique insights into specific problems: audio and visual data from police arrests. Again we see the power of defensible data. Who can compete with 4.9 Petabytes of existing data?

The applications for such data include facial recognition, voice identification and stress prediction. As well as predictive and prescriptive policing policies.

3. Stitch Fix

Stitch Fix is one of the most recent and popular examples of the power of data in analytics.

Bill Gurley, arguably one of the greatest venture investors of all time, led Stitch Fix’s Series B round for $12M. He wrote a great article on the company in 2013 where he strikes at the heart of SFIX’s main competitive advantage, unique data (emphasis mine):

“Stitch Fix’s personalization technology creates a very similar dynamic within women’s fashion. Through a better understanding of the customer, and using data to predict future orders, Stitch Fix has an engine that simultaneously better serves the individual desires of the customer and also contributes to higher inventory turns, fewer write-downs, higher capital efficiency and higher ROIC. This is business model nirvana.”

In short, SFIX’s customers help make the product better. This makes for more happy customers, which creates better data for an even better product. It’s Bill Gurley’s “business model nirvana” and what Vorheis described as defensible data.

This leads us to an important (albeit nuanced) conclusion: data network effects are better signals for potential moats than regular network effects.

Matt Turk (VC at FirstMark) wrote an article on the differences between generic network effects and data network effects. Here’s his take on data network effects (emphasis mine):

“Data network effects occur when your product, generally powered by machine learning, becomes smarter as it gets more data from your users.  In other words:  the more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes (which can mean anything from core performance improvements to predictions, recommendations, personalization, etc. ); the smarter your product is, the better it serves your users and the more likely they are to come back often and contribute more data – and so on and so forth.”

What’s interesting is that companies can leverage both types of network effects. Matt gives two examples in Facebook (FB) and Uber (UBER):

“Part of the magic of Uber is that it benefits from both for its core mission: a standard network effect (Uber becomes more valuable for everyone as more drivers and more customers “join” the service) and a data network effect (more data enables Uber to constantly improve its routing algorithms to get customers a car as quickly as possible and to ensure its drivers get as many jobs as they can handle, making everyone happy and more likely to be long term members of the network).  Similarly, Facebook benefits from both a “standard” network effect (the more people are on Facebook, the more interesting everyone’s experience is) and data network effects, as the newsfeed, for example, keeps getting more personalized based on massive data learning loops.”

The truly great AI companies leverage both sets of network effects to create the best product and service for its customers. They do this by solving unique problems via insights from differentiated datasets. Defensible datasets. Welcome to the new economic moat reality.

Alright, now that we know what makes a great AI company, let’s learn about the four quadrants of AI Applications. Understanding where an industry sits in its Quadrant helps us determine what type of investment we should make in that space. Or if we should make one at all.

The Four Quadrants of AI Applications

AI’s industry Adoption cycle mirrors that of a company lifecycle. There’s the early stage, high-growth period, maturity and decline. Let’s break each down with Deloitte’s definition and our translated version. Note that for the majority of this section we’ll refer to Deloitte’s whitepaper on the AI industry. It’s a must-read for anyone interested in the space.

Transition

Level of application of AI in the relevant industry is relatively high, but market opportunities are limited currently and the market scale is expected to expand further in the future

Translation: This quadrant has high industry penetration rates and low market scale (opportunities).

Germination

Level of application in the industry and market opportunities are not yet mature, although AI has played certain functions but is still at the initial stage overall speaking.

Translation: This quadrant has low market scale (opportunities) and low industry penetration. Think of Germination as the early-stage venture company with loads of potential but no clear direction.

Growth

Level of application in the industry is not sufficient, but application will broaden in the future with more market opportunities.

Translation: This quadrant has high market scale (opportunities) and low industry penetration. Lots of ideas and not enough solutions.

Developed

AI technology has already had a profound impact on such areas with high level of industry application and market opportunities

Translation: This quadrant has high market scale (opportunities) and high industry penetration. In other words, these industries knew the importance of AI, adopted the technology early and are reaping the benefits of their decision.

Where Industries Fit In Each Quadrant

Now that we know each specific quadrant, we can place each industry into their respective bucket. This is important because it helps us frame where (and when) we want to invest in these industry-specific AI applications.

Transition Quadrant: Digital Government

Germination Quadrant: Public Utilities, Healthcare, Smart Cities, Energy

Growth Quadrant: Natural Resources and Materials, Autonomous Driving, Communication Media & Services

Developed Quadrant: Communication Media & Services, Education, Financial, Manufacturing, Retail

Generally, we want to focus on the right side of the above graph. We want to invest in industries with large market opportunities and growing industry penetrations. Specifically, we’re looking at four main industries:

• • Retail
  • Healthcare
  • Education
  • Financial

These four industries offer the perfect mix of opportunity and penetration. Yet it’s not enough to take these industries and call it a day. We must find companies within each industry solving unique problems using differentiated datasets.

The rest of the essay will focus on each industry’s specific pain-points and how companies can use AI to solve those issues. This week we cover Retail and Healthcare.

Retail: All About Customer Stickiness

Today’s customers buy for different reasons than their parents. Now, consumers care more about the social implications of what they buy versus the efficacy of the product. This presents challenges for many retailers as they struggle to understand the “millennial” consumer mind. And they’re fighting an uphill battle. Most retailers’ boards are full of 65+ year old men.

Packy McCormick nails this idea in his newsletter Software Is Eating The Markets, when he says (emphasis mine):

“Like angel investments in the Bay Area, when you add social and experiential value to other asset classes like stocks, sneakers, and cryptocurrencies, price is divorced from hard math and becomes more emotional. That doesn’t mean that people are sitting at home bored and gambling; they may be making perfectly rational decisions when accounting for the many roles their investments are doing for them now.”

Luckily, AI can help retailers keep a pulse on the consumers’ ever-changing behavior.

Deloitte outlined four ways AI can help retailers take back control:

1. 1. Attracting Consumer Engagement
   2. Goods Management
   3. Redefine Stores
   4. Smart Supply Chain

Two of them (Goods Management and Smart Supply Chain) assist mainly in inventory management and ensuring the retailers orders enough of what’s selling and less of what’s not. For example, companies like ImageDT help retailers organize their shelf space.

Using ImageDT, retail associates can take a photo of a store shelf, and within minutes the company’s AI algorithms recommend optimized product placement, informing you of competitor pricing and how to fill empty space.

Another interesting aspect in the retail space revolves around Redefining Stores. In Gavin Baker’s latest piece, Why category leading brick and mortar retailers are likely the biggest long term Covid beneficiaries, he explains the importance of physical stores for retails future (emphasis mine):

“The future was always going to be omnichannel. Pundits have been prematurely predicting this for many years, but it is finally happening. There is a strange belief in certain circles that the future will be e-commerce only and that brick and mortar stores have no value. This is strange because the worlds largest, most sophisticated e-commerce companies are all opening stores. Lots of stores.”

Gavin goes further with this claim, saying (emphasis mine):

“Brick and mortar stores have tremendous online value in addition to enabling true omnichannel commerce. Nothing matters more for an e-commerce company than marketing efficiency expressed either as gross margin $ payback period or the ratio of CAC to LTV. Brick and mortar stores significantly lower online CAC by improving marketing efficiency (higher click through rates, higher quality scores for ads). Consumers are more likely to trust a brand they have seen in the real world. Ironic in a world where “CAC is the new rent” that one of the best ways to lower your online rent, i.e. CAC, is to pay rent offline for physical stores. Brick and mortar stores also enable BOPIS (buy online pickup in store) and the in-store return of items purchased online, which consumers value.”

If Gavin’s right (and I think he is), this means companies like Geohey become very valuable to commercial retail stores. Geohey leverages its database of economic indicators to help retailers pick the best spot for their new store. Here’s how the company explains their product (emphasis mine):

“The “Jihai Site Selection Solution” quantifies the customer flow, competing products, ecology, consumption, behavior, and location data around the store, and provides full-dimensional indicators for business operations through spatial calculations and geographic statistics.”

Artificial Intelligence helps retailers find and buy the best spots for their specific niche.

Healthcare: The Three Main Drivers of A Complex Industry

It’s astounding to ponder the amount of AI applications in the Healthcare space. It’s also easy to get overwhelmed. Thankfully Deloitte distilled the Healthcare industry’s AI applications to three main focus areas:

• • Intelligent Health Management
  • Intelligent Medical Imaging
  • Intelligent Diagnosis and Treatment

In general, AI Healthcare applications care about one thing: personalizing the approach to health and medicine. AI will usher in new waves of personalized medicine, care and treatment. We’re currently seeing this with applications in risk identification, virtual doctors, online diagnosis and precision-based interventions.

Intelligent Medical Imaging is another clear winner from AI adoption. Through image recognition algorithms, scientists can train an algorithm to detect cancer better than humans. And as we mentioned earlier, these algorithms only get better with each incremental image viewed. This ensures better diagnostic accuracy over time, unlike humans which decrease in cognitive function as they age.

Wrap-Up & Lead-In To Next Week

We’re just scratching the surface on industry-specific AI applications. Next week we’ll cover our two remaining industries: education and finance. We’ll dissect major pain points in each of these industries and reveal ways AI is helping solve these issues.

After that, we’ll dive into specific investment ideas fixated on this core theme of Defensible Data and Data Network Effects.

AI is one of the most intriguing thematics to study. There’s tremendous potential for multi-baggers and wide-moat businesses in this space. We can’t wait to find some of those companies with you!

Brandon Beylo

Student of value investing for over 13 years spending his time in small to micro-cap companies, spin-offs, SPACs and deep value liquidation situations.

Murata is the world’s largest producer of multi-layer ceramic chips (MLCCs) with over 40% market share. The company cranks out over 150 billion MLCC pieces per-month. The next closest peer, Samsung Electric (SEMCO) produces 100B pieces per-month.

Here’s why we’re interested in Murata now: The company’s main revenue source (MLCCs) will see a demand explosion from two sources: electric vehicles and 5G telecommunications.

The company owns the entire manufacturing process from start to finish, vertically integrating the supply of raw materials, sheet-casting, sintering, processing and finishing and inspection/packaging. They do all this in-house, allowing them to process, create and deliver customer orders faster than competition.

Murata’s dominant market share position allows it to invest more money in R&D than its competitors while keeping R&D expenses a lower percentage of total revenue. As an example, Murata will spend $1B on R&D this year compared to SEMCO’s (MLCC competitor) $416M R&D investment.

It also doesn’t hurt that the company’s long-term stock chart is tantalizingly bullish (see below):

These facts below should get anyone excited about the coming MLCC demand boom (emphasis mine):

• • The required number of automotive MLCCs increased from 3,000 units in 2012 to 8,000 units in 2018
  • Battery EVs (BEV) require much more MLCCs than internal combustion cars. They are expected to require around 30,000 MLCCs.
  • TESLA’s Model 3 contains over 9,000 MLCCs and their Model S & X each have over 10,000 MLCCs.
  • Murata predicts the MLCC usage of smartphones in 2024 will be 1.5 times more than that in 2019.
  • For instance, 5G smartphones which support sub-6GHz frequency band will need 10～15% MLCC more than those of 4G smartphones; and 5G smartphones which support mmWave will need 30～35% MLCC more than those of 4G smartphones. In estimation, each 5G smartphone will need more than 1000 units MLCC in the future.

It’s clear MLCCs will be vastly more important in five years than they are now. And we’re going to need a heck of a lot more of ‘em. In turn, MLCC producers, most notably the world’s largest, should be worth significantly more five years from now.

If that’s not convincing enough, The Global and China Multi-Layer Ceramic Capacitor (MLCC) Industry Report (2019 to 2025) expects MLCC supply to reach 6.1T/year by 2025. That’s six times higher than today’s production levels!

This essay will review the following:

• • What is an MLCC and Why Is It Important?
  • Murata’s Founding & Culture of Innovation
  • Murata’s Growth Story: Betting on EV & 5G

By the end of the essay, you’ll know what an MLCC is, why it’s important, where future demand will surface, and why Murata is the best-positioned company to capture that demand.

And before I forget, because a number of you asked for it we went ahead and created a monthly option for the Collective. You can find that here. This order form will only be live for another day or two so if you’d like to join our group make sure to sign up soon!

Now back to our regularly scheduled programming.

What Is An MLCC and Why Are They Important?

Multilayer Ceramic Capacitors (MLCC) are devices that store energy in the form of an electric field between layers of ceramic and metal material. Think of MLCCs like an Italian sub. Each layer consists of alternating genoa salami and capicola. MLCCs are also used to differentiate between high/low frequencies.

They were born out of necessity in Germany during the 1920s. The Germans ran out of the material, Mica. Luckily, they had spare porcelain (ceramic derivative) and tried it. The rest as they say is history.

MLCCs main benefits include the best high-frequency performance of any capacitor, as well as better stability in high temperatures. They also have a high voltage threshold, meaning they can withstand heavy electrostatic discharge without damage. Both of these features come in handy with electric vehicles (we’ll discuss that later).

The main disadvantage is smaller capacitance (the ability of a system to store an electric charge) per volume. MLCCs present a tradeoff between higher capacity for temperatures and voltage at the expense of electrical charge storage.

Two classes of MLCCs: Class I and II

Class 1 capacitors work best when high stability and low loss is required.

Class 2 capacitors have a higher capacitance per volume with thermal stability of typically 15%, so are better suited for less sensitive applications (source: JJSManufacturing)

They come in surface-mounted and leaded versions. Surface mounted means they “mount” surface boards (like CPUs and processors). Leaded capacitors have an epoxy coating and wires to assist in conduction.

There’s an MLCC In Every Device You Use

Chances are you use devices that use MLCCs every single day. Ceramic capacitors are the most produced capacitors in the world. Manufacturers crank out 1 Trillion of these microscopic devices every year.

They’re used in everything from smartphones to EV batteries to bluetooths and camera sensors.

Point to an electronic product or device and odds are, you’ll find an MLCC in it.

Why Are MLCCs Important?

MLCCs are inexpensive, extremely reliable and can withstand high voltages, frequencies and temperatures. This makes them the go-to capacitor for consumer electronics, 5G telecommunications and EV components.

They’re also incredibly scalable, with the smallest MLCC the size of a grain of sand.

As more devices need higher computing power at the edge, OEMs will look for the smallest MLCCs available. The smaller the MLCC the smaller OEMs can make their devices (see: wearable smart watches).

But perhaps the biggest reason why MLCCs are important is their ability to perform in EV batteries/cars and 5G smartphones and base stations. These two categories will drive MLCC growth for the next 5-10 years.

There’s one company that dominates the MLCC market: Murata Manufacturing. But before we understand where Murata sits in the current market, we need to understand the history of the company and its cultural fabric.

The Birth of Murata Manufacturing: The Story of How One Man Turned $102K Into A $14B Empire

Akira Murata founded the company in 1944 as a personal venture. At the start it was merely a small, family-run operation.

The company originally specialized in basic capacitors to solve the needs of a small number of customers.

But Murata wanted more. He wanted to help more customers and find new business. In 1947, he asked one of his school professors, Mr. Tanaka about a new ceramic product called barium titanate. Before discovering barium titanate, Murata used titanium-oxide. Titanium-oxide boasted a dielectric constant of 70-100. Barium titanate on the other hand had a whopping 1,000 – 10,000 constant.

Murata reorganized the company three years later with $102K of his own money (adjusted for inflation). It was also at this time Murata built his first factory outside Kyoto. The story on how he found the land to build the factory is fantastic.

In 1950, Murata received a call from a former work colleague at the Kyoto Research Institute, Mr. Senda. Senda happened to be the director of a ceramic laboratory. Senda showed Murata a large plot of land with massive pottery-stone reserves (i.e., ceramics).

This moment sparked Murata’s vision of factories, employees and subsidiaries across Japan.

Today, Murata generates over $14B in revenue and $2B in operating income. All that from a $102K investment. That’s a cool 19,607% return on Murata’s initial capital. Not bad.

So how did Murata go from a single factory to 77,00 employees and 150B capacitor production per month? To answer that question we have to understand the company’s culture and philosophy.

Murata’s Founding Philosophy: Pursuit of Originality & Moving First

Electronic component manufacturers are commodity businesses. They win by offering the lowest priced product to their customers. In doing so, they hope to gain market share at the expense of operating profit margins. Make it up in volume, right?

Murata’s competitive advantage is simple: they offer products that their competitors don’t. They’ve done that since their founding in 1950. The company notes in its 2019 report: “Murata offers products that competitors do not offer, to people that need them. This marked the start of Murata’s ‘pursuit of originality,’ which has led to originality across all aspects of our business, including our technology development capabilities, manufacturing capabilities, networks and organizational cooperation to integrate these elements.”

These strong competitive advantages took over half a century to solidify. And they were built on the back of solving a customer’s specific problem. Akira was the master of solving specific problems. He did things that didn’t scale (custom-building parts vs. mass production) to win the trust and adoration of customers.

This Pursuit of Originality is best shown in the company’s R&D segment, monozukuri manufacturing process and dogged determination to do everything in-house.

By keeping everything in-house, Akira could tinker with new raw materials, diagnose different manufacturing processes and create unique orders for specific customers. All the while keeping costs down and completion times quick.

Murata’s early success allowed it to scale quickly while reinvesting most of its profits into R&D to develop new products. This paved the way for Murata’s First Mover Advantage. Something it’s kept since its founding.

A History of Moving First

Murata’s history is earmarked by first-mover advantage. From its beginnings in 1940, the company recognized technological shifts and developed products for those rising industries. Murata was first-to-market with Japan’s first mass produced temperature compensating barium titanate ceramic capacitor for radios. That was five years after the company’s founding. Yet they saw the technological shift happening in radio during WWII.

The company executed during the 50s and 60s. They developed ceramic capacitors and semiconductors for black-and-white TV in the 50s and commercialized ceramic filters during the color-TV boom in the 60s.

Next came the 70s with the CB transceiver boom in the US and expanded use of audio-visual equipment in cars. Murata launched GIGAFIL dielectric filters for microwaves, commercialized ceramic resonators, chip ferrite beads and multi-layer LC filters during the decade.

The 90s and early 2000s gave rise to the Internet, miniaturization of mobile phones and spread of PCs. This led Murata to commercialize bluetooth modules, develop 0.4×0.2mm multilayer ceramic capacitors and MEMS gyro sensors.

Today, Murata continues to innovate and develop market-leading technology.

Murata’s scale is one of the many competitive advantages it has over its peers. It’s helped the company deliver 16.5% CAGR over the last decade (362.5% return).

As we’ll see in the final section, Murata is the best-positioned company to capture the positive MLCC demand shock from Electric Vehicles and 5G telecommunications technology.

Murata Today, Murata Tomorrow

Despite operating in a cyclical industry, Murata’s financial results are impressively consistent.

Since 2004 the company’s averaged 35%+ Gross Margins, 12-18% Operating Margins and reported only one losing year, 2008. During that time they’ve grown revenue from $4B to $14B, operating income from $539M to $2.24B and FCF from ~$400M to ~$900M.

Given those stats, one would expect to pay a lofty multiple of EBIT for the entire business. Not quite. At the time of writing, Murata trades roughly 18x NTM EBIT and 3x revenues.

Murata relies on three competitive advantages to keep its top-dog spot in an otherwise commoditized industry:

1. They anticipate market changes and customer needs better than competitors

This goes back to Murata’s early days of solving the needs of its initial customers. The company still embodies this tradition by focusing on their customers’ future problems, and how they can develop technologies to meet those needs.

Murata can do this better than any company in its industry. It’s 92 subsidiaries and thousands of customers provide tremendous insight other companies can’t get. With all these data points, Murata can spot trends and potential product failures before anyone else.

2. Continuous R&D investment enables new product development and IP accumulation

Murata has increased their R&D spend every year going back to 2004. In fact, if trends continue, they’ll invest over $1B in R&D in 2021. This is a massive competitive advantage against its competitors. The sheer dollar amount of R&D capital allows Murata to develop more products and fail more often in search of what works (see graph below).

For comparison, Samsung’s Electronics business (A009150) invested $419M in R&D last year.

Murata generates (on average) 36% of its revenue from new products. And on average the company spends ~6% of net sales on R&D. That’s important because while total R&D investment looks daunting ($1B), it still represents <10% of total revenue.

TDK is the only other company that compares to Murata’s R&D spending (TDK actually spends >$1B in R&D).

Part of this R&D strategy is to accumulate IP, specifically patents. As of 2018 the company had over 20.500 patent applications globally. That’s good for 29th highest in the world and 10th highest in Japan. It’s tough to compete with a company that has that many patents.

3. Strong Monozukuri Capabilities Enable Timely Supply

Translated literally, monozukuri means “making of things” or “production” in Japanese. But it goes deeper than that. Monozukuri encompasses, “the synthesis of technological prowess, know-how and spirit of Japan’s manufacturing process.”

Murata owns the entire manufacturing process from start to finish. The company vertically integrates the supply of raw materials, sheet-casting, sintering, processing and finishing and inspection/packaging.

Here’s Murata’s Director of Components discussing the power of Monozukuri (emphasis mine):

“A major factor in acquiring this high market share is the fact that we can complete everything from development to manufacturing internally. In other words, everything from ceramic material selection to production facilities and manufacturing process technology is taken care of by our own internal framework. As a result, customer requests can be quickly led forward into development, and products can be supplied at lower cost due to various cost reduction options.”

Murata Today: The Clear Leader in Filters and Capacitors

Murata competes on four major products (see global market share %):

• • Chip multilayer ceramic capacitors (40%): Discussed above
  • SAW filters (50%): Extract only the required portion of a radio signal. Key devices in high frequency circuits.
  • Noise suppression products EMI suppression filters (35%): Eliminate external noise and protect delicate electronic circuits
  • Connectivity Modules (55%): Allow various devices to access the Internet via radio signals

The company generates 36.5% of their sales from capacitors, 27.1% from Communications Modules, 25% from “Other”, 8% from Piezoelectric Components and 2.6% from Power supplies and other modules. Broken down by industry the company generates 48% of its revenues from Communications, 16.4% from Automotive Electronics, 15.8% from Computers & Peripherals and 15% from Home & Others.

From a regional standpoint, Murata earned 50.5% of its revenues from China, 16.5% from Asia, 15% from the Americas, 9% from Japan and 8% from Europe.

Murata’s MLCC segment grew 27% YoY and is the biggest factor in the company’s continued success. Let’s see how they plan to capture the coming demand shock.

Murata Tomorrow: A Management Team Pointed In The Right Direction

It’s worth reiterating that the only thing that matters for Murata’s future success is their ability to capture the growing demand for MLCCs in the Automotive and Communications markets via EV and 5G.

Management knows this, too. Both Murata’s President and Director of Components stress the importance of EV and 5G in their 2019 Annual Report.

Here’s what President Tsuneo Murata (the founder’s son) had to say about the automotive industry (emphasis mine):

“We are also seeing changes in the automotive industry that will have a significant impact on the world of electronics. The potential of semiconductors and communication functions will increase through electrification and automated driving, and automobiles are expected to move closer to being considered electronic or communication devices just like smartphones.”

Murata’s Director of Components, Toru Inoue echoed Tsuneo’s sentiment on its two key markets (emphasis mine):

“For 5G, it is naturally important to consider which applications will become mainstream as data volumes increase significantly, but the evolution of smartphones and all kinds of wearable devices will continue to centralize on becoming smaller and thinner and featuring increased functionality, and capacitors will be required to accommodate larger capacities … In addition, EVs, V2X and automated driving require high reliability in products that will never fail even in harsh environments such as high temperatures, high humidity, high voltage and high currents.”

Before diving into valuation, let’s summarize the bull thesis for EV and 5G.

Bull Thesis: Autonomous Driving

Murata’s MLCCs will meet heightened demands as powertrains move to EV and Autonomous Driving levels increase. The company expects Mild HEV (Hybrid/Electric) powertrains to reach ~15-20M units by 2024. At the same time, Autonomous Driving levels should increase from zero to a majority at Level 1 and Level 2.

This is important because higher levels of EV and Autonomous Driving mean more MLCCs in the car (see graph).

More importantly, automotive sales don’t need to be stellar for Murata to experience automotive MLCC growth (see below):

Imagine how valuable Murata’s Automotive MLCC segment will be when nearly every car on the road is fully electric with Level 3 Autonomous Driving capabilities.

Bull Thesis: 5G Communications

TSR estimates that by 2025 the world will have nearly 1B 5G smartphones in circulation. And remember, 5G smartphones need more MLCCs than LTE and “dumb” phones.

2020 MLCC Demand and Supply Analysis notes that (emphasis mine), “5G smartphones which support sub-6GHz frequency band will need 10～15% MLCC more than those of 4G smartphones; and 5G smartphones which support mmWave will need 30～35% MLCC more than those of 4G smartphones. In estimation, each 5G smartphone will need more than 1000 units MLCC in the future.”

That’s a lot of future MLCC demand.

Murata notes the reasons for increased MLCC demand in 5G phones, including:

• • More frequency bands
  • Higher frequencies
  • Advanced communication technologies
  • More sensors and cameras
  • Larger batteries

The company has the components to meet this demand (see below):

As the largest producer of MLCC’s in the world, Murata is well-positioned to supply the needs of EV/Autonomous vehicles and 5G. But how could they fail? If we look back in five years and we’re wrong, where would the errors lie?

Risks

There’s a few major risks to Murata’s future success:

• • Rise in Chinese-owned MLCC production
  • Increased demand from alternative capacitors like aluminum polymer and tantalum due to high lead times in MLCC supply
  • Inability to correctly anticipate changing customer needs/landscape resulting in poor R&D investment (i.e., burning cash)
  • Increased losses in the company’s lithium-ion battery business (small part of the company’s operations)
  • Economic recession (or COVID-related scare) that delays demand for 5G and EV/Autonomous vehicle investment

Valuation

Murata is a durable, consistent company that cranks out 30%+ gross margins and 12-17%+ EBIT margins like clockwork.

The company generates a 23% Operating Cushion, which isn’t bad. Yet more than 100% of that operating cushion is eaten by working capital. Last year inventory alone sucked 21% of the company’s Operating Cushion, followed by 18% from accounts receivables.

With supply gluts in the past, Murata should experience improved inventory/receivables management. This in turn will help it turn FCF positive over the next five years.

Today’s stock price assumes a few things on an EBITDA exit basis over the next five years:

• • Average revenue growth of 5%
  • 37% average gross margin
  • 16% average EBIT margin
  • 25% average EBITDA margin

These above assumptions get us $18B in revenues, $4.74B in EBITDA and $2.24B in after-tax operating earnings. A 15x 2025 EBITDA multiple at a 10% discount rate gets us a little over $43B in shareholder value. Which is where we stand today.

If the future demand from EV/Autonomous Driving and 5G are real and materialize, the above revenue growth rate is too low. What happens if we assume Murata grows 13%/year for the next five years?

In that world we get $25.9B in revenue, $6.77B in EBITDA and $3.2B in after-tax profits. Sticking with our 15x multiple on EBITDA gets us $62.5B in shareholder value (~42% higher than current price).

Concluding Thoughts

Today’s price assumes a low-growth environment for a company whose industry tailwinds are just getting started. The market doesn’t realize this, of course, because it’s terrible at predicting exponential growth. Who knows, maybe 13%/year top-line growth is too low? We don’t know.

What we do know is the future looks bright for Murata’s core products and markets. And Mr. Market doesn’t seem to think that.

Brandon Beylo

Student of value investing for over 13 years spending his time in small to micro-cap companies, spin-offs, SPACs and deep value liquidation situations.

Cintas Corp (CTAS) provides corporate identity uniforms and related business services primarily in North America, Latin America, Europe, and Asia. It operates through Uniform Rental and Facility Services and First Aid and Safety Services segments. The company rents and services uniforms and other garments, including flame resistant clothing, mats, mops and shop towels, and other ancillary items; and provides restroom cleaning services and supplies, and carpet and tile cleaning services, as well as sells uniforms. It also offers first aid and safety services, and fire protection products and services.

Cintas is a boring business.

But this boring business has returned over 1,000% for investors since 2009?

Our CTAS case study dives into the early days of the company. We examine initial investor sentiment. What did the company do well? What were investors’ priorities? We place ourselves in the shoes of those first investors. Those that saw what others could only recognize in hindsight.

We’ll review past newspaper articles and investor write-ups. The bull and bear cases.

Consider this case study a time-travel machine.

Our goal: to know what really made these super stocks soar.

Analyzing past winners won’t guarantee future success. But it does paint a picture of what a winner might look like and hopefully after doing a number of these we can tease out some commonalities that’ll help us identify super stocks down the road.

So let’s dive into the wonderful world of uniform renting, cleaning, and laundering.

The Early Days: Acme Industrial Laundry Company

Richard “Doc” Farmer founded Cintas in 1929 under the name Acme Industrial Laundry Company. Doc got his start collecting chemical-soaked rags from various factories. He would then wash and re-sell the rags to factories for a fee.

By 1956, Doc Farmer’s grandson Richard “Dick” Farmer joined the family business. Fresh with ideas from an undergraduate degree, Dick Farmer grew sales from $300K in 1959 to $847K in 1963. Dick assumed the role of CEO in 1968.

Farmer then drew up a new business plan for the company: Open uniform rental plants across the United States.

Acme opened its first uniform rental plant in Cleveland, OH in 1968. In 1972 they changed the name to Cintas. Then in 1983 Cintas went public and traded on the OTC (Over-The-Counter) market.

Let’s learn how CTAS became today’s Super Stock.

Lesson 1: Cintas’ Fast (and Early!) Growth

CTAS hit the ground running after its IPO. A 1990 newspaper article highlights CTAS’ success as a public company (right):

During its seven years on the market, CTAS put up:

• • 24% compounded annual earnings growth
  • 24% compounded annual sales growth
  • 20% average ROE

Growth, growth and more growth. That was the key to CTAS’ early success. The company’s strategy was working. And investors could see it on the top and bottom-line.

Where did that growth come from? Expansion into new and existing markets. By 1990, CTAS commanded 10% share in the uniform rental market. The more uniform rental plants CTAS could fit on the map, the more money they made. This snippet from a 1995 interview outlines CTAS’ core business strategy (emphasis mine):

“In good economic times, uniform companies grow by increasing the size of existing accounts and convincing companies that have never rented workers’ uniforms that it’s cost-efficient and enhances their corporate image, says analyst Jim Stoeffel of Smith Barney. In bad times, the big companies grow more through acquisition, because more of the 700 or so small uniform-rental companies become willing to sell their businesses for lower prices, says analyst Craig.”

Between its IPO and 1990, CTAS expanded to 37 new markets and operated in 26 states.

Moreover, the company’s story made sense. Here was a boring business servicing a high-demand, highly fragmented market. The roll-up strategy worked. The business did near 17% EBIT margins and had a long runway for growth. Insiders owned over half the company. They had little debt on the balance sheet.

CTAS outlines the bull thesis in their 1996 Annual Report:

Is it a stretch to say that screenshot is all the due diligence needed to invest in the company in 1996?

It’s easy to see the growth potential in hindsight. But what were the analysts thinking at the time? Here’s a snippet from a 1991 Kiplinger’s Personal Finance Magazine article (emphasis mine):

“Because Cintas operates in only three-fourths of the largest markets, brokerage firm Alex. Brown & Sons considers its potential for growth by expansion to be “substantial.” Analyst Sally Smith estimates long-term earnings growth at 15% to 20% a year. Similarly, Shearson Lehman Brothers picks Cintas to outperform the market over the coming year. It cites the company’s ability to sustain sales and earnings growth of existing outlets while spending on expansion. Shearson estimates $1.73 per share in earnings for fiscal 1992, versus $1.47 in fiscal ’91, and projects a possible share price of $52 to $53 by spring.”

Revenue and earnings growth are staples of super stocks. CTAS had both in spades. The company expanded market share and by 1996 was generating $730M in annual revenue. Read this clip from CTAS’ 1996 Annual Report (emphasis mine):

“When Cintas went public in August 1983, we were comparable in size to the other public companies in our industry.  Since then, Cintas has grown at a faster rate and now is the largest public company – almost twice as large as the next largest company in the industry.”

And that’s the most important question: why was CTAS able to grow twice as large as the next largest company in the industry? There’s a few reasons for this success, much of which we’ll cover later in this piece.

1. 1. Aggressive Expansion: The company expanded into more territories faster than its competition. This created a flywheel effect. More facilities allowed the company to service more companies in their local areas. It also helped them command better pricing from its suppliers. Each new distribution center brought the company closer to its customers, which further reduced costs.
   2. Leader in Acquisitions: The uniform rental business was loaded with mom and pop operations. And nobody rolled-up more of these companies than CTAS. The company’s consistently strong balance sheet allowed them to buy in any market cycle. So, while their competition shored up operations to conserve their (little) cash, CTAS got aggressive.
   3. Higher Spending On Facility Improvement: More facilities meant more customers, which meant more revenue. The increased revenue allowed CTAS to invest heavily in optimizing the performance of their facilities. The company was constantly investing in making their facilities run better and sport the latest technology.

CTAS created a snowball effect. And at that point the goal was simple: expand into as many territories as possible. This snowball helped take CTAS from <5% market share to 17% by 1996.

All investors had to do was follow the number of new facilities CTAS opened, and confirm that their existing facilities were operating profitably.

But it’s not that easy. Many investors sat idle as CTAS made 20%+ annual returns. Why? The answer to that question lies in our second lesson.

Lesson 2: Super Stocks Always Look Expensive

Super Stocks will always look expensive on a short-term valuation basis. There’s intuitive logic behind this idea. You don’t buy an Armani suit for the same price as an off-brand Target sport coat. One is clearly higher quality.

You get what you pay for. Super Stocks look expensive because they’re higher quality businesses. You shouldn’t expect to pay Five Below prices for something that belongs behind a glass display case.

CTAS was no exception. In the previous 1995 article, portfolio manager Mark Fuller described CTAS’ valuation (emphasis mine):

“Cintas, which earned $1.12 a share in fiscal (May) 1994, traded recently at 363/4, a seemingly pricey 33 times trailing earnings. “Seemingly” is the operative word, however. The p/e ratio has been sky-high throughout Cintas’s decade as a public company, with 28 as its historical mid-range. ‘“Some of our best investments look sort of expensive,“’ says Mark Fuller, a portfolio manager at William Blair Investment Management.”

Let’s focus on the phrase “sort of expensive”. This implies a few things. First, it suggests that it’s not a traditional “value” stock. You wouldn’t have found CTAS on a deep value scan. Yet at the same time the price never reached astronomical levels. It’s this in-between valuation that propels Super Stocks higher.

And I get it. A 38x P/E multiple assumes a 2.60% forward return. But that’s without considering actual earnings growth. CTAS traded at 38x earnings in 1994. But they were growing those earnings over 25% annually. That’s a 1.52x PEG (Price-to-Earnings Growth). Again, not bad.

Looking expensive comes at a cost. It’s around this time that short sellers come out of the woodwork. The hope, of course, is selling short a company trading above their estimate of “fair multiple”. CTAS fell victim to various short reports. Check out this one below from a 1999 Streetwalker report:

Here’s their reasoning for shorting CTAS at 38x earnings:

• • “Back out the acquisitions, though, and you have a much more stagnant-look-ing company”
  • “The biggest growth in employment is coming from technology, service and healthcare jobs, many of which don’t require uniforms.”

There’s one major problem with the Streetwalker report. It didn’t actually address why the company couldn’t continue to grow over the next five-to-ten years. The main crux of the argument was “it’s at 38x earnings and grows via acquisitions.”

Moreover, CTAS outlined their acquisition strategy from the beginning. It was always a large part of their growth plans. Sam Rovit and Catherine Lemire described CTAS’ strategy in their 2003 Forethought letter:

Here’s the best part of that snippet: “Since the 1960s, Cintas has supplemented its organic growth with a steady diet of small acquisitions.” Knowing this, Streetwalker’s report falls mute.

By 2004, CTAS owned 30% of the uniform market. Their second largest competitor, Aramark, couldn’t compete. The former chemical rag washer generated 60% higher EBIT margins than Aramark in 2004. With competitive advantages like that, It makes sense the company traded at near 40x earnings.

The above is a monthly chart of CTAS stock price since its IPO in 1984. Notice the stock’s torrid gains from 1984-1999. It wasn’t until the Dotcom Bubble that CTAS experienced its first true test/trading range.

Lesson 3: Insider Ownership & A Boring Business

CTAS had ample skin in the game from the top down. Doc Farmer owned 52% of the company during his stint as chairman. Early CTAS management didn’t rely on salaries and bonuses to generate wealth. They needed operational success and stock price appreciation. Most of CTAS’ management’s net worth was in their stock ownership.

On top of that, the company reduced its share count from 171M in 2004 to 104M in 2020.

The uniform rental business is a boring business. A 1991 article titled, Unappetizing suggestions for tasty stock profits, defined CTAS’ business as, “dull, dull and dull.” Twitter user @PermanentCap shared similar thoughts (see right).

It’s part of what made CTAS such a great company. Boring industries usually have loads of mom-and-pop shops. Family-run businesses with no exit plan because their son Jeremy wants to develop software, not clean clothes.

Boring industries are the perfect hunting grounds for a CTAS roll-up strategy.

Lesson 4: Low Debt

CTAS used little debt. The 1996 Annual report shows a Debt/Capital ratio of 22.5%. In fact, CTAS stayed around that range for most of its decade price advance. It wasn’t until 2017 that we saw Debt/Capital breach the 50% mark.

While the Dotcom bubble showered markets with lackluster balance sheets, CTAS stood tall. The company’s balance sheet strength allowed them to buy businesses in any market cycle. What a huge advantage! Again, it all goes back to CTAS’ core strategy: buy and expand into new and existing markets.

Wrapping Up: Yesterday’s Winners Provide Today’s Clues

CTAS possessed a few key attributes that made it one of the highest returning stocks of the decade:

• • Early and fast revenue/earnings growth
  • Industry-leading EBIT margins
  • Boring industry with small competitors (apt for roll-up)
  • Heavily incentivized management team
  • Little debt

There’s a reason why early and fast revenue/earnings growth is the number one attribute listed. Earnings and revenue growth drive long-term stock price performance. If a company is consistently growing earnings and exceeding expectations, the stock will eventually reflect that growth.

Check out CTAS’ revenue growth since 2009:

Like we mentioned, revenue growth isn’t enough. We need earnings per share growth. Here’s what CTAS’ EPS looked like during the same time frame:

CTAS increased it’s EPS from $1.59/share in 2004 to $8.36/share in 2020. But to achieve Super Stock status, we need one more thing: multiple expansion.

Here’s a chart of CTAS’ P/E ratio since 2009:

Next let’s look at EV/Sales from the same time frame:

Again we see the same story. 1x sales to 5.42x sales in ten years.Next let’s look at EV/Sales from the same time frame:

Revenue growth + Earnings growth x Multiple Expansion = Super Stock

Not every Super Stock will possess these exact attributes. But it gives you a few ingredients needed for long-term success. A few more filters to screen potential investments.

Macro Ops Composite System (MOCS) Analysis

We’re stoked to bring you our new Macro Ops Composite System (or MOCS, for short). MOCS allows us to get a full picture of a company’s health at a point in time. Think of it like taking the temperature of a patient. A doctor starts there, and dives deeper. We shamelessly cloned much of this system from our friend Brian Feroldi.

The MOCS analyzes the following categories:

• • Financials
  • Moat
  • Potential
  • Customers
  • Company-specific factors
  • Management & Culture
  • Stock Price

Each category has a different weighting. For example, financials score between 0 – 5. Moats score between 0 and 15. Etcetera, etc. This reinforces what we want to look for in investments.

Those seven categories give us our Pre-Garbage Bin Score. From here, we run each company through our Garbage Bin Test (GBT).

The GBT reduces a company’s overall score for negative characteristics like:

• • Increased share dilution
  • Customer concentration
  • Outside forces
  • Large share price decline
  • Complicated financials
  • Currency risk

These criteria subtract from the total score. The most you can lose in the Pre-Garbage Bin Test is -44.

The highest rating a company can get is 100. The lowest, -44. The higher the better. When it comes to making the final investment decision, the scoring goes:

• • >80: Why haven’t we bought (or bought more)?
  • 65 – 79: We should invest
  • <60: Avoid for now

Cintas (CTAS) MOCS Score

CTAS scored an 85 before the GBT. We deducted 9 points from the GBT for a total score of 76. That scores CTAS in the 76% percentile of top companies. And it falls under the “We should invest” range above.

Macro Ops Collective members can access the MOCS score in the Operators dashboard.

Reading The Tape: Cintas Chart Analysis

We know the fundamental story behind CTAS’ success. Now let’s hit the tape and analyze the company’s stock chart. We’re using the weekly time frame and going all the way back to the IPO.

We’ll reveal chart patterns that provided an optimal, low-risk entry point.

Let’s get after it!

Chart 1: 1983 – 1987

Chart 2: 1989 – 1991

Chart 3: 1994 – 1997

Chart 4: 1999 – 2001

Chart 5: 2013 – 2015

Chart 6: 2016 – 2018

Brandon Beylo

Student of value investing for over 13 years spending his time in small to micro-cap companies, spin-offs, SPACs and deep value liquidation situations.