Digital Economics: Revisited
Physical vs. Digital
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Hello fellow investors,
Welcome back! In today’s newsletter, we will be discussing the differing economics of the traditional (Old Economy) and Modern (New Economy). We will also analyze the qualitative advantages of digital business and explain why we believe current digitization trends will continue into the future.
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TL;DR Version:
Digital businesses can grow faster, easier and with less capital than businesses constrained by the physical world.
Adaptable product design has significant advantages over traditional product design for both consumers and businesses.
Modern businesses succeed by (1) Digitizing the customer experience for data collection capabilities, (2) Encoding the data into a form that is understandable by computers, and (3) Utilizing machine learning technology to achieve scale and manage operational complexity that would otherwise be impossible.
Physical vs. Digital Economics
When thinking about our investable universe from a high level, we categorize our businesses into two major camps: Old Economy and New Economy. Old economy industries include logistics, steel, mining, oil, gas, chemicals, agriculture, and manufacturing, among others. New economy industries include social media, biotech, fintech, software as a service, cloud computing, artificial intelligence, and our personal favorite, online marketplaces. To understand the current digitization trends occurring in every industry, it is vital to recognize the structural economic differences between physical and digital businesses.
Let’s start by looking at an Old Economy business: auto-manufacturing. In this example, an owner would have to source capital to build a factory, equip it with machinery, hire workers and have enough cash in the bank to cover production and operational expenses until the factory scaled up to a profitable production volume. As the business grows and sells more cars, profitability improves linearly because of operating leverage. (I.e. variable costs grow in tandem with production volume, but fixed capital and operating costs are spread across more cars being produced)
However, once production goes beyond the factory’s capacity, the owner needs to build a new factory, raise more capital to fund it, and go through the profitability curve all over again.
In this case, growth is a mixed blessing for the physical world business. On the one hand, profits are growing on an absolute basis. But each time, growth requires new capital and increasingly complex organizational layers, which increase costs even further. As these businesses grow larger, they require deep managerial, operational, and manufacturing infrastructure to produce more. If the business wants to expand beyond its immediate geography, complexities around logistics, time, and space create additional friction to scale. This is what economists refer to as the Law of Diminishing Returns
Now, let’s compare this to a growing online platform business. The differences are apparent when evaluating a 10x customer growth scenario for both models. In the platform business, growth from 1m users to 10m users has the highest capital intensity until the business generates network effects. Once the business reaches sufficient scale, the value created through the network effect becomes self-propelling. In other words, for modern business, the velocity of growth increases with scale. This is what is referred to in the finance world as non-linear growth.
Conversely, a 10x growth in the customer base for an auto-manufacturer would require several reorganizations, large capital investments, increasing administrative costs, and diminishing incremental returns on capital.
In summary, digital businesses can grow faster, easier and with less capital than businesses constrained by the physical world.
We believe that industrial businesses will continue to digitize as a way to benefit from these superior economic characteristics. In our Q1 Letter to Partners, we discussed our thoughts on current business trends that we believe will continue to shape our investments over the next decade, stating,
“The physical world will continue to move toward digital channels as it is more productive and efficient for both businesses and consumers. Because capitalism rewards optimization with higher profits, the business world naturally adopts what is optimal over time. The new, more efficient business process becomes the standard once it is fully adopted by all operators.” - Q1 Letter
Adaptable Products, Datafication, and Machine Learning
One of the major differences between traditional and modern business is in product design. Adaptable product design has notable advantages over traditional as it:
Significantly broadens the customer base and thus, market opportunities.
Allows for data-intelligence capabilities which the firm can then use to improve its products and services in real time.
Allows businesses to better adjust to market conditions and trends, thus potentially increasing business durability and lifespan.
The adaptable product interface connects the customer to the firm, so that the firm can observe customer behavior and preferences and create a unique product experience at an individual level. Modern businesses use technology to coordinate business activity across an unlimited number of interconnected parties, disrupting traditional processes.
Take, for example, Spotify’s streaming platform. While individual artists produce music that adheres to a segmented part of the music listening customer base, Spotify, as a platform, is not limited by such segmentation. Spotify’s product can be enjoyed by fans of over 1,500 genres of music. This expands the market opportunity for a new economy business in ways that could not be accomplished otherwise. For many traditional industries, creating an adaptable product is a serious challenge. Imagine, for example, if Ford Motors had to design a vehicle that would appease the tastes of every potential automotive customer. It would be impossible as the product is non-adaptable and constrained by the physical world.
Some other examples of businesses that excel in adaptable product design include Netflix, Meta, Microsoft, Amazon, Alphabet and Apple.
Better Data = Better Business
A key advantage of a new economy business is in the data-intelligence capabilities inherent to digital commerce. By having direct access to customer behavior online, companies no longer have to rely on costly third-party market research, or ineffective customer surveys dependent on customer responses (as opposed to their actions.) We find it logical that better data equates to better business decisions for any industry. To illustrate this point, let’s analyze the data-collection capabilities of an e-commerce company like Amazon Inc. compared to a traditional retailer.
Amazons ability to collect and analyze customer data is unmatched by physical retail stores. By owning the platform on which all business is conducted, Amazon can record how often each individual customer uses the website, whether they shopped quickly or lingered for hours, and whether they toggled back and forth to compare prices at other sellers. It knows in real time not only what show the customer is watching on Prime Video, but what episode the customer has reached. By triangulating granular data on customer behavior, Amazon knows exponentially more about a customer’s lifestyle and preferences than could be possible for a traditional retailer in the old economy model. The more data the business can amass, the more reliably it will be able to use prompts, promotions, and pricing to increase sales at an individual customer level.
For a second example, let’s analyze one of the oldest industries, lending.
Lending is a simple business to understand. At the most basic level, lending institutions need to answer three questions when faced with a potential borrower: Do we lend to them, how much should we lend, and at what interest rate? In a traditional lending business, the standard method for this assessment is looking at third-party information such as their credit score and employment history, and then processing paperwork, with the hope of obtaining useful information. This process relies on static data from multiple parties and is subject to a relatively limited number of data points.
To understand the advantages of a digital lender, we can analyze Alibaba’s Ant Group. Ant Group is a financial services business that provides loans to small and medium sized enterprises who conduct business on Alibaba’s e-commerce platforms in China. By leveraging merchant data on Alibaba’s Taobao and Tmall shopping sites, the company is able to generate loans based on richer data sets than could be obtained by traditional methods. For example, the company uses data points regarding the amount of time merchants spend working on their online stores, product trends, and a plethora of customer reviews and references. The business also looks into the credit history for close associates of the potential borrower. Better data models lead to a more-accurate understanding of how much to lend and how much interest to charge. In essence, as a fully digital lender, the company can build a richer borrower profile, leading to much lower delinquency rates and higher profits when compared to traditional lenders.
Dynamic Pricing
Dynamic pricing, sometimes referred to as demand pricing, is the practice of varying the price for a product or service to reflect changing market conditions, in particular the charging of a higher price at a time of greater demand. On a personal note, I experienced dynamic pricing first-hand during a day out in New York City. It began to rain unexpectedly and I, without an umbrella, stopped at a local shop to purchase one. The umbrellas were stored in a bin marked $5.00. When I got to the counter to pay, I was informed that the price of the umbrella was now $8.00. (To a man without an umbrella in a storm, a $5 umbrella can cost him $8.)
While a local store can change pricing manually, a new economy business with the assistance of machine learning and algorithms can dynamically change prices on a much greater scale and with greater speed. Take for example, the ride-sharing business, Uber Technologies.
Uber uses a dynamic pricing algorithm, which adjusts rates based on a number of variables, such as time and distance of your route, traffic and the current rider-to-driver demand. When demand increases, Uber uses variable costs to encourage more drivers to get on the road and help deal with the number of rider requests. Once more drivers get on the road and ride requests are taken, the demand becomes more manageable and fares revert to normal. Dynamic pricing helps the business to make sure there are always enough drivers to handle ride requests, so customers can get a ride quickly and easily.
Some other common examples of industries that use dynamic pricing include airlines, hospitality, e-commerce, and event ticketing.
In summary, modern businesses succeed over traditional by employing the following three steps:
Digitizing the customer experience for data collection capabilities.
Encoding the data into a form that is understandable by computers.
Utilizing machine learning technology to achieve scale and manage operational complexity that would otherwise be impossible.
Thank you for reading! We hope that you found this discussion valuable. All feedback is welcomed in the comment section below. Until next time!
Sincerely,
Jack Beiro, MBA
JB Global Capital
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The information contained herein represents the author’s opinion and is for informational purposes only. Nothing in this newsletter should be construed as legal, tax, investment, or financial advice. No opinion expressed by the author should be construed as a specific inducement to make a particular investment or follow a particular strategy. The author may hold positions in securities mentioned in the newsletter and may buy or sell securities at any time. The author may express opinions based on information he considers reliable, but no guarantee or warranty is made with respect to such information’s completeness or accuracy, and the author is under no obligation to update or correct any information provided. Please consult your own financial or investment advisor before acting on any information provided herein.