We operate in a world where search, social, and sales platforms like Google, Facebook, Alibaba, and Amazon define a significant percentage of growth opportunities for businesses in the United States and in Europe.
As global revenue streams are funneled through a few digital pipelines and opportunities to buy narrowed to a few search results, relatively significant portions of economies center around the revenue driven through the data gathered by the pixels and clicks owned by a few large public companies. Have you considered the “friction” and “forces” encountered by the consumers as they try to complete those sales?
Using data, we can, in theory, trace the point of capture either digitally or in person all the way back to when a person initially searches or voices their desire for a product. With that in mind, starting at the beginning of the process, there are many places for a sale (“a conversion”) to be disrupted. The information garnered by pixels combined with ever increasing knowledge owned by big data may ensure that certain concepts, ideas, and businesses either succeed or fail. This is true in many places around the world as sites like Google, Amazon, Facebook, and Twitter are the primary sources of traffic for most retail-based websites. The new “trusted shelf” is page one of the natural search listings or to be naturally present in a social media feed. Through my blog, I’ve gradually been teaching some new concepts that have helped me to better understand my own business and it’s uniquely precipitous position in the new marketplace as Google, Amazon, Facebook, and Twitter attempt to balance the interest of individuals, intellectual property holders, small companies, artists, and large corporations. As the world is learning, algorithms have the ability to cause success and failure based on the type of data analyzed. Algorithms owned by public companies tend to make increasingly self-interested decisions over time as the need to increase shareholder value goes up and constraints involved in the sale change over time.
“Psychological Constraints” and “Functional Constraints” are described in detail in another post entitled “Why Anti-Trust Law Matters“. I suggest reading this article before diving into the content of this post. In this particular article, I’m introducing another concept called “The Ease of The Sale“.
DEFINITION: The ease of the sale is a governing force that functional constraints and psychological constraints are working against. If the constraints are removed, the sale could be considered “easy”. If the constraints are increased, the sale could be considered “difficult” because the constraints provide friction against the force of the sale, preventing the sale from happening.
We’ve been learning about these concepts in the context of Amazon’s growth. Within this context, Amazon had to learn how to move into new industries. This process was iterative, meaning they had to go through the process repeatedly. The first time an industry was attempted, there was very little data and very little technology, so it was difficult and expensive. As data and technology was acquired or learned, constraints were slowly removed, making the sale easier to obtain. Today, there is a lot of data and a lot of technology so, for Amazon, an industry move would be easy and less expensive than it might be for a competitor with less data and technology [Figure 2].
Given these circumstances, assuming that learning occurs, every time Amazon enters a new industry, it might be easier for Amazon to move into the next new industry.
Here’s why:
Entering an industry requires knowledge. Amazon has the ability to learn through competitive business intelligence, but denies using data for this purpose. In fact, the European Union is investigating Amazon’s use of competitive data to make strategic decisions to its own advantage.
Anytime a business sells a product or a service through Amazon, that data may enable Amazon to make more informed decisions about how to offer similar, lower-cost, Amazon-branded products and services to customers.
- Use of knowledge about industry behaviors before making an investment would be considered a “removal of constraints”.
- Artificial intelligence mining consumer shopping behavior would be considered a “removal of constraints”.
- The addition of acquired innovative technology would be considered a “removal of constraints”.
Prior interpretations of anti-trust law have previously held that it is best for the consumer if corporations engage in efficient behavior designed to produce the lowest possible price for the consumer. This is a true statement when multiple corporations are actively engaged in truly competitive behavior. Competitive behavior is essential to ensure real price competition and low prices.
In my opinion, it may be impossible to ensure competition when data is co-housed and able to be mined for the benefit of a single large corporation in a market dominant position. With its competitive platform and knowledge of both the consumer and the business side of the data-space, Amazon may be able to enter and be almost immediately competitive in some new industries through search positioning, acquisition of brands, and threat of dominance.
Amazon has scaled their platform effectively over the last twenty years. They’ve scaled in a world where the rules of engagement were written for traditional businesses completing transactions face to face. Prior to the impact of coronavirus, those face-to-face businesses were disappearing as other businesses with deep cash resources stepped forward to compete with Amazon in some interesting ways, creating new, platform-based competitive environments. During coronavirus, the idea of a face-to-face world has all but disappeared as people stopped wanting to do business in person.
As other types of retail platforms develop, the real challenge faced in the retail industry is going to be product differentiation as the product selection may stagnate. Amazon drives over half of the retail click traffic. Google clicks tend to stop after the second page. Consumers eyes are limited to the top half of the first page. Who owns the rights to what gets seen and where? How is that determined when shelves and feet no longer drive diversity of product exposure? What is a product launch? Do companies now owe both Amazon and Google a theoretical tax to even be “known”?
I am often asked how to measure how these platforms are doing with their efforts. As I consider that question, I look at balance sheets first to see how the business is behaving. Are they investing? Are they changing other behaviors? Have they reached a point where they cannot be stopped? How “easy is their sale”?
In his book, Good to Great, Jim Collins described something called the “The Flywheel Effect”. In his most recent book, Turning The Flywheel, Collins publishes Amazon’s flywheel and describes his business planning session with Amazon’s executive team in the fall of 2001.
While I don’t endorse all of Collins’ concepts, I do believe in the flywheel with one caveat. It does eventually “max out” in a business’ financial analysis (see Graph 1 below). A flywheel can’t continue to become easier and easier. It will not produce profitable revenue growth forever because it exists within an economic and a competitive environment. The business must refine its processes or it will eventually roll over a financial hill and find it difficult (if not impossible) to stop the decline. When that happens, the business will find that sales have become more difficult and expensive to obtain.
With that in mind, I believe Amazon seems to have refined their processes to the point that they can be iterated, or copied, across industry. Each new industry sets the stage for Amazon to grow and learn something new, developing new technology and another new set of data about consumer behaviors, products, companies, and countries. As that data is enmeshed with other data, Amazon is eventually able to dominate regardless of the nature of the business.
“Trace your way around the Amazon flywheel a few times in your mind, and you can almost get swept up in the momentum. Each component in the flywheel sets you up for the next component, indeed, almost throwing you around the loop.”
Collins, Jim. Turning the Flywheel (Good to Great) (p. 3). HarperBusiness. Kindle Edition.
If a successful flywheel is copied, the “ease of the sale” (the force that the firm, in this case, Amazon, must PULL against to produce revenue) is lessened, improving operating income. This is only true because Amazon has so much data to mine and so much cash to use.
If you’re interested, you can measure and track how effective a public company is implementing a “flywheel” concept by looking at the firm’s operating income.
If we look at this graph another way, just ask yourself, “How easy would it be to slow down or stop this company?” As operating income flattens or slopes down, it shows the firm having to work harder to produce top line revenue and profitable net income. As operating income shows a steeper slope up, it shows a deep wealth of revenue that can be tapped to fund growth. Downward sloping lines indicate investment or unhealthy companies.
Figure 2: A quick study of this graph shows Amazon’s learning process taking effect. The top line is overall revenue. The middle line is operating revenue. The bottom line is net income (profit). You’re observing how hard a business has to work (or doesn’t have to work) to make profitable revenue in a particular economic environment.
Now let’s take all of this back to Whole Foods. Over the last few years, I’ve written three articles about Amazon purchasing Whole Foods and why that mattered to the natural products industry. Just prior to the purchase of Whole Foods, a small-format, inexpensive grocery store had been proposed by Whole Foods that was designed to compete with Trader Joes.
My theory was that Amazon would scale grocery across the United States as a small format grocery store using a private label grocery strategy. I believed Amazon bought Whole Foods for the formulas. This is, indeed, the playbook.
- On March 1, 2019, The Wall Street Journal ran this headline: Amazon to Launch New Grocery-Store Business.
- The same day, Bloomberg ran this headline: Amazon Plans to Open Dozens of Grocery Stores
- On July 28, 2019, The New York Times ran this headline: Amazon Wants to Rule the Grocery Aisles, and Not Just at Whole Foods.
- October 8, 2018, Food & Wine ran this headline: Everything We Know About Amazon’s New Grocery Stores
- On December 14, 2019, Yahoo Finance ran this headline: Every We Know About Amazon’s New Grocery Stores in LA
- A critical thinker interested in competition might ask the following questions:
Is grocery going to be better?
Will there be more jobs?
More revenue?
More competition?
More diversity?
More manufacturers in the grocery space?
More brands in the grocery space?
More shelf space for those brands in grocery?
Will food prices be better for consumers today?
Will food prices be better for consumers in 10 years?
Walmart has long held a leading position in the grocery industry. Will Amazon take over this position? As this happens, is the grocery industry at risk of a monopsony forming or will Walmart be able to update their services and technology fast enough (and in enough locations) to provide healthy competition to Amazon?
I started this article describing the “ease of the sale” and talking about the removal of constraints. We looked at the financial impact of what happens when constraints are removed and a business is allowed to remove constraints and move rapidly across industries with abandon. The strategy is a sucessful one for public company shareholders and, temporarily, for consumers on a platform seeking ease of consumption. I’m ending the article with an example of an industry impacted by constraint removal, acquisition, and change.
By the way, before you get too excited about analyzing Amazon’s flywheel based on the image that I shared above, remember that it is based on boardroom work from 18 years ago. A truly great executive team would have refined that strategy before that book was published.
Can you identify Amazon’s current flywheel? What are the implications to our current and future global economy if other corporations are allowed to gather up data in a similar fashion and grow unchecked across all industries and economies world-wide?
Thank you for reading.
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