Back in 2005, Amazon.com had already become a massive success. A website, turned into a bookstore with a wide selection of books, had started in July 1994.
The escalation continued, and by 2005, Amazon.com had evolved in a store offering a wide range of products, from books to music.
Amazon had started from a niche, which had dominated and quickly expanded. Indeed, in 2005 the company had sold almost eight billion and a half worth of goods!
A company that had started as a bookstore, challenging existing giants (like Barnes & Noble) had become a recognized brand not only in the US but also in Europe.
As Jeff Bezos recounted back in 2006, “many of the important decisions we make at Amazon.com can be made with data. There is a right answer or a wrong answer, a better answer or a worse answer, and math tells us which is which. These are our favorite kinds of decisions.”
Where retail stores would focus either on lowering prices or customer experience. Amazon obsessed on both.
For instance, as pointed out back in 2006, when it came to deciding whether to open up a new fulfillment center Amazon used historical data to estimate seasonal peaks and to model alternatives for new capacity.
It also looked at shapes, dimensions of products to optimize its fulfillment capacity. Or computed and shortened with maximum precision, the outbound transportation costs, based on the proximity of customers.
In short, in making decisions, Amazon is more of a lab made of scientists relying on data and math than a company relying on human judgment.
However, as Jeff Bezos admitted back then, there were certain kinds of decisions that cannot be modeled and made through math and data.
With Prime, Amazon can hook customers to purchase more things in the online store. While they also pay a subscription fee that makes Amazon revenues more predictable and at higher margins, compared to the online store.
Short-term, operating decisions, based on quantitative analyses, and long-term, strategic decisions based on opinion and judgment
Indeed, opinion and judgment, in that case, mattered way more. As Jeff Bezos recounted in 2006:
As our shareholders know, we have made a decision to continuously and significantly lower prices for customers year after year as our efficiency and scale make it possible. This is an example of a very important decision that cannot be made in a math-based way. In fact, when we lower prices, we go against the math that we can do, which always says that the smart move is to raise prices. We have significant data related to price elasticity. With fair accuracy, we can predict that a price reduction of a certain percentage will result in an increase in units sold of a certain percentage. With rare exceptions, the volume increase in the short term is never enough to pay for the price decrease. However, our quantitative understanding of elasticity is short-term. We can estimate what a price reduction will do this week and this quarter. But we cannot numerically estimate the effect that consistently lowering prices will have on our business over five years or ten years or more. Our judgment is that relentlessly returning efficiency improvements and scale economies to customers in the form of lower prices creates a virtuous cycle that leads over the long term to a much larger dollar amount of free cash flow, and thereby to a much more valuable Amazon.com. We’ve made similar judgments around Free Super Saver Shipping and Amazon Prime, both of which are expensive in the short term and—we believe—important and valuable in the long term.
In particular, Jeff Bezos cited a paper called “The Structure of ‘Unstructured’ Decision Processes” published in 1976 by Henry Mintzberg, Duru Raisinghani, and Andre Theoret.
More, in particular, the paper highlighted how, when an institution made decisions, primarily based on data and math, that made them take efficient operating decisions.
Yet, as long-term, strategic and “unstructured” (based on processes that have not been encountered in quite the same form and for which no predetermined and explicit set of ordered responses in the organization) decisions, might not rely on quantitative understanding, will get underestimated.
That happens, because decisions that can be taken on a quantitative basis can be measured, thus institutions but also companies and managers in the field focus too much on measurable analyses.
Yet while those decisions might be good for the short-term. They might prevent an organization to focus on long-term, hard and strategic decisions.
Also relied a lot on judgment, opinion, and human decision-making when it came to long-term, strategic decisions, that could not be based on previous experience or scenarios, but needed to be tackled.
This point is very important. In a world of management that focuses more and more on the quantifiable, and measurable. Getting data-driven might mean losing the strategic focus.
You just need a simple framework, made of a few core principles for your strategic decisions, and you need to stick to them!
- We will continue to focus relentlessly on our customers.
- We will continue to make investment decisions in light of long-term market leadership considerations rather than short-term profitability considerations or short-term Wall Street reactions.
- We will continue to measure our programs and the effectiveness of our investments analytically, to jettison those that do not provide acceptable returns and to step up our investment in those that work best. We will continue to learn from both our successes and our failures.
- We will make bold rather than timid investment decisions where we see a sufficient probability of gaining market leadership advantages. Some of these investments will pay off, others will not, and we will have learned another valuable lesson in either case.
Those bold decisions that made Amazon the company we know today were not based on quantitative analyses but rather on controversial human judgment.
The compass for Amazon was based on customer focus, long-term game, launching programs fast and killing them even faster, invest massively in areas where the company sees a sufficient probability of gaining market leadership!
Where managers and practitioners get bogged down by complex and quantitative analysis to make short-term decisions.
In reality, companies like Amazon did rely on those quantitative analyses to make short-term decisions to maximize their fulfillment centers, the shapes of their products, the shipping time and so on.
However, a whole new set of strategic and unstructured decisions that could not rely on math and data (like the launch of Amazon Prime) were made based on human judgment.
To make these decisions, Amazon defined since the start a clear framework, based on a few guiding principles.
If you want to make an impact on your organization you need to have that framework ready each time math and data can’t help you out!
Other resources for your business:
- What Is Business Model Innovation And Why It Matters
- What Is a Business Model? 30 Successful Types of Business Models You Need to Know
- What Is a Business Model Canvas? Business Model Canvas Explained
- Blitzscaling Business Model Innovation Canvas In A Nutshell
- What Is a Value Proposition? Value Proposition Canvas Explained
- What Is a Lean Startup Canvas? Lean Startup Canvas Explained
- How to Write a One-Page Business Plan
- The Rise of the Subscription Economy
- How to Build a Great Business Plan According to Peter Thiel
- What Is The Most Profitable Business Model?
- The Era Of Paywalls: How To Build A Subscription Business For Your Media Outlet
- How To Create A Business Model
- What Is Business Model Innovation And Why It Matters
- What Is Blitzscaling And Why It Matters
- Snapshot: One Year Of “Business Model” Searches On Google In Review
- Business Model Vs Business Plan: When And How To Use Them
- The Five Key Factors That Lead To Successful Tech Startups
- Top 12 Business Ideas with Low Investment and High Profit
- Business Model Tools for Small Businesses and Startups