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Jeff Bezos Teaches You When Judgment Is Better Than Math And Data

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.

By 1995 it had sold already half a million worth of inventory. Its growth was exponential and by 1997 sales skyrocketed at over a hundred forty-seven million dollars!

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!

To indeed get Amazon’s growth, let’s visualize it:

is-amazon-profitable
Amazon was profitable in 2021. The company generated over $33 billion in net income, primarily driven by the Amazon AWS business, which contributed to over 55% of its operating margins and other profitable parts like Amazon Prime and Ads. The Amazon e-commerce platform runs at tight operating margins since it’s built for scale.

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. 

Why not stop there? Yet in 2005, Amazon kept making hard choices. For instance, the company launched a program called Amazon Prime.

Why Amazon Prime was a controversial judgment call rather than a quantitative analysis

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.”

Indeed, Amazon had always been more of a tech and software company than a retail store. If all Amazon had turned the retail store business model upside down.

Where retail stores would focus either on lowering prices or customer experience. Amazon is obsessed with both.

Amazon turned to quantitative analysis each time it had to make critical operating decisions related to the business.

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 the shapes, and dimensions of products to optimize their 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.

Nonetheless, massive growth, for instance, in 2005 Amazon launched Amazon Prime. And there was no way to assess quantitatively, whether that initiative would have been successful.

Fast forward to 2021, Amazon Prime has become a key ingredient in Amazon’s business model mix. 

amazon-business-model
Amazon has a diversified business model. In 2021 Amazon posted over $469 billion in revenues and over $33 billion in net profits. Online stores contributed to over 47% of Amazon revenues, Third-party Seller Services,  Amazon AWS, Subscription Services, Advertising revenues, and Physical Stores.

Amazon Prime is a key ingredient to the overall Amazon success.

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 the 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.

Amazon, is a company that relied over and over again on quantitative analysis of things that could be measured, optimized, and maximized.

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.

How did Jeff Bezos as manager and executive and Amazon as a company handle it?

You just need a simple framework, made of a few core principles for your strategic decisions, and you need to stick to them!

amazon-leadership-principles

Amazon fundamental principles that drove and drive the company are:

  • Customer Obsession
  • Ownership
  • Invent and Simplify
  • Are Right, A Lot
  • Learn and Be Curious
  • Hire and Develop the Best
  • Insist on the Highest Standards
  • Think Big
  • Bias for Action
  • Frugality
  • Earn Trust
  • Dive Deep
  • Have Backbone; Disagree and Commit
  • Deliver Results

Amazon laid out the foundation of its decision-making process, based on a few key principles, defined in 1997, in the first Shareholders letter:

  • We will continue to focus relentlessly on our customers.
customer-obsession
Customer obsession goes beyond quantitative and qualitative data about customers, and it moves around customers’ feedback to gather valuable insights. Those insights start by the entrepreneur’s wandering process, driven by hunch, gut, intuition, curiosity, and a builder mindset. The product discovery moves around a building, reworking, experimenting, and iterating loop.
  • 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! 

Key takeaway

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 onset 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!

Key Highlights:

  • Amazon’s Evolution: Amazon.com began as an online bookstore in 1994 and rapidly expanded its product offerings. By 2005, it had become a major online retailer with billions in sales.
  • Amazon Prime’s Controversial Decision: In 2005, Amazon launched Amazon Prime, a program offering subscribers benefits like free two-day shipping. This strategic decision was not based on quantitative analysis but on human judgment.
  • Quantitative vs. Qualitative Decision-Making: Amazon was built upon quantitative analysis for optimizing operational decisions. However, certain strategic decisions, like launching Amazon Prime, relied on qualitative factors and judgment due to their complexity.
  • Long-Term vs. Short-Term Decisions: Short-term decisions were data-driven and aimed at operational efficiency. Long-term, strategic decisions required human judgment and opinion, as their outcomes couldn’t be accurately predicted through data alone.
  • Amazon’s Decision-Making Principles: Amazon’s core principles, defined in its 1997 Shareholders Letter, guided decision-making. These principles included Customer Obsession, Ownership, Invent and Simplify, Think Big, Bias for Action, and others.
  • Balancing Data and Human Judgment: Amazon’s success came from combining quantitative analysis with qualitative judgment. While data-driven approaches were crucial, strategic decisions that couldn’t be quantified were guided by principles and human insight.
  • Customer Focus and Long-Term Strategy: Amazon’s focus on customers and its commitment to long-term market leadership over short-term profitability shaped its decision-making process.
  • Bold Investment Decisions: Amazon was willing to make bold investments to gain market leadership advantages, even if some of these decisions didn’t pay off. Learning from both successes and failures was a part of its approach.
  • Framework for Impactful Decisions: To make an impact, organizations should establish a decision-making framework that incorporates both quantitative analysis for short-term decisions and guiding principles for long-term, strategic choices.

Read Next: Amazon Business Model

Connected to Amazon Business Model

Amazon Business Model

amazon-business-model
Amazon has a diversified business model. In 2021 Amazon posted over $469 billion in revenues and over $33 billion in net profits. Online stores contributed to over 47% of Amazon revenues, Third-party Seller Services,  Amazon AWS, Subscription Services, Advertising revenues, and Physical Stores.

Amazon Mission Statement

amazon-vision-statement-mission-statement (1)
Amazon’s mission statement is to “serve consumers through online and physical stores and focus on selection, price, and convenience.” Amazon’s vision statement is “to be Earth’s most customer-centric company, where customers can find and discover anything they might want to buy online, and endeavors to offer its customers the lowest possible prices.” 

Customer Obsession

customer-obsession
In the Amazon Shareholders’ Letter for 2018, Jeff Bezos analyzed the Amazon business model, and it also focused on a few key lessons that Amazon as a company has learned over the years. These lessons are fundamental for any entrepreneur, of small or large organization to understand the pitfalls to avoid to run a successful company!

Amazon Revenues

amazon-revenue-model
Amazon has a business model with many moving parts. With the e-commerce platform which generated over $222 billion in 2021, followed by third-party stores services which generated over $103 billion, Amazon AWS, which generated over $62 billion, Amazon advertising which generated over $31 billion and Amazon Prime which also generated over $31 billion, and physical stores which generated over $17 billion.

Amazon Cash Conversion

cash-conversion-cycle-amazon

Working Backwards

working-backwards
The Amazon Working Backwards Method is a product development methodology that advocates building a product based on customer needs. The Amazon Working Backwards Method gained traction after notable Amazon employee Ian McAllister shared the company’s product development approach on Quora. McAllister noted that the method seeks “to work backwards from the customer, rather than starting with an idea for a product and trying to bolt customers onto it.”

Amazon Flywheel

amazon-flywheel
The Amazon Flywheel or Amazon Virtuous Cycle is a strategy that leverages on customer experience to drive traffic to the platform and third-party sellers. That improves the selections of goods, and Amazon further improves its cost structure so it can decrease prices which spins the flywheel.

Jeff Bezos Day One

jeff-bezos-day-1
In the letter to shareholders in 2016, Jeff Bezos addressed a topic he had been thinking quite profoundly in the last decades as he led Amazon: Day 1. As Jeff Bezos put it “Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”

Read Next: Qualitative Data, Quantitative Data.

Connected Analysis Frameworks

Failure Mode And Effects Analysis

failure-mode-and-effects-analysis
A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage.

Agile Business Analysis

agile-business-analysis
Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.

Business Valuation

valuation
Business valuations involve a formal analysis of the key operational aspects of a business. A business valuation is an analysis used to determine the economic value of a business or company unit. It’s important to note that valuations are one part science and one part art. Analysts use professional judgment to consider the financial performance of a business with respect to local, national, or global economic conditions. They will also consider the total value of assets and liabilities, in addition to patented or proprietary technology.

Paired Comparison Analysis

paired-comparison-analysis
A paired comparison analysis is used to rate or rank options where evaluation criteria are subjective by nature. The analysis is particularly useful when there is a lack of clear priorities or objective data to base decisions on. A paired comparison analysis evaluates a range of options by comparing them against each other.

Monte Carlo Analysis

monte-carlo-analysis
The Monte Carlo analysis is a quantitative risk management technique. The Monte Carlo analysis was developed by nuclear scientist Stanislaw Ulam in 1940 as work progressed on the atom bomb. The analysis first considers the impact of certain risks on project management such as time or budgetary constraints. Then, a computerized mathematical output gives businesses a range of possible outcomes and their probability of occurrence.

Cost-Benefit Analysis

cost-benefit-analysis
A cost-benefit analysis is a process a business can use to analyze decisions according to the costs associated with making that decision. For a cost analysis to be effective it’s important to articulate the project in the simplest terms possible, identify the costs, determine the benefits of project implementation, assess the alternatives.

CATWOE Analysis

catwoe-analysis
The CATWOE analysis is a problem-solving strategy that asks businesses to look at an issue from six different perspectives. The CATWOE analysis is an in-depth and holistic approach to problem-solving because it enables businesses to consider all perspectives. This often forces management out of habitual ways of thinking that would otherwise hinder growth and profitability. Most importantly, the CATWOE analysis allows businesses to combine multiple perspectives into a single, unifying solution.

VTDF Framework

competitor-analysis
It’s possible to identify the key players that overlap with a company’s business model with a competitor analysis. This overlapping can be analyzed in terms of key customers, technologies, distribution, and financial models. When all those elements are analyzed, it is possible to map all the facets of competition for a tech business model to understand better where a business stands in the marketplace and its possible future developments.

Pareto Analysis

pareto-principle-pareto-analysis
The Pareto Analysis is a statistical analysis used in business decision making that identifies a certain number of input factors that have the greatest impact on income. It is based on the similarly named Pareto Principle, which states that 80% of the effect of something can be attributed to just 20% of the drivers.

Comparable Analysis

comparable-company-analysis
A comparable company analysis is a process that enables the identification of similar organizations to be used as a comparison to understand the business and financial performance of the target company. To find comparables you can look at two key profiles: the business and financial profile. From the comparable company analysis it is possible to understand the competitive landscape of the target organization.

SWOT Analysis

swot-analysis
A SWOT Analysis is a framework used for evaluating the business’s Strengths, Weaknesses, Opportunities, and Threats. It can aid in identifying the problematic areas of your business so that you can maximize your opportunities. It will also alert you to the challenges your organization might face in the future.

PESTEL Analysis

pestel-analysis
The PESTEL analysis is a framework that can help marketers assess whether macro-economic factors are affecting an organization. This is a critical step that helps organizations identify potential threats and weaknesses that can be used in other frameworks such as SWOT or to gain a broader and better understanding of the overall marketing environment.

Business Analysis

business-analysis
Business analysis is a research discipline that helps driving change within an organization by identifying the key elements and processes that drive value. Business analysis can also be used in Identifying new business opportunities or how to take advantage of existing business opportunities to grow your business in the marketplace.

Financial Structure

financial-structure
In corporate finance, the financial structure is how corporations finance their assets (usually either through debt or equity). For the sake of reverse engineering businesses, we want to look at three critical elements to determine the model used to sustain its assets: cost structure, profitability, and cash flow generation.

Financial Modeling

financial-modeling
Financial modeling involves the analysis of accounting, finance, and business data to predict future financial performance. Financial modeling is often used in valuation, which consists of estimating the value in dollar terms of a company based on several parameters. Some of the most common financial models comprise discounted cash flows, the M&A model, and the CCA model.

Value Investing

value-investing
Value investing is an investment philosophy that looks at companies’ fundamentals, to discover those companies whose intrinsic value is higher than what the market is currently pricing, in short value investing tries to evaluate a business by starting by its fundamentals.

Buffet Indicator

buffet-indicator
The Buffet Indicator is a measure of the total value of all publicly-traded stocks in a country divided by that country’s GDP. It’s a measure and ratio to evaluate whether a market is undervalued or overvalued. It’s one of Warren Buffet’s favorite measures as a warning that financial markets might be overvalued and riskier.

Financial Analysis

financial-accounting
Financial accounting is a subdiscipline within accounting that helps organizations provide reporting related to three critical areas of a business: its assets and liabilities (balance sheet), its revenues and expenses (income statement), and its cash flows (cash flow statement). Together those areas can be used for internal and external purposes.

Post-Mortem Analysis

post-mortem-analysis
Post-mortem analyses review projects from start to finish to determine process improvements and ensure that inefficiencies are not repeated in the future. In the Project Management Book of Knowledge (PMBOK), this process is referred to as “lessons learned”.

Retrospective Analysis

retrospective-analysis
Retrospective analyses are held after a project to determine what worked well and what did not. They are also conducted at the end of an iteration in Agile project management. Agile practitioners call these meetings retrospectives or retros. They are an effective way to check the pulse of a project team, reflect on the work performed to date, and reach a consensus on how to tackle the next sprint cycle.

Root Cause Analysis

root-cause-analysis
In essence, a root cause analysis involves the identification of problem root causes to devise the most effective solutions. Note that the root cause is an underlying factor that sets the problem in motion or causes a particular situation such as non-conformance.

Blindspot Analysis

blindspot-analysis

Break-even Analysis

break-even-analysis
A break-even analysis is commonly used to determine the point at which a new product or service will become profitable. The analysis is a financial calculation that tells the business how many products it must sell to cover its production costs.  A break-even analysis is a small business accounting process that tells the business what it needs to do to break even or recoup its initial investment. 

Decision Analysis

decision-analysis
Stanford University Professor Ronald A. Howard first defined decision analysis as a profession in 1964. Over the ensuing decades, Howard has supervised many doctoral theses on the subject across topics including nuclear waste disposal, investment planning, hurricane seeding, and research strategy. Decision analysis (DA) is a systematic, visual, and quantitative decision-making approach where all aspects of a decision are evaluated before making an optimal choice.

DESTEP Analysis

destep-analysis
A DESTEP analysis is a framework used by businesses to understand their external environment and the issues which may impact them. The DESTEP analysis is an extension of the popular PEST analysis created by Harvard Business School professor Francis J. Aguilar. The DESTEP analysis groups external factors into six categories: demographic, economic, socio-cultural, technological, ecological, and political.

STEEP Analysis

steep-analysis
The STEEP analysis is a tool used to map the external factors that impact an organization. STEEP stands for the five key areas on which the analysis focuses: socio-cultural, technological, economic, environmental/ecological, and political. Usually, the STEEP analysis is complementary or alternative to other methods such as SWOT or PESTEL analyses.

STEEPLE Analysis

steeple-analysis
The STEEPLE analysis is a variation of the STEEP analysis. Where the step analysis comprises socio-cultural, technological, economic, environmental/ecological, and political factors as the base of the analysis. The STEEPLE analysis adds other two factors such as Legal and Ethical.

Related Strategy Concepts: Go-To-Market StrategyMarketing StrategyBusiness ModelsTech Business ModelsJobs-To-Be DoneDesign ThinkingLean Startup CanvasValue ChainValue Proposition CanvasBalanced ScorecardBusiness Model CanvasSWOT AnalysisGrowth HackingBundlingUnbundlingBootstrappingVenture CapitalPorter’s Five ForcesPorter’s Generic StrategiesPorter’s Five ForcesPESTEL AnalysisSWOTPorter’s Diamond ModelAnsoffTechnology Adoption CurveTOWSSOARBalanced ScorecardOKRAgile MethodologyValue PropositionVTDF FrameworkBCG MatrixGE McKinsey MatrixKotter’s 8-Step Change Model.

Other strategy frameworks:

Additional resources:

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