Working Backwards: Insights, Stories, and Secrets from Inside Amazon

by Colin Bryar

distillation is still that of founder Jeff Bezos (hereafter referred to as Jeff): “We have an unshakeable conviction that the long-term interests of shareowners are perfectly aligned with the interests of customers.”2 In other words, while it’s true that shareholder value stems from growth in profit, Amazon believes that long-term growth is best produced by putting the customer first.

“Our culture is four things: customer obsession instead of competitor obsession; willingness to think long term, with a longer investment horizon than most of our peers; eagerness to invent, which of course goes hand in hand with failure; and then, finally, taking professional pride in operational excellence.”

“We need to plant many seeds,” he would say, “because we don’t know which one of those seeds will grow into a mighty oak.” It

When I (Bill) first read through the long list of competencies as a new hire in 1999, I recall feeling a mix of inspiration and intimidation thanks to the combination of super-high standards applied across such a breadth of disciplines. I thought, “I will have to work harder and smarter than I have ever worked if I am to live up to these.”

company. S-Team goals are mainly input-focused metrics that measure the specific activities teams need to perform during the year that, if achieved, will yield the desired business results. In chapter six, we will discuss in more detail how Amazon develops such precise and specific metrics to ensure teams meet their business objectives. S-Team goals must be Specific, Measurable, Attainable, Relevant, and Timely (SMART). An actual S-Team goal could be as specific as “Add 500 new products in the amazon.fr Musical Instruments category (100 products in Q1, 200 in Q2…),” or “Ensure 99.99 percent of all calls to software service ‘Y’ are successfully responded to within 10 milliseconds,” or “Increase repeat advertisers from 50 percent to 75 percent by Q3 of next year.” S-Team goals are aggressive enough that Amazon only expects about three-quarters of them to be fully achieved during the year. Hitting every one of them would be a clear sign that the bar had been set too low. S-Team goals for the entire company are aggregated and their metrics are tracked with centralized tools by the finance team. Each undergoes an intensive quarterly review that calls for thorough preparation. Reviews are conducted in multihour S-Team meetings scheduled on a rolling basis over the quarter rather than all at once. At many companies, when the senior leadership meets, they tend to focus more on big-picture, high-level strategy issues than on execution. At Amazon, it’s the opposite. Amazon leaders t...

For example, if the interviewer owns the principle of Insist on the Highest Standards, they might ask, “Can you give me an example of a time when your team proposed to launch a new product or initiative and you pushed back on their plan because you didn’t think it was good enough?”

probe carefully for the truth. The method that Amazon interviewers use for drilling down goes by the acronym STAR (Situation, Task, Action, Result): “What was the situation?” “What were you tasked with?” “What actions did you take?” “What was the result?” A good interviewer continues to ask questions until they feel they have a good understanding of what the interviewee personally accomplished versus what the team did. Other questions that can reveal this information include “If you were assigned to work on a different project instead of Project X, what would have changed about Project X?” and “What was the toughest call on Project X, and who made it?”

“If given the chance, would you hire this person again?” Another is, “Of the people you have managed or worked with, in what percentile would you place this candidate?”

“most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.”5

We found it helpful to think of such cross-functional projects as a kind of tax, a payment one team had to make in support of the overall forward progress of the company. We tried to minimize such intrusions but could not avoid them altogether. Some teams, through no fault of their own, found themselves in a higher tax bracket than others. The Order Pipeline and Payments teams, for example, had to be involved in almost every new initiative, even though it wasn’t in their original charters.

“The best way to fail at inventing something is by making it somebody’s part-time job.”6

be stubborn on the vision but flexible on the details.

The reason writing a good 4 page memo is harder than “writing” a 20 page powerpoint is because the narrative structure of a good memo forces better thought and better understanding of what’s more important than what, and how things are related. Powerpoint-style presentations somehow give permission to gloss over ideas, flatten out any sense of relative importance, and ignore the interconnectedness of ideas.3

The fact that most PR/FAQs don’t get approved is a feature, not a bug. Spending time up front to think through all the details of a product, and to determine—without committing precious software development resources—which products not to build, preserves your company’s resources to build products that will yield the highest impact for customers and your business.

If, after the PR/FAQ process, the leadership team still believes in the product and wants it to become a reality, the process will have given them a thorough understanding of the problems that would need to be solved in order to move forward with it. Perhaps a problem can be solved through an acquisition or a partnership. Perhaps it can be solved with the passage of time—new technologies may become available, or the costs of the technology might come down. Perhaps the company decides that the problem or constraint is solvable, that the solution will require risk and cost, and that they are willing to assume that risk and cost because the TAM is large and therefore the potential rewards are great.

and didn’t know if we could solve. Jeff would say something to the effect of, “We shouldn’t be afraid of taking on hard problems if solving them would unlock substantial value.”

Watch what happens when we improve customer experience: Better customer experience leads to more traffic. More traffic attracts more sellers seeking those buyers. More sellers lead to wider selection.

Wider selection enhances customer experience, completing the circle. The cycle drives growth, which in turn lowers cost structure. Lower costs lead to lower prices, improving customer experience, and the flywheel spins faster. The Amazon flywheel captures the major aspect of what makes Amazon’s retail business successful. Therefore, it should be no surprise that almost all the metrics discussed in the WBR can be categorized into one of the flywheel elements. In fact, the first page of the WBR deck has a picture of the very same flywheel above.

One of the metrics we initially chose for selection was the number of new detail pages created, on the assumption that more pages meant better selection. Once we identified this metric, it had an immediate effect on the actions of the retail teams. They became excessively focused on adding new detail pages—each team added tens, hundreds, even thousands of items to their categories that had not previously been available on Amazon. For some items, the teams had to establish relationships with new manufacturers and would often buy inventory that had to be housed in the fulfillment centers. We soon saw that an increase in the number of detail pages, while seeming to improve selection, did not produce a rise in sales, the output metric.

When we realized that the teams had chosen the wrong input metric—which was revealed via the WBR process—we changed the metric to reflect consumer demand instead. Over multiple WBR meetings, we asked ourselves, “If we work to change this selection metric, as currently defined, will it result in the desired output?” As we gathered more data and observed the business, this particular selection metric evolved over time from number of detail pages, which we refined to number of detail page views (you don’t get credit for a new detail page if customers don’t view it), which then became the percentage of detail page views where the products were in stock (you don’t get credit if you add items but can’t keep them in stock), which was ultimately finalized as the percentage of detail page views where the products were in stock and immediately ready for two-day shipping, which ended up being called Fast Track In Stock.

Sounds simple, but with the wrong input metrics or an input metric that is too crude, your efforts may not be rewarded with an improvement in your output metrics. The right input metrics get the entire organization focused on the things that matter most. Finding exactly the right one is an iterative process that needs to happen with every input metric.

Having an independent person or team involved with measurement can help you seek out and eliminate biases in your data.

Start with the customer and work backwards by aligning your metrics with the customer experience.

Emerging patterns are a key point of focus. Individual data points can tell useful stories, especially when compared to other time periods. In the WBR, Amazon analyzes trend lines to highlight challenges as they emerge rather than waiting for them to be summed up in quarterly or yearly results. Graphs plot results against comparable prior periods. Metrics are intended to trend better over time. Care is taken to ensure that prior periods are structured to provide apples-to-apples comparisons so as not to highlight false variances due to predictable things like holidays or weekends. Graphs show two or more timelines, for example, trailing 6-week and trailing 12-month. Trend lines for the short term can magnify small but important issues that are hard to spot when averaged out over longer periods.

A good deck uses a consistent format throughout—the graph design, time periods covered, color palette, symbol set (for current year/prior year/goal), and the same number of charts on every page wherever possible. Some data naturally lend themselves to different presentations, but the default is to display in the standard format.

The WBR is a tactical operational meeting to analyze performance trends of the prior week. At Amazon, it was not the time to discuss new strategies, project updates, or upcoming product releases.

more attention, and to point out when high standards have not been met. Still, success demands an environment where people don’t feel intimidated when talking about something that went wrong in their area. Some Amazon teams were better at exemplifying this than others were, and, quite honestly, it’s an area where the company could improve. Sometimes WBRs can devolve into downright hostile environments, especially at times when a major slip-up caused the comments to focus more on the presenter than on the issue. While fear may be a good short-term motivator, it will ultimately cause more problems than it solves.

mistakes should be acknowledged as a chance to take ownership, understand the root cause, and learn from the experience. Some tension is unavoidable and appropriate, but we think it’s better to establish a culture where it’s not just okay, it’s actually encouraged to openly discuss mistakes.

Adding YOY growth rates in addition to the underlying metric you are measuring is a great way to spot trends. In this example, YOY growth has actually decelerated 67 percent since January with no signs of plateauing. The business may look healthy at a cursory glance, but trouble is looming on the horizon. The enhanced graphic reveals the need for action that the simpler graph obscures.

output metrics—the data we graphed above—are far poorer indicators of trend causes than input metrics. It turned out in this case that the cause of our decelerating growth was a reduction in the rate of acquiring new customers—but

The key to these meetings is to create a balance between extremely high standards and an atmosphere where people feel comfortable talking about mistakes.

Amazon’s approach to metrics embodies the Customer Obsession leadership principle. The relevance of Customer Obsession becomes evident in the company’s focus on input versus output metrics. If you look at the input metrics for Amazon, they often describe things customers care about, such as low prices, lots of available products, fast shipping, few customer service contacts, and a speedy website or app. A lot of the output metrics, such as revenue and free cash flow, are what you’d typically see in a company’s financial report. Customers don’t care about those. But as we stated at the beginning of this book, Amazon has an unshakable conviction that the long-term interests of shareowners are perfectly aligned with the interests of customers. Controllable input metrics are a quantitative (diving deep with data) and qualitative (anecdotes) way of measuring how well the organization is satisfying these customer interests so that the output metrics trend the way the company desires.

“We want to be a large company that’s also an invention machine. We want to combine the extraordinary customer-serving capabilities that are enabled by size with the speed of movement, nimbleness, and risk-acceptance mentality normally associated with entrepreneurial start-ups.”

“I believe we are the best place in the world to fail (we have plenty of practice!), and failure and invention are inseparable twins. To invent you have to experiment, and if you know in advance that it’s going to work, it’s not an experiment. Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there.”

Invention is not the solution to every problem. For instance, when Amazon started, the company did not create its own computer hardware. On the flip side, when we were planning our e-book business, we decided to get into the hardware game with Kindle. The reason: invention works well where differentiation matters.

And what was true yesterday may not be true today. In fact, today Amazon does make some computer hardware to power its data centers. That’s because this special-purpose hardware designed for AWS’s data centers reduces costs and increases reliability in a nontrivial way. These benefits can be passed on to AWS customers in the form of meaningful price decreases and services that offer higher reliability.

Even the best process can only improve the quality of your decision-making; no process will make the decision for you.

“We all know that if you swing for the fences,” he wrote, “you’re going to strike out a lot, but you’re also going to hit some home runs.” Unlike baseball, where a home run can bring in no more than four runs, a big business hit can score an almost infinite number of runs. What’s crucial to understand is that a small number of very big winners can pay for a large number of experiments that fail or succeed only modestly. In an interview after the Fire Phone was withdrawn, Jeff was asked about its failure and answered, “If you think that’s a big failure, we’re working on much bigger failures right now—and I am not kidding.”4 The magnitude of your inventions, and therefore your mistakes, needs to grow in lockstep with the growth of your organization. If it doesn’t, your inventions will likely not be big enough to move the needle.

Big companies tend to develop a decision-making process that is designed to manage one-way door decisions, precisely because poor decisions can lead to big problems, even disaster. The process is typically slow, cumbersome, and riddled with risk aversion. This process tends to become the dominant one in large companies, and it is routinely, almost thoughtlessly, applied to two-way door decisions. The result is reduced speed, impaired idea generation, stifled innovation, and longer development cycles.

To win in digital, because those physical retail value adds were not advantages, we needed to identify other parts of the value chain where we could differentiate and serve customers well. Jeff told Steve that this meant moving out of the middle and venturing to either end of the value chain. On one end was content, where the value creators were book authors, filmmakers, TV producers, publishers, musicians, record companies, and movie studios. On the other end was distribution and consumption of content. In digital, that meant focusing on applications and devices consumers used to read, watch, or listen to content, as Apple had already done with iTunes and the iPod. We all took note of what Apple had achieved in digital music in a short period of time and sought to apply those learnings to our long-term product vision. Our core competencies did not extend to either end of the value chain.

Steve did not let this get in the way. In one of our meetings, he said that a typical company that wanted to grow would take stock of its existing capabilities and ask, “What can we do next with our skill set?” He emphasized that Amazon’s approach was always to start from the customer and work backwards. We would figure out what the customers’ needs were and then ask ourselves, “Do we have the skills necessary to build something that meets those needs? If not, how can we build or acquire them?” Once we determined what was necessary to create value for our customers and to differentiate ourselves from our competitors, we didn’t let our lack of ability deter us from achieving this important end result—our own device.

“strong general athlete” (SGA). These customer-obsessed, inventive, long-term thinkers take pride in operational excellence and embody the Amazon Leadership Principles. Amazon often puts SGAs like Charlie into leadership positions and gives them the tools to become subject matter experts.

We couldn’t even accurately estimate the cost, because our models could not really predict how customers would react, so we were relying more on judgment and educated guesses than on data.

The “institutional no” is a big reason why Amazon could have made an error of omission in this case. Jeff and other Amazon leaders often talk about the “institutional no” and its counterpart, the “institutional yes.” The institutional no refers to the tendency for well-meaning people within large organizations to say no to new ideas. The errors caused by the institutional no are typically errors of omission, that is, something a company doesn’t do versus something it does. Staying the current course offers managers comfort and certainty—even if the price of that short-term certainty is instability and value destruction later on. Moreover, the errors of omission caused by the institutional no can be notoriously tricky to spot. Most businesses don’t have the tools to evaluate the cost of not doing something. And when the cost is high, they only realize when it’s too late to change. The institutional no can infiltrate all levels of the organization. It’s what causes a board of directors to say no to a big change of strategy (think Nokia and Microsoft missing the turn on smartphones). It’s what drives frontline managers to keep their top performers working on a current project and say no to their involvement in high-risk experiments that could fail but could also pay off handsomely later—especially if that payoff is likely to be after the manager has moved on to another role.

think at some point he had decided that it wasn’t the idea that was flawed, but the decision-making process, a process encumbered by institutional risk-aversion. The “October surprise” email arose out of his realization that you simply could not prove a priori that free shipping would work. You just had to try it.

stubborn on the vision and flexible on the details.

customers are divinely discontented, and “yesterday’s ‘wow’ quickly becomes today’s ‘ordinary.’”

He said that about half the day was probably the same as it was for many of the people in the audience—scrambling to keep their technology platform one step ahead of the rapid growth of their business. They worked on scaling their databases, web servers, software, and hardware. Stewart said they did not spend as much time as he would like on innovating things that were unique to Flickr. After the meeting, Jeff and I had a brief chat about Stewart’s comments. We’d both noticed the same thing—a phenomenon that Amazon would later refer to as “undifferentiated heavy lifting,” that is, the tasks that we could do for companies that would enable them to focus on what made them unique. This was an opportunity.