贝索斯致股东信(2005)

qimoe 发布于 2 个月前

致我们的股东:

在Amazon.com我们做出的很多决定都是依据数据。数字告诉我们一个答案是正确还是错误,是好还是坏。这是我们最喜欢的那些决定。

比如我们建设了一个新的物流中心(fulfillment center)。我们通过已有的物流中心的网络的数据预测了季度性的峰值,来为建设新的仓储地点做出指导。我们还会预测物品包装的组合。通过商品的大小和重量来确定需要如何运输这些商品。我们也会考察运送地点的周边情况来规划合理的运货路线。量化分析帮助我们进一步提升用户体验和改善成本结构。

同理,我们大部分库存的购买决策也是基于数据建模分析的。我们希望商品一上架就能让客户买走,也希望总的库存量减少,继而降低成本最终降低价格。为了同时达到这两个目标,我们必须保持合适的库存量。我们使用用户购买的历史数据来确定需求的弹性,并且用供应商的历史数据来预估补货的频率。我们还能够通过物流网络的运输进出费用、存储成本和顾客方位来明确如何存储货品。通过这种方法,我们让超过一百万货品在我们的掌控之中,随时可以送达用户,而于此同时依然保持每年超过14次的库存周转。

上述的决策要求我们做出估计和假设,但是在这些情景下,真正对决策起决定性作用的是数学计算。

正如你所想象的,不是所有重要的决策都能基于数学计算做出。有时候我们根本没有历史数据或做试验的条件。虽然数据、分析和数学做出了很大贡献,但做决定最重要的还是判断力。(译者注:此处作者脚注提到一篇论文“The Structure of ‘Unstructured’ Decision Processes”,1976 by Henry Mintbzberg, Duru Raisinghani, and Andre Theoret 稳重研究了机构如何做战略性的“非结构性”的决策的)

译者注:最喜欢下面这一段

我们的股东们应该知道,我们做出了不断降价的决定,我们的增长和高效管理确保了这一点的实现。这就是一个无法通过数学计算做出的结论。事实上,我们降价是“反模型”的,因为无论怎么算都是抬价更合算。我们有数据来显示商品的需求弹性,也就是在价格下降一定百分比的同时需求量会增加多少。只有在非常罕见的情况下,我们的降价所带来的需求量增加能覆盖降价的损失。不过,我们对价格弹性的量化认知是短视的。我们可以知道这一波降价对这一周乃至这一个季度的营业影响,但我们算不出持续的大范围降价在五年、十年乃至更久对公司造成的影响。我们做出这样的决定就是基于一个判断,也就是在我们无遗余力提高效率和规模的同时通过降价来回馈用户,并让这个良性循环持续进行下去,使得我们在长期能有更多的自由现金流,而这也就意味着Amazon.com的价值不断提高。我们在做出推出免邮费和Prime项目的决定同时也是出于类似理由,这些看起来短期代价很大的决定可能会在长期为我们带来不可估量的价值。

再举一个例子,在2000年我们在自己的平台上引入了第三方商家。在同一个商品页面提供好几个不同来源的购买选择是非常有风险的。很多心怀好意的内部和外部人士都劝我们不要这么做,认为这有可能让我们自己的生意玩火自焚,因为我们没法预知这么做了会产生什么后果。事实上这种事情在很多消费者导向的创新中常常发生。我们的采购团队说引入第三方会让我们无法准确预估该备多少库存,如果用户都从第三方购买了,我们的备货就烂在仓库里了。但不管怎样,我们的判断非常简单:如果一个第三方平台卖的又便宜又好,我们的的用户有权利非常便捷的获得这样的服务。随着时间的推移我们发现这是一个非常成功的决定,从2000年的6%,今年第三方占比总单位销售已经到了28%,这还是在我们总营收翻了三倍的情况下。

基于数学的决策常常因来一片叫好,而基于判断的决策却常常引发争议。任何不想引发这种乱象的组织都倾向于采用前者的决策方式。但在我们看来,这虽然减少了争议,但也限制了创新和长期的价值创造。

我们的决策方式已经在1997年的第一封致股东信中明确提出,我也附在了后面。

你要相信我们可以将冷静的量化分析的文化和大胆决策的文化有机结合。我们依然会从用户出发,因为这也是对股东最好的出发点。

Jeffrey P. Bezos

(Vinchent翻译)


英文原文

To our shareholders:

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.

Opening a new fulfillment center is an example. We use history from our existing fulfillment network to estimate seasonal peaks and to model alternatives for new capacity. We look at anticipated product mix, including product dimensions and weight, to decide how much space we need and whether we need a facility for smaller “sortable” items or for larger items that usually ship alone. To shorten delivery times and reduce outbound transportation costs, we analyze prospective locations based on proximity to customers, transportation hubs, and existing facilities. Quantitative analysis improves the customer’s experience and our cost structure.

Similarly, most of our inventory purchase decisions can be numerically modeled and analyzed. We want products in stock and immediately available to customers, and we want minimal total inventory in order to keep associated holding costs, and thus prices, low. To achieve both, there is a right amount of inventory. We use historical purchase data to forecast customer demand for a product and expected variability in that demand. We use data on the historical performance of vendors to estimate replenishment times. We can determine where to stock the product within our fulfillment network based on inbound and outbound transportation costs, storage costs, and anticipated customer locations. With this approach, we keep over one million unique items under our own roof, immediately available for customers, while still turning inventory more than fourteen times per year.

The above decisions require us to make some assumptions and judgments, but in such decisions, judgment and opinion come into play only as junior partners. The heavy lifting is done by the math.

As you would expect, however, not all of our important decisions can be made in this enviable, math-based way. Sometimes we have little or no historical data to guide us and proactive experimentation is impossible, impractical, or tantamount to a decision to proceed. Though data, analysis, and math play a role, the prime ingredient in these decisions is judgment.1

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

1 “The Structure of ‘Unstructured’ Decision Processes” is a fascinating 1976 paper by Henry Mintzberg, Duru Raisinghani, and Andre Theoret. They look at how institutions make strategic, “unstructured” decisions as opposed to more quantifiable “operating” decisions. Among other gems you will find in the paper is this: “Excessive attention by management scientists to operating decisions may well cause organizations to pursue inappropriate courses of action more efficiently.” They are not debating the importance of rigorous and quantitative analysis, but only noting that it gets a lopsided amount of study and attention, probably because of the very fact that it is more quantifiable. The whole paper is available at www.amazon.com/ir/mintzberg.

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.

As another example, in 2000 we invited third parties to compete directly against us on our “prime retail real estate”—our product detail pages. Launching a single detail page for both Amazon retail and third-party items seemed risky. Well-meaning people internally and externally worried it would cannibalize Amazon’s retail business, and—as is often the case with consumer-focused innovations—there was no way to prove in advance that it would work. Our buyers pointed out that inviting third parties onto Amazon.com would make inventory forecasting more difficult and that we could get “stuck” with excess inventory if we “lost the detail page” to one of our third-party sellers. However, our judgment was simple. If a third party could offer a better price or better availability on a particular item, then we wanted our customer to get easy access to that offer. Over time, third party sales have become a successful and significant part of our business. Third-party units have grown from 6% of total units sold in 2000 to 28% in 2005, even as retail revenues have grown three-fold.

Math-based decisions command wide agreement, whereas judgment-based decisions are rightly debated and often controversial, at least until put into practice and demonstrated. Any institution unwilling to endure controversy must limit itself to decisions of the first type. In our view, doing so would not only limit controversy —it would also significantly limit innovation and long-term value creation.

The foundation of our decision-making philosophy was laid out in our 1997 letter to shareholders, a copy of which is attached:

• 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.

You can count on us to combine a strong quantitative and analytical culture with a willingness to make bold decisions. As we do so, we’ll start with the customer and work backwards. In our judgment, that is the best way to create shareholder value.

Jeffrey P. Bezos

Founder and Chief Executive Officer