2005年Amazon致股東信：那些不能靠數據決策的事，我們靠哲學｜見識之旅

導讀

這封信是2005年Amazon的第9封致股東信。

在這封信中，Jeff Bezos首先提到了數據在短期決策的重要性。

「如何減少運送次數」這類的問題，Amazon靠數學、模型等定量分析來解決，成效非常好。

定量分析的數據決策，讓Amazon的倉儲商品每年能夠周轉14次以上。商品周轉速度越快，Amazon的商業效率也越高。

來到信件的下半部分，Jeff Bezos話鋒一轉，開始講起「那些不能靠數據決策的事」。

總有些事情是不能夠靠數據決策的，例如是不是應該要讓第三方賣家到Amazon平台上銷售商品。

從數據上來看，第三方賣家上來搶官方的生意，會影響Amazon本身的零售業務，數據會告訴Jeff Bezos：不要開放第三方賣家上架商品。

但從顧客角度來看，第三方賣家的商品能夠增加消費者可選擇的品項，這是有利於顧客的。

顧客想要，那就應該做。不能靠數據決策的事，Amazon靠的是決策哲學。

好了，導讀結束，以下致股東信正文開始。

正文

To our shareholders:

致我們的股東：

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.

Amazon做出的許多重要決策，都是基於數據。數學將告訴我們，哪一個是正確答案，哪一個是錯誤答案；哪一個是更好的答案，哪一個是較差的答案。我們喜歡這樣子做決定。

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.

設立新的出貨中心就是一個例子。我們使用現有物流網絡中的歷史記錄，來估算季節性高峰，並建立模型尋找解決需求的方案。我們會預估產品的組合，包括尺寸和重量，以決定我們需要多少空間。同時，評估我們是否要為體積較小的「可分類」物品，或通常單獨運送的較大物品建立額外的設施。為了減少運送次數和出站運輸成本，我們根據與客戶、交通樞紐和現有設施的距離，來分析預期的設立地點。這樣的定量分析，可以改善客戶體驗和我們的成本結構。

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.

同樣的，我們大多數的購買庫存的決策，都可以進行數值建模和分析。我們希望庫存產品能夠即時提供給客戶，也希望庫存量盡可能少，以維持較低的庫存成本，進而提供客戶較低的商品價格。要實現兩者的平衡，需要有適量的庫存。我們運用歷史數據，來預測客戶對商品的需求，以及需求的變化程度。我們運用供應商的歷史數據，來預測補貨時間。我們根據入站和入站的運輸成本、庫存成本和預期的客戶所在位置，來決定我們的庫存要放在哪個出貨中心。如此一來，我們可以保留超過100萬件庫存商品，即時供貨給客戶，又可以實現每年14次以上的周轉次數。

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.

上述決策需要先做出假設和判斷，但在這樣的決策中，個人的判斷和意見只是基礎要素，更重要的是數學構成的理性分析。

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.

然而，正如你可能預期的那樣，並非所有重要的決策，都可以透過這種基於數學的方式做出決定。有時，我們只有很少的歷史數據可以指導我們，而進行相關實驗是不切實際的，或者對於做出決策沒有幫助。儘管數據、分析和數學起了重要作用，但這些決策的主要原因還是判斷力。

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.

正如股東所知道的，由於我們的效率和規模做得到，我們決定要持續為消費者盡可能的降價。這是非常重要的決策，而且沒辦法根據理性的數學方法做出決定。事實上，我們持續降低價格的做法，和基於數學的分析結論是對立的。數學方法告訴我們，最聰明的作法是漲價。我們有與價格彈性相關的數據，可以在一定範圍內，預測價格下降的百分比與銷售額上升的百分比之間的關係。除了極少數的例外，銷售額的短期增量，很難彌補價格下降帶來的損失。然而，我們的定量分析，只能用於短期。我們可以估計價格這周和這個季度的降幅，但是我們沒辦法預估長期持續降價政策下，五年、十年以後會帶來什麼結果。我們的判斷是，持續將效率進步的益處分享給消費者，藉此創造出龐大的經濟規模，長期來說，這樣的循環會給我們帶來大量的現金流，因此可以創造一個更有價值的Amazon。我們對Free Super Saver Shipping服務、Prime服務都有類似的判斷。這兩項服務，在短期來說都是昂貴的投資，但我們認為，這兩者長期來看都會是重要且有價值的。

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, thirdparty 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.

另一個例子是，2000年，我們邀請了第三方賣家進駐Amazon，在我們最重要的資產，也就是產品詳情頁上面與我們競爭。讓第三方賣家和Amazon使用同一款產品詳情頁，看起來是一件很冒險的事。有些好心人認為，這會蠶食我們的零售業務。而且，以消費者為中心的創新，通常很難在事前就證明它會起作用。我們的買家指出，邀請第三方賣家進駐Amazon，而且第三方賣家表現比我們更好時，會使我們的庫存預測難以進行，Amazon將會陷入庫存過多的泥淖中。但是，我們的判斷其實很單純，如果第三方賣家提供比我們更好的商品、更低的價格，那麼我們希望消費者可以輕易買到第三方賣家的東西。長期來說，第三方賣家將會成為Amazon業務中不可或缺的一部分。在零售收入提升3倍的背景下，第三方賣家的銷售額佔比從2000年的6％，提升到2005年的28％。

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.

基於數學的決策，通常能夠形成廣泛的共識。相對來說，基於價值觀判斷的決策經常是彼此矛盾的，在公諸於世之前只經過適度的辯論，所以無法像基於數學的決策取得廣泛共識。任何一間不願意接受矛盾的公司，都會把自己限制在極度安全的決策上，也就是基於數學的決策。而我們認為，這麼做不只是減少矛盾，也在極大程度上也減少了創新與創造。

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

我們在1997年發布的第一封致股東信，闡述我們的決策哲學。這封信的要點節錄：

• We will continue to focus relentlessly on our customers.
• 我們將會持續把注意力放在客戶身上。
• 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.
• 我們將繼續根據長期的市場領導地位，而不是短期的獲利能力，或華爾街的短期反應，來進行投資決策。
• 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.
• 我們將繼續評估和分析計劃和投資的有效性，拋棄那些無法提供可接受報酬的計劃，加大投資成效最好的計劃。無論是成功或失敗，我們將繼續從中學習。
• 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.
• 若有足夠高的可能性可獲得市場領導優勢，我們選擇做出大膽，而不是保守的投資決定。這些投資中，有一部分會有回報，有一部分不會。無論是哪種情況，我們都會學到寶貴的經驗。

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.

你可以期待我們將強大的定量分析能力和願意冒險的價值觀相結合。當我們這麼做時，我們將一如往常的聚焦於客戶。根據我們的判斷，這是創造股東價值的最佳途徑。

Jeffrey P. Bezos

Founder and Chief Executive Officer

傑夫·貝佐斯

Amazon創始人暨CEO

以上就是2005年Amazon致股東信。

想看隔年的Amazon致股東信，請至《2006年Amazon致股東信：AWS元年，未來的巨無霸在耐心中萌芽》。

想看全系列導讀目錄，請至《Amazon 1997–2019年致股東信導讀目錄》。