2014年11月8日

馬特發布編者按:它是科學帕洛阿爾托的董事總經理風險。交通、牽引、增長。我們都知道,這些術語是成功的先決條件。當我們推出創業公司首次客戶接受,我們希望這將導致交通,牽引和增長(TTG)。在某些情況下,我們願意支付交通。在其他情況下,我們晝夜不停地工作,點燃有機測試。當我們讀到成功的共同創始人Yelp, Pinterest,或WhatsApp,我們發現自己受他們的動力和智慧,但是我們經常離開想知道真的是給這些創業公司天文TTG,我們都想要的。當然沒有短缺的想法和看法如何創業取得了成功,但作為分析的創始人,從《從優秀到偉大》規定的路徑往往不能滿足我們。我們渴望更多的數學指導。一個學科轉向為了理解業務操作的底層機製研究(或)。 OR principles not only guide us to optimize and run our businesses smoothly but also provide us with statistical analysis of underlying business concepts via modeling and simulation. One of the most interesting studies in OR which provides relevant guidance to today’s applications is queuing theory . And inside queuing theory, Little’s law is a hidden gem that gives us profound hints on where to focus to achieve superior traffic, traction and growth. Queuing theory in its simplest terms tackles problems within the context of the following flow in a store: Arrival –> Service–> Departure In a queuing system, there are items that arrive at some rate to the system. Then they depart. An item can be a customer or inventory. When we think about it, this is exactly what we have on a website or app. Visitors arrive, they stick around for a while, then they leave. The most valuable company is the one with the most visitors that stay the longest. Little’s Law says that, under steady state conditions, the average number of items in a queuing system equals the average rate at which items arrive multiplied by the average time that an item spends in the system. Letting L =average number of items in the queuing system, W = average waiting time in the system for an item, and λ =average number of items arriving per unit time, the law states the following: “The long-term average number of customers in a stable system is equal to the long-term effective arrival rate multiplied by the average time a customer spends in the store.” This statement sounds trivial. Its magic, however, lies in the simplicity that the relationship is not influenced by the service distribution, service order or anything else. It’s not influenced by the color of the site, the distribution of the content or the price of the product. The only thing that matters is how fast the visitors are coming and how long they’re staying. Everything else is secondary. Little’s law doesn’t only apply to queues in physical stores; it applies to networks and to any system where there’s a flow of items. To examine a real-life situation, it’s safe to claim that Google, as a search engine, has the highest arrival rate of visitors, namely λ. But the visitors don’t stick around much. They quickly click through to another site via organic or paid links. Then they come back later for another search only to leave quickly. Google has done a phenomenal job at building up that arrival rate that made the company what it is today. But take a look at the acquisitions, research or any other top initiative at Google, and you’ll easily see that all of them target the second part of Little’s law: W, the average time a customer spends at a Google property, whether that’s email, phone, calendar or web browser. According to Comscore, Google received about 13 billion search queries in March 2014 . This translates to 433.3 million queries per day, 18 million per hour, 300 thousand per minute and only 5,000 per second. A quick comparison to Bing looks like this: