作者：Owen Martin

假设你正在开发一款游戏，并且你也希望从中找到最有利可图的玩家。想象你获得了一个幂律法则，你想要以此去评估形状参数以及所有相关的数据。就好像你将把所有内容分割开来同，好象你正在展开一场“捕鲸”行动。

Fail Whale(from news.cnet.com)

在赌界中，所谓的“大赌客”便是我们说的鲸鱼用户。道理很简单：他们好像总是志在必得地投下大笔钱想去打败庄家，但最终他们总会为赌场贡献出最大收益。虽然这种表达方法带有稍许诋毁性，但是从中我们便可以很明显地看出赌场的收益来源：许多小额赌资外加一些偶然的大笔投入。

过去Facebook也包含了一些自己的鲸鱼标签（字母“g”便是代表玩家），什么样的用户属于鲸鱼用户？一些统计数据表明，更愿意花钱消费的用户便属于鲸鱼用户。所以Facebook便选择利用这类用户标签去吸引更多应用开发商的注意；但是考虑到亚马逊在2000年所经历的价格调整风波，他们便需要尽力避免这种差别取价情况的出现：

“为了保护用户的隐私，该API调用并不会公开玩家的任何购买信息，而是将玩家归到一个广泛的群组中。除此之外，这种调用也不能用于市场营销，或者以此提升高消费玩家的购买价格。”

的确如此。Facebook总是积极地保护其用户免受哄抬物价的影响。但是这种现象也招致了一些发人深思的思考：

1.这种政策将迅速引起法律上的争议。即为何哄抬物价是非法的？它的法律基础是什么？差别取价是基于种族，性别还是收入所决定？卖家尝试获得更多需求的做法是否会遭到阻碍（不管是从法律上还是道德上来看）？

2.其次我们便需要思考如何解决这些问题。答案便是，我们必须提供给所有玩家相同的应用。并且根据一些深刻的分析，应用开发者将针对鲸鱼用户而重构整个应用。然而对我来说，如果有人愿意进行这种深入分析（如使用移动社交分析平台Kontagent），那么Facebook的“游戏”标签也就不再有效了（除了能够帮助开发者明确一些合法责任）。

3.最终我们将意识到只有特定的应用类型和盈利模式需要明确或指向鲸鱼用户。它们便是面向“长尾”人口而发布的应用。从二八法则来看，我们所获得的80%收益是来自于20%的用户。但是这种法则却只适用于特定的商业模式，而非所有应用类型。如此我们便需要一名理解能力较强的研究人员通过使用一些合适的工具，帮助我们明确收益，发行渠道，主要用户，合适的政策，是否面向鲸鱼用户的元素等问题。

当你在研究用户的盈利行为时，记得问自己：我的最大买家是否属于“长尾”群体中的组成部分，或者说他们具有不同的特性？如果答案是肯定的，你就可以针对他们设计盈利模式。（本文为游戏邦/gamerboom.com编译，拒绝任何不保留版权的转载，如需转载请联系：游戏邦）

Finding the “Whales” of Your Game

Owen Martin

Let’s say you’re developing a game and you want to see who your most profitable players are. Imagine you’ve got a power-law so heavy-tailed you don’t even have a mean. You want to estimate your shape parameter and your statistics are all over the place as expected (no pun intended). Looks like you’re going to have to chop off that long tail and treat it separately. Looks like you’re going whale hunting.

In the casino industry, so-called “high-rollers” are often referred to as whales. It’s easy to see why: while they might come in with an attitude of being able to beat the house by wagering enough money, ultimately they wind up providing the biggest payouts to the casino. A somewhat disparaging term, to be sure, but it’s easy to understand how casinos view their revenue stream: lots of small change with the occasional big kill.

Facebook used to include a little whale tag of its own, the letter ‘g’ for gamer, that the company would include among the usual demographic data that a Facebook app would usually access. What constitutes a gamer? Oh, just some aggregate statistic indicating a high propensity to spend. No actual transaction history. Facebook would have obviously liked to market this tag to app developers, but given the heat that Amazon went through for price adjustments back in 2000, it have obviously tried put the kibosh on this sort of price discrimination:

“To protect user privacy, this API call does not disclose specific purchasing information about a particular player, but instead categorizes players into a broad set. In addition, this call must not be used for marketing purposes, or to increase prices for the set of higher monetizing players.”

Fair enough. Facebook is actively protecting its customers (at least with this clause) from price gouging. But the existence of this phenomenon begets a few interesting lines of thought:

1.First, this sort of policy can be quickly handed off to the attorneys for some serious debate. Why is price gouging illegal? What is its legal foundation? Does the discrimination of price fall under the umbrella of discrimination in general, à la race, gender and income? Are sellers barred, legally and morally, from trying to capture more of the demand curve?

2.The second thought is how to work around this sort of clause. The answer is that you have to provide the exact same app experience to everyone, gamer or not. But with some incisive analytics, an app developer can refactor the entire app to target the whales. It seems to me, however, that if one is going to be doing such deep analytics (i.e., using Kontagent), that Facebook’s little ‘g’ tag won’t be contributing that much information (except exposing the developer to perhaps some legal liability).

3.The final thing to realize is that only certain types of app and styles of monetization demand identifying and targeting the whales. These are precisely the apps that target the “long-tail” of the population. We recall the 80-20 rule, wherein 80% of revenue generally comes from 20% of customers. But this rule of thumb only applies to certain business models, not all of them. It requires a savvy investigator with the right tools (again, Kontagent), to identify revenue distribution, key customers, and optimal policy, whether he targets the whales or not.

So when investigating the monetization behavior of your user base, ask yourself this: Are my big spenders simply the long tail of a distribution that everyone falls under, or are they qualitatively different? If so, happy hunting.(source:Kontagent Kaleidoscope)