作者：Nicholas Lovell。转自：游戏邦。2011-11-17
http://gamerboom.com/archives/41643
免费游戏的成功关键不在于数量。它的目标不是获得大量用户，而是依靠少数能够创造大量收入的用户。
这涉及能量定律。免费游戏的成功要诀是：
免费模通过让玩家免费体验游戏移除体验障碍；且还通过去除价格或订阅费用消除忠诚粉丝的消费上限。
我知道某家公司注册用户25万，年收入300万美元，还有一家公司的MAU不到150万，但年收入达2000万美元。这就是能量定律商业模式的作用所在。
能量定律及其于在线平台的运作
能量定律传递这样的理念：用户并非人人平等。有些人喜欢你的游戏，有些人则觉得游戏马马虎虎。有些人有大量金钱和时间，有些人则相反。用户掏钱的原因也各不相同，或图方便，或希望提高社会地位。
通过合理设计游戏，让用户能够进行不同程度的消费（游戏邦注：通过提供消耗品、美化道具、升级道具及以金钱换时间机会），开发者得以令游戏忠实粉丝在游戏中投入大笔资金。
（术语注释：将游戏的高消费者称作“鲸鱼”如今非常普遍。我不喜欢这个称呼，因为这过于直白。我更喜欢真实粉丝这个叫法。但后来我发现鲸鱼用户和真实粉丝间存在差异：真实粉丝因你而掏钱；鲸鱼用户因自己而掏钱。真实粉丝因喜欢你的作品而投入大笔资金，这合情合理；但瞄准缺乏自控能力的鲸鱼用户则就不是如此。）
模式化免费增值能量定律
我将玩家分成三大类：
* 每月投入极少资金的小鱼，通常是1美元。
* 花费“中等”数额的海豚。他们平均每月花费5美元。
* 投入大量资金的鲸鱼。他们平均每月花费20美元。
* 免费体验者属于第四类。
三类用户的分布比例如下：
* 小鱼：50%的付费用户
* 海豚：40%的付费用户
* 鲸鱼：10%的付费用户
注意这是能量定律模型的近似估值。你可以调整分布比例和ARPPU数值。但调整分布比例和ARPPU数值会改变预期的曲线。
划分基准
我们很难设定区分小鱼、海豚和鲸鱼的准确普遍标准。很多公司都只是粗略谈到自己的ARPPU（游戏邦注：例如，Bigpoint只是表示其ARPPU高于《魔兽世界》，这就避免将焦点转移到鲸鱼用户）。
我们不断探究鲸鱼、海豚和小鱼的分界点。但这不过是能量定律曲线的近似估值，所以我们可能还要花费很长时间。
我建议你采用此标准：
* 小鱼：50%
* 海豚：40%
* 鲸鱼：10%
（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）
Whales, Dolphins and Minnows–the beating heart of a free-to-play game
By Nicholas Lovell
The secret to a free-to-play game is not volume. It is not about getting millions of users and relying on only a tiny percentage of that enormous volume to cover your costs.
It is about understanding the power-law. You can read more about the concept in my post How much is your game worth? but the secret to success in free-to-play is this:
Free to play not only removes barriers by letting players play the game for free; it removes the upper limit on how much a committed fan spends by removing the purchase price or subscription.
For an ironic take on this concept, see this cartoon on free-to-play from Penny Arcade.
I know of one company with around a quarter of a million registered users that is grossing $3 million a year and another with fewer than 1.5 million MAUs that grosses $20 million. The power-law business model works.
What is the power-law, and how does it work with online
The power law simply expresses the idea that not all customers are equal. Some love your game, some will think it’s so-so. Some will have lots of time and no money, others will be vice versa. Some users are happy spending money for many reasons, ranging from convenience to social status.
By designing your game to allow users to spend different amounts of money – by offering consumable items, aesthetic items, power-ups and the ability to exchange time for money – you unlock the ability to let your biggest fans spend a lot of money with you.
(A note on terminology: the term “whales” for your biggest spenders has become dominant. I don’t like it, because it is a deeply unflattering term. I prefer true fans. But then I realised there is a difference between whales and true fans: true fans spend money because of what you do; whales spend money because of who you they are. Enabling true fans to spend lots of money because they love what you do is entirely ethical; targeting whales who can’t help themselves may not be. For more on this, read Whales, true fans and the ethics of free-to-play gaming.)
Modelling the freemium power-law
For the purposes of this spreadsheet, I split your gamers into three groups:
* Minnows spend the smallest amount possible in a month, typically $1
* Dolphins spend a “middling” amount. Typically I forecast they spend an average of $5 per month
* Whales spend a lot. Typically I forecast they spend an average of $20 per month.
* Freeloaders (see Whales, power-laws and the future of media) are, of course, the fourth group. They are covered by the conversion rate and not considered here)
For more details on ARPPU, see the separate post on this topic – ARPPU in freemium games.
My starting point for what percentage of your users fit in which bucket is:
* Minnows: 50% of payers
* Dolphins: 40% of payers
* Whales: 10% of payers
Note that this is an approximation of the shape of the power law. You can change the percentages and change the ARPPUs as you like. Just be aware that changing the percentages and ARPPU changes the curve that you are predicting. The diagram below illustrates how the spreadsheet approximates the curve.
Benchmarks
It is pretty hard to get accurate, public benchmarks for how to separate the minnows from the dolphins and the whales. Many companies talk about their ARPPU in round terms. Bigpoint, for example, says that its ARPPU is larger than that of World of Warcraft. That hides a massive concentration amongst the whales.
We’ll keep digging to find publicly available splits of users into whales, dolphins and minnows. However, since it is an approximation to the power law curve, that may take us a long time.
In the meantime, I suggest you work with:
* Minnows: 50%
* Dolphins: 40%
* Whales: 10%
It’s what I’ve seen across many of my clients, but you’ll just have to take that on trust.（Source：gamesbrief）
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