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社交游戏不应迷信数据编辑本段回目录

作者:Tadhg Kelly

当社交游戏领域看似具有时代性,而不仅仅是技术性突破时,我们的游戏开发者开始认识了Eric Ries。Ries(以及Steve Blank)是精简型创业团队运动中的关键人物,当时他所提出的快速迭代和用户检验的概念对我们来说极有实用价值,原因有二。

首先,当时我们已经十分厌倦于连续多年为同一个项目效力,而这些项目更像是体现设计师一人独有的理念而已,并没有明确的目标。其次,我们将其视为游戏公司的新运营理念,并且也似乎获得了投资界的认同。毕竟,游戏公司是公认的投资难题,所以任何我们承诺能够让开发过程更好更快的理念,对他们来说都会更为入耳。

在那个时期,你很容易辨别出哪些开发者参悟了“最小可行性产品”(游戏邦注:minimum viable product,简称MVP)以及“免费增值模式”(free-to-play,简称F2P)的门道。小型团队可以借此快速迭代产品。这些理念也意味着游戏最初体验是免费的,可以引进大量用户,其中有些会转化为消费者。这些理念很快就获得了认知和认同。

MVP(from snagglepop.co.uk)

MVP(from snagglepop.co.uk)

MVP和F2P最终也加入其他一系列词汇的行列,成为游戏行业的常规术语。这一领域几乎每家公司都在谈论DAU、LTV、ARPU、ARPPU、ARPDAU以及ARPPDAU等术语。他们讨论人口特征分析,多数人甚至不屑于再讨论病毒K系数,而是关注玩家的CPA。对于一个曾经只知担忧Metacritics网站评分的行业来说,这无疑是一个重大转变。

然而,这些术语也常被滥用。与此相同的是,每家工作室都声称要采用“敏捷”方法,但实际上很少人真正做到这一点,多数人都忽略了这些数据背后的要点。当他们遇到MVP时却总因游戏产品开发到哪一阶段才算具有“可行性”(viable)而百思不得其解。所以他们还是重走自己的旧路,也就是模仿他人做法但却甚少创新。

狂热的数据分析思维

这一切都起源于数据的迷惑性。我之前所谓的“旧金山变革”(游戏邦注:作者在此是指以旧金山湾为中心的游戏行业变革,其中以Facebook、iPhone和Android平台的社交及手机游戏为主)中的一个原理直接来源于精简型思维,也就是如果你无法衡量数据也就无法提升产品。换句话说,如果你增加或扣除了一些东西,但却并没有直接让某个关键参数上升,那么这种变化和调整就是没有意义的。

这个原理很有诱惑性,因为它意味着数据可以揭露游戏中的问题,避免游戏沦为一个无法破解的黑盒子。理论上看,它应该是一个创新,因为我们由此可知关于玩家行为和想法的大量信息,然后使用这些信息优化游戏。衡量参数并找到结果,正是整个精简型方法所关注的问题。

当时Facebook游戏市场还是个好去处,关于这一领域的一些利好消息也不绝于耳。例如游戏中添加的圣诞树一周内就出售了100万棵,而由玩家社区建议添加的物品三天之内就销售一空。这些游戏道具看起来很有趣,但玩家的热情很快就消退了。现在每款游戏都会推出常规的促销活动,它们都有自己的节日主题道具,但没有一者能够超越这个范畴。

实际上,这种已经验证的方法只是赌博领域老套操作惯例的一个翻版。我个人对赌博行业并无偏见,但其意图证明一切的倾向让人觉得它仅关注一部分可行的关键游戏规则。这也正是所有赌场都大同小异的原因所在,而社交游戏开发商做法也同样多有雷同。

但社交游戏领域并没有通过衡量数量而持续创新,而是将自己逼近了一条死胡同。这个行业清楚可行的数据衡量公式(但却不清楚为何它们可行),知道如何创建这些内容,然后持续重复相同的操作。所以,同赌博行业一样,社交游戏领域的本质也演变成了谁拥有最佳商业手段,就会引来众多竞争者相效仿。《FarmVille》其实并不是Zynga复制《Harvest Moon》的天才策略,而是Zynga最先最快让游戏通过Facebook展示于众人的一种创举。

趋利避害的模仿之风

社交游戏的悲剧在于行业中的公司都在不遗余力地寻找史上最佳发布渠道,但却疏于向平台提供出色的游戏。他们目前所提供的最佳产品是扑克游戏,一些简单休闲游戏的社交版本,一些超级简单的模拟游戏,以及成千上万种《龙与地下城》的变体游戏。

这部分要归咎于市场局限性。Facebook平台政策变化多端,难以捉摸,有些开发者不得不将所有精力用于解决游戏曝光度的问题。他们沉迷于发送通告和交叉推广内容是因为用户并不记得自己所玩游戏的名称,也不知道如何找到这些游戏。

技术也是一部分原因。《Mob Wars》等PHP游戏基本上只能让玩家执行一点静态的点击操作,而Flash技术虽然能够制作体面的模拟游戏,但却无法支持动作型游戏。很显然这一领域需要更好的技术,但奇怪的是,社交游戏开发商仍在制作一些相同而具有局限性的游戏。Supercell如今在iOS平台的成功也不过是重现Facebook三年前的辉煌景象罢了。

最主要的还是开发文化的问题。社交游戏的行业思维与电视领域并无不同。电视领域的会计通常也认为这其中必定存在一种数据,这些数据可以让他们找到电视节目获得收视率的方法(游戏邦注:但他们还没有找到这种数据)。他们坚信获取观众是一个过程,取悦他们是一个过程,如果可以找到一个合适的数据衡量方法和公式,那么电视行业定会前途光明。

因为没有这种数据,他们只能通过收视率、用户特殊数据以及观看模式,试图推断相关结果。他们根据这种推理制作产品,推出能够迎合这些数据需求的电视节目。而这一方法行不通时,他们又返回原路通过复制其他成功电视节目的模板来制作产品,就好像赌场的做法。这种文化开始周而复始地循环,最终变成了他们的执行标准。

固守“只有衡量数量才能提升产品”这一理念的荒谬性就在于,让数据衡量手段来拍板创意决策。我曾无数次听到有设计师抱怨自己无法为游戏进行一些深度调整,因为项目经理要求这种调整必须能够用数据证实效果。 双方都知道游戏有些地方不对劲,但这一原理就是要求用数据来反馈问题,否则游戏就不存在问题。这种认知失调现象常让团队受挫,并产生刻板的决策,而游戏也在此过程中趋于平庸。

趣味性无法用数据衡量

社交游戏开发商痴迷于用数据衡量一切,并以数据形式确定一切问题,他们认为数据就是王道,这也正是扼制他们创新的源头。与电视工作从业者一样,他们也积极寻找与趣味性相关的数据。他们认为这其中必定存在一种可虏获趣味因子的数据合体(例如LTV与ARPU或DAU相结合等等),总之这样的数据一定存在。

看看人们参与体育运动或桌面游戏,体验《使命召唤》或《愤怒的小鸟》的过程,你就会开始发现一些端倪。这些游戏中都有一些产生小结果的小决策,而这些小结果又都彼此相关并最终铸成更大的结果。在大型游戏中,这看似一种模式,而我们通常将其称为“机制”。

只要你不去纠结于游戏机制到底是什么,那就很好理解这一概念。机制就是一种会影响玩家的操作或规则。如果不看DAU等亚参数,游戏工作室通常会衡量机制实例。他们会观察多少玩家购买物品,在特定时间段中升级,或者使用某项道具。然后他们就会根据这些向量提升产品,但游戏总体趣味性则有所缺失。

Mario-Power-Tennis(from fanpop.com)

Mario-Power-Tennis(from fanpop.com)

试图通过研究机制而衡量和提升游戏,就好像只是通过衡量几次犯规来提升网球技能一样。这些数据在许多方面都很管用,但对游戏来说未必如此。例如,你想增加赢球次数,也许可以通过降低拦网高度,加强球拍性能或改变使用的网球来实现目标。这些变化可能会给比赛带来立杆见影的效果,但也很可能产生这种结果,即你赢了很多次球,但比赛却毫无趣味可言。

这种趣味存在于网球运动中多种动态机制相互影响的过程,而这种动态又很容易受到一些小变动的影响。趣味动态的本质也正是如此,也这是优秀游戏开发者在创建原型时经常强调“找到趣味所在”的一个原因。

游戏本质上是一个混乱系统。在这个混乱系统中,你可以看到起始条件,模拟结构,并大胆预测奇异吸引体的交互结果,但如果不观察它的运行状态,你根本不可能看到模拟情况如何。这个系统很敏感,我们很难解释其中的模式,它发生的情况也是即时性的。

有趣的游戏有点类似于这种混乱系统,它会产生美丽的碎片。在一些游戏中,所有机制的平衡会产生一些令人兴奋的结果,但如果只看游戏规则却并不能预测会出现什么情况。只有在体验过程中才能看到什么操作可行,什么不可行。

如果你不试玩原型,你根本就不会知道游戏机制是否有趣,或者是否将产生游戏令人爱不释手的美妙结果。衡量机制实体有助于实现这一目标,但你仍然得已承认游戏中的确存在无法破解的未知因素。

与其他主流游戏(游戏邦注:如街机、主机、PC、休闲、MMO等游戏)变革不同,社交游戏开发商一直无法悟出趣味具有动态性这个道理。这也正是为何没有一款社交游戏能够像《魔兽世界》、《愤怒的小鸟》、《植物大战僵尸》、《超级马里奥》这样备受推崇的原因,它们一直在寻找所谓的趣味要素,但这实际上并不存在。

开发商若想让社交游戏顺利进入下一个时代,就必须汲取这个教训。(本文为游戏邦/gamerboom.com编译

What Games Are: The Fun Boson Does Not Exist

Tadhg Kelly

Back when the social game scene looked like it might be generationally, rather than merely technologically, disruptive, we game makers discovered Eric Ries. Ries (along with Steve Blank) is the key figure behind the lean startup movement, and at the time his message of fast iteration and customer validation rang true for us for two reasons.

First, we were very frustrated working on multi-year-long projects with no clear goal that seemed to be more about some designer’s ego. Second, we saw it as a new way to look at the games business and found that the investment community was inclined to agree. Game companies are notoriously difficult investment propositions after all, so anything we could do or say that promised to manage the creation process better was music to their ears.

During those days you could immediately tell who got it and who didn’t with the use of the terms “minimum viable product” (MVP) and “free-to-play” (F2P). You’d explain this ideal process whereby a tiny team would iterate on ideas quickly. It would measure everything, too. It would offer this kind of game where the initial experience was free, and that would pull in a lot of users, some of whom would become customers. The eyes of those who knew what you meant would suddenly spark in recognition.

MVP and F2P eventually passed into regular industry jargon along with a boat load of other terms. Most every company involved in the space now talks about DAU, LTV, ARPU, ARPPU, ARPDAU and even ARPPDAU. They talk about performing cohort analyses. Some of them ask whether they are working on an MVP or an MDP? Most don’t really bother discussing viral K-factors any more, and instead obsess about the CPA of players. These are significant changes for an industry that used to worry more about Metacritic ratings.

However they are also often misused. In much the same way that every studio claims to be “agile,” but few actually are, most of them miss the point of all these numbers. They get badly stuck when considering their MVP because they realise that they have no idea what “viable” is supposed to mean in the context of games. So they do what they’ve always done, which is to copy the other guy and invent very little.

How We Got Here

It all starts with the delusion of numbers. One of the axioms of the San Francisco Revolution, derived straight from lean thinking, is that you can’t improve what you can’t measure. In other words, if you add or subtract something and it does not cause a key metric to go up in some significant way, then that change was meaningless.

This axiom is seductive because it promises to expose the game and stop it being treated like a mysterious black box. In theory it’s supposed to unlock a whole wealth of innovation, because we could then know a great deal about how players behave and think, and then use that. Measuring to find an outcome that might scale is, after all, what the entire lean method is about.

When Facebook developer garages were interesting places to be, the sector got very excited by stories that seemed to prove this assertion. There was the story of the Christmas tree put into the game which sold a million units in a week. There was also the story of the object suggested by the community and then put into the game not three days later. All of these emotive images seemed to validate the validation, but the energy around them didn’t last. Now every game conducts regular sales, every game has its holiday boondoggles and none have really taken those ideas to a next level (if there is one).

In practise what the validation-led method actually turned out to be was a sanitised version of age-old processes from the gambling industry. Personally I have no problem with the gambling industry (as long as it behaves responsibly around addiction), but its tendency toward validation of everything means it tends to only focus on a couple of key game formulae that are proven to work. That’s why every casino is identical. That’s also why every social game maker is identical.

Rather than continuing to innovate through measurement, the social sector as a whole rationalised itself into a corner. It knew of a couple of formats of game that seemed to work with measurements (but not really why they worked), knew how to build those, and then continued to repeat the same format again and again. So, just like the gambling industry, social gaming became about who had the best commercial processes in place to push their identikit product around as fast as possible. Farmville really wasn’t about Zynga’s genius at replicating Harvest Moon. It was about their genius at getting that game in front of everyone on Facebook faster than anyone else.

But, again just like the casino business, that kind of thinking can only get you so far.

Local Maxima

The tragedy of social games is that the companies involved discovered the greatest distribution tool in the history of the industry, and yet proved inept at providing great games to go with it. The best things that they’ve come up with so far is Poker, some socialised versions of simple casual games, some super-simple sims and about 100,000 variations of Dungeons and Dragons.

In part that’s because of market conditions. Facebook proved pretty tricky to understand, as some developers devoted almost their entire energies to overcoming visibility issues. All those notifications and cross-promotion obsessions happened because users didn’t really remember the names of the games they were playing, nor how to find them.

It’s also because of technology. PHP games like Mob Wars could do little more than be static click-object games, and while Flash could handle sims well, it was (and still is) very weak for making action-oriented games. Arguably the sector needed a better technology to work with, which in time it got through iOS. The weird thing, however, is that social developers are still making the same limited games. What’s happening on iOS with Supercell is really just a repeat of what happened on Facebook three years ago.

Mostly it’s about development culture. The thinking behind social games is not unlike the thinking behind television. The bean counters in TV land tend to think that there is a number, perhaps not yet discovered, that will one day explain television viewing to them. They believe that attaining viewers is a process, entertaining them is a process, and that if only the right measurement and formula can be found, television would become a predictable industry.

In the absence of that number they look at ratings, demographic data and viewing patterns and try to infer what it might be. They build products based on that inference, to make shows which satisfy those numbers. And when that doesn’t work they fall back to copying other successful show formats and trying to put a spin on them, just like casinos do. And that culture becomes circular and inward-looking over time, so eventually that’s all they know how to do.

The ultimate fallacy of sticking with “you can only improve what you can measure” is that measurements eventually determine all of your creative decisions. I’ve lost count of the number of times I’ve heard a designer complain that they need to enact a deep change in their game, but are not allowed to do so by a manager who demands it be proved with numbers first. Both understand that something is not right, but the axiom mandates that the problem must be expressed in numeric terms, or else it does not exist. The resulting cognitive dissonance leads to frustration and formulaic decisions, and so the game becomes like every other game.

That’s called being trapped in a local maximum.

Measuring The Wrong Thing

Obsessed with measuring everything and therefore defining all of their problems in numerical terms, social game makers have come to believe that those numbers are all there is, and this is why they cannot permit themselves to invent. Like TV people, they are effectively in search of that one number that will explain fun to them. There must, they reason, be some combination of LTV and ARPU and DAU and so on that captures fun, like hunting for the Higgs boson. It must be out there somewhere.

Watch any sport or exciting board game, some gamers playing Call of Duty or Angry Birds, and you start to notice how there’s a certain yin and yang of play. There are many small decisions of little consequence, but they tend to bounce off one another and lead up to bigger moments. In great games this seems to have a pattern, and we often try to describe this in terms of “mechanics.”

Once you sidestep some of the more feverish interpretations of what a game mechanic is, they’re actually pretty easy to understand. A mechanic is an action on the part of, or a rule that affects, a player. When not looking at large meta-metrics like DAU, studios typically measure instances of mechanics. They look at how many players buy an object, level-up within a certain time frame or use an item in-game. Then they try to improve along those vectors, yet for some reason the overall fun of the game seems lacking.

Now to be clear, there are many arguments to be made for soul, culture and the importance of building an identity in a game that tells a marketing story, but this argument is not about those qualities. By “fun” I have a very simple definition (“the joy of winning while mastering fair game dynamics“).

Trying to measure and improve a game through only studying mechanics is like trying to improve tennis solely by measuring how many aces occur, or how many foot faults happen. Those numbers are very useful in many ways, but they are not the game. If, for example, you wanted to increase the number of aces in the game, then perhaps you might lower the net, lengthen the racket or change the type of ball used. That change would have massive knock-on effects through the game, however. And it’s quite likely that you would get more aces but make a worse game.

It is in the interplay of various mechanics that the dynamic of tennis emerges, and that dynamic is surprisingly sensitive to small changes. The dynamic nature of fun is always like that, and is why good game developers often talk about the importance of “finding the fun” in games through prototyping.

Games are essentially chaotic systems. In a chaotic system you can look at the initial starting conditions and the topology of a simulation and try to predict the interactions of strange attractors as much as you like, but you generally don’t really get what the hell the simulation is doing until you observe it in motion. The system is too sensitive, the patterns too hard to interpret and the situation too emergent.

Fun games are a little bit like those chaotic systems that produce beautiful fractals. In some games the balance between all the mechanics produces an inherently exciting set of outcomes, but are hard to predict just from looking at their rules. They have to be played to see what does and doesn’t work, to be genuinely iterated upon in the true sense of the lean startup (not just built) and allowed to be validated in their dynamics. Everything else is just nonsense.

Until you prototype it and try it, you really just don’t know whether a dynamic is fun or whether it produces beautiful outcomes that compel you to play again. Measuring mechanical instances may help you do that, but you still need to accept that there’s an x-factor involved.

Unlike every other major game revolution (arcade, console, PC, casual, MMO, etc.), social game developers have proved consistently unable to understand that fun is dynamic in this way. This is why there is, as yet, no social game that has achieved the genuine love and admiration of a World of Warcraft, an Angry Birds, a Plants vs Zombies or a Super Mario Galaxy. They are hunting for the fun boson, but it does not exist.

This is the hardest lesson that they need to learn if they are to get to generation-two.(source:techcrunch



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