游戏邦注：本文作者为Brice Morrison，他是The Game Prodigy网站编辑，这是一个针对游戏设计师的新网站。他曾参与《模拟人生3》、《快乐水族馆》和《It Girl》的制作。

Brice Morrison

“不，你们无法理解。大部分的玩家都达不到22等级，大多数玩家此前就退出游戏。我们需要尽快推出新功能。”

“不对！我们很多玩家都达到这个等级，这就是为什么我们这个阶段需要更多关注游戏设置。”

我无法说出在我的职业生涯当中，参加过多少次设计辩论。讨论玩家需求，如何有效利用我们工程和艺术资源，游戏精髓何在。置身掌机和零售PC游戏行业，我们只能获得有限用户反馈信息供做决定参考。开展体验测试、问卷调查以及发行前亲自体验游戏是我们避免批评声的唯一途径。一旦游戏磁盘投放市场，便尘埃落定。

然后社交游戏世界却呈现截然不同的光景。对Facebook或MySpace平台而言，游戏发行不过是第一步。如今开发商能够挖掘和使用的数据不胜枚举（游戏邦注：如今天每个等级分别有多少玩家；玩家在道具上消费了多少资金；玩家体验了多久，什么时候开始体验；玩家主要体验什么游戏内容）。虽然其他平台要求开发商对于游戏发行采取“置之，忘之”的态度，但社交游戏却能够在发行后为开发商带来无数反馈信息，使得开发商能够基于玩家行为完善游戏体验。

知道如何充分利用这些数据的开发商将能够深入洞察玩家体验行为，领先竞争对手。下面就来谈谈数据挖掘所能带来的好处。

深入研究的决定

多年以来，具有传统观念的游戏设计师大多依靠直觉决定玩家需求。这些更多是基于直觉，认为玩家喜欢袭击其他太空飞船胜过建造、提升自身飞船。或者通过白板讨论，决定每场游戏平均体验时间（游戏邦注：几分钟、1小时或几小时）。或者也许会讨论玩家体验风格——他们倾向通过晋级提高地位，或者更喜欢掏钱购买道具？

过去，诸如此类的讨论、辩论的解决途径就是看哪个设计师能够发表最佳见解，将谈话导向自己的结论。虽然如果开发商团队人才济济，那么这个方法将颇有成效，但其实这个方式更多导致错误决定，无法反映玩家真实行为。

Data Graph

通过获取即时游戏数据，这类辩论也就能够马上得到结论。在就玩家是否喜欢每次登陆升级建筑的众说纷纭声中，有人表示，“嗨，伙伴们。我认为是这样的，1-30等级的玩家平均每次登陆都要升级4个建筑。”其他人就点头附和，然后据此做决定，然后接着开展其他讨论。或者在是否需要于游戏中给予玩家更多资金的激烈讨论中，有人表示，“嗨，各位。这张图告诉我们现在玩家拥有软货币数量。你们会发现玩家拥有的货币其实已超出正常3倍。”营收减少，就在市场投放更多高价商品，一切就迎刃而解。

我认为有时获悉关键数据极其重要。通过了解玩家实际体验活动，开发商能够学习如何通过数据辅助决定。

展开试验

数据和即时用户反馈的优势在于其为社交游戏设计师提供了试验机会。符合游戏新功能的选择很多，但对于哪种功能能够提高DAU，增加营收，或者最大限度提高用户留存率，我们通常无从知晓。

通过快速获得反馈，开发商能够观察用户反应、变化和流动，或者获悉新功能能否立马奏效。为付费用户发行能为之带来好处的新更新系统？没问题，我们可以先就几款游戏进行试验，看看所产生影响。独角兽喷泉作为病毒式道具销量更好，还是作为Facebook Credit商品？（游戏邦注：我们可以实施A/B测试，然后隔日查看结果）。开展试验，投放市场，然后查看结果是迅速开发热门游戏的最佳方式。

facebook credits

试验对社交游戏而言至关重要，许多标经典戏设置几年前尚未问世。随着Facebook和其他社交网站越来越受青睐，我们需要采取达尔文的进化方式不断开发、测试和取舍新设计，以谋求成功。如果我们无法快速查看数据，筛选信息，行业就无法像今天这样快速成熟。

体验优化

鲜少游戏平台能将游戏打造得尽善尽美。有时稍作调整，更改几个数字，替换少量数值，游戏便能够从落选之马变成轰动巨作。众多社交游戏数据不可小视：例如升至等级5需要120个XP，日收入达到17可升至等级3，游戏汽车价格是700，200个coins可兑换10个Facebook Credits，可以为游戏创造100美元收益，但假如将这些兑换数量分别设定为115、12、900和12，则可能扩大10倍收益。

很幸运，通过不断调整各种数字，观察结果，社交游戏开发商得以优化游戏直至其达到几近完美状态。如果玩家在游戏中赚到的钱太少，开发商可以试着增加他们的收入，观察结果。如果玩家进展过于缓慢，开发商可提高XP值设定，或者给予奖励查看效果。观察反馈参数，将其同用户终身价值进行比较是众多数据的最主要用途之一。

虽然及时挖掘大量玩家数据受益匪浅，但其中也蕴藏颇多陷阱。有幸获悉玩家行为详细信息的开发团队，若不知如何应对这些数据，将陷入困境当中。下面就让我们来看看数据将带给社交游戏开发团队的失误。

数据瘫痪

数据对于社交游戏开发商的优化工作帮助很大。但游戏开发并不仅限稍微调整。有时针对那些不尽如人意的作品，我们需要添加大型新功能，根本调整设计，或者重新设置，更好抓住游戏本质。

例如，我们知道玩家通常每天登陆2次，从城市建筑中获得收入，但我们同时发现城市建筑设置长期来看存在问题（游戏邦注：导致玩家出现厌倦情绪，或需要消耗大量金钱，或者最终脱离游戏）。这将使我们陷入困境：正确的做法就是移除该功能，添加新功能。但如果他们全都放弃登陆呢？我有数据，数据告诉我们这不是好主意。下降趋势显而易见，上升趋势遥遥无期。

此类变化不能简单量化。但如果DAU或ARPU下滑显著，我们就该采取相应措施。出现诸如此类的情况，依赖数据将导致停滞不前，设计师也束手无策。他们能够简单量化数据，但这不是解决方案所在。

arpu

出现这种情况的补救办法是使用游戏开发领域的传统工具。团队具备富有眼见的成员十分重要，体验测试、调查和核心群体有助于我们了解玩家，而只有能力卓越的领导人才则能把团队带向未知领域。我们不能完全依赖数据做决定，经验丰富人士会发现数据无法帮助他们做决定，但我们依然得做决定。拥有数据以外的强大系统能够帮助团队做出最佳决定，从而促使团队向前迈进。

虚无反馈参数

社交游戏存在信息过多现象。如果数据成为关注焦点，那么大家就会习惯查看图表。（游戏邦注：图表能够显示对比上周玩家减少了多少文化交流。图表显示55等级玩家的平均树莓收获趋势。图表显示创建切断车间25天以上玩家每周增长情况）。

这听起来似乎很有趣，但我们能做些什么呢？这些真的举足轻重吗？获知这些真的能够提高我们的营收底线、DAU或者玩家的终身价值吗？

图表数量众多，它使我们感觉自己好像获悉某些信息，它提供能够触及的具体数字，使我们觉得自己对于游戏理解透彻。但并非所有数据、图表或者表格都是具有实际操作价值。这就是所谓的“虚无参数”。能够掌握这些数据固然很棒，开发商们也愿意花费大量时间挖掘、制作和讨论这些数据，但它们并不能够提供真正有用任务项。

判断有些数据是否为虚无参数的方法之一是开发商扪心自问：如果这些数据发生根本变化，我们的行动会相应改变吗？如果答案是否定的，那么这些数据就毫无意义，我们应该着眼其他更为重要的元素。

之所以认为虚无数据存在危险原因有二。首先，花费时间分析数据徒劳无益，这占用了原本可用于开发新功能、发展业务的时间。其次，由于常常耗时于无关紧要的数据，团队可能对数据丧失信心，最终错失关键参数。

所以当挖掘大量数据时，开发团队首先要对数据用途心中有数。沉溺于数据无益于制定决策。

数据分析也是一种强大武器

及时获悉游戏数据是开发商的一大设计法宝。盲目前行过去尚能运行，但如今门槛有所提高。所以如果游戏设计不甚理想，用户们还有其他众多高质量竞争者可供选择（游戏邦注：这些竞争者深入研究玩家行为，开发极富趣味的体验）。切记大量的玩家数据只是为游戏设计锦上添花，而不是取长补短。但若应用得当，我们则能够通过数据深入了解玩家和游戏情况。（本文为游戏邦/gamerboom.com编译，如需转载请联系：游戏邦）

Designing Games with Massive Social Data

[Editor's note: This is a guest post by Brice Morrison, former CrowdStar designer and editor of The Game Prodigy, a new site for game designers.]

“No no, you don’t understand. Most players don’t even get to level 22. Most of them drop out before then. We need to release the feature earlier.”

“That’s not true! A lot of our players are at that level, which is why we need to focus on more gameplay at that stage.”

I can’t tell you how many design discussions I’ve been in during my career. Discussions over what players want, what is the best way to spend our engineering and art resources, and what the spirit of our game is. When working on consoles and retail PC titles, we only had only limited feedback available to make decisions. Playtests, surveys, and playing the game ourselves before it was on the shelf were really the only ways to back up claims. And once the discs were out the door, then it was all said and done.

With social games however, it is a much different world. On Facebook or MySpace, the launch of a game title is just the beginning. Today the amount of data that developers are able to mine and manipulate is massive. How many players are at each level today. How much money players spend on what items. How long they play, when they play, and what they all do when they’re playing. Whereas other platforms require developers to adopt a “set it and forget it” attitude towards their games, social games spew mountains of feedback to their developers from launch day, allowing them to tweak and improve their social games based on player behavior.

Developers who understand and know how to make use of this data will have incredible perception into the behavior of their players and a leg up over their competitors. Let’s look at some of the advantages that data mining can provide.

Better Researched Decisions

For years, “classically trained” game designers in the industry have relied on their gut instincts to make decisions as to what players want. Much of this was based on intuition, imagining that yes, of course players would enjoy attacking other spaceships more than building and upgrading their own. Or a whiteboard discussion revolving around how long an average session is – a few minutes, for an hour, or for hours at a time. Or maybe an argument about player’s play styles – do they prefer to level up in order to improve stats, or do they like to spend money on items?

In the past, discussions and arguments like this are usually resolved by whichever designer can make the best points and steer the conversation towards their personal conclusion. While this approach is often effective if the development team is a talented one, it is often faulty and can produce decisions that don’t reflect player’s actual behavior.

By being able to pull live data from a game, arguments like this can be resolved almost instantly. In the middle of a shouting match on whether or not players like to upgrade their buildings every time they log in, someone can say, “Hey, guys, I looked it up, and yes, actually players level 1-30 upgrade 4 buildings on average every time they log in.” Everyone nods their head, makes the decision, and moves on. Or in a heated discussion of whether or not players need to be given more money in the game, someone can say, “Hey everyone, here is a graph showing the amounts of soft currency that people have right now. You can see that actually, most players have three times too much.” Income is slashed, expensive items are put on the market, and the job is done.

Sometimes knowing a key stat is just what the doctor ordered. By filling in the blanks as to what players are actually doing (not just what it seems like they’re doing), developers can learn to back up their decisions with data.

Experimenting

One of the best capabilities of data and instant user feedback is the ability it gives social game designers to experiment. There are often many options for the next feature that could be implemented in a game, and the right choice as to which will lift DAU, increase monetization, or improve retention to the greatest degree is not always clear.

By being able to get feedback quickly, developers can see how players react and change, tweak, or can the feature in no time flat. Releasing a new upgrade system that will give an advantage to paying users? No problem, let’s try it out on a few, watch them, and see how it affects their games. Wondering whether the unicorn fountain would sell better as a constructable viral item or a Facebook Credit purchase? Do an A/B test and look at the results tomorrow. Designing an experiment, putting it out into the wild, and then looking at the results is the best way to rapidly develop a hit title.

Experimenting is especially important in social games, where many of the designs that are now standard in the industry didn’t even exist several years ago. As Facebook and other social platforms have exploded in popularity, new designs needed to be created, tested, and discarded in darwinian fashion in order for companies to succeed. Without being able to look at the numbers quickly and decide what is and isn’t working, it would have been unlikely that the industry would have matured as quickly as it has.

Optimizing Where It Counts

Few game platforms have the luxury of being able to tweak their game to perfection. Sometimes just a few simple tweaks, a few numbers changed, or a few tuning values moved is all it takes to go from an also-ran to a blockbuster hit. There are so many numbers in social games that it can be overwhelming: the amount of XP to get to level 5. The amount of daily income that players receive at level 3. The cost of the in-game car. The exchange rate of 200 coins to Facebook Credits. It can be intimidating to realize that setting those numbers to 120, 17, 700 and 10 could result in $100 in revenue, while setting them to 115, 12, 900 and 12 could result in 10x that amount for your company.

Luckily, by being able to tweak different numbers endlessly and monitor the results, social game developers are able to optimize the tuning of their game until it’s nearly perfect. If players are making too little money, then the developer can just tick up their income a bit and see the results. If players are going too slowly, then they can increase the XP given or give out a bonus to see the effects. Watching these metrics and measuring them against the lifetime value indicators for players is one of the primary uses of massive data.

While these are all wonderful advantages to being able to mine massive data from players in real time, there are also many dangerous pitfalls. Teams who are suddenly blessed with being able to look at their players activity in detail without the experience of how to handle it can run into trouble. Let’s look at some of the common missteps that data can cause for social game teams.

Data Paralysis

Data is great for helping social game developers to make optimizations. However, game development isn’t just about making small optimizations. Sometimes for games that are doing mediocre or downright poorly, what’s really needed is a large new feature, a fundamental design change, or a reboot that better captures the essence of the game.

For example, let’s say we know that players consistently log in to get money from their city buildings twice a day, but we also see that the city building gameplay is causing long term problems, such as players becoming fatigued, racking up huge amounts of currency, or dropping out. This puts us in a tough spot: the right thing to do is to remove the feature from the game and try something else. But then what if they just stop logging in altogether? We have the data, and the data is telling us that is a bad idea. The downside is clear; the upside is fuzzy.

Changes like this aren’t easily quantified. But if DAU or ARPU are dropping like a rock, then something needs to be done. In cases like these, dependency on data can lead to paralysis, with designers not knowing what to do either way. They are able to easily quantify the problem but not the solution.

In cases like these the remedy is to use other traditional tools of the game development trade. It’s important to have someone on the team with a strong vision, an understanding of what players want backed up by playtests, surveys, and focus groups, and the leadership capabilities to lead the team into unknown territory. By not being completely dependent on data to make decisions, experienced developers will understand that sometimes the numbers aren’t there to help make the decision, but a decision will still need to be made. Having a robust system beyond data for making the best decision can help keep the team going.

Vanity Metrics

In social games, there is such a thing as too much information. When the numbers are something that you care about, then everyone likes looking at a graph. Any graph. A graph showing how many cultural exchanges players lost versus the week before. A graph showing the trend of raspberries farmed on average by level 55 players. A graph that shows the percentage increase of 25+ day users who built a chop shop week over week.

Ok, that sounds interesting, but what can we do with that? Does any of that actually matter? Does knowing it actually increase our bottom line, our DAU, or our players’ lifetime value?

Graphs are pretty, they make you feel like you know something, and they give you a concrete number to hold onto, giving the impression that you know and understand your game very well. However, not all numbers, graphs, or tables are actionable. I like to call these “vanity metrics”. They’re cool to have, and developers are willing to spend enormous amounts of time mining, producing, and debating them, but they don’t provide truly useful action items.

A good test for whether something is a vanity metric is for developers to ask themselves this question: if this number was radically different, would we act any differently? If the answer is no, then the data is useless and focus should be shifted to more important matters.

Vanity metrics are dangerous for two reasons. One, by spending time analyzing data that is actually useless, it takes away from time that could be spent developing features or improving the business. Second, by constantly sinking time into numbers that don’t matter, teams may begin to lose faith in data all together, eventually missing out on the metrics that do matter.

So when going to mine massive amounts of data, teams need to make sure that they know what they would do with the data first. Drowning in numbers is no way to improve your decision making.

Another Tool in the Toolbelt

Being able to pull instant data from games is a wonderful tool for developers to have in their belts. Flying blind may have worked in the past, but today the bar is higher than ever before, and if a game is designed or tuned poorly, there are plenty of high-quality competitors to flee to, competitors who have studied player behavior and chiseled the experience for maximum enjoyment. It’s important to remember that massive data on player behavior is a supplement to good design practices, not a replacement. But when used well, data can give invaluable insights into a company’s players and game.

A game designer who has worked at EA and CrowdStar, Brice Morrison is the editor of game design website The Game Prodigy and has been with teams for major titles like The Sims 3, Happy Aquarium, and It Girl.（Source：Inside Social Games）