作者：William Grosso

免费游戏世界中的参数很容易让人感到混淆，因为这里有无数参数并且还有许许多多有关这一主题的介绍文章。许多开发者甚至选择完全放弃参数追踪或者只是遵循着《游戏开发者需要铭记的15个游戏参数》，并且不再进行更深入的分析。还有些开发者会专注于自己的分析并希望从每日甚至每个小时的动态中找出趋势所在。考虑到大多数免费游戏的自然属性，这就好似将一只鸡开膛破肚并尝试着从它的内脏中看清楚游戏是否能够取得成功一样。

之所以会出现这种混淆是有原因的：游戏产业呈献给了我们一种源于基准且非常复杂的参数语言。这便是所有游戏会议报告会出现的内容。这也是所有分析包所衡量的内容。

但这却没有多大用处。

产业参数并不会告诉你任何有关你的免费游戏虚拟经济的状态。结果便是你找不到任何能够向你解释如何使用参数去完善游戏的特定指南。除了那些拥有资源去创造自己内部分析系统的大型发行商外，所有开发者，甚至是整个产业都会因为这种模糊的内容而受伤。

让我进一步向你们解释。

关于分析的简单描述

也许上面的段落听起来有点轻浮，所以让我再添加一些简单但却重要的修饰内容：一个基本分析包能够追踪到像ARPPU，转换率以及其它值得所有开发者关注的标准参数。现在市场上有许多分析服务提供者，他们中的大多数人都能够免费，或近乎免费地帮助我们追踪这些重要参数。

尽管这些基本分析并不能告诉你你的虚拟经济的状态，但它们还是非常重要的：例如你总是希望能够了解你的游戏相对其它游戏的性能。或者你也可以追踪到像你想要获得多少玩家并在软发行时创造出多少用户留存等内部目标，如此你才能更好地决定何时去推动用户获取。产业标准数据也能帮助你的投资者去判断你的游戏是否值得获得更多资源。

让我们以三个标准参数为例并着眼于它们所缺乏的元素：

首先说说ARPDAU

日活跃用户平均收益（ARPDAU）是手机/免费游戏中最常出现的参数。值得肯定的是这是一种有用的参数，它能帮助你了解游戏每天的表现，同时这也是你在经历任何用户获取活动之前和之后去追踪相关参数的关键指标。

当然了还有很多东西是ARPDAU并未告诉我们的：例如付费玩家是否会在某一天再次购买东西？广告和应用内部购买各创造了多少收益，这些收益在整体玩家基础中的分配是怎样的？

当你的ARPDAU下降时你要做什么？我想首先你会去修改游戏，但是你要怎么修改，该修改哪里？如果下降数量不多，你可以再观察看看数值是否会恢复。如果下降数量很多，你便需要开始寻找原因。

ARPPU

每付费用户平均收益（ARPPU）只能计算那些在游戏中花钱的用户。如此看来它比ARPDAU参数更有用些。但是再一次地，ARPPU的作用也很局限，因为基于不同游戏类型ARPPU的作用也是不同的。就像硬核和中核游戏（游戏邦注：主要是策略游戏，RPG游戏盒射击游戏）拥有更高的盈利参数，但在休闲游戏中这就没什么用了。

就像《战争游戏：火力时代》的ARPPU值便远高于《Candy Crush》—-即使这两款游戏在去年所赚取的收益是持平的。

转换率是否有价值？

ARPDAU和ARPPU能够帮助你明确付费玩家能够为你创造多少收益，而转换率则能够计算在特定时期内在总用户中有多少特殊用户在游戏中花钱了。（你也可以计算免费游戏中的广告转换率。）让用户在他们可以免费玩的游戏中花真钱是非常困难的事，如果你能够获得5%的转换率，这就说明你做的很好了。所以这是衡量一款免费游戏在市场上的表现的有效基准。

但再一次地，转换率也并非一种全面的参数。就像在其它产业中那样，在免费游戏中重复的消费者创造了绝大部分的收益。如果你让5%的玩家一个月在游戏中进行一次IAP然后便能够获得成功，那么这些玩家将不会在游戏中进行多次购买。

最终我们会发现这三种参数都是虚势—-这是开发者在GDC趴上喝了几杯酒后会自夸的一些内容。他们可能会告诉你是否要去获取用户，但是他们不会指出你的游戏中存在的任何问题。

更糟糕的是，尽管ARPDAU，ARPPU和转换率都是经济参数，但是它们却都不能清楚地告诉你玩家是如何看待你的游戏内部经济，他们在游戏中购买了什么，以及他们是否会再次消费。而这些恰恰才是提高你的游戏收益的关键，所以了解ARPDAU，ARPPU和转换率并不能帮助你真正做到这点。漏斗参数也是如此，即虽然能够用于衡量市场营销以及用户流失的地方，但却不能清楚地向你解释原因。

为什么我们所使用的游戏参数如此有限

我们该如何使用如此有限的参数而发展？这需要进行更多的解释，因为我们首先需要基于两条轴线去考虑这些参数：

特定游戏vs一般游戏：前者指的是一些特殊游戏或定位明确的游戏子类型，而后者则指代所有游戏，甚至是一些非游戏应用。

警示vs基准vs诊断vs控制：像ARPPU，ARPDAU和转换率都属于警示和基准类别，即将告知开发者他们的游戏在产业标准下的盈利表现。而漏斗参数属于诊断型，即能够指出用户体验中的问题，其中控制参数是最有用的，能够真正明确一些特殊的相互作用。我认为这便是问题产生的根源。

metrics(from gamasutra)

从上述表格中我们可以发现，如果开发者要去讨论在自己的特定游戏中怎样的参数最重要是非常困难且费时的，而即使他们能够这么做，他们也会因为担心将更多特殊方法泄露给竞争者而不敢这么做。他们也总是不愿去正视一些糟糕的参数。与此同时像Flurries和App Annies都拥有专注于适合大众消费的警示和基准参数的有效服务。

结果呢？我们讨论了一些公共参数并引用了一些使用了这些参数的公司。尽管开发者可以在许多分析服务中添加一些定制要求，但是对于该添加什么内容却不存在真正有效的指南，因为这是不在公众谈论范围中的。如果你在考虑如何与你的首席执行官谈论游戏或者与外部人员谈论游戏，你最好去使用一些通用参数。

但这些便都不属于控制参数，即那些真正对开发者有利的参数。这才是真正的问题所在。我们拥有的是一个完全基于警示和基准参数所创造的复杂参数语言。

朝着基于控制的游戏参数

我们的产业真正需要的是围绕着对游戏成功最有帮助的参数而展开的公共交谈。

我们将开始看到一些致力于创造这些目标的分析/盈利服务。例如DeltaDNA面向免费游戏发行商Thumbspire的案例研究。他们基于产业标准衡量了第一天的用户留存并发现其数值较低，所以他们继续进行一次定性分析。从中他们发现20%的玩家会在教程期间离开游戏，他们便知道该为首次游戏的玩家完善这部分内容。于是他们的第一天用户留存便提高了66%。如此看来我们或许可以创造一个像“废弃教程”的参数作为和转换率一样普遍的标准参数？

（本文为游戏邦/gamerboom.com编译，拒绝任何不保留版权的转发，如需转载请联系：游戏邦）

Why Industry Standard Game Metrics Don’t Matter (Enough)

by William Grosso

The world of free-to-play metrics can be very confusing, with literally hundreds of metrics out there, and literally thousands of introductory articles on the topic. Many developers largely give up tracking metrics at all, or simply stick with the 15 Metrics All Game Developers Should Know by Heart, using these instead of conducting a deeper, more qualitative analysis. Other developers obsess over their analytics, struggling to discern trends on a daily or even hourly basis. Given the natural variance in most free-to-play-games, that’s a lot like cutting open a chicken and reading the entrails in a vain attempt to figure out whether the game is going to prosper.

There’s a very good reason for this all confusion: The game industry has given us a complex metrics language that is almost entirely built out of alerts and benchmarks. It’s what all the game conference presentations are in. It’s what all the analytics packages measure.

And it’s not very useful.

Industry metrics don’t tell you anything about the state of your F2P game’s virtual economy. Consequently, there’s very little specific guidance out there which explains how to use metrics to make your game better, or goes beyond elementary advice such as “promote relevant offers”. Outside the giant publishers which have the resources to build their own in-house analytics system, this lack of clarity is hurting just about every developer, and hurting the industry as a whole.

Let me explain.

Quick Qualifier on Analytics

The previous paragraph might have sounded a bit dismissive. So let’s add a quick but important qualifier: A basic analytics package that can track standard metrics like ARPPU, conversion rates and all the other items I’m about to discuss is a worthwhile investment every developer should get. There’s a lot of analytics providers out there, most of which track these core metrics for free, or close to it.

While these entry level analytics won’t tell you much about the state of your virtual economy, they’re still important: You want to understand your games’ performance relative to other games, for one thing. For another, you can track internal goals for how many players you want and retention levels during a soft launch, and then better decide when it’s time to boost acquisition through through ad campaigns. Perhaps just as key, industry standard data will help your investors decide if your game is good enough to get more resources.

That said, let’s take three canonical metrics and look at how far they fall short:

Let’s Talk about ARPDAU

The Average Revenue Per Daily Active User (ARPDAU), is one of the most commonly discussed metrics in mobile/F2P games. And to be sure, it is a useful metric, allowing you to understand how your game performs on a daily basis, while also a key reference for tracking the before and after results of any user acquisition campaign.

Still, there’s a lot in there that ARPDAU doesn’t tell us: For instance, do the paying players on a given day purchase again? How much of this revenue is generated by advertising versus IAP, and how is this revenue distributed among your entire player base? ARPDAU doesn’t tell us that.

And what do you do when your ARPDAU drops? Well, you fix the game, I guess — but how, and at what point? If the drop wasn’t very big, you wait and see if the game recovers. If the drop was very big, you start looking for causes. And for most games, that’s usually a qualitative exercise, instead of a quantitative one.

How About ARPPU?

Average Revenue Per Paying User (ARPPU) measures only the subset of users who have completed a purchase in a game. For that reason, it’s somewhat more useful than the aforementioned ARPDAU. Once again, however, ARPPU has limited use, because it can vary dramatically based on game genre. Hardcore and midcore games (primarily strategy, RPG, and shooters) tend to have much higher monetization metrics in regards to ARPPU, but they also lack the mass appeal of more casual games.

You can bet, for example, that ARPPU rates for Game of War: Fire Age are much, much higher than Candy Crush, even though both games earned about as much revenue last year.

Okay, But Surely Conversion Rates Are Valuable?

While ARPDAU and ARPPU give you (limited) insights into how much money your monetized players are paying you, the conversion rate measures the total percentage of unique users who have made a purchase out of the total number of users during a given period. (You can also measure the conversion rate of ads served in a free-to-play game.) Getting users to pay real money in a game that they can play for free is a difficult assignment, and if you can earn conversion rates over 5%, you are doing extremely well. So this metric is a good benchmark for how well your F2P game is doing on market.

But once again, conversion rates are not a be-all, end-all metric. As with many other industries, repeat purchasers generate the majority of revenue in free-to-play games. You could have 5% percent of your players making an IAP one month, and declare victory — only to watch, crestfallen, as these same players never again make another purchase.

In the end, all three are largely vanity metrics — the kind that developers humblebrag about after a few drinks during a GDC party. They might tell you whether to acquire users or not, but they don’t point to any fixable issues in your game. Changes in them signal that something is wrong (or right), but they don’t give any hints as to what.

Even worse, while ARPDAU, ARPPU and Conversion are all financial metrics, they don’t even give you a clear picture of how players are engaging with your in-game economy, what they’re buying within it, and whether they’re likely to buy again. That’s the key to increasing your revenue, and knowing your ARPDAU, ARPPU, and conversion rates won’t tell you much about this. The same can be said of funnel metrics, which may be essential for marketing, and measure where users falls off the train, but still don’t give you sufficient diagnostics to help explain why.

Why the Game Metrics We Use Are So Limited

How did we end up with such limited metrics to guide us? That takes a bit more explanation, because we first need to think about metrics in terms of two axes:

Game-specific versus generic: The former relate only to a particular game or clearly defined sub-genre of game, while the latter apply to all games and even to non-game apps.

Alerting versus Benchmarking versus Diagnoses versus Control: The standard metrics like ARPPU, ARPDAU, and Conversion fall into both the Alerting and Benchmarking categories, telling developers how well their game is monetizing in relation to industry standards. Funnel metrics fall into the Diagnostic category, pointing out problem points in the user experience, while Control metrics are the most useful at all, giving you actionable insights on specific interactions. I believe this is where the problem arises.

As this chart above hopefully suggests, it’s very difficult and/or time-consuming for any particular developer to discuss the metrics which matter most in their particular game, and even when they can (say, at an in-depth GDC talk), they worry that they’re giving away too much special sauce recipe to potential competitors. Alternately, they’re often too embarrassed to acknowledge bad metrics. At the same time, the Flurries and App Annies of the world are able to run successful services which focus on the top level alerting and benchmarking stats that work best for public consumption.

The upshot? We wind up talking about the public metrics and cite the companies made to measure them, and we maintain an entire industry around both. And while developers can add custom events in many analytics services, there’s no guidance for what to add, because that falls outside the public conversation. And if you think about how you’re going to talk about your game to your CEO, or to outside parties, you have to speak in terms of generic metrics.

But these are not control metrics, the kind of data developers need most to survive and possibly thrive. And that’s the ultimate problem. We have a complex metrics language that is almost built entirely out of alerts and benchmarks.

Toward Control-Based Game Metrics

What our industry needs is a public conversation around the metrics that matter most to the success of our games, and some consensus around them.

We’re starting to see analytics/monetizations services work on creating these. For instance, see this excellent DeltaDNA case study for Thumbspire, a F2P publisher. They measured Day 1 retention against industry benchmarks, found them lacking, so then went on to do a qualitative analysis (presumably guided by event logs). This showed them that 20% of players dropped out during the tutorial, which pointed them to improving this for first-time users. (Which improved D1 retention by 66%.) Perhaps, therefore, we should make a metric like “Tutorial Abandonment” as standard and widely-cited as conversion rates?

In my next Gamasutra post, I’ll start to propose some other metrics worth standardizing, particularly those related to revenue. As a preview, here’s some reliable KPIs my team and I have added to our own tracking system, which customizable analytics services on the market can also measure:

Percent Of Spend by End of Day One: I.E. the % of users who complete a purchase within one day of downloading the app (out of the total number of users who download the app on that same day). The specific percentage rate varies quite a bit depending on the game and the genre, of course, but a relatively high percentage means the game is connecting with an audience. After pushing an update to your game, substantial changes to this D1 % spend immediately alert you to how well (or not) those changes are working with your userbase.

Percent of Spenders Who Buy a Second Time: You want at least 40% of your paying players going on to make a second IAP — a strong indicator that they enjoyed the first purchase, found it valuable, and wanted more. If this number is significantly less than 20%, you should worry that these first time purchasers might have (for example) felt cheated by the experience, or realized that your game’s free daily rewards and other free giveaways are sufficient for play.

Time Until Second Purchase: Most purchases should occur within two weeks of one another. If the number is substantially higher than 14 days, your game probably provides too little incentive for the users to make subsequent purchases. As with D1 % spend, this two week rule of thumb can vary somewhat by genre, since successful core/mid-core games typically enjoy extended gameplay over many months, while more casual games on average have a shorter shelf life.(source:gamasutra)