作者：Dmitri Williams

我们已经清楚用户互动对于游戏盈利的重要性，而本文将通过讨论游戏内部受社区推动的盈利而进一步延伸这一概念。本文将区分游戏社区管理与其它形式的社区管理（如社交媒体），并提供关于游戏内部社区如何帮助或伤害开发者的概述。引文同时也会讲述如何使用一系列工具，包括预测分析去创造更加强大的游戏社区。

向玩家出售游戏是一门不错的生意。唯一的问题在于你的工作并不只是如此。就像开发者Raph Koster所指出的那样：

“这是一种服务。不是单纯的游戏。这是一个世界。不是单纯的游戏。这是一个社区。不是单纯的游戏。任何说着‘这只是一款游戏’的人都未能抓住要领。”

这点真的很重要。是否有参数能够支持它呢？

当然了。让我们这样想：大多数游戏业务的运作都是基于B2C，即企业对消费者模式。但什么又是C2C呢？即消费者对消费者，或者在这里应该说是玩家对玩家模式。

最终证明玩家对于彼此之间都具有很重要的影响—-这是社区管理者所清楚的事，但是他们却从未去证实它。这一社会价值通常占据你的收益的25%至50%。社交网络分析也证实了这一点。

市场细分

将你的用户分成不同的群组是一种明智的做法。你可能会想到男性用户与女性用户的不同表现，或者法国人与澳洲人的不同消费标准，以及年长用户不像年轻用户那样频繁地发送信息。而市场细分能让你将这些群组的价值与其它参数匹配在一起。

让我们先说说ARPPU。如果整个群组的价值为25美元，但是你发现男性用户为45美元而女性用户只有5美元，你便能够清楚问题的所在。一开始便明确细分能够提供给你断代分析。或者你可以基于应用版本1和应用版本2去划分不同群组。很快地，你便能够进行AB测试。

更复杂的细分变量还能呈现更多不同的类型。例如能够告诉我德国女性用户使用iOS与美国男性用户使用Android的情况。通常情况下，我们应该先思考正确的问题，然后再选择工具。

降低游戏开发风险

随着过去几年里电子游戏领域的不断发展，即从专注于主机向手机平台的扩展，市场也变得比以前更大了。但是一个巨大且充满竞争的市场意味着那些想要进入这一领域的人将面临更大的财务风险。

所以游戏开发者该如何降低风险？来自SuperData的全新研究显示，超过40家电子游戏公司发现了降低风险的最佳方式。研究中的第一个结论表示，当决定一个目标消费者基础时，使用B2B和B2C混合模式是最有效的：“使用这种双重方法能够通过收获多个收益流而降低业务模式的风险。”

该研究发现利用合作方去帮助游戏开发者到达一个更大的市场的重要性，同时评估创造性的效能也能够缓解风险并创造持久的收益。

通过预测分析提高玩家留存

在2年多时间里，手机游戏玩家的价值不断飙升—-2012年，1百万用户的价值仅有130万美元，而现在他们的价值已经达到了340万美元。

因为手机游戏玩家的价值不断提升，所以确保他们不断回到游戏中变得更加重要。为什么？因为市场变得越来越拥挤，更多竞争者便意味着你将可能失去更多忠实玩家。而现在获取全新玩家也变得更加困难且更加昂贵。

提高用户留存的一种方法便是提供一个稳定的消费者关系管理（CRM）基础，包括消费者支持和整体的社区管理。再加上一个预测分析平台，你便能够分析它是如何影响你的结果。

让我们看看Julian Runge在Gamasutra上的一篇文章，在这里他应用了基于用户留存战术的一些预测分析去决定什么才是对玩家最具影响力的内容。

我们必须注意到预测游戏分析虽然很有效，但是你需要有足够的信心去利用任何系统预测。并非所有平台都会呈现给你这个数值。你应该依赖于你所使用的平台。你应该着眼于自己的预测的精准线。如果现在的你只是在寻找一个分析平台，那么你便需要问自己这些问题并判断平台是否能够提供给你做出真正的业务决定所需要的精准性。

不同玩家

是什么原因导致玩家间的区别？根据研究公司EEDAR的全新研究，是源自平台而非性别。

多亏了先进的技术，各种不同的电子游戏平台让现在的玩家能够拥有比10年前更加独特的体验。现在的我们拥有手机，PC，主机，甚至是VR，游戏开发者能够为各种年龄和性别的玩家定制专属于他们的电子游戏了。

预测性分析

预测性分析将告诉你一些事情发生的可能性。再一次地你可以从总体或个体进行思考。让我们假设你想要了解用户流失的可能性。你可以从总体去观察数值，或着眼于每个人的数值。你会想看到它们在不同情况下的消费。你不应该将具有80%的离开可能性并且已经花费10美元的玩家与具有70%离开可能性但却已经花费了2000美元的玩家混为一谈。

例如，如果你知道玩家X在接下来10天里不会花任何钱，你便会考虑采取一定的行动，对吧？如果你知道玩家Y在4天内会退出游戏，但是他已经在游戏中花了100美元，那么你便会想办法去留住他，对吧？

当然了，这一切的前提是你了解这些情况。

这就像预测天气一样

当你在研究游戏的预测分析时，你很快便会发现这与预测天气没有什么区别。你拥有数据，趋势，以及对于明天可能发生什么的估测。最聪明的气象学家会使用科学数据模式。随着时间的发展你将清楚哪种模式才是值得你信赖的。

作为一名游戏开发者，你会重视对于人们的预测，特别是他们采取特定行动的可能性。举些例子来说：

用户流失

人们会在哪里退出游戏？

为什么他们会在那时候退出游戏？

为什么最近流失率在上升（或下降）？

用户转换

是什么原因让玩家从观察者变成免费玩家？

是什么原因让玩家从免费玩家变成付费玩家？

游戏内部盈利

是什么原因让玩家决定开始花钱？

是什么原因让玩家花更多钱

是什么原因让玩家去点击广告？

玩家终身价值

玩家总的花了多少钱？

社交价值

玩家网络有何价值？

是否有些玩家比其他玩家更有价值？

Asimov预测就是你的现实

如果你看过Isaac Asimov的《Foundation Trilogy》，你可能会记得一个先进文明，即通过理解人类和社会从而更精确地预测个体行动的科学。

但那毕竟只是科幻小说。我们不可能真正预测到未来。

不管从技术上来看，这也不是不可能。只是这并非魔法，而是关于数学。

幸运的是现在的科学让我们能够更轻松地做到这点，因为我们拥有更多更厉害的辅助工具。你是否需要聘请一些博士？你可以这么做，但这却是不实际的。你必须理解真正发生了什么，如此你才会清楚结果是否有用。

自信地识别不同模式

一开始，让我们假设在一款冒险游戏中只可能存在两种模式。

1.玩家登录并玩了第一个关卡。一些玩家进入了Dark Forest，而20%的玩家退出了游戏。

2.玩家登录并玩了第一个关卡。一些玩家进入了Blue Bayou，而40%的玩家退出了游戏。

这是显而易见的结果，这告诉你Blue Bayou更糟糕，也就是在这里玩家更容易退出游戏。

现在让我们假设一个更现实的场景，即玩家将做35件事，并且他们将基于各种不同序列去做这些事。在这个随机的事件序列中，复杂的算式能够加工数据并开始“学习”模式。

这也是为何这些程序会被冠予“学习机器”的称号。然后这些程序将开始着眼于玩家并说道，“基于其他玩家所做的，这周这个玩家有80%改变，退出或点击广告的可能性。”

就这样，你便拥有了预测分析！

（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）

The Tenets of Community Management Series: Part 1 of 3

by Dmitri Williams

We’ve already established that user interaction is key to game monetization, and this series will take that concept one step further by discussing in-game community-driven monetization. This article will differentiate game community management from other forms of community management (social media, for example), and give an overview on how in-game communities can help or hurt developers. The introductory article will also prep users on how to build stronger gaming communities through a mix of tools, including predictive analytics.

Selling games to players is a great business. The only problem is that it’s not all of what you actually do. As respected developer Raph Koster has pointed out:

“It’s a SERVICE. Not a game. It’s a WORLD. Not a game. It’s a COMMUNITY. Not a game. Anyone who says, ‘it’s just a game’ is missing the point.”

That’s a big claim. Do the metrics bear it out?

Absolutely. Think about it this way: most games businesses operate and think as B2C, or business-to-consumer, models. A few are B2B. But what about C2C? Consumer to consumer, or in your case, player to player?

It turns out that players have a massive and important impact on each other—something community managers have always known, but have never been able to quantify. This Social Value? actually accounts for usually 25-50% of all of your revenue. Social network analyses make capturing it possible.

Segmentation

It’s always smart to think about your users as being in different buckets and groups. You might guess that men will behave differently than women, that the French will spend differently than the Australians, or that the older users will send fewer messages than the younger ones. However you want to slice and dice it, segmentation lets you layer those group values on to other metrics.

Let’s stick with ARPPU for a minute. If the whole population is $25, but you find that men are $45 and women are $5, you’ve just diagnosed a problem. Segmenting can be done based on start date, which gives you cohort analysis. Or, you may segment based on one group you had see version 1 of the app and one group that saw version 2. Presto, you have AB testing.

More complex variations of segmentation allow multiple types. For example, show me the female Germans using iOS vs. the male Americans using Android. As always, the key is thinking about the right questions first, then picking the tool second.

Lowering Game Development Risks

As the video game landscape has evolved over the past few years from console-only to the explosion of mobile, the market is larger than ever. But a large and competitive market means an even larger financial risk for people who want to dive into the industry.

So how can a game developer minimize their risks? A new study by research firm SuperData surveyed over 40 video game companies to find out the best ways for lowering risks. The first conclusion in the study found that when determining a target customer base, a mix of B2B and B2C is the most effective: “Adopting a dual approach can play a role in derisking a business model by bringing in multiple revenue streams.”

Internally, the study found that it’s important to put an emphasis on partners to help game developers reach a larger market, while also valuing efficiency over creativity and innovation, to lessen risks and generate consistent revenue.

Increasing Player Retention with Predictive Analytics

In just a little over two years, the value of mobile game players has skyrocketed – in 2012, 1 million users were worth just $1.3M, and now they’re worth nearly $3.4M.

Since the value of mobile gamers is on the rise, it’s important to keep them coming back for more. Why? The market is becoming increasingly flooded, and more competition means that you’re at a higher risk of losing loyal players. It’s also more difficult and costly than ever to acquire new players.

One way to increase customer retention is by providing a solid customer relationship management (CRM) foundation – which includes customer support and overall community management. Couple that with a predictive analytics platform, and you’ll be able to analyze how it affects your bottom line, over time.

Take a look at this article by Julian Runge on GamaSutra, where he applies predictive analytics with a few customer retention tactics, to determine what has the biggest impact on players.

It’s important to note that predictive game analytics are great, but you need a high confidence score to make the most out of any systems predictions. Not all platforms will show you this number. Depending on what platform you are using. It is worth taking a look at how accurate your predictions are. If you are just looking for an analytics platform now, it’s important to ask these questions and determine if that platform will provide the accuracy needed to make real business decisions.

Gamer Differences

What drives gamer differences? According to a new study by research firm EEDAR, it’s platforms, not gender.

Thanks to the advancement of technology, the abundance of video game platforms has allowed players to experience more unique experiences than were possible 10 years ago. Now that we have mobile, PC, console and even VR, game developers are able to tailor their video games to a variety of ages and genders.

Predictive Analytics

Predictive analytics tell you the likelihood that something will happen. Again, you can think about this in the aggregate, or by individuals. Let’s say you want to know the likelihood of churn. You can see that value overall, or for each person. And, you should want to see their spending for context. The player who is 80% likely to leave and has spent $10 should not be treated like the player who is 70% likely to leave who has spent $2,000.

For example, what if you knew that player X is not going to spend any money for at least the next 10 days, you might think about taking some sort of action, right? If you knew that player Y is going to quit in 4 days and she’s worth $100 to you, you’d do something to intervene. right?

Of course you would….if you only knew about it beforehand.

It’s Like Predicting the Weather…But With People

As you delve into predictive analytics for games, you quickly discover that it’s not radically different from forecasting the weather. You have data, you have trends, and you have an estimate for what’s going to happen tomorrow. The smartest meteorologists use scientific data-driven models. And over time you learn which ones you can trust.

As a game developer, you care about making predictions about people; specifically their likelihood to take certain actions. For example:

Churn

Where do people quit the game?

Why do they quit at that point in the game?

Why has churn rates increased (or decreased) recently?

Conversion

What makes players go from observer to freemium?

What makes players go from freemium to paying?

In-Game Monetization

What makes people decide to first start spending money?

What makes them spend more?

What makes them click on ads?

Player Lifetime Value

How much do players spend in total?

Social Value

What is the value of player’s networks?

Are some players worth more than others?

Asimov’s Predictions Are Your Reality

If you’ve read Isaac Asimov’s Foundation Trilogy, you may remember that there’s an advanced civilization that made a science out of understanding humans and societies so well that they could accurately forecast the actions of individuals.

But that’s science fiction. It’s impossible to really predict the future, right?

Technically, no, it’s not impossible. And it’s not magic either. It’s math.

Luckily, the science here is getting easier to deal with as more and better tools become available. Do you need to hire a bunch of PhDs? You can but it’s not terribly practical (or cheap). Still, it’s important that you understand what actually happens so you understand how usable and actionable the results are.

Recognizing Patterns with Confidence

To start, let’s keep it simple and imagine that only two patterns are possible in an adventure game.

1.Players login and play Level 1. Some go into the Dark Forest and 20% of them quit.

2.Players login and play Level 1. Some go into the Blue Bayou and 40% of them quit.

That’s pretty handy to know because it also starts to tell you that the Blue Bayou is in some sense, worse–and by worse, I mean it leads to quitting.

Now imagine a more realistic scenario where players are doing maybe 35 things and they do them in a wide variety of sequences. From this seemingly randomn series of events, sophisticated algorithms can mine the data and start to “learn” patterns.

This is why these programs are dubbed “machine learning.” Those programs then start to be able to look at a player and say, “based on what other players have done, there’s an 80% chance of this player converting or quitting or clicking an ad this week.”

And bam, you have predictive analytics!

In future posts, we’ll look at Individual metrics and cohort analysis as a further dive into this topic.(source:gamasutra)