篇目1，阐述游戏用户分析学的定义及作用

作者：Anders Drachen

近年来，游戏分析学已经引发人们极大的关注。

研究玩家对游戏公司的商务、设计等各个部门都非常有帮助，应此要求，有必要向游戏开发人员介绍这种分析技术。游戏分析学也因此逐渐成为行业的商业智能中的重要领域。通过遥测技术、市场调查、QA系统、基准测试等各种来源获得的定量数据成为商业智能管理、决策的重要参考。

将分析学引入开发过程时，有两个重要问题要考虑，一是追踪什么，二是如何分析获得的数据。选择收集什么资料的过程叫作特征选择。特征选择是个困难的任务，特别是当针对的是用户行为时。决定追踪什么用户

行为并没有唯一正确的答案或标准模型；相反地，我们要根据不同的目标选择不同的策略；例如，改进用户体验或增加赢利。在本文中，我们将概述与游戏玩家分析学有关的基本原理，其中特征选择是重点。首先，我们会概述可追踪的用户数据的类型，然后介绍特征选择的过程，即在什么地方如何选择什么来测量。最重要的是，本文并不针对免费游戏或在线游戏——分析学适用于所有游戏。

分析学的数据

游戏分析学的数据来源主要有三种：

1、性能数据：这些是与技术和硬件有关的数据，尤其是在线或持续型游戏。普遍的性能指标包括游戏在客户端硬件平台的执行帧率或游戏服务器的稳定性。

2、过程数据：这些是与开发游戏的实际过程有关的数据。游戏开发或多或少是一个创造性过程，但仍然需要监控，即估计任务量和制定计划等。

3、用户数据：这是最常见的数据来源，这些数据来自玩我们的游戏的用户。这里，我们既把用户当作消费者（收益来源）也当作玩家（与游戏产生特定交互活动的对象）。我们计算与收益有关的指标如每用户平均收益（ARPU）、日活跃用户（DAU），或者执行与收益有关的分析如用户流失分析、用户支持性能分析或微交易分析时，我们称游戏用户为消费者。

当研究人们与游戏系统、系统的组件以及其他玩家如何产生交互作用（游戏邦注：主要是游戏内行为如平均游戏时间等）时，我们称游戏用户为玩家。本文针对的是这类数据。这三类数据不包含一般的商业数据如公司市值、公司收益等。我们不把这类数据当作游戏分析学中的特定领域，而是把它归入商业分析学的一般范畴中。

用户数据的指标

过去几年，已经有不少人提出分类用户数据的不同方法。从自上而下的角度看，开发导向型的分类法是很实用的，因为它可以在三种不同利息相关者的这三个方向上过滤用户指标：

1、消费者指标：包括用户作为消费者的各个方面——例如，消费者开发和留存的成本。这些指标对游戏的营销和管理以及游戏开发特别有意义。

2、社区指标：包括所有忠实程度的用户社区的活动，如论坛活动。这些指标对社区经理很有用。

3、玩法指标：与用户作为游戏内的玩家的实际行为（游戏邦注：例如使用道具、交易物品、探索游戏世界等）有关的任何变量。玩法指标对评估游戏设计和用户体验最为重要，但从游戏开发的收益链这个传统的角度上看，它通常不是优先考虑的指标。这些指标对从事游戏设计、用户研究、QA或主要研究用户实际行为的人员最有用。

具体分析

1、消费者指标：作为消费者，用户可以下载和安装游戏、在游戏内外商店中购买任意数量的虚拟商品、支付真钱或虚拟货币。与此同时，消费者与客户服务互动、提交漏洞报告、请求帮助、申诉等。用户也可以参与官方或非官方的论坛活动、登录其他社交互动平台，从这些渠道可以获取和分析关于这些用户以及他们的游戏行为、对游戏的满意度的信息。我们还可以收集消费者的国家、IP地址和甚至年龄、性别、邮件地址等信息。将这些玩家群体信息、行为数据相结合，有助于深入地了解游戏的消费者基础。

2、社区指标：如果有条件，用户之间会产生互动行为。这种互动行为可以是玩法上的（如通过游戏机制进行战斗或合作），也可以是社交上的（如游戏内聊天）。玩家-玩家的互动可以发生在游戏内，也可以发生在游戏外，或二者混合——如通过“分享到Facebook”的功能发送炫耀新装备的信息。在游戏内，玩家可以通过内置聊天功能互相交流；在游戏外，玩家可以使用即时会话工具如TeamSpeak或Skype，或者通过游戏论坛来沟通。

这类玩家之间的互动活动形式是重要的信息来源，具有广泛作用。例如，通过分析免费游戏的用户社区的社交网络，可以知道哪些玩家有很发达的社交关系网——这类玩家有助于创造良好的社交环境，从而留住大量其他玩家（相当于MMORPG中的公会领袖）。与此类似，聊天日志和论坛帖子可以提供关于游戏设计的问题的信息。例如，从在线游戏的聊天日志中抽取数据可以发现漏洞或其他问题。监控并分析玩家-玩家互动行为在任何情况下都是重要的，因为多玩家，特别是对于致力于创造和支持稳定型玩家社区、采用在线商业模的游戏，包括许多社交在线游戏和免费游戏。这些例子只是冰山一角，收集、分析和报告关于玩家-玩家互动行为的游戏指标是可以轻轻松松就写出好几本书的话题。

3、玩法指标：这个用户指标的亚类可能是目前在使用中的游戏遥测技术中最广泛记录和利用的一类。玩法指标主要用于衡量玩家行为：导航、道具和技能的使用、跳跃、交易、奔跑等玩家在虚拟的游戏环境（无论是2D还是3D）中的活动。当玩家在游戏中做某事或某事发生在游戏中的玩家身上时，有四种信息可以记录：发生什么？在哪里发生？什么时候发生？与谁有关？

玩法指标对游戏设计特别有用，因为设计师可以通过它了解几个关键问题，例如是否有哪些游戏世界区域使用过多或过少、玩家是否按设计意图使用游戏功能、和是否有拖延玩家进度的障碍等。在游戏开发的任何阶段仍至游戏发布以后，都可以记录这些游戏指标。

在一次游戏过程中，玩家可能产生上千种行为——每一次玩家在游戏系统中做出行为，游戏就必须产生回应。玩家活动的准确评估可能包含按秒来测量的各种行为。例如，在典型的MMORPG如《魔兽世界》中，测量玩有行为可能包括记录玩家角色的位置、当前命值、精力值、魔法值、法术效果的影响时间、主动行为（奔跑、挥动斧头等）、状态（战斗、交易、探索等）、NPC敌人对玩家的态度、玩家角色的名字、种族、等级、装备、金钱等等——所以这些小信息都要从安装的游戏客户端流向收集服务器。

从实用的角度来说，你可以把玩法指标进一步分类成如下三种类型（为了使你的指标更容易搜索）：

游戏内：包括所有游戏内活动和玩家行为，如探索、经济行为、使用物品等。这类信息最主要来自用户遥测技术。

界面：包含所有玩家与游戏界面和菜单发生的互动活动。这包括设置游戏变量如鼠标灵敏度和显示器亮度。

系统：系统指标包括游戏引擎及其亚系统（AI系统、自动事件、MOB/NPC活动等）对玩家活动产生的反应。例如，如果玩家角色移动到怪群的仇恨范围内，怪群就会攻击玩家角色，或者当玩家角色满足预设条件时，就会升到下一级。

综上，从游戏用户（或游戏服务）中得到的可能测量工作量也许会大得惊人，通常我们应该把目标确定为记录和分析最必要的信息。这个选择过程存在偏好，但通常必须避免数据过多，以确保分析工作的可行性。

整合分析

在选择要监控的特征和评估要采用的策略时，往往会对收集什么数据产生偏见，特别是当分析工作没有确定的根据时。如果那些负责分析的人不能与利益相关者交流沟通，那么极有可能遗漏关键信息，同时分析也实现不了全部价值。

因为分析人员来自不同部门，如用户研究、营销和赢利，这导致分析团队只服务或优先服务于自己的母部门。保证分析团队与所有团队保持交流，有助于缓和这种情况。这也有助于减轻另一个常见的问题，也就是分析团队因为不能接触到设计团队，被迫自行选择特征来追踪和分析，这样他们的工作就缺少游戏设计及其赢利模式作为根据。

甚至对于雇用业余分析人员的小开发者，这也可能是个问题。另一个典型的问题是，在没有分析团队参与的情况下，就决定追踪什么行为。这可能导致的结果是，在处理不必要的数据上浪费大量时间，或不得不记录额外的数据。团队之间良好的沟通也有助于缓和分析和设计之间的摩擦。

重要的是，分析应该与生产相结合——贯穿早期设计环节。应该在早期计划好要追踪什么行为和追踪的频率。这有利于确保分析对设计、赢利、营销等产生价值。永远不应该在测试之后才进行分析。如果是这样，分析学的作用就无异于用户研究，在理想的情况下，分析学应该贯穿于开发过程和游戏发布后。

特征选择

知道我们可以测量的用户行为有很多，但我们怎么选择？我们确实必须做选择吗？很遗憾，是的。在现实生活中，我们几乎没有可用于追踪和分析所有用户行为的资源，这意味着我们必须开发一种分析方法，能考虑到追踪、储存和分析用户遥测技术/指标所需的资源，和可获得的价值之间的成本-收益关系。还应该意识到在不同生产阶段和发布后所需的分析方法也是不一样的。例如，在开发的后期阶段，协调设计是关键，但许多与赢利有关的指标无法计算，因为目标受众还没开始消费。

我们将在下文中进一步讨论，但简单地说，根据这一系列推断，我们应该追踪、储存和分析的用户属性至少应该包含以下几点：

1、一般属性：所有游戏的用户都具有的属性（作为玩家和消费者）。对于任何电脑游戏，都可以随时收集这些属性形成的核心指标——例如，用户开始或结束游戏的时间、用户ID、用户IP、入口点等等。这些是所有游戏分析学数据集的核心。

2、核心机制/设计属性：这些是与核心玩法和游戏机制相关的必要属性（例如，与游戏持续时间有关的属性、花费的虚拟货币、杀敌数量等等）。确定核心设计属性应该直接以游戏的关键玩法机制以基础，且应该提供允许设计师推测用户体验（如玩家的进度是否符合设计、流量是否持续、死亡率、关卡完成、得分等）的信息。

3、核心业务属性：例如，有关公司的业务模式核心的必要属性会记录下用户每次购买一件虚拟道具（该道具是什么），在游戏中建立好友联系或向Facebook好友推荐游戏—-或者其它有关收益，用户留存，病毒式传播等属性。对于一款手机游戏来说，地理定位数据能够推动目标市场营销的发展。但是在传统的零售产业中，这些内容便没有任何意义。

4、利益相关者的要求：此外，我们还需要考虑一类利益相关者的要求。例如，管理人员或市场营销者会更加看重日活跃用户指数（DAU）。这些要求可能与上述提到的类别结合在一起。

5、QA和用户研究：最后，如果你想要使用遥测数据去进行用户研究/用户测试和质量保证（记录崩溃出现和崩溃原因，用户端系统的硬件配置，显著的游戏设置等），你就需要相应地在功能列表上扩大属性。

当你在创建最初的属性组并规划指标时，你需要确保选择过程足够开放，并包含所有利益相关者。这能够避免你在之后再次回到代码中并添加额外内容—-只要谨慎规划便可以无需浪费这些时间。

不管游戏在制作过程中还是之后的发行时发生变化。我们都必须往代码中嵌入新内容，如此才能追踪全新属性并支持不断发展的分析过程。样本是另一个值得考虑的重要元素。虽然它不是追踪每次玩家开枪的必要元素，但是样本本身就是一个大问题，不过这并不是我在本文所强调的内容，我只能说样本能够帮助我们有效地削减游戏分析的资源要求。

预先挑选功能

在功能选择过程中，一个需要考虑的重要元素便是你的属性组能够受到预先计划所驱动的程度，即通过定义我们从用户遥测和选择属性中获得游戏指标和分析结果。

减少复杂性非常重要，但是当你约束数据收集过程的范围时，你便会趟进措施用户行为中重要模式的风险（这是使用预选属性所检测不到的）。而当游戏指标和分析也是预先定义好的时，这种问题将会恶化—-例如，基于一组关键绩效指标（如DAU，MAU，ARPU，LTV等）便有可能降低你在行为数据中找到任何模式的机会。总之，根据可行的分析资源，这两种情况的平衡才是最佳解决方法。例如，完全专注于KPI的话你便不可能掌握游戏内部行为，例如为什么35%的玩家在第8个关卡时选择退出，而为了找出这些答案我们就需要着眼于与设计和性能相关的指标。

我们需要注意的是，当提到用户分析时，我们面对的是不可预测的人类行为。这便意味着预测用户分析要求变得更具有挑战性。这便强调了同时使用探索性（我们着眼于通过用户数据去找出它们所包含的模式）和假设方法（我们知道自己想要衡量并知道可能结果，而不只是什么才是对的）的必要性。

受设计师知识所驱动的策略

在游戏过程中，用户创造一个持续行动循环和回应将让游戏状态不断变化着。这便意味着在任何时候总会出现许多用户行为功能会改变价值。隔离分析过程中的第一步便是有关游戏及玩家间所有可能互动的综合和详细列表。设计师非常了解游戏和玩家间所有可能的互动；所以利用这些知识并让他们一开始就编辑这些列表将非常有帮助。

其次，考虑到最简单游戏中的大量变量，我们就需要通过缩减受知识驱动的元素而降低复杂性：设计师可以轻松地定义同构互动。这些是关于基本相同的互动，行为和状态改变的群组，尽管在形式上可能有点不同。举个例子来说吧，“使用绷带恢复5个HP”或“使用药剂恢复50个HP”从形式上看来可能不同，但从本质上来看它们却是相同的行为。之后同构互动也被整合到更大的区域中。最后，我们需要明确能够获得每个区域中所有同构互动的方法。例如，在“治愈”区域中，我们便不需要追踪使用药剂和绷带的数量，只需要记录玩家“健康情况”的每次状态改变。

我们不能通过缩小客观元素去推断这些区域；而关于让人们在类别中组合元素则存在一个明显的解释偏见，即设计师总是拥有许多专业知识。这些更大的区域总是包含所有玩家可能在游戏中传达的行为，并同时能够帮助我们选择需要监控哪些游戏变量，以及如何监控。

受机器学习驱动的策略

机器学习是关于让计算机在没有编程的前提下拥有学习能力的研究领域。除了作为手设计师驱动策略的替代选择，自动功能选择也是减少玩家与游戏互动所创造的各种状态改变的复杂性的一种补充方法。从传统上来看，自动方法是用于现有的数据集，关系数据库或者数据库中，即代表分析游戏系统，定义变量以及为这些变量创造方法的过程（超过了自动策略的范围）；而我们已经定义了该追踪哪些变量以及如何追踪。因此自动方法只能突出所有受监控的变量中，最重要且最明确的功能。

自动功能选择是根据算式去探索与其它内容相关的属性空间和掉落功能；算式的范围是从简单到复杂。而方法则包括聚集，分类，预测和序列挖掘。这也能够用于寻找最重要的功能，因为如果功能的呈现与影响相似性测度的类型不相干，便会大大降低算式所发现的聚类的质量。

收益递减

在拥有有限资源的情况下，你可以追踪，保存并分析所有用户发起行动—-所有服务器端系统信息，移动角色的每一步，每一次购买，每一条聊天信息，每次按键按压，甚至每次敲击。这么做将引起带宽问题，并要求大量资源往游戏代码中添加信息内容，但是从理论上看，这种分析游戏的“蛮力”方法也是可行的。

然后它将创造出非常大的数据集，并反过来可能引出巨大的资源需求（为了转换并分析这些数据）。举个例子来说，在一款FPS游戏中，追踪武器类型，武器修改，范围，伤害，目标，杀戮，玩家和目标位置，子弹轨迹等等将能够实现一次具有深度的武器使用分析。但是评估武器平衡的关键指标可能只是某些范围以及每种武器的使用频率。添加一些附加变量/功能也许不能带来全新的见解，甚至有可能混淆分析。同样地，它并不能用于获取所有游戏玩家身上的行为元素，只能涉及一些百分比（当涉及销售记录时，这就没有意义了，因为你需要追踪所有收益）。

总之，如果选择合理的话，那么最先追踪，收集和分析的变量/功能将提供许多有关用户行为的见解。当追踪更多用户行为的细节时，储存，处理和分析成本也会提高，而遥测数据中所包含的信息的附加值率将会减少。

这便意味着在游戏遥测技术中存在着成本效益关系，能够描写收益递减的简化理论：在分析过程中提到数据来源的数量将生成较低的每单位返回。

在经济文学中一个经典的例子便是，为一块地施肥。在一个不平衡的系统中（未施肥的）添加肥料将促进庄稼的生长，但是在某个点之后，这也可能缩减甚至停止庄稼的生长。而在一个已经得到平衡的系统中施肥则不可能再促进庄稼的生长，甚至有可能抑制它们的生长。

从根本上来看，游戏分析也是遵循着相同的原则。我们能在使用全新功能前在一个特殊点上优化分析，并提供一个特定的输入功能/变量。除此之外，在一个分析过程中提高数据数量将减少返回，或者在极端情况下会因为额外数据所造成的混淆而创造出负返回值。这当然也存在例外—-例如出现问题行为模式的原因将降低社交在线游戏的用户留存（可能是基于一个较小的设计缺陷），从而导致我们很难去判断是否追踪了相关缺陷的特殊行为变量。

用户导向分析的目标

用户导向游戏分析具有各种各样的目的，但是我们可以将其概括性地划分为：

策略分析，即根据用户行为和业务模式的分析而瞄准游戏将如何发展的全局观点。

战术分析，致力于在短期内告知游戏设计，如一个新游戏功能的A/B测试。

操作分析，注重对游戏现状的分析和评估。例如，传达你需要做出哪些改变去创造一款持久的游戏而匹配实时用户行为。

在某种程度上，操作和战术分析将传达技术和基础设施问题，而策略分析则注重整合用户遥测数据与其它用户数据或市场研究。

如果你正在规划一个处理用户遥测的策略，那么你最先需要考虑的便是现有的三种用户导向游戏分析类型，它们所需要的输入数据类型，你该如何保证这三种分析的有效执行以及最终呈献给利益相关者的结果数据。

其次你需要考虑如何同时满足公司和用户的需求。游戏设计的基本目标便是创造能够提供有趣用户体验的游戏。但是运行一家游戏开发公司的基本目标则是赚钱（游戏邦注：至少从投资者的角度来看是这样的）。我们必须确保分析过程所生成的结果能够支持这些目标的相关决定。推动游戏分析的潜在动机主要源自两方面：1）为了获取并留住用户而保证高质量的用户体验；2）确保盈利周期能够生成收益—-而不考虑业务模式。用户导向游戏分析将同时传达设计和盈利。那些在免费市场中取得成功的公司们便证实了这一方法的合理性，即他们使用了像A/B测试等方法去评估一个特定设计改变是否会提高用户体验和盈利。

总结

到目前为止有关功能选择的讨论一直处在一个抽象层面上，尝试着去创造分类指导选择，确保涉及范围的综合性而不是生成一些具体指标列表（如每种武器发射的弹药数/分钟，死亡率，跳跃成功率）。这是因为我们不能为所有游戏类型和使用情况的指标创造一个通用指南。这不只是因为游戏不属于整齐的设计分类（游戏邦注：游戏具有很大的设计空间并且不能聚集在一个特定区域内），同时也因为设计的更新率较高，这将快速导致推荐不再具有效用。因此我们关于用户分析的最佳建议是自上而下创造模式，如此你便能够确保数据收集的综合覆盖面，并能从主要机制出发去推动用户体验（帮助设计师）和盈利（帮助确保设计师能够获得回报）。同时还可以添加额外的细节作为资源许可。最后，你需要努力确保决定和过程够顺畅且可被修改；这在一个复杂且令人兴奋的产业中非常重要。

篇目2，Marczewski阐述游戏化用户新分类法

作者：Andrzej Marczewski

用户类型2.0

我曾试图简化和提升我的游戏化用户类型。这第2个版本已经比原来更深入。经过更多调查和研究了他人调查之后，我总结了一些新结论。

四种基本类型：成就者、社交家、慈善家和自由者的归类确实具有可取之处。我认为外向型（消费者、沟通者、自我探索者和开拓者）的分类也还可以，但是它们会给人们造成不少困惑。我太过于极端化了，人们似乎把我的内向型和外向型玩家视作好坏之分！

所以，我要在此推出新的分类法。这并非意在取代原来的分类，只能算是一种增订版本。

以下就是这种分类的基本示意图。

6种用户类型

从上图可以看到，目前有6种类型。以前的慈善家、成就者、社交家和自由者仍在此列，并且仍然代表4种内在动机类型，不过我们现在还添加了破坏者和玩家这两种类型。这两者也并非新概念，“玩家”最初已经出现于我原来的用户分类中，代表外在动机的用户。“破坏者”是我最近给“消极”用户分类所引进的类型。

用户或系统之间仍有动作和交互之分，不过此时的破坏者和玩家似乎并不局限于某一行为。破坏者在此是向用户和系统执行操作，而玩家则是与用户和系统互动。

表面价值

*社交家的动机是相互关系。他们希望与他人互动并创造社会联系。

*自由者的动机是自主权。他们希望创造和探索。

*成就者的动机是精通掌握。他们希望学习新知识并提升自己，他们想克服挑战。

*慈善家的动机是目标。这一群体是利他主义的希望以某种方式帮助他人，丰富他人的生活。

*玩家的动机是奖励。他们会尽一切所能从系统中搜集奖励。

*破坏者的动机有多种多样，但总体来说他们就是想以直接或间接方式对系统搞破坏。

玩家很乐于“玩”你的游戏，从中搜集点数和奖励。破坏者却并不想从中得到什么，要引起他们的兴趣并不那么简单。

这种情况就像下图所示：

灰色地带

我花了些功夫才意识到这一点，但谈论人们的行为表现时的确不可以用非黑即白来描述。在此更适用的是灰色地带，所以我在新用户类型之间也创造了一些灰色地带。虽然玩家和破坏者可以视为独善其身的用户类型，但也可以视为其他四种类型的变体。

玩家

如果你看过我原来的用户类型描述，就会知道以下四种外向型分类法：

所以玩家对系统奖励感兴趣的特点，可以视为内在动机类型的变体。

破坏者

破坏者也同此理。他们的兴趣是破坏游戏化系统。原因不一而足，这可以视为是一种目的。他们觉得破坏系统具有深远意义，可能是让开发者认识到瑕疵，或者证明该系统出了问题。这也可能是因为自主权。在内在用户类型中，自主权是一种积极的动机、探索和创造力。但是，这也可以视为是玩家试图摆脱系统限制的行为——如果系统中具有你并不喜欢的规则，你怎么可能实现真正的自主权呢？他们在破坏系统的过程中可以精通掌握系统，而这一行为也可以让他们因被人所识而获得一种相互关系。

积极的负面因素

这些都与积极动机有关，但对多数人来说它们可能是两个极端。现在值得我们思索的是“破坏”所带来的现代意义。现在破坏性通常与提升系统有关，这可以通过打破常规和呈现新的改进方式得到体现。

正如我所言，这会创造许多灰色地带。我们不提倡让破坏者一直居于稳定的系统中。如果他们就是爱好无理由地破坏规则，那就要把他们移除出去。否则，就应该将他们视为揭示系统弱点以及改进方法的积极因素。

以上就是我当前的游戏化用户分类。如果你选择采用这个用户分类法，就会发现它更具灵活性，更有助于了解用户动机的灰色地带。

篇目3，探讨制作游戏可以采用的16种动机模式

作者：Gabriel Recchia

自1995年到1998年有关人类行为（游戏邦注：地位、饥饿等）基本动机的一系列调查研究中，Dr.Steven Reiss与其同事从一系列“可能动机”入手，包括心理学研究、精神科分类手册等资源提出的上百种可能性。

接着，他们将这些可能因素缩减到仅剩384条，并通过调查2500多位被测者，衡量各个动机的重要程度。

然后通过因素分析法找到其中的不同基本要素，最终筛选出15个人们认为具有高度重要性的动机（在1998年又补充一条）。具体内容如下：

动机名称 目的 内在感受

权利 渴望影响他人（包括领导权、统治权） 效力

好奇心 渴望了解 好奇

独立性 渴望独立自主 自由

地位 获得社会地位（包括引起关注） 自我重要感

社会联系 获得同伴相陪（共同玩耍） 乐趣

复仇 报仇（包括对抗取胜） 自我证明

荣誉 渴望遵守传统道德准则 忠诚

理想主义 渴望改变社会（包括大公无私） 怜悯

锻炼身体 渴望锻炼肌肉 活力

浪漫 性需求（包括求偶） 欲望

家人 渴望养育孩子 爱

整理 组织需求（包括宗教仪式） 稳定

饮食 食物需求 饱腹感（免受饥饿）

接受 获得认同 自信心

宁静 避免紧张、恐慌 安全、放松

存储 收集、节约需求 拥有权

（此表根据《Multifaceted Nature of Intrinsic Motivation: The Theory of 16 Basic Desires》制定）

上表并不符合将动机分为内外两类这种常用方法，而且从理论角度上看显得更加混乱。但心理学家通过研究指出，我们没有必要期待诸如人类动机这种复杂理论应是整整齐齐，而不是混乱无序。

我们人类拥有50多个不同的皮质区域，100多个不同的神经递质，以及成千上万个蛋白质。为何不把固有动机类别归纳成少数规模？

当然，该理论本身也存在弊端。大量证据显示，人们并没有充分了解刺激因素（从而限制我们的调查研究），而且该理论无法充分证实我们对“想要之物”与“想避免之物”的反应其实是受到了不同神经系统的控制。但它确实提出了一个有趣观点。

不少设计师惊讶于《Farmvile》的盛行，因为其核心机制违背了游戏设计准则，而Zynga的持续式微也表明，优秀游戏应具有强大的内在动机，而不只是提供荣誉和勋章等浅层的外在动机。

然而，《Farmvile》核心机制利用的动机在本质上并不比其它动机“拙劣”，比如收集道具（Reiss理论上的“存储”），以及希望回报赠送Blue Doohickey的玩家一个Green Whatsit（即互利主义，也就是Reiss理论上的“理想”动机），但由于缺少其它刺激性能，这款游戏就难以长期维持这些动机。许多出色的MMO游戏都很擅长充分利用这两种以及其它额外动机。

我在上篇博文中主要强调在游戏中设计内在动机会遇到的困难。尽管我们有必要阐明内外动机设计的差别，但这一理念在现实世界中颇具实施难度，因而对设计师而言多因素理论可能更具实用性。

Jesse Schell在《The Art of Game Design》一书中将它们当作“透镜”，该书包括100个发人深省的透镜，设计师可以借此观察和改进游戏。可能会有人设想开发出对应Reiss基本动机的透镜（游戏邦注：比如，独立透镜：我的游戏会让玩家获得独立感吗？玩家会获得控制感吗？他们能够自由选择有意义的选项吗？）事实上，Schell的列表中已囊括不少与上述动机相关的透镜（比如竞争透镜、合作透镜、需要透镜、控制透镜、社区透镜）。

虽然大多数设计师已经知晓刺激用户的因素，但着眼于这16个尤为重要的维度还是具有一定价值，即便他们只是利用这些动机鉴别游戏的出彩之处，以及它的改进方向。

除了传统观点所提出的会刺激玩家的性能（奖励技能提升、引人注目的故事、逐渐加大的困难等）之外，“16种基本渴望”理论也可以激发人们进一步思考其他有待发掘的刺激性能。

篇目4，点评4种玩家类型的个性及其对立面

作者：Alfons Liebermann

本文内容是我们同Andrzej Marczewski以及（直接与）Richard Bartele进行的讨论所得结果，这两者都曾研究玩家类型学这一问题。除此之外，业内还出现了Nicole Lazzaro提出的趣味类型理论，“4个关键2种趣味”。

我们所提出的并非自己原创的模型，只是一些现存玩家类型的扩展，并增加了一些自己的经验和理论推测。

1.玩家类型学的存在需求

它的优势在于针对一个反应系统（AI）使用这种类型学。你可以借此允许系统分别回应不同的玩家心理。虽然这类定制化选项还很少，不只是因为它需要一些复杂的编程技术，还因为它要求人们详细描述极具弹性的玩家个性模型，这一模型不但包含基本和已知的玩家类型，还包括其中的大量微妙差别。因此，正如Richard Bartle之前所言，这一分类系统的关键问题并不在于列出玩家心理，而在于良好的理论基础。

这里我们可以回顾一下法国心理分析学家Jacques Lacan评论。Lacan曾指出大学演讲的另一面是种癔病，这表现于两个原本应该毫无关联的环节交织在一起的时候。

也许有人会更直接指出：动机正起源于人们所捍卫事物——正是这种冲突组成了游戏的核心。

如果你去看看与这个背景相左的模型，就会发现单个玩家心理并不起源于特定的原型，但它指出了可能具有多种形式的冲突（心理学坐标描述了其主导因素）。

2.关于渴望的坐标

我们有两个轴线 ：从右到左是一个代表个人和集体这两个极端的轴线。它反映了玩家更愿意让自己服从于集合意志，还是喜欢自主行事的情况。也许有人会据此划分多人玩家vs单人玩家游戏的玩家关系，但这种方法很容易产生误解。

关于“政治家”这一类型的代表或许就是《魔兽世界》用户群体中的领袖，这种看法是一个很大的误解。某人是否属于集体主义者，这与其个人社交举措无关。这一点很关键，尽管“政治家”会将自己视为一个集体秩序的代表，一个实体的形象代言人。这表明战略玩家这一身份，才是主导其身份认知的因素。

这里就要提到第二个轴线。它用于区分严格和松散的玩法，介于规则与破坏规则之间，也可以说是传统与创新之间。

显然战略游戏玩家会选择服从规则并排斥无序行为（游戏邦注：包括随机性、机遇、敌对部队的入侵）。

尽管这种冲突描绘了他的内在心理及其青睐的意图，要依靠重复和积累，法律的持续重复，以及持续发展的必要性。全能的力量正是驱使玩家的幻想，他所得到的结果就是自己在游戏中所处的地位。

鉴于这种心理，我们可以看到图表中“政治家”的对角线所指向的正是他的对映体“自由主义者”，也就是他实际上试图禁止的情况。“自由主义者”藐视社会秩序，无视系统规则，这使他注定成为“政治家”的敌人。

“自由主义者”最爱冒险，他并不喜欢重复，但却追求独特的体验。他无视规则，试图打破常规。追求刺激正是他的兴奋来源。

他渴望自由，因此无暇顾及社会秩序。他会为省事而选择捷径，是一个积极分子，是具有美感和精明的先驱，但却没有什么追随者。

在他看来，系统构成了一个自然甚至是个性化的对手。但这个系统却像磁铁一样，神奇地吸引他。背叛系统是一种巨大的创新，是其独特的满足感来源。

现在让我们看看左上方象限的“成就者”。他会将精通掌握某个机器的用法视为终极目标。如果我们将其置到“自由主义者”象限中，可以将他们视为近亲，因为这两者都怀有极强的个人主义。

而“成就者”和“自由主义者”二者的区别就在于，前者更倾向于在规则的框架之内行事。他无意背叛系统。相反，他志在主宰系统。获取高分，展示技能，可以让他攀向系统的最高峰。而展示自己的伟大成就无疑就是他追求的目标。

战略玩家总沉浸于获得超人力量的幻想中，“成就者”却更为机器的幻想所着迷：他渴望获得单独的绝对力量。在电脑游戏术语中，我们可以将其视为阻止大批敌人冲击的“自我”射击者——乱世中的独立居民。

他同“自由主义者”的共同点在于自由漫游的态度，但他也不乏与“政治家”对应的行为。他也热衷于重复和积累，这种持续的重复可提升他的力量，而积累则可让他完善技艺。虽然他在游戏中的目标是完美控制环境，其实际意象却是一个孤独的斗士（游戏邦注：在游戏世界之外，他可能并不会喜欢这种评价）。

看看对角线，其对手一目了然——“社交玩家”，这类玩家并不在乎精通掌握系统规则，也无意追求玩游戏的高超技巧。事实上，我们很容易看出这两者区别好比是采用高科技的射击游戏与Zynga游戏等产品的差异。

“社交玩家”具有内在随意性。游戏只是他打发时间的一种方式。这里我们又看到一种矛盾：战略玩家，“政治家”催生了完美的社会秩序，而“社交玩家”却唤醒了自己实际所缺乏的人际关系。这两个不相干的领域在此发生重叠，这也正是“政治家”与“社交玩家”为何落于同一侧象限的原因。

我们还可以看出他与“自由主义者”的不同之处。“自由主义者”追求的是即兴玩法，而“社交玩家”只对社交标准、简单、容易掌握、可预测的情况感兴趣。

尽管如此，“社交玩家”的玩法并非来源于社会交换，而是自己表现杰出的需求。《FarmVille》玩家会花钱购买拖拉机增加自己的产量，提高自己的地位，由此可见他重视的并非合作而是竞争。这里我们又可以看到他的对立面：“成就者”要与自己的NPC敌人较劲，而“社交玩家”却把自己的同伴玩友视为NPC。

3.微妙差别

显然这4种类型极少孤立存在，我们从中可以看到微妙的心理差异和多种玩法行为。

同轴度意味着我们可将心理区域视为一个映射，而距离则可理解为亲密关系的梯度。比这种空间对齐方式更重要的是，每个玩家类型都无法在脱离其对手的情况下解释说明。

可见游戏像电影故事一样向我们揭示了一个逻辑：如果你不知道人物的内心冲突，就不法真正了解一个角色。冲突才是关键。

篇目5，探讨四种游戏类型在移动平台发展潜力

作者：Kevin Corti

对电脑游戏有所兴趣的人应该都很清楚，手机游戏市场发展极为迅速，智能手机在主流市场仍然仅占比40%，因此未来数年这一领域仍有许多发展空间。

而对于制作手机游戏的开发者来说，情况也很明显，制作人人都想玩的游戏极为困难，而制作人人都愿意付费的游戏更是难上加难。现在每天都有超过13万款的游戏向苹果App Store提交审核，《CSR Racing》这类游戏首个月就可以创收1200万美元，但更多手机游戏并没有这么幸运，据称平均来看，每款手机游戏收益不足4000美元。虽然理论上看，仅用数千美元开发一款手机游戏仍是可行做法（游戏邦注：但即使没有实际的财政支出，开发者在无报酬的情况下开发游戏仍然需要付出一定的机会成本），但多数来自专业工作室的游戏仍需要准备5万至100万美元的开发预算。

不仅是游戏制作成本，营销预算也在大幅上涨。寄希望于用某些免费以及自然传播形式来实现计划的开发者很可能遭遇失败。对于iOS游戏来说，这一点尤为正确，因为只有跻身榜单前列才有可能获得曝光度，而跻身榜单前列也意味着开发者需耗费大量营销成本。简而言之，让应用获得曝光度很费钱。

开发者需频频向一些用户获得服务撒下大笔资金，例如Tapjoy（提供奖励让玩家下载游戏）或者FreeAppADay（为玩家免费提供原先需付费下载的游戏）。这两种以及其他方法都需要开发者在第一天就支出至少1万美元的费用。一款游戏若要成功盈利，就必须先实现以下目标：

1）ARPU（每用户平均收益）超过ACPU（每用户获取成本）；

2）收获大量用户以确保“净”利润足以抵消最初开发成本。

我们还要考虑到这里的“净”收益是扣除包括苹果30％抽成、营业税、授权成本、发行商抽成、合作伙伴收益分红，以及持续的运维成本（例如服务器）之后的总销售额。

甚少出现开发者制作了一款游戏，将其发布后就置之不理的情况。我们已经步入“游戏即服务”时代，并且游戏通常需要绑定一些用户行为数据收集和分析工具，这意味着开发者可以经常查看哪些元素可行哪些不可行。这意味着开发者不但需要修复技术漏洞，优化用户界面，重制新手教程，而且还要重访游戏变量以实现平衡，编辑故事情节，创造新内容和新功能。表现出色的游戏常会移植到其他平台（游戏邦注：例如Android、Windows手机平台、亚马逊Kindle等），或者针对其他市场推出本土化版本，这就更需要大笔成本和开支。

所以，制作游戏，推广游戏和维护游戏需要耗费大笔资金。这是个竞争激烈的市场，这一领域的用户忠诚度也很低，每天都会涌现许多不同的新游戏。因此，如果你想针对手机和平板电脑制作游戏，希望实现收支平衡甚至是创造巨大利润，那就最好先明确自己想做哪种游戏。那么你该如何选择呢？我将此归结为4种游戏类型：

1.休闲游戏（运用于移动设备）——“在出行途中独自玩游戏”

这本来也可以包括那些两人参与的游戏（两人在同一个屏幕上各出一个手指进行操作），但这主要是单人模式的游戏。这些游戏需针对特定硬件设备的性能（或者说是“局限性”）而量身定制，但有许多游戏却是直接将网页、PC或主机游戏复制移植到移动设备，这种做法可行但未必明智。《割绳子》、《植物大战僵尸》和《水果忍者》属于这类游戏中的优秀典型，但这一领域还有更多克隆版井字游戏和劣质的平台游戏。如果你制作的是这类游戏，那你就必须时刻牢记自己的游戏唯一超越主机、PC和网页游戏的优势就在于，用户可以随时随地体验游戏。

2.休闲社交游戏——“拥有社交层，允许好友相互挑战成就的游戏”

换句话说，这类游戏因为好友的加入而进入了另一个层次。

这些游戏本质上是单人游戏体验，但在此基础上添加了“挑战好友”或者积分排行榜功能。这已迅速成为手机游戏的默认设计样式。我认为这是一个懒惰并且可能存在商业局限性的方法。它通常采用OpenFeint或GameCenter这类第三方所提供的基本功能添加在游戏之上，而不是从加强玩家体验的角度出发来制作游戏。这种做法通常也会通过与游戏美术及UI格格不入的注册、登录和弹出成就屏幕等形式频频干扰玩家的游戏体验。如果合理采用这种做法，例如通过赋予玩家超越他人的机会来增强游戏体验，那么它确实能提供了一定的终端用户价值。但即便如此也掩盖不了这种产品骨子里仍然是单人游戏的事实。并且这些第三方服务的终极目标是创建属于自己的用户基础（游戏邦注：例如通过广告或交叉推广插页广告来吸引用户），而这一商业目标往往又会与游戏开发商吸引和保留自己用户的目标相冲突。

这种类型的游戏中又有第二种与Facebook/网页“社交游戏”极相似的游戏。有不少社交游戏都移植到了移动设备（例如《FarmVille》、《CityVille》和《Ravenwood Fair》），但其游戏玩法本质上依旧是单人游戏，只是增加了赠送礼物、分享和访问等会提供免费虚拟商品、虚拟货币或其他价值的社交机制而已。尽管这种游戏看似让用户频频与好友互动，但这种互动的意图是让开发商免费获取新用户，而不是传递内在的趣味。在这种情况下，你只是不得不与好友互动，而不是因为这样玩更有趣。

3.同步多人游戏—–“在移动设备上与他人结盟或对抗所玩的游戏（多为硬核游戏）”

这类游戏较稀少，原因有两个：首先，它们对游戏的技术框架和服务有一定的要求，而设置并维护这些组件的成本极高；其次，并非所有玩家都能找到在同一时间中喜欢玩同一款游戏的好友。此外，如果他们想一起玩游戏，可能还需要持有同种设备/平台，因为有些游戏可能仅支持运行于苹果iPhone手机而非三星Android设备。

同步合作或竞争型游戏玩法比较适合PC和主机游戏体验，其游戏回合更长，体验时间更有规律，并且主要发生于适合玩家头戴耳机并随心所欲地吼几嗓子的环境。同步游戏玩法更适合传统或硬核游戏，而这些游戏并不瞄准大众市场。手机游戏玩法具有短暂而随心所欲的特点，用户可以随时随地掏出手机体验游戏。我认为同步（即时）多人游戏是一个能够提供创新空间但商机有限的小众市场。

4.异步多人游戏—–“玩家与好友共同体验，但并不会立即进行数据交换的游戏”

我认为这种手机游戏最符合“社交手机游戏”的定义。虽然第3种类型（同步多人游戏）确实是用户之间的真正互动，但它仅适用于一小部分手机游戏玩家，我认为它具有“反社交”的特点。异步手机游戏在这一点上如果处理得当，就能够传递加强玩家参与度的游戏体验，同时又能够同移动设备使用习惯（即随时随地玩游戏的特点）相兼容。这类游戏确实能够传递内在趣味，因为它们搭载的是支持身处异地的用户随时沟通和互动的设备，这些设备并不需要随时插带电源或网线等累赘的配件。异步游戏的劣势就在于其数据交换并不及时。

我认为这类游戏的典型就是《Draw Something》（OMGPOP/Zynga）。其成功周期确实很短暂（大约6个月）但实现了8000万次下载量，并创收了可观的收益（据称是5000万-7500万美元）。

本文的主旨是探讨手机游戏如何以较少的成本创造可观收益，并成功获取用户，那么以上四种游戏类型又该如何实现这一目标呢？

休闲手机游戏——并没有直接的用户获取优势。这些游戏缺乏让用户向他人传播游戏的工具，也缺乏让用户采取这一行为的内在动力。你只是在自己的移动设备上玩单人游戏，你的游戏进程和愉悦感与你的好友是否也在玩游戏毫无瓜葛。

社交休闲手机游戏——如果开发商掌握了用户数据，那么这些游戏就有一定优势。但如果开发商使用的是类似于OpenFeint这种第三方API那就未必了。Zynga拥有多款属于这种类型的With Friends系列游戏，它们都创建了一个意在获取用户数据然后向其交叉推广游戏的生态圈（这样开发商就无需通过其他渠道支付每名用户2美元的成本）。多数开发商都无力效仿这一做法，创建出类似的生态圈。另外，由于这些游戏更适合单人体验，所以接入社交网站帐号并与好友“分享”的设置就不是很有必要了。将游戏链接到Facebook是一个积极做法，但如果你并不在同一部移动设备上玩游戏，那么这种设置就不存在什么优势了。

同步多人手机游戏——虽然这种游戏理论上需要玩家找到能够在同一时间参与的好友，但游戏在此的潜在用户覆盖率的重要性相对较小，并且玩家可以通过系统随机匹配的陌生人共同体验游戏，并不一定需要将自己已经认识的熟人引进游戏。

异步多人手机游戏——这就是需要玩家拥有好友一起玩游戏（否则就无法体验游戏），具有病毒传播特点的游戏定义。这类游戏，例如《Draw Something》总会在早期弹出一些屏幕要求玩家登录Facebook或Twitter帐号，或者向好友发送邀请邮件。这里还存在用户信任门槛，拥有一款出色的游戏当然很重要，但如果你做法得当，游戏的整个用户基础就会自动扩展。制作一款十分有趣，并且可长期传递趣味的出色游戏，那么你就相当于胜券在握，只需要一些最初的付费获取用户基础即可。

所以，异步多人游戏就是手机游戏开发者的首先，但如何才能制作出优秀的异步游戏？

手机游戏玩法不但要适合其运行的设备，还需要依据设备用户特点而设计。在此开发者通常会忽视一些情况。需注意的是，不要因为iPhone 4/iPad 2可传递堪比主机游戏的高质量图像，就误以为你真的可以照搬其他平台的做法。试想有多少玩家会在iPhone上投入20多个小时持续玩每个关卡至少需要20分钟才能玩的游戏？

虽然人们通常会在等绿灯，或者在星巴克排队时掏出手机打发时间，但也不能忽视有50％玩家是在床上或沙发上玩手机游戏这个事实，他们在此时每回体验游戏时长不再是数秒，而是数十分钟。因此开发者不但要牢记间歇而短暂的用户游戏习惯，还要注意这并非游戏设计的唯一考虑因素。

开发者向移动平台投放手机游戏需考虑的问题如下：

(1)在随时随地体验游戏的情况下，游戏是否依然有趣？

(2)玩家能否轻而易举地开启游戏，停止游戏和重启游戏？

(3)游戏是否因为围绕社交性设计游戏而呈现出趣味性？

如果以上问题皆为肯定回答，那么你就放心制作游戏吧！

篇目1，篇目2，篇目3，篇目4，篇目5（本文由游戏邦编译，转载请注明来源，或咨询微信zhengjintiao）

篇目1，Intro to User Analytics

by Anders Drachen

The science of game analytics has gained a tremendous amount of attention in recent years. Introducing analytics into the game development cycle was driven by a need for better knowledge about the players, which benefits many divisions of a game company, including business, design, etc. Game analytics is, therefore, becoming an increasingly important area of business intelligence for the industry. Quantitative data obtained via telemetry, market reports, QA systems, benchmark tests, and numerous other sources all feed into business intelligence management, informing decision-making.

Two of the most important questions when integrating analytics into the development process are what to track, and how to analyze the data. The process of choosing what to collect is called feature selection. Feature selection is a challenge, perhaps especially when it comes to user behavior. There is no single right answer or standard model we can apply to decide what behaviors to track; there are instead several strategies that vary in goals: e.g., improve the user experience or increase monetization. In this article, we will attempt to outline some of the fundamental concerns in user-oriented game analytics, with feature selection as an overall theme. First, we’ll walk through the types of trackable user data, and then introduce the feature selection process, where you select how and what to measure. Importantly, this article is not focused on F2P and online games — analytics is useful for all games.

Data for Analytics

The three main sources of data for game analytics are:

Performance data: These are related to the performance of the technical- and software-based infrastructure behind a game, notably relevant for online or persistent games. Common performance metrics include the frame rate at which a game executes on a client hardware platform, or in the case of a game server, its stability.

Process data: These are related to the actual process of developing games. Game development is to a smaller or greater degree a creative process, but still requires monitoring, e.g., via task-size estimation and the use of burndown charts.

User data: By far the most common source of data, these are derived from the users who play our games. We view users either as customers (sources of revenue) or players, who behave in a particular way when interacting with games. The first perspective is used when calculating metrics related to revenue — average revenue per user (ARPU), daily active users (DAU) — or when performing analyses related to revenue (churn analysis, customer support performance analysis, or microtransaction analysis).

The second perspective is used for investigating how people interact with the actual game system and the components of it and with other players, by focusing on in-game behavior (average playtime, damage dealt per session, and so forth). This is the type of data we will focus on here. These three categories do not cover general business data, e.g., company value, company revenue, etc. We do not consider such data the specific domain of game analytics, but rather as falling within the general domain of business analytics.

Figure 1: Hierarchical diagram of sources of data for game analytics emphasizing user metrics.

Developing Metrics From User Data

Many people have proposed different methods of classifying user data over the past few years. From a top-down perspective, a development-oriented classification system is useful, as it serves to funnel user metrics in the direction of three different classes of stakeholders — for example, as follows.

Customer metrics: Covers all aspects of the user as a customer — for example, cost of customer acquisition and retention. These types of metrics are notably interesting to professionals working with marketing and management of games and game development.

Community metrics: Covers the movements of the user community at all levels of resolution, such as forum activity. These types of metrics are useful to community managers.

Gameplay metrics: Any variable related to the actual behavior of the user as a player inside the game (object interaction, object trade, and navigation in the environment, for example).

Gameplay metrics are the most important for evaluating game design and user experience, but are furthest from the traditional perspective of the revenue chain in game development, and hence are generally underprioritized. These metrics are useful to professionals working with design, user research, quality assurance, or any other position where the actual behavior of the users is of interest.

Customer metrics: As a customer, users can download and install a game, purchase any number of virtual items from in-game or out-of-game stores and shops, spending real or virtual currency,over shorter or longer timespans. At the same time, customers interact with customer service, submitting bug reports, requests for help, complaints, and so on. Users can also interact with forums, official or not, or other social-interaction platforms, from which information about these users, their play behavior, and their satisfaction with the game can be mined and analyzed. We can also collect information on customers’ countries, IP addresses, and sometimes even age, gender, and email addresses. Combining this kind of demographic information with behavioral data can provide powerful insights into a game’s customer base.

Community metrics: Users interact with each other if they have the opportunity. This interaction can be related to gameplay (combat or collaboration through game mechanics) or social (in- game chat). Player-player interaction can occur in-game or out-of-game, or some combination thereof — for example, sending messages bragging about a new piece of equipment using a post-to-Facebook function. In-game, users can interact with each other via chat functions, out-of-game via live conversation (TeamSpeak or Skype), or via game forums.

These kinds of interactions between players form an important source of information, applicable in an array of contexts. For example, a social-network analysis of the user community in a F2P game can reveal players with strong social networks — who are the players likely to help retain a big number of other players in the game by creating a good social environment (think guild leaders in MMORPGs). Likewise, mining chat logs and forum posts can provide information about problems in a game’s design. For example, data-mining datasets derived from chat logs in an online game can reveal bugs or other problems. Monitoring and analyzing player-player interaction is important in all situations where there are multiple players, but especially in games that attempt to create and support a persistent player community, and which have adopted an online business model, which includes many social online games and F2P games. These examples are just the tip of a very deep iceberg, and the collection, analysis, and reporting on game metrics derived from player-player interaction is a topic that could easily take up several volumes.

Gameplay metrics: This subcategory of the user metrics is perhaps the most widely logged and utilized type of game telemetry currently in use. Gameplay metrics are measures of player behavior: navigation, item and ability use, jumping, trading, running, and whatever else players actually do inside the virtual environment of a game (whether 2D or 3D). Four types of information can be logged whenever a player does something or something happens to a player in a game: What is happening? Where is it happening? At what time is it happening? And: Who is involved?

Gameplay metrics are particularly useful for informing game design. They provide the opportunity to address key questions, including whether any game world areas are over- or underused, if players utilize game features as intended, and whether there are any barriers hindering player progression. These kind of game metrics can be recorded during all phases of game development,as well as following launch.

Players can generate thousands of behavioral measures over the course of a single game session — every time a player inputs something to the game system, it has to react and respond.

Accurate measures of player activity can include dozens of actions being measured per second. Consider, for example, players in a typical fantasy MMORPG like World of Warcraft: Measuring user behavior could involve logging the position of the player’s character, its current health, mana, stamina, the time of any buffs affecting it, the active action (running, swinging an axe), the mode (in combat, trading, traveling), the attitude of any NPC enemies toward the player, the player character name, race, level, equipment, currency, and so on — all these bits of information simply flow from the installed game client to the collection servers.

From a practical perspective, you may want to further subdivide gameplay metrics into the following three categories (in order to make your metrics more searchable, for instance):

In-game: Covers all in-game actions and behaviors of players, including navigation, economic behavior, as well as interaction with game assets such as objects and entities. This category will in most cases form the bulk of collected user telemetry.

Interface: Includes all interactions the player performs with the game interface and menus. This includes setting game variables, such as mouse sensitivity and monitor brightness.

System: System metrics cover the actions game engines and their subsystems (AI system, automated events, MOB/NPC actions, and so on) initiate to respond to player actions. For example, a MOB attacking a player character if it moves within aggro range, or progressing the player to the next level upon satisfaction of a predefined set of conditions.

To sum up, the array of potential measures from the users of a game (or game service) can be staggering, and generally we should aim for logging and analyzing the most essential information. This selection process imposes a bias, but is often necessary to avoid data overload and to ensure a functional workflow in analytics.

Integrating Analytics

Bias is introduced in the dataset both by the selection of the features to be monitored and also by the measuring strategies adopted, and that happens to a large degree when analysts work in a vacuum. If those responsible for analytics cannot communicate with all relevant stakeholders, critical information will invariably end up missing and the full value of analytics will not be realized.

Analytics groups are placed differently across companies due to analytics arriving to the industry from different directions, notably user research, marketing, and monetization, and this can lead to a situation where the analytics team only services or prioritizes their parent department. Having a strong lateral integration — making sure that the analytics team communicates with all the teams, for example — helps to avoid this issue. This also helps alleviate the common problem that the analytics teams, without having sufficient access to design teams, are forced to self-select features to track and analyze, without having the proper grounding in the design of the game and its monetization model.

Even for a small developer with a part-time analyst this can be a problem. Another typical problem is that the decision about which behaviors to track is made without involving the analytics team. This can lead to a lot of extra time spent later on trying to work with data that are not exactly what is needed, or needing to record additional datasets. Good communication between teams also helps alleviate friction between analytics and design.

Importantly, analytics should be integrated from the onset of a production — all the way back in the early design phases. Early on it should be planned what kinds of behavior that should be tracked and with what types of frequencies. This allows for optimal planning of how to ensure value from analytics to design, monetization, marketing, etc. Analytics should never be slapped on sometime after the beta. In this way analytics is similar to other tools like user research, in that it ideally is embedded throughout the development processes, and after launch.

Feature Selection

Knowing that there is an array of things we can measure about user behavior, how do we then select among them? And do we really have to make choices here? Sadly, yes. In real life, we rarely have the resources to track and analyze all possible user behaviors, which means we have to develop an approach to analytics that considers cost-benefit relationships between the resources required for tracking, storing, and analyzing user telemetry/metrics on one hand, and the value of the insights obtained on the other. It is also important to be aware that the analyses needed during different stages of production and post-launch varies. For example, during the latter phases of development, tuning design is vital, but many metrics related to monetization cannot be calculated because purchases have not been made by the target audience yet.

We will discuss this in more detail below, but in short, by following this line of reasoning, the minimum set of user attributes that should be tracked, stored, and analyzed should include considerations as to the following:

1) General attributes: The attributes that are shared for users (as customers and players) across all games. These form the core metrics that can always be collected, for any computer game– for example, the time at which a user starts or stops playing, a user ID, user IP, entry point, and so on. These form the core of any game analytics dataset.

2) Core mechanics/design attributes: The essential attributes related to the core of the gameplay and mechanics of the game. (For example, attributes related to time spent playing, virtual
currency spent, number of opponents killed, and so on.) Defining the core design attributes should be based directly on the key gameplay mechanics of the game, and should provide information that lets designers make inferences about the user experience (whether players are progressing as planned, if flow is sustained, death ratios, level completions, point scores).

3) Core business attributes: The essential attributes related to the core of the business model of the company, for example, logging every time a user purchases a virtual item (and what that item is), establishes a friend connection in-game, or recommends the game to a Facebook friend — or any other attributes related to revenue, retention, virality, and churn. For a mobile game, geolocation data can be very interesting to assist target marketing. In a traditional retail situation, none of these are of interest, of course.

4) Stakeholder requirements: In addition, there can be an assortment of stakeholder requirements that need to be considered. For example, management or marketing may place a high value on knowing the number of Daily Active Users (DAU). Such requirements may or may not align with the categories mentioned above.

5) QA and user research: Finally, if there is any interest in using telemetry data for user research/user testing and quality assurance (recording crashes and crash causes, hardware configuration of client systems, and notable game settings), it may be necessary to augment to attributes on the list of features accordingly.

When building the initial attribute set and planning the metrics that can be derived from them, you need to make sure that the selection process is as well informed as possible, and includes all the involved stakeholders. This minimizes the need to go back to the code and embed additional hooks at a later time — which is a waste that can be eliminated with careful planning.

That being said, as the game evolves during production as well as following launch (whether a persistent game or through DLCs/patches), it will typically be necessary to some degree to embed new hooks in the code in order to track new attributes and thus sustain an evolving analytics practice. Sampling is another key consideration. It may not be necessary to track every time someone fires a gun, but only 1 percent of these. Sampling is a big issue in its own right, and we will therefore not delve further on this subject here, apart from noting that sampling can be an efficient way to cut resource requirements for game analytics.

Figure 2: The drivers of attribute selection for user behavior attributes. Given the broad scope of application of game analytics, a number of sources of requirements exist.

Preselecting Features

One important factor to consider during the feature selection process is the extent to which your attribute set selection can be driven by pre-planning, by defining the game metrics and analysis results (and thereby the actionable insights) we wish to obtain from user telemetry and select attributes accordingly.

Reducing complexity is necessary, but as you restrict the scope of the data-gathering process, you run the risk of missing important patterns in user behavior that cannot be detected using the preselected attributes. This problem is exasperated in situations where the game metrics and analyses are also predefined — for example, relying on a set of Key Performance Indicators (such as DAU, MAU, ARPU, LTV, etc.) can eliminate your chance of finding any patterns in the behavioral data not detectable via the predefined metrics and analyses. In general, striking a balance between the two situations is the best solution, depending on available analytics resource. For example, focusing exclusively on KPIs will not tell you about in-game behavior, e.g., why 35 percent of the players drop out on level 8 — for that we need to look at metrics related to design and performance.

It is worth noting that when it comes to user analytics, we are working with human behavior, which is notoriously unpredictable. This means that predicting user analytics requirements can be challenging. This emphasizes the need for the use of both explorative (we look at the user data to see what patterns they contain) and hypothesis-driven methods (we know what we want to measure and know the possible results, not just which one is correct).

Strategies Driven by Designers’ Knowledge

During gameplay, a user creates a continual loop of actions and responses that keep the game state changing. This means that at any given moment, there can be many features of user behavior that change value. A first step toward isolating which features to employ during the analytical process could be a comprehensive and detailed list of all possible interactions between the game and its players. Designers are extremely knowledgeable about all possible interactions between the game and players; it’s beneficial to harness that knowledge and involve designers from the beginning by asking them to compile such lists.

Secondly, considering the sheer number of variables involved even in the simplest game, it is necessary to reduce the complexity through a knowledge-driven factor reduction: Designers can easily identify isomorphic interactions. These are groups of similar interactions, behaviors, and state changes that are essentially similar even if formally slightly different. For example “restoring 5 HP with a bandage” or “healing 50 HP with a potion” are formally different but essentially similar behaviors. The isomorphic interactions are then grouped into larger domains. Lastly, it’s required to identify measures that capture all isomorphic interactions belonging to each domain. For example, for the domain “healing,” it’s not necessary to track the number of potions and bandages used, but just record every state change to the variable “health.”

These domains have not been derived through objective factor reduction; there is a clear interpretive bias any time humans are asked to group elements in categories, even if designers have exhaustive expert knowledge. These larger domains can potentially contain all the possible behaviors that players can express in a game and at the same time help select which game variables should be monitored, and how.

Strategies Driven by Machine Learning

Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. More than an alternative to designer-driven strategies, automated feature selection is a complementary approach to reducing the complexity of the hundreds of state changes generated by player-game interactions. Traditionally, automated approaches are applied to existing datasets, relational databases, or data warehouses, meaning that the process of analyzing game systems, defining variables, and establishing measures for such variables, falls outside of the scope of automated strategies; humans already have defined which variables to track and how. Therefore, automated approaches individuate only the most relevant and the most discriminating features out of all the variables monitored.

Automated feature selection relies on algorithms to search the attribute space and drop features that are highly correlated to others; algorithms can range from simple to complex. Methods include approaches such as clustering, classification, prediction, and sequence mining. These can be applied to find the most relevant features, since the presence of features that are not relevant for the definition of types affects the similarity measure, degrading the quality of the clusters found by the algorithm.

Diminishing Returns

In a situation with infinite resources, it is possible to track, store, and analyze every user-initiated action — all the server-side system information, every fraction of a move of an avatar, every purchase, every chat message, every button press, even every keystroke. Doing so will likely cause bandwidth issues, and will require substantial resources to add the message hooks into the game code, but in theory, this brute-force approach to game analytics is possible.

However, it leads to very large datasets, which in turn leads to huge resource requirements in order to transform and analyze them. For example, tracking weapon type, weapon modifications, range, damage, target, kills, player and target positions, bullet trajectory, and so on, will enable a very in-depth analysis of weapon use in an FPS. However, the key metrics to evaluate weapon balancing could just be range, damage done, and the frequency of use of each weapon. Adding a number of additional variables/features may not add any new relevant insights, or may even add noise or confusion to the analysis. Similarly, it may not be necessary to log behavioral telemetry from all players of a game, but only a percentage (this is of course not the case when it comes to sales records, because you will need to track all revenue).

In general, if selected correctly, the first variables/features that are tracked, collected, and analyzed will provide a lot of insight into user behavior. As more and more detailed aspects of user behavior are tracked, costs of storage, processing, and analysis increase, but the rate of added value from the information contained in the telemetry data diminishes.

What this means is that there is a cost-benefit relationship in game telemetry, which basically describes a simplified theory of diminishing returns: Increasing the amount of one source of data in an analysis process will yield a lower per-unit return.

A classic example in economic literature is adding fertilizer to a field. In an unbalanced system (underfertilized), adding fertilizer will increase the crop size, but after a certain point this increase diminishes, stops, and may even reduce the crop size. Adding fertilizer to an already-balanced system does not increase crop size, or may reduce it.

Fundamentally, game analytics follow a similar principle. An analysis can be optimized up to a specific point given a particular set of input features/variables, before additional (new) features are necessary. Additionally, increasing the amount of data into an analysis process may reduce the return, or in extreme cases lead to a situation of negative return due to noise and confusion added by the additional data. There can of course be exceptions — for example, the cause of a problematic behavioral pattern, which decreases retention in a social online game, can rest in a single small design flaw, which can be hard to identify if the specific behavioral variables related to the flaw are not tracked.

Goals of User-Oriented Analytics

User-oriented game analytics typically have a variety of purposes, but we can broadly divide them into the following:

Strategic analytics, which target the global view on how a game should evolve based on analysis of user behavior and the business model.

Tactical analytics, which aim to inform game design at the short-term, for example an A/B test of a new game feature.

Operational analytics, which target analysis and evaluation of the immediate, current situation in the game. For example, informing what changes you should make to a persistent game to match user behavior in real-time.

To an extent, operational and tactical analytics inform technical and infrastructure issues, whereas strategic analytics focuses on merging user telemetry data with other user data and/or market research.

When you’re plotting a strategy for approaching your user telemetry, the first factors you should concern yourself with are the existence of these three types of user-oriented game analytics, the kinds of input data they require, and what you need to do to ensure that all three are performed, and the resulting data reported to the relevant stakeholder.

The second factor to consider is to clarify how to satisfy both the needs of the company and the needs of the users. The fundamental goal of game design is to create games that provide a good user experience. However, the fundamental goal of running a game development company is to make money (at least from the perspective of the investors). Ensuring that the analytics process generates output supporting decision-making toward both of these goals is vital. Essentially, the underlying drivers for game analytics are twofold: 1) ensuring a quality user experience, in order to acquire and retain customers; 2) ensuring that the monetization cycle generates revenue — irrespective of the business model in question. User-oriented game analytics should inform both design and monetization at the same time. This approach is exemplified by companies that have been successful in the F2P marketplace who use analysis methods like A/B testing to evaluate whether a specific design change increases both user experience (retention is sometimes used as a proxy) and monetization.

Summing Up

Up to this point, the discussion about feature selection has been at a somewhat abstract level, attempting to generate categories guiding selection, ensuring comprehensiveness in coverage rather than generating lists of concrete metrics (shots fired/minute per weapon, kill/death ratio, jump success ratio). This because it is nigh-on impossible to develop generic guidelines for metrics across all types of games and usage situations. This not just because games do not fall within neat design classes (games share a vast design space and do not cluster at specific areas of it), but also because the rate of innovation in design is high, which would rapidly render recommendations invalid. Therefore, the best advice we can give on user analytics is to develop models from the top down, so you can ensure comprehensive coverage in data collection, and from the core out, starting from the main mechanics driving the user experience (for helping designers) and monetization (for helping making sure designers get paid). Additional detail can be added as resources permit. Finally, try to keep your decisions and process fluent and adaptable; it’s necessary in an industry as competitive and exciting as ours.

篇目2，Marczewski’s Gamification User Types 2.0

by Andrzej Marczewski

The following blog post, unless otherwise noted, was written by a member of Gamasutra’s community.

The thoughts and opinions expressed are those of the writer and not Gamasutra or its parent company.

User Types 2.0

I was trying to simplify and improve my gamification user types. Version 2 is just that and a little more. After more research and the results of mine and others surveys on the matter, I have realised a few things.

The four basic types; Achiever, Socialiser, Philanthropist and Free Spirit are all fine. They work and can be left exactly as they are. I am also happy that the extrinsic types (Consumer, Networker, Self Seeker and Exploiter) are ok, however – they have caused a lot of confusion with people. I made everything a little too black and white – it was as if people saw my intrinsic types and extrinsic types as good and evil!

As such, I offer this new version. It is not a replacement, more an addition. If you are using the current four or even eight types – keep using them, they work just fine! However, this is where my thinking and research has led me, so I wanted to present it here properly.

The image below shows the basics.

Six User Types

As you can see, there are now six names on the board. Philanthropist, Achiever, Socialiser and Free Spirit are still there and still represent the four intrinsic motivations of RAMP, however we now have Disruptor and Player. Neither of these is new, Player was first introduced in my original work on user types as a name for the extrinsically motivated users. Disruptor was introduced recently as my “negative” user type.

There is still a split between action and interacting on users or systems, though this time Disruptor and Player straddle more than one segment. Disruptor is seen here as Acting on users and systems, where Player interacts with users and systems.
Face Value

Socialisers are motivated by Relatedness. They want to interact with others and create social connections.

Free Spirits are motivated by Autonomy. They want to create and explore.

Achievers are motivated by Mastery. They are looking to learn new things and improve themselves. They want challenges to overcome.

Philanthropists are motivated by Purpose. This group are altruistic, wanting to give back to other people and enrich the lives of others in some way.

Players are motivated by Rewards. They will do what is needed of them to collect rewards from a system.

Disruptors are motivated by various things, but in general they want to disrupt your system, either by directly or through other users.

Players are happy to “play” your game, where points and rewards are up for grabs. Disruptors want nothing to do with it and the others need a bit more to keep them interested.

This looks a bit like this

willing to play

Creating Grey

It took me a while to realise this, but black and white is actually not all that much use when talking about how people behave. Grey is a much more usable area for this. So, I have created a little grey with the new user types. Whilst Players and Disrupters can be seen as distinct user types in their own right, they can also be viewed as modifiers for the other four types.
Players

If you have seen the original user type descriptions, that is how I created the extrinsic groups.

Player types

So the Player characteristics of being interested in the rewards a system can give them can be seen as modifying the motivations of the intrinsic types.

Disruptor

The same can be said of the Disruptor. Their interest is in disrupting the gamified system. The reason for this can be varied. It may be considered purpose. They feel that disrupting the system has a greater meaning, be it educating the creators of flaws or proving that the system is somehow wrong. It could be autonomy. In the intrinsic types, autonomy is seen as a positive motivation, exploration and creativity. However, this can just as easily be seen as wanting to break free from the confines of the system – how can you have true autonomy when there are rules in place that you don’t like. Mastery can be achieved as they learn how to disrupt the system and Relatedness can be seen in the status that such acts can give them.

Positively Negative

All of these things relate to the positive motivations I talk about, but they would be considered by most as the polar opposite. Rather than helping, destroying. However, at this point it is worth considering the more modern meaning of Disruptive. These days disruptive refers to improving the system by breaking down the norms and showing new and improved ways.

As I say, this creates a lot of grey areas. Disruptors should for the most part be discouraged from being in a stable system. If they are hell bent on breaking the rules for no reason other than because they can, they need to be removed. However, they may well be the key to unlocking better levels of engagement by showing you what is wrong with a system and how to improve it!

There you have it. My current thoughts on the gamification user types. It may seem like I am making the waters muddy, but if you choose to use this version of the user types, you will see that it gives you much more flexibility and a better understanding of the grey areas of user motivations!

篇目3，Sixteen ways to motivate – is your game tapping into them?

by Gabriel Recchia

The following blog was, unless otherwise noted, independently written by a member of Gamasutra’s game development community. The thoughts and opinions expressed here are not necessarily those of Gamasutra or its parent company.

7 Habits of Highly Effective People. The 8 Essential Steps to Conflict Resolution. I’ll be the first to agree that including an arbitrary number in a headline makes an article sound like something that you’d find in the bargain bin of your local bookstore, but in this case there’s a rationale.

In a series of studies from 1995 to 1998 that investigated fundamental human drives/motives for action (status, hunger, sex, etc.), Dr. Steven Reiss and colleagues started with a list of “every motive they could imagine,” including hundreds of possibilities drawn from psychological studies, psychiatric classification manuals, and other sources.

They whittled this down to a mere 384, and distributed a survey designed to measure the importance that survey-takers assigned to each motive to over 2,500 people.

Plugging the results into a factor analysis to find out how many distinct underlying dimensions were necessary to account for the majority of variance yielded 15 distinct clusters of motives that people rated as of particularly high importance. (They added one more in 1998). In no particular order, they are:

Based on Multifaceted Nature of Intrinsic Motivation: The Theory of 16 Basic Desires, Table 1.

This is at odds with the reigning approach of dividing motivations up into extrinsic vs. intrinsic, and is much messier from a theoretical perspective. But as the psychologists who conducted the studies argue, there’s no reason to expect that an adequate theory of something as complex as human motivation should be anything but messy.

We have over 50 distinct cortical regions, over 100 different neurotransmitters, and thousands of proteins. Why not at least a handful of innate motivational categories?

Certainly, the theory has its flaws. There is ample evidence that people don’t have a good grasp of what really motivates them (which puts limits on what we can learn from surveys), and the theory doesn’t do justice to fact that our reactions to “things we want” vs. “things we want to avoid” are subserved by different neural systems. But it certainly provides an interesting perspective.

Many designers were astounded at the popularity of Farmville, whose key mechanics flew in the face of received game design wisdom, and Zynga’s continuing demise has been heralded by some as proof that the intrinsic motivation provided by a good game ultimately trumps the extrinsic motivation of praise and badges. Maybe so.

But it’s also possible that the motives that Farmville’s core mechanics tap into—accumulating items (Reiss’ “saving” motive) and the desire to give a Green Whatsit to someone who gave you a Blue Doohickey (reciprocal altruism, which falls under Reiss’ “idealism” motive)—are not inherently ‘worse’ than other motives, just hard to sustain in the long term in the absence of other motivating features. Arguably, many good MMOs take ample advantage of both of these motives and many more besides.

My previous post highlighted some of the difficulties of designing intrinsic motivators into a game. Even if the intrinsic/extrinsic distinction is a meaningful and important one to make, the difficulties of navigating this space in a real-world game may make multi-factor theories more useful to game designers in practical terms.

In particular, they can be used as “lenses” in the sense of Jesse Schell in The Art of Game Design, which contains 100 thought-provoking lenses through which one’s game can be viewed and improved. One can imagine developing corresponding lenses for each of Reiss’ fundamental motives (e.g. “The Lens of Independence: Does my game make people feel autonomous? Do players have a sense of control over their actions? Do they feel free to select from meaningful choices?”)—and in fact, Schell’s list already includes several that are relevant to some of the motives above (The Lens of Competition, The Lens of Cooperation, The Lens of Needs, The Lens of Control, The Lens of Community).

(Drawing up lens cards for Reiss’ remaining motives, and designing a game that satisfies the motives of “desire to eat,” “desire for sex,” and “desire to raise own children” is left as an exercise to the reader.)

Although most designers already have a sense of what motivates their audience, focusing one’s attention on the sixteen dimensions that have emerged as particularly important in large-scale studies of human motivation may be a worthy endeavor, if for no other reason than to identify which motives one’s game already addresses best, and to evaluate whether ramping those up even more would improve it further.

In addition to features that conventional wisdom suggests are motivating to players (rewards for skill development, compelling narrative, gradually increasing difficulty, etc.), ’16 Basic Desires’ theory may inspire further ideas for underappreciated features worthy of consideration.

篇目4，4 Temperaments – Some Remarks on Gamer Typology

by Alfons Liebermann

The following blog was, unless otherwise noted, independently written by a member of Gamasutra’s game development community. The thoughts and opinions expressed here are not necessarily those of Gamasutra or its parent company.

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The following thoughts are an offspring of a discussion we had with Andrzej Marczewski and – indirectly – Richard Bartle, both of whom have dealt with the problem of a gamer typology. In addition – once again mediated by Andrzej Marczewski – there are traces of Nicole Lazzaro’s typology of fun, “4 keys 2 Fun”. Although one cannot find her marks anymore, it is crucial for the underlying reward system.

Presenting our model we do not claim originality but just an extension of some already elaborated gamer typologies. What we can add though is our experience as well as certain theoretical assumptions that guide us.

Gamer Typology

1. Why there is a need for a gamer typology

The biggest advantage is that using such a typology for a reaction system (an AI). On this basis you allow a system to individually respond to different gamer psychologies. Although a common request, this kind of customization is somewhat rare, not because it implies some sophisticated programming but because one has to elaborate a highly flexible model of gamer temperaments, a model that does not cover basic and known prototypes but also the multitude of nuances. Therefore – as Richard Bartle rightfully pointed out – the crucial question of such a classification system does not lie in the listing of possible gamer psychologies but in a sound theoretical foundation.

Here a remark of the famous French psychoanalyst Jacques Lacan was inspiring. Lacan once noted that the other side of the university discourse is hysteria, and that signification arises where two presumably unlinked signifying chains glide past each other.

One may put it more bluntly: motivation is fed exactly by the things people fight with – and it is this very conflict that becomes the core of the game.

If one reads the model against this background one realizes that the respective gamer psychology is not derived from a certain prototype, but that it points to a conflict which may have many forms (and in which the psychological axes describe the dominant forces).

2. The axes of desire

We have two axes: From the right to the left we have an axis that signifies the polarity between the individual and the collective. It reflects the question as to what extent a gamer entrusts himself to a collective order or whether he feels obliged to act on his own account. One could be tempted to derive certain gamer affinities towards multi-player vs single-player games – but this is an approach that could easily lead to misinterpretations.

The conclusion at least that the POLITICIAN could be identified with the leader of a World of Warcraft cohort would be a gross misunderstanding. Whether or not somebody refers to a collectivist mindset has nothing to do with the respective social practice. It is essential though that the POLITICIAN conceives of himself as a representative of a collective order, a corporate identity so to speak. This reveals him as a strategy gamer who – from his god’s perspective – is supervising his realm.

Here the second axis gains importance. It is oscillating between strictly ordered and ad-libbing gameplay, between rules and breaking the rules, or if you prefer: between tradition and innovation.

It is evident that the player of a strategy game opts for the rule (i.e. for law and order) and that he abhors the irregular: randomness, chance, the intrusion of hostile units.

Nevertheless this conflict describes his interior map as well as it profiles his preferred means. He relies on repetition and accumulation, the perpetual repetition of the law, and at the same time on the necessity of steady growth. The phantasm that drives the gamer is omnipotence – and his reward the resulting status.

Given this short psychology the diagonal points to the POLITICIAN’s perfect antipode, hence the reality that he actually tries to ban. This is the appearance of the FREE SPIRIT – and the double break of the axes. Disrespecting the social order and neglecting the rules reveal him as the politician’s true antagonist.

The FREE SPIRIT’s kick is the adventure. He does not care for repetition but is striving for the unique moment. Ignoring the rules he tries to outdo them instead. That is his thrill: the rush of adrenaline, instant karma, paradise now.

His desire of freedom leaves no room for social arrangements. In case of doubt he opts for the shortcut. Enthusiast that he is, he constitutes an aesthetic and sophisticated avantgarde – without followers though.

From his point of view the system constitutes a natural, even personalized adversary. The system however (that you can depict as a dark imago, a punishing father) acts as a magnet, attracting him magically. To betray the system is a big motivation – and in case of success, a respective satisfaction.

Now let us focus on the upper left square. Here we have the ACHIEVER, him who takes the mastery of the machine as his very objective. If we put him on the side of the FREE SPIRIT, we could take them for relatives – and rightfully so, since both of them lean towards the pole of individualism.

What differentiates the ACHIEVER from the FREE SPIRIT is that he prefers to play within the rules. He is not interested in betraying the system. On the contrary: he is determined to dominate it. The highscore, his skill­fullness, shows him at the height of the system, and HIS awesomeness is actually what he is looking for.

While the strategy gamer is indulging in fantasies of omnipotence, the ACHIEVER is obsessed by the phantasm of the machine: he yearns for the individually sensed absolute power. In the terminology of the computer games we can identify him as the ego-shooter that stops the surging horde: the last independent, the dweller of an apocalyptic world that made warfare his home.

What he has in common with the FREE SPIRIT is the free roaming attitude, but his favored strategems correspond to the POLITICIAN’s behaviour. Like him he is obsessed with repetition and accumulation. The continuous repetition helps him to improve, the accumulation serves his as an imprint of boosted competence. Whereas the objective of the games resides in the perfect control of the environment, his imago depicts him as grandiose lone fighter (an appraisal he might not able able to enjoy outside the game).

Taking again the diagonale into focus his anatgonist becomes visible: It is the SOCIAL GAMER gamer that does no care about mastery (ruling the sytem) nor struggles for a considerable excellence in playing the game. In fact this conflict can easily be discerned as the gap between high-tech shooter games and their poor equivalents à la Zynga.

The SOCIAL GAMER is casual by heart. Gaming is just a way of killing time: a dialogue without dialogue. Once again we face a paradox: Whereas the strategy gamer, the POLITICIAN, evokes a perfect social order, the SOCIAL GAMER invokes the human contact he is actually missing. In this invoca­tion of society the disparate areas overlap – and that’s why the POLITICIAN and the SOCIAL GAMER are located on the same side.

Here we can see the difference to the FREE SPIRIT. Whereas the former is looking for the state of emergency, the SOCIAL GAMER is just interested in social standards, simple, easy-to-learn, predictable constellations.

Nevertheless the SOCIAL GAMER’s approach is not inspired by the need of social exchange, but by his will to excel. The farmville gamer that buys himself a tractor and augments his capacities and position thereby, demonstrates that his currency is not cooperation, bus competition instead. Once again we can see the repercussion of the antipode: While the ego-shooter stuggles with his NPC-adversaries, the social gamer degrades his co-gamers to NPCs.

3. Nuances

It is evident that these 4 prototypes may seldomly be found in their cristalline form. Instead we encounter psychological nuances and a variety of behaviour instead.

The axiality permits to understand the psychological field as a map, where distance may be translated as a gradient for kinship. More important than this spatial alignment though (which is ideal for implementation) is the fact that each gamer prototype cannot be explained by itself but only through his antagonist.

In this sense the absent part of the field is ever-present – and should be understood as a key for the gamer psychology.

Hence the game reveals the logics that cinematographic narration has taught us: You will not understand a character unless you know about its inner conflicts. It’ all about conflict, stupid!

篇目5，The opportunities in mobile gaming are in asynchronous social multiplayer games

by Kevin Corti

It should be clear to anyone that is interested in computer games that the mobile gaming market is growing very fast and, with smartphone penetration still accounting for only 40% in even major markets, that there is room for a lot more growth and for several years still.

It is also clear, to anyone who is actually making mobile games, that creating a game that people want to play en masse, let alone pay for (or in) en masse, is extremely hard. There are already over 130,000 games already submitted to the Apple App Store. Games like CSR Racing may be pulling in US$12million in their first month, but there is a very long tail in action here and the average revenue for a mobile game is reportedly less than US$4,000. Whilst it is still theoretically feasible to develop a mobile game for a few thousand dollars (working unpaid still has an opportunity cost even if there is not an actual monetary expenditure) most games from professional studios will have development budgets ranging from US50,000 to as much as US$1million.

The costs do not stop at simply making a game; far from it, next comes the marketing cost. Developers that base their plans/hopes/dreams around some form of free, natural virality are most likely going to fail. This is especially true of iOS games where (a) getting discovered requires being at the top of the charts, and (b) getting to and staying at the top of the charts costs lots of money. Putting that even more succinctly; getting visibility for your app WILL cost money….and no small amount of it.

Developers frequently drop a pot of money into user acquisition services such as Tapjoy (players are incentivised to download your game) or FreeAppADay (where players go to find normally paid-for apps being offered for free temporarily). These, and other methods, invariably cost from $10,000 and upwards on ‘day 1’. For a game to be profitable it needs to:

(1) generate revenue per user (ARPU) at a rate that exceeds the average cost per user (ACPU)

(2) reach a critical mass of users to ensure that the ‘net’ profit covers the initial development cost.

We must also consider that ‘net’ revenue is the gross sales revenue minus a whole host of direct costs starting with Apple (30%) but possibly also including any sales taxes, licensing costs, publisher’s cut, partner revenue share and on-going infrastructure (e.g. server) costs.

It is also very rare for a game to be created then launched then left unattended. We are in a ‘games as a service’ era and games are usually hooked up to some form of user behaviour data collection and analytics tool nowadays, meaning that developers can see what is working and what is not. That means not just technical bug fixes but user interface improvements, tutorial re-working, revisiting game variable to achieve better balancing, editing narrative, creating new content and new features. A game that does at all well will invariably be ported to other platforms (Android, Windows mobile/8, Amazon Kindle) and/or be localised for different territories. That’s more cost folks.

So, making games, marketing them and maintaining them costs a lot of money. It is a crowded market and one where customer loyalty is low and where new/different games are foisted at players from all angles. If, therefore you want to make games for the mobile phone and tablet market, you had better be clear about what kind of games you are going to make if you want to have a chance of achieving breakeven let alone amassing huge profits. What are the options? I boil these down into four (broad but distinctly different) game types. These are:

[1] Casual games (that work on mobile devices) – ‘play by yourself on the move’

Conceivably this can includes games that involve more than one player – e.g. two players, one finger each on same screen – but is invariably about single player games. If done right then the games are designed for the specific hardware capabilities (some might say ‘limitations’) of mobile devices but many are copies of web, PC or console games which are simply ported to mobile because it is feasible to do so not because it is sensible to do so. Cut The Rope, Plants vz Zombies and Fruit Ninja are exemplars of this category of game but for each of these there are a hundred (make that ten thousand) Tic Tac Toe clones and shoddy platformers. If you make this class of game then you need to be highly aware that the only benefit you have over console, PC and browser games is that your game can be played on the move. Design for that modality of use not for what is technically achievable.

[2] Casual social games – games that have a (vaguely) social layer where you ‘play by yourself….then see if your friends can beat your score’. Put another way; ‘games that are given another dimension because your friends are involved to some degree’.

These games are usually characterised by being a fundamentally single player experience on top of which is bolted a ‘challenge friends’ and/or leader-board functionality. This is rapidly becoming the de facto design pattern for mobile games. I regard this as a somewhat lazy and possibly an commercially finite approach. It is often achieved with basic functionality provided by third party services such as OpenFeint or GameCentre that very much looks and feels ‘bolted on’ rather than having been crafted to enhance the player experience. This also leads to several frequent interruptions to the playing experience in the form of registration, login and pop-up leader-board or achievement screens that look completely different to the game art and UI. If this is done well, e.g. where the playing experience is genuinely enhanced by the ability to try to perform better than people you know, then there is quantifiable end user value. This doesn’t disguise the fact, however, that the product is essentially still a single player game. These services also all exit to ultimately build a user-base for the service itself (e.g. to engage the user with advertising or cross-promotion interstitial ads) and that commercial goal conflicts with the game developer’s goal of engaging and retaining their player as long as is possible.

There is a secondary type of game in this class that closely resembles the Facebook/browser-based ‘social game’ type. Numerous social games have made their way to mobile devices (e.g. Farmville,

CityVille and Ravenwood Fair) however the game-play remains fundamentally of a single player nature which is augmented with the social mechanics of, for example, gifting, sharing and visiting and where such behaviour is rewarded with free virtual goods, in-game currency or other utility value. Despite seemingly interacting with friend’s in-game on a frequent basis, the nature of those interactions exist solely to bring about free user acquisition for the developer rather than to deliver intrinsic fun from playing. You interact with your friends because you have to not because it makes the game more fun in of itself.

[3] Synchronous multiplayer games – ‘play with or against other (probably quite hard-core) players in real time….on a mobile device’.

These kinds of games are rare and for two good reasons: firstly, they require a level of technical infrastructure and service provision that is typically very expensive to put in place and to maintain, and, secondly, because it is statistically unlikely that any one player has many friends that likes (an downs) the same game they do and whom are able to play that game at exactly the same time on a regular basis as they do. There is also the factor that in order to do so they may also require the same device/platform as you. ‘Android on a Samsung? Sorry you need an iPhone 4 or higher to play this game”.

Synchronous collaborative or competitive play is major aspect of the PC and console gaming experience where play sessions are much longer, happen at more regular (often coordinated) times and in environments conducive to that activity e.g. where you can strap on a headset and swear a lot. The very nature of synchronous gameplay tends to lend itself to more traditional, or ‘hard-core’, games genres which is not mass market (when expressed as a subset of the mobile phone gaming market overall). Mobile game play typically happens at unplanned opportunistic times, for very much shorter sessions spread throughout the day at a wide variety of locations many of which do not offer a reliable cellular or wifi network connectivity. I see synchronous (‘real time’) multiplayer gaming as a small niche that offers creatively interesting but commercial limited opportunities.

[4] Asynchronous multiplayer games – games where ‘the entirety of the fun is derived because you are playing with (or against) friends but which do not require an immediate data exchange’.

This is the class of mobile game that I think truly fit the ‘social mobile game’ definition. Whilst a real time (type 3) game is clearly about a genuine interaction with other (real) people and fundamental to gameplay, the very fact that this will be practical to only a very minor subset of mobile gamers make it, IMHO, by definition ‘antisocial’. Asynchronous mobile games, when done well, deliver playing experiences that are very much enhanced by the involvement of others but which do not fail to cater for the very real modality of mobile device usage (‘anytime, anywhere’).

Indeed, these games deliver an experience that is intrinsically fun because they are using a device that exists to enable communication and interaction between people who are not physically together in the same location and which does not require cumbersome peripherals or – at least not all of the time – power supply or data connectivity. Asynchronous games can be somewhat ‘lossy’ in that the exchange of data isn’t overly time-sensitive.

My archetypal example of this kind of game is Draw Something (OMGPOP/Zynga). It’s success may have been over a fairly short time frame (approx. 6 months) but it reached 90million downloads and delivered outstanding revenues (reportedly $50-75million).

The title of this ’blog is about where I believe the (greatest) opportunities lie for mobile gaming. Given that commercial success is highly dependent upon successfully acquiring users and at a cost that is less than the revenue that they generate, how then do the different types of game (as defined above) contribute, or not, towards this goal?

Casual mobile games – no direct user acquisition benefit. These games lack both the instruments for users to spread the word to other users and the intrinsic motivation for them to do so. You are playing a single player game on your mobile device. Your progress in game and enjoyment of it are totally unrelated to whether or not your friends may be playing it. Score 0/10

Social casual mobile games – some benefit if the developer owns the user data, however that is rarely the case when using third party APIs such as OpenFeint. Zynga have a whole raft of ‘X with friends’ games in this category and have built an eco-system aimed at capturing that user data and then cross-promoting their games (thus avoiding the $2/user cost of acquiring users through other channels). Most developers are unlikely to be able to afford to replicate that ecosystem too any degree. Equally, as these games can be played as a single player experience, the user’s motivation to connect social network accounts and to enable ‘sharing’ etc is not necessarily high. Visibility of the game name and link on Facebook is a positive factor but one that is limited by the fact that the game isn’t immediately playable on that platform if you are not using Facebook on the same mobile device. Score 5/10

Synchronous multiplayer mobile games – whilst there is the logical argument that players must have other players with whom to interact with in this case, (a) the potential user reach is fairly insignificant, and (b) the likelihood is that you will be paired with/against strangers by the system (in order to ensure there are enough people to take part) rather than being required/motivated to bring new players that you actually know into the game. Score 2/10.

Asynchronous multiplayer mobile games – these are the very definition of what makes the foundation for a genuine virally-promoted game as you have to have friends to play with or against or you can’t play yourself. There is not alternative state. These games – such as OMGPOPs Draw Something – invariable involve a very early screen asking you to connect Facebook or Twitter accounts or to send out email invitations. There is certainly a trust barrier here and having a genuinely stellar game offering is unquestionably of fundamental importance, but get that right and your entire user-base is acting to expand itself. Make a great game that is unquestionably fun and which delivers that fun over a sustained time period (e.g. has longevity to the play experience) and you have a hit on your hands that should only need seeding with an initial paid-for user-base. Score 10/10.

So, asynchronous multiplayer games it is then…..but what makes for a good asynchronous game?

Mobile gameplay needs to be designed not simply just to work on mobile devices but also to be designed for the mobile device user. These are quite different things that are often overlooked. Just because the iPhone 4/iPad2 could deliver highly impressive raw computational and graphical power capable of delivering ‘near console’ game experiences doesn’t make it appropriate to do so. Who has 20+ hours to play a game on their iPhone where each level takes 20minutes or more?

An inelegant but essentially accurate term to describe the prevalent modality of use is ‘dip in and dip out’ gameplay. Contextual scenarios involving stops at traffic lights or being in the queue in Starbucks typically get used to illustrate this and these resonate with casual geeks and professional analysts alike. They also ignore the fact that something like 50% of mobile game play time actually happens in bed or on the sofa where the user sessions are not measured in seconds but dozens of minutes. ‘Dip in and dip out’ gaming is certainly very important but it is not the only factor.

We are only just beginning to understand the specialist craft of effective mobile game design but a crude rule of thumb of revaluating any game concept’s appropriateness for mobile deployment (versus PC, Facebook etc) could simply be:

[1] Is this game fundamentally fun because I can play it anytime and anywhere?

[2] Can I start playing, stop playing and re-start playing with minimal ease?

To those questions we can then assess the level of genuine organic user acquisition by asking:

[3] Is this game made fun because people being able to play with or against their friends is central to it’s design?

If you can answer ‘yes, yes and yes’ then go build that game!