作者：Neil Vidyarthi

一组调查人员在去年发布了一份关于社交游戏传播动态的调查报告，主要研究了社交游戏的好友邀请成交，以及这一用户群体的游戏习惯。

该报告的作者包括密歇根大学的Xiao Wei、Jiang Yang，巴西佩洛塔斯联邦大学的Ricardo Matsumura de Araújo，Lolapps公司高管Manu Rekhi。这项研究主要围绕用户邀请哪些好友，哪些用户接受邀请，以及邀请发生过程等方面展开调查，本文是游戏邦编译的其中一部分核心调查结果。

需要指出的是，该报告主将好友邀请的传播过程定义为“扩散”，并采用Lolapps旗下的两款社交游戏《Yakuza Lords》（以下简称YL）和Diva Life（以下简称DL）辅助调查工作，这两款游戏当时的用户数据情况如下：

2010年1月，YL活跃用户达到100万，其中有85%以上是男性玩家，而DL虽然是在两个月后才上线，但同一时期的月活跃用户却超过了200万，并且96%以上的用户为女性玩家。这两者的用户年龄介于青少年至70岁老者之间，但主要集中于18至38岁的年轻用户群体。

该报告最重要的环节之一就是用户的行为分化研究，从下图中可以看出，有相当比例的用户是看到这两款游戏的道具和人物角色才开始接触游戏，但他们深入体验游戏，并逐渐上手的时候，其关注重心就会集中于游戏任务和战役环节。这一点恰好印证了人们的通常看法，社交游戏设计师也应该根据用户的这些变化来优化游戏，以减少玩家流失率。

YL和DL的玩家行为

除此之外，该报告还显示了游戏用户分享信息的习惯：“约90%的用户分享地理位置信息，40%用户分享好友列表，仅有1%分享自己的人际关系状态”。这个结果表明，许多用户并不点击Facebook的预览应用界面，有相当部分的用户只选择与他们分享自己体验过的应用服务。

这项报告还分析了邀请者与受邀者之间的关键因素，研究了邀请者成功邀请到他人的不同因素，以及影响受邀者作出选择的条件。

·在所有下载这两款游戏的受访者当时，37%以上的YL用户和25%的DL用户是因收到好友的邀请才加入游戏；

·与未接受邀请的游戏玩家相比，这些受邀者停留游戏的时间更为长久。约有80%的非受邀者在第一天之内就会离开游戏，而他们体验游戏的时间一般也不会超过80天。但那些受邀玩家则明显不同，超过50%的受邀者停留游戏的时间长达一天以上，20%的受邀者体验游戏的时间超过了80天。

·每名邀请者平均邀请26名好友，不过这个中间值是10个好友。

·在这两款游戏中，用户前6轮邀请好友的过程，可以极大刺激游戏的用户数量增长。但在第15轮邀请过程后，受邀用户的数量增长就没有原先那么明显（如下图所示）。

YL和DL的好友邀请波状图

·仅有10%的用户可以获得50%的邀请成功率。

该报告以“成效”来定义用户邀请好友的成功率，并分析了影响其成效的相关因素 ：

·规模（邀请的好友数量）：用户邀请的好友越多，成功率就越低。

·频率（每次邀请之间相隔的时间长度）：相隔更长时间后再发出的邀请更容易成功。

·重复率（向同一位好友发送邀请的次数）：发送邀请次数越多，成功率就越高。

·选择性（同一次邀请时选择的好友数量）：用户挨个邀请好友时更容易获得成功，因为他们知道哪些好友最有可能接受邀请。

该报告还定义了几种成功率最高的邀请者类型：

·人口统计学特征：最易成功的邀请者通常随机分布于用户群体中，但调查发现年长者更有公信力和影响力。

·个人关系网（好友结构）：朋友数量越多，意味着用户与好友之间的关系越缺乏稳定性，所以邀请成功率也就更低。

·“令人不解的发现是，用户的Facebook交际网络并不能准确反映该用户的人际关际情况。”有一种解释是这些社交网络是静态事物，它们并不能反映用户与好友之间的互动层次。

·游戏行为：玩家体验游戏的时间越长，发送的邀请也就越多。

·从总体上看，比起玩家的人口结构情况，用户的邀请策略才是决定邀请成功率的重要因素。（本文为游戏邦/gamerboom.com编译，如需转载请联系：游戏邦）

Report: 50% Of Successful Social Game Invites Come From 10% Of Players

By Neil Vidyarthi

Understanding the viral invitation process and successful virality of Facebook social games is a subject that warrants deep investigation, and a formal paper on just such a topic has been released by a group of researchers and industry veteran Manu Rekhi. In “Diffusion Dynamics of Games on Online Social Networks”, the group investigates the invitation process of users within social games, specifically focusing on “predicting invitation efficiency and understanding the group and social dynamics of invitation networks”.

Written by Xiao Wei, Jiang Yang from the University of Michigan, Ricardo Matsumura de Araújo from the Federal University of Pelotas, Brazil and Manu Rekhi, VP of strategy, marketing, business and corporate development for Lolapps, the investigation begins by defining a smart terminology for the analysis of the viral spread of an application. That is, who is inviting who, who is accepting, and how/why are these processes occurring. Below are some of the key findings and statistics, but I suggest you view the entire paper available here, because I only analyze a small section of their very complete findings.

Firstly, the paper defines the process of transmitting invitations between each other as ‘diffusion’, and adopts models of disease transmission to analyze the diffusion. To get data for the analysis, the group uses the extremely detailed data that is available for two of Lolapps popular games, Yakuza Lords (YL) and Diva Life (DL). As a testament to the level of analysis in the paper, here’s a quote about the information they know about their userbase:

As of February 2010, YL had reached one million active users, with over 85% being male. Although launched 2 months later than YL, DL gained over 2 million monthly active users in the same period.

As a game targeted at women, DL has more than 96% female users. The age distributions are similar and range from teens to 70s, with the majority being in the 18 to 38 years old range.

One of the first striking elements of the paper is their breakdown of user behavior. Looking at this graph below, entitled “Figure 2: Distribution of Different Actions in YL/DL”, we can see that for both games, a great percentage of users begin by looking at the items and character screens, but as they last longer in the game and become more expert, most of their time is spent on the mission and battle areas. This corroborates well with common sense, but to see that the transition area is around 5 to 10 actions is the precise kind of tool that social game designers need to iterate their games and reduce the number of players that leave.

Yakuza Lords and Diva Life Breakdown

Furthermore, they go on to say that since each user has to explicitly share their information with the application, they can understand a bit more about their sharing habits: “around 90% users share their locale information, 40% users share their friend list but only 1% share their relationship status”. This is interesting to note, as Facebook’s pre-application screen is not just clicked through for many users, a great percentage choose what they will share with the applications they play.

The paper goes on to analyze some key factors of inviters and invitees. Specifically, they look at the various factors that cause an inviter’s invite to be successful, and then examine what we can learn about invitees based on their choices to accept invites. I’ve included some short form points from the paper that I found interesting, and strongly suggest you read the entire paper here.

Out of all players who downloaded the two games analyzed here, more than 37% (for YL) and 25% (for DL) received invitations from their friends before starting to play the game.

Compared to players who have never been invited, invited users remain in the game longer. Around 80% of non-invited players leave the game within the first day
and almost none keep playing longer than 80 days. But among invited players, over 50% kept on playing for more than a day and 20% of all invited users were still playing 80 days later.
On average, each inviter has invited 26 friends while the median number is 10.

For both games, there is a quick expansion in the first 6 generations of invitations. We see in the graph below the invitation patterns of a typical inviter. This shows that on average, the first few cascades generate an increasing number of ‘invited people, where a cascade refers to the total number of invites sent by people who have been just invited to the game. This total number of invites increase until we get to the fifth level of invites, where we see that the fifth group if inviters generates, on average, the peak number of total invites into the system. The paper discusses this diffusion of invites in more depth, and finds some fascinating results.

Just 10% of users account for 50% of successful invites.

The paper breaks down the invitation strategies of users to determine which invites will be successful. They break the “efficiency” of an invite, meaning the success rate, into several interesting factors and determines their affect on the efficiency.

volume (# of friends invited): the more friends invited, the less success per invite pacing (time between invites):  invitations that are more spread out in time are more likely to succeed

repetition (# of invites to a friend): higher number of invites represent a higher chance of success selectivity (number of invites per invite session): uusers who invite friends individually tend to have a higher yield, possibly because they target their invitations to those who are more likely to accept.

The paper also attempts to determine the demographics of the best type of inviter:

demographics: no correlation: influential inviters are distributed randomly across various demographics with the exception of age: we observe that being older does confer a bit more authority and influence.

ego-network profile (structure of friends):high friend count means weaker connections and lower overall success rate

“It is puzzling that the shape and density of one’s FB network has little predictive power. A possible explanation is that these networks can be largely static and do not reflect the level of interaction between friends.”

gamer activity: the longer they play, the more invites they send

Overall, they find that invitation strategy is more important than demographics in determining invitation success rate.

The paper goes on to cover the details of the invitee and which factors determine whether someone will accept an invite. （source:socialtimes）