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In: Computers and Technology

Submitted By hewenjun0210
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《信息技术和信息社会》案例分析
(共两个案例,请尽可能在案例背景下,进行批判性思维critical thinking,给你的你想法。你的逻辑、创意、所运用的课程中的知识点是我所看重的)

案例1. Zynga Wins with Business Intelligence

这是一家高速发展的游戏公司,但它并不是以逼真的图像,令人心悸的动作,或者史诗般的故事著称,也不为Playstation, Xbox, 或者Wii开发游戏。这家公司就是Zynga。如果你有Facebook的账户,很大可能你会很熟悉它所开发的游戏。Zynga爆炸式发展表明社会游戏的潜力以及社会化网络在提供关键数据理解公司客户的能力。

Mark Pincus和一群创业者在2007年创建了Zynga,如今这家公司已经成为社交网络游戏的领行者,它的游戏包括CityVille、Texas HoldEm Poker、FarmVille、Empires & Allies等。这四款游戏是Facebook上最为流行的应用。Zynga的游戏有将近3亿月活跃用户以及6千多万日活跃用户,他们每天就会产生3TB的数据。自创建以来,Zynga便将数据分析放在优先级位置上,以方便对其游戏的管理以及公司的商业决策。

这家公司极度依赖数据来提高用户保有,增加游戏玩家的相互协作。用Zynga的首席数据官的话来说,有用的数据必须是“可行动的” – 它必须使能够帮助Zynga对其游戏做出显著改进的信息。游戏数据的产生和储存只是这个战争的一般。Zynga还必须有俩个分析团队(数据分析、可视化)来处理数据,在数据的基础上给出商务改进建议。

促进社会化游戏的经济性有三个关键指标:流失率、口碑传播系数、每个用户所带来的收入。流失率是指游戏玩家的的流失率。社会化游戏的流失率很高,平均而言,每月可以高达50%。这意味着今天注册的用户在一个月后只有一半人会继续。传播系数是衡量现有游戏玩家能够吸引新客户的有效性,这是社会化网络平台的一个重要能力。具体来讲,如果在一个月内,有100个FarmVille的玩家能够带来5个新的玩家,那么传播系数为1.05。用户收益是一个游戏玩家在整个生命周期所能够产生的预期收益,它是在每个用户的月收益和流失率的基础上计算。例如,如果每个用户的月均收益为5美元,流失率为50%,那么他的预期收益为5元第一个月 + 2.5元第二个月 + 1.25元第二个月等等依次类推,总共为10美元。

第一波Facebook上的社会化游戏关注提高传播系数,他们通过游戏玩家在游戏时的墙张贴广告来提高传播系数。但这样的做法产生了太多的“墙垃圾链接”或者游戏相关的帖子,使得用户很难看到他朋友推荐推荐过来的墙贴。为此,Facebook以及其他社会化网络平台严令要求游戏公司减少他们的墙贴。

为此,Zynga转向了社交图谱分析(social graph analysis)。对于社会化游戏,社交图谱,或者说朋友之间的关系,和社交网络平台有一些不同。例如,Zynga的Mafia Wars游戏,玩家可能有两类朋友:一类是对这个游戏很积极,另一类是对该游戏相对消极,只是为了朋友而加入到这个游戏,很少玩这个游戏或者很快就离开了这个游戏。玩家和这两类朋友的互动方式也不一定一样,例如积极分子朋友圈中礼物和提供帮助更多些。指导玩家对不同朋友采用不同的沟通方式,提高了收益降低了流失率。类似Zynga的社会化游戏公司必须从各个方面提升玩家体验才能提高游戏的利润。

来自于Vertica Systems(一家数据库管理公司)的技术能够解决这个问题。Vertica的大规模并行处理(massively parallel processing)架构允许用户用标准硬件或者云解决方案(即“节点”)来使用它的分析平台。用户可以建立群,包括1,10,100或者更多的节点,将上千个的处理器,TB的电脑内存,以及PB的硬盘空间整合到一个群里。一个小公司可以只用一个节点,根据需要可以增加节点。

Vertica的数据仓库是列,即以每一列而非每一行来存储数据,这样有利于更好地将数据压缩(压缩比为10:1)。传统的数据库无法处理压缩数据,但是Vertica的数据仓库能够对压缩了的数据进行管理,从而提高了绩效,降低了对处理能力、内存的要求。采用这个数据仓库,Zynga在数据处理上要比其他公司要快50-100%。Vertica的数据还能够为社交图像对数据进行预处理,将各个用户和其它用户的互动信息很快就转置成一列。这是传统的关系型数据库所做不到的。…...

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