1 / 79

people & data | weigend.com

Veränderungen in einer vernetzen Welt – die METRO Group im neuen Mitmach -Web Andreas Weigend 2. August 2007 | Düsseldorf. people & data | weigend.com. What is Web 2.0?. Tim O’Reilly (2005): Web 2.0 is a set of economic, social, and technology trends

mary
Télécharger la présentation

people & data | weigend.com

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Veränderungen in einervernetzen Welt –die METRO Group imneuenMitmach-Web Andreas Weigend 2. August 2007 | Düsseldorf people & data | weigend.com

  2. What is Web 2.0? • Tim O’Reilly (2005): • Web 2.0 is • a set of economic, social, and technology trends • that collectively form the basis for • the next generation of the Internet—a more mature, distinctive medium • characterized by user participation, openness, and network effects.

  3. Agency of the Year : The Consumer (Advertising Age 2007.01) 年代理商：消费者广告时代

  4. The five levels Web 2.0 Web1.0

  5. Web1.0 vs Web2.0 Web1.0 与 Web2.0 • Web 1.0 : E-business Web 1.0 ：电子商务 • about pages, commerce关于网页和商业 • Data gathering数据搜集 • Experimentation试验 • about impressions关于印象 • Web 2.0: Me-BusinessWeb 2.0：我的商务 • about people, individuals 关于人和个人 • Participation, Contribution参与，贡献 • Interaction互动 • Group, Community, Collaboration团体，社区，协作 • Crowdsourcing more than social networks不同于社会网络的扎堆 • Future of work未来的工作 • about the impressed关于留下印象的

  6. Web1.0vs Web2.0Web1.0 与 Web2.0

  7. From Targeting to Discovery从定位到发现

  8. Attributes of Web2.0 • User focus (E-Business  Me-business) • User is at the center of Web2.0 • Transparency • Google Maps: Create API rather than tighten security • Technology • Lightweight • System engineered for feedback • System improves as people use it • Network effects • Demand-side economies of scale (not only supply-side economies of scale) • Incentives • Pay people (e.g., Yahoo directory index) • Get volunteers (e.g., wikipedia) • Create self-interest (e.g., BitTorrent; file sharing sites)

  9. Web 1.0  Web 2.0 • Background (2007) • 1 bn PCs, 2 bn mobile phones • 150M registered eBay users, more transactions per day than NYSE • 80M blogs total, 20 new entries per second, 2 new blogs created every second • US online advertising USD 15…20bn (12.5bn in 2005)

  10. Technology innovation • Military • Enterprise • Consumer • Centralized • Top-down • Consumer • Enterprise • Consumer • De-centralized • Bottom up

  11. User generated content (UGC)用户自创内容 • Paying a few experts to create content  Users generated content雇几个专家来创造内容用户自己创造的内容 • Implicit data and explicit data 隐含的数据+明确的数据 • Incentives?激励运作? • Metadata元数据 • Example: Music例子：音乐

  12. Social search社会搜索 • Algorithmic search  Social search规则搜索社会搜索 • Use information of files, email etc. on your computer to determine relevance使用你的电脑文件里的信息来决定相关性 • Example: Illumio例如：Illumio • Customers helping customers: People answer questions消费者互助: 人们回答问题 • Exmple: Yahoo Answers例如：Yahoo Answers

  13. Social search – Korea韩国的社会搜索 • KoreanClick 2007.07 • Naver • 77% of searches占77%的搜索 • 110M queries per day by 16 M unique users per day out of 48M citizens4800万人口中，每天有11000万条询问来自于1600万个用户 • Knowledge iN (since 2002)Knowledge iN（始于2002年） • 44k questions posted per day每天有44000条问题 • 110k answers received per day每天会收到110，000条回复 • Cumulative 70M累计达到7000万条 • Other search engines • Daum.net 11%Daum.net占11% • Yahoo 4.4%Yahoo占4.4% • Google 1.7%Google占1.7%

  14. Knowledge Search, Yahoo 2003 in Korea韩国版的Yahoo知识搜索工具

  15. Popular questions in Korea什么问题在韩国最流行？ • Why do people close their eyes while they are kissing?为什么人们接吻的时候要闭上眼睛？ • The most common name in the world?世界上最通用的名字是什么？ • What’s the truth of Bruce Lee’s death?李小龙的真正死因是什么？ • Why do people get drunk more when drinking alcohol with a straw?为什么当人们使用吸管喝酒的时候更容易喝醉？ • Are there any animals that commit suicide? 什么动物会自杀？ • Is that true that most of The Great Commanders are short? 大多数的大人物都很矮，这种说法对吗？ • Why is the Ocean salty? 为什么海水是咸的？ • Why do people lift up their hands to show their joy of victory? 为什么人们要举起手来表示他们胜利的喜悦？ • The Secret of Popularity of Harry Potter Series!! 哈里波特系列如此畅销的秘密。 • Badly want to lose my weight.... Help. 非常非常想减肥….请帮忙！

  16. Amazing user adoption…网络搜索的惊人增长 每周的网页浏览数 知识搜索

  17. …with a Web search halo网络搜索的惊人增长 每周的网页浏览数 网络搜索 知识搜索

  18. Yahoo Taiwan: DesignYahoo台湾：设计

  19. Total Search Market Share (PVs) Yahoo! 68% Google 29% MSN 1.5% Yahoo Taiwan: Launch 2004.12Yahoo台湾（2004年12月诞生） Total Search Market Reach Total Search Market Share (PVs) Total Search Market Reach 总的搜索市场范围 总的搜索市场份额 Yahoo! 89% Yahoo! 68% Y! Knowledge 67% Google 51% MSN 24% Source: InsightXplore ARO

  20. Yahoo Answers US: DesignYahoo Answers美国：设计

  21. Yahoo Answers US: Launch 2005.12Yahoo Answers美国（2005年12月诞生） Answer.yahoo.com Answer.google.com Qna.com.com

  22. Typical interaction典型的互动 • Example: I need a 7th grade science experiment using food and how quickly it molds例如：我需要七年级的用食物来观察多久发霉的科学实验

  23. US web search美国的网络搜索 • US web search market share: Y! flat, Google taking share from MSN and AOL美国的网络搜索市场份额：Yahoo和Google超过了MSN和AOL Source: ComscoreqSearch reports Quarterly data reflects end of period data

  24. Landscape of Web Search网络搜索行业的特点 • Levers to gain market share两个赢得市场份额的要素 • Superior quality • Distribution质量和分销渠道 • Mechanisms to lock-in share锁定市场份额的办法 • Differentiated content差异化的内容 • Superior monetization较好的货币化 • Country leader has advantage本土领先优势 • US (Google), KR (Naver), CN (Baidu)Google在美国，Naver在韩国，Baidu在中国 • New players face significant barrier to entry进入壁垒高 • Brand and user habits dominate品牌和用户习惯起主导作用：

  25. Social search has the potential to change the game Social search 很有可能改变游戏规则 • Consumer need消费者需求 • Platform enabling communities of people to share 创造世界上最大的平台，使交易各方能够共享来自于经验的和后来学习的知识 • Experiences • Long tail knowledge • Game changing potential游戏规则改变前景 • Critical mass of content and community of knowledge enthusiasts创造一个必不可少的信息群以及一个由热心人建立的知识社区 • Change the search experience改变搜索经历

  26. Share Knowledge 知识共享 Ask Answers询问答案 Ask Google/Yahoo ask family/friends . . . Facebook . . . Wikipedia Blog Share Person with Knowledge有答案的人 Why is this game changing?为什么游戏规则会改变？ Person with a Question?有疑问的人

  27. Learnings经验之二 • The expertise is in the tail专家位于尾部（占少数） • It’s about the people以人为本 怀孕和做父母的最佳答案

  28. Use in politics: Hilary Clinton asking about healthcare Hilary Clinton 根据你的个人家庭生活经验，你认为应该怎样提高美国的健康护理水平？

  29. Social Networks (“Contacts”)社交网络（“联络”） • Build your knowledge network by connecting with the people you trust and the topics you care about通过与你信任的人和你关心的问题联系建立你的知识网络 • Benefits 好处 • More personal experience更私人化的经历 • More productive experience更有成果的经历 • Faster, easier access…更快 • … to more useful, helpful, relevant information更简单地找到更有用、更相关的信息

  30. The five levels Web 2.0 Web1.0

  31. Increase of Communication: Five levels增进沟通的5个层次 • Architectures of Collaboration, Community协作社区体系 • Architectures of Interaction交互体系 • Architectures of Participation参与贡献体系 • Remember, share, discover记住，共享，发现 • Empower and incentivize people to contribute给予人们贡献的权力并激励他们来贡献 • Architectures of Experimentation实验体系 • Act: A/B test, active learning, surveys …做法：A/B测试，主动学习，问卷设计… • Data Strategy 数据收集分析 • Collection, mining: Describe, predict数据挖掘：描述，预测 Web 2.0 Web1.0

  32. 1. Data collection and analysis (Amazon.com)1.数据收集和分析(Amazon.com) • Level层次 • Customer消费者 • Orders订单 • Session aggregates访问总计 • Clicks 点击 • Data collectedin 20072007年搜集的数据 • 100 MB • 10 GB • 1 TB • 100 TB Amount of data 数据量

  33. Sources of data • Order data订单 • E.g., Amazon.com例如：亚马逊网站 • A few GB per year每年几百亿 • Click data点击数据 • E.g., Facebook web logs例如：Facebook网络链接 • A few TB per day每天几万亿 • Intention data意图数据 • E.g., Google search logs例如：google的搜索网页 • Attention data关注数据 • E.g., Del.icio.us tags例如Del.icio.us • Interaction data信息数据 • Social network data, email headers从社会网络，从电子邮件标题 • Location data地点数据 • GPS, mobile phones从全球定位系统，从移动电话

  34. Web 1.0 vs Web 2.0Web 1.0与Web 2.0

  35. 3. Participation3.参与 • 1. Data Analysis数据分析 • Data mining: Description, prediction数据挖掘：描述，预测 • 2. Architectures of Experimentation实验体系 • A/B test, active learning, survey design…A/B测试，主动学习，问卷设计 • 3. Architectures of Participation参与体系 • Remember, share, discover记住，共享，发现 • Empower and incentivize people to contribute给予人们贡献的权力并激励他们来贡献 • Self-expression自我表达

  36. Platform: Yellow Pages平台：黄页

  37. Click to Call Business Click to Call!点击 直接通话

  38. Make customer feedback trivially easy获取消费者反馈易如反掌 • Capture context automatically自动捕获内容 I went to the bathroom and came back, and the page was still loading!!我去了浴室，又回来，网页却还在下载！！

  39. Click点击 Money Economy  Intention Economy  Attention Economy货币经济意图经济注意力经济 Only able to click on links given on site.只能点击网站链接

  40. Search搜索 Click点击 From intention to attention从意图到注意力 Express intention. Doesn’t depend on result.表达意图。独立于结果 Only able to click on links given by site.只能点击网站链接

  41. Tag书签 Search搜索 Amount of specificity increases针对性愈加明显 Click点击 From intention to attention从意图到注意力 Label item. To remember, share，discover.留下标签。记忆，共享，发现 Express intention. Doesn’t depend on result.表达意图。独立于结果 Only able to click on links given by site.只能点击网站链接

  42. Example: del.icio.us例子：del.icio.us • Tags are distilled attention, a pure form of attention.书签是过滤的了注意力，是纯粹的注意力 • You are what you tag.书签展示真我 • You are what you are tagged as / who you are tagged by.书签决定你的存在 Discover other users who tagged the page找到给同一页面贴上书签的其他网友 Follow a tagand discover a topic跟随书签发现话题 Follow a user and discover what he is interested in探索该网友的兴趣爱好

  43. Example: flickr例子：flickr Quentin Lee, Filmmaker (Drift, Ethan Mao) 你想评论一下吗？

More Related