《新媒体技术基础（双语）》课件-5.3 新媒体数据分析

模块五：新媒体数据收集与分析技术单元三：新媒体数据分析Module5:NewMediaDataCollectionandAnalysisTechniquesModule3:NewMediaDataAnalysis02互动数据构成与分析典型平台数据分析传播数据构成与分析转化数据构成与分析目录040103directoryCompositionandanalysisofdisseminateddataInteractivedatacompositionandanalysisConversiondatacompositionandanalysisTypicalplatformdataanalysis01传播数据构成与分析PARTONECompositionandanalysisofdisseminateddata传播数据构成与分析（一）内容传播数据指标构成1发稿量/转载量可以按不同时间段如一个月、一周和近24小时，展示新媒体发稿量趋势及转载趋势。媒体传播量2用数据展现企业下属网站、微博、微信、APP在各平台信息的传播量。传播力属性3网站、APP传播力排行榜，包括相似文章数量、影响地域、传播趋势及转载列表。展示信息包括发布内容题、作者、发布时间、新闻来源、转载量；内容的传播效果分析，分析结果包括传播路径、影响地域、传播趋势及转载列表。Compositionandanalysisofdisseminateddata(1)ThecompositionofcontentdisseminationdataindicatorsNumberofpublications/reprintsItcanbeusedfordifferenttimeperiods,suchasonemonth,oneweek,andnearly24hours,todisplaythetrendofnewmediapublicationvolumeandreprinttrend.MediaCommunicationUsedatatoshowtheamountofinformationdisseminationonthecompany'ssubordinatewebsites,Weibo,WeChat,andAPPonvariousplatforms.PropagationpropertiesWebsiteandAPPcommunicationpowerrankings,includingthenumberofsimilararticles,influenceregions,communicationtrendsandreprintlists.Thedisplayinformationincludesthetitleofthepublishedcontent,theauthor,thereleasetime,thenewssource,andthenumberofreprints;Theresultsincludethecommunicationpath,influencearea,communicationtrendandreprintlist.传播数据构成与分析（一）内容传播数据指标构成4实时动态包括今日发稿总量、发稿的转载与原创比，各媒体平台发稿量的占比，新闻热点信息等。实时展示企业下属网站、微博、微信、APP的内容发布情况，包括发稿量/转载量趋势分析，媒体类型转载量占比，媒体传播量，网站、微博、微信、APP传播力排行榜。发布内容及热点头条文章5发布内容的阅读量、评论量、转发量、传播时长、覆盖网站、覆盖地域，展示传播引爆点及稿件的传播路径图。媒体类型转载量占比6展示企业下属网站、微博、微信、APP发布的稿件转载量的占比，对单篇稿件可设置更新频率，对用户指定的内容进行分析展示。Compositionandanalysisofdisseminateddata(1)ThecompositionofcontentdisseminationdataindicatorsIncludingthetotalnumberofarticlespublishedtoday,theratioofreprintstooriginality,theproportionofarticlespublishedonvariousmediaplatforms,newshotinformation,etc.Real-timedisplayofthecontentreleaseofthecompany'ssubordinatewebsites,Weibo,WeChat,andAPP,includingtheanalysisofthetrendofthenumberofpublications/reprints,theproportionofmediatypesofreprints,thenumberofmediadissemination,andtherankingofwebsites,Weibo,WeChat,andAPPcommunicationpower.Thenumberofreads,comments,forwards,disseminationduration,websitescovered,andregionscoveredbythepublishedcontentaredisplayed,andthecommunicationtippingpointandthecommunicationpathmapofthemanuscriptaredisplaPublishcontentandhotheadlinesyed.Displaytheproportionofmanuscriptspublishedbythecompany'ssubordinatewebsites,Weibo,WeChat,andAPP,settheupdatefrequencyforasinglemanuscript,andanalyzeanddisplaythecontentspecifiedbytheuser.Real-timedynamicsPublishcontentandhotheadlinesProportionofmediatypesreprinted传播数据构成与分析（二）产品传播数据指标构成基于产品运营的新媒体平台，产品具有内嵌分享功能，对内容型平台或者依赖传播做增长的产品，对病毒式增长的衡量就会变的至关重要，因此，数据指标包括分享率、传播率、病毒K因子和传播周期。分享率转化率即现有用户中有多少比例的用户进行分享。按单篇内容来计算分享率，即点击分享按钮的人占阅读人数的比例。转化率是指分享后有多少比例的用户被转化为新用户，即新用户数占分享次数的比例。Compositionandanalysisofdisseminateddata(2)ThecompositionofproductdisseminationdataindicatorsBasedonthenewmediaplatformofproductoperation,theproducthasanembeddedsharingfunction,andthemeasurementofviralgrowthwillbecomeveryimportantforthecontent-basedplatformorproductsthatrelyoncommunicationforgrowth,sothedataindicatorsincludesharingrate,transmissionrate,viralKfactorandtransmissioncycle.SharerateThatis,whatpercentageofexistingusersshare.Thesharerateiscalculatedbasedonasinglepieceofcontent,whichistheproportionofpeoplewhoclickthesharebuttontothenumberofreaders.ConversionrateConversionratereferstotheproportionofuserswhoareconvertedintonewusersaftersharing,thatis,theproportionofthenumberofnewuserstothenumberofshares.传播数据构成与分析（二）产品传播数据指标构成病毒K因子传播周期又称病毒系数，衡量现有用户能够成为产品获取的新用户数。病毒K因子计算公式：K=（分享数/现有用户数）*（新用户数/分享数）=分享率*转化率当K因子大于1时，每位用户能至少能带来一个新用户，用户量会像滚雪球般变大，最终达成自传播。当K因子足够大时，就是口口相传的病毒营销。传播周期指用户完成从传播到转化新用户所需要的时间，通常传播周期越短，意味着用户裂变传播的效果越好。Compositionandanalysisofdisseminateddata(2)ThecompositionofproductdisseminationdataindicatorsAlsoknownastheviralityfactor,itmeasuresthenumberofexistinguserswhocanbecomenewusersacquiredbytheproduct.FormulaforcalculatingviralKfactor:K=(Shares/ExistingUsers)*(NewUsers/Shares)=SharingRate*ConversionRateWhentheKfactorisgreaterthan1,eachusercanbringatleastonenewuser,andthenumberofuserswillsnowball,andfinallyachieveself-propagation.WhentheK-factorislargeenough,it'sword-of-mouthviralmarketing.ViralKfactorPropagationcycleThepropagationcyclereferstothetimeittakesforuserstocompletethetransitionfrompropagationtoconversionofnewusers,andtheshorterthepropagationcycle,thebettertheeffectofuserfissionpropagation.Thepropagationcyclereferstothetimeittakesforuserstocompletethetransitionfrompropagationtoconversionofnewusers,andtheshorterthepropagationcycle,thebettertheeffectofuserfissionpropagation.02互动数据构成与分析PARTTWOInteractivedatacompositionandanalysis互动数据构成与分析新媒体的互动数据是用户参与运营活动产生的，用户可以在官网、互动活动页面、微博、BBS等活动或栏目中参与互动，也可自愿留下信息，这些活动过程会形成互动数据。新媒体平台可通过互动数据驱动制定互动策略、设定互动规则为用户量身打造互动活动，来吸引用户参与。互动数据分析包含如下：

内容互动数据分析用户互动数据分析活动互动数据分析InteractivedatacompositionandanalysisTheinteractivedataofnewmediaisgeneratedbyusers'participationinoperationalactivities,anduserscanparticipateininteractionsinactivitiesorcolumnssuchasofficialwebsites,interactiveactivitypages,Weibo,BBS,etc.,orvoluntarilyleaveinformation,andtheseactivitieswillforminteractivedataintheprocessoftheseactivities.Newmediaplatformscanattractuserstoparticipatebyformulatinginteractionstrategiesandsettinginteractionrulesthroughinteractivedata-driveninteractionstrategiesandcustomizedinteractiveactivitiesforusers.Engagementinsightsincludethefollowing:UserinteractiondataanalysisContentengagementdataanalysisEventinteractiondataanalysis互动数据构成与分析（一）用户互动数据的构成与分析用户互动数据指引入新用户、留存老用户、保持用户活跃、促进用户付费、挽回流失或者沉默的用户等新媒体运营中所产生的互动数据。0201用户精准画像数据分析活跃、留存、付费的分析你的用户是男性多还是女性多，他们分布在什么样的年龄层次，集中于哪些省份，他们受教育程度如何，兴趣点有哪些，等等这些都是做基础用户分析的指针，而对基础用户的分析会决定运营人员应当采用何种运营策略、使用何种运营工具、发布哪些运营活动和内容。用户运营需要了解用户的规模以及增长或衰退情况，并进行适当的用户分层。新用户有多少、老用户又有多少、每日增长规模如何、用户处于怎样的生命周期。明确了这一点，才能了解产品处于什么样的阶段，用户处于什么样的阶段，然后才能了解对用户进行运营的目标所在，从而选择合适的运营方式。InteractivedatacompositionandanalysisUserinteractiondataisdirectedtotheinteractiondatageneratedinnewmediaoperations,suchasnewusers,retainingoldusers,keepingusersactive,promotinguserpayment,andrecoveringlostorsilentusers.AccurateuserportraitdataanalysisWhetheryourusersaremoremaleorfemale,whatageleveltheyaredistributed,whichprovincestheyareconcentratedin,howeducatedtheyare,whataretheirinterests,andsoonarethepointersforbasicuseranalysis,andtheanalysisofbasicuserswilldeterminewhatoperationstrategyoperatorsshouldadopt,whatkindofoperationtoolstheyuse,andwhatoperationactivitiesandcontenttheyshouldpublish.Useroperationsrequireunderstandingthesizeofusersandhowtheyaregrowingordeclining,andhavingtherightusertiering.Howmanynewusers,howmanyoldusers,whatisthescaleofdailygrowth,andwhatisthelifecycleofusers.Onlybyclarifyingthispointcanweunderstandwhatstagetheproductisinandwhatstagetheuserisin,andthenwecanunderstandthegoalofoperatingtheuser,soastochoosetheappropriateoperationmode.(1)Compositionandanalysisofuserinteractiondata互动数据构成与分析（一）用户互动数据的构成与分析03用户行为数据分析与转化通过对用户行为数据的分析，灵活运用事件分析模型、留存模型、转化漏斗等，了解用户为什么来、为什么走、为什么活跃、为什么留存。对新用户的增长，已有用户的活跃和留存，活跃用户促付费，流失用户的挽回都有对应的措施，让所有的决策有迹可循而不是“拍脑袋”，才能真正提升活跃、留存和付费。（1）留言点赞用留言或点赞与阅读量的比值来衡量用户对该篇文章的互动情况，数值越高，互动越明显。（2）用户消息新媒体运营人员为了引导用户互动和关注，经常会策划一些送福利的活动，用户想要了解活动的具体情况，就需要与平台活动，就会产生用户消息。消息越多，说明活动吸引力大。Interactivedatacompositionandanalysis(1)CompositionandanalysisofuserinteractiondataUserbehaviordataanalysisandconversionThroughtheanalysisofuserbehaviordata,wecanflexiblyuseeventanalysismodels,retentionmodels,conversionfunnels,etc.,tounderstandwhyuserscome,whytheyleave,whytheyareactive,andwhytheyareretained.Therearecorrespondingmeasuresforthegrowthofnewusers,theactivityandretentionofexistingusers,thepromotionofpaymentbyactiveusers,andtherecoveryoflostusers,sothatalldecisionscanbetracedinsteadof"pattingthehead",soastotrulyimproveactivity,retentionandpayment.(1)LeaveamessageandlikeTheratioofcommentsorlikestoviewsisusedtomeasuretheuser'sinteractionwiththearticle,andthehigherthevalue,themoreobvioustheinteraction.(2)UserMessagesInordertoguideuserstointeractandpayattention,newmediaoperatorsoftenplansomeactivitiestosendbenefits,anduserswanttounderstandthespecificsituationoftheactivity,theyneedtointeractwiththeplatform,andusermessageswillbegenerated.Themoremessagesthereare,themoreattractivetheeventis.互动数据构成与分析（二）内容互动数据的构成与分析内容互动是通过采集、创造、组织、呈现内容提高新媒体营销的内容价值，创造出对用户粘性、用户活跃产生促进作用的运营内容。内容互动数据分析指用户连接进来之后，需要根据用户对内容的偏好及各种行为的数据分析来了解用户，对用户分组，定向推送合适的内容。1.内容评论数据指标分析内容评论量/内容评论人数内容评论量指的是在统计周期里的总内容评论次数。评论量越高，意味着用户对文章发表见解的意愿越强烈。内容评论人数指在统计时间周期里，产生过内容评论行为的用户总和。内容评论量会略高于内容评论人数，因为有的用户可能发布了多条评论。Interactivedatacompositionandanalysis(2)CompositionandanalysisofcontentinteractiondataContentinteractionistoimprovethecontentvalueofnewmediamarketingbycollecting,creating,organizing,andpresentingcontent,andtocreateoperationalcontentthatpromotesuserstickinessanduseractivity.Contentinteractiondataanalysismeansthatafterusersareconnected,theyneedtounderstandusersaccordingtotheirpreferencesforcontentanddataanalysisofvariousbehaviors,groupusers,andpushappropriatecontentinatargetedmanner.1.AnalysisofcontentcommentdataindicatorsNumberofcontentcomments/numberofcontentcommentsContentcommentsrefertothetotalnumberofcontentcommentsinastatisticalperiod.Thehigherthenumberofcomments,thestrongertheuser'swillingnesstoexpresstheiropiniononthearticle.Thenumberofcontentcommentersreferstothetotalnumberofuserswhohavecommentedoncontentduringthestatisticaltimeperiod.Thenumberofcommentsonthecontentisslightlyhigherthanthenumberofcommentsonthecontent,assomeusersmayhavepostedmultiplecomments.互动数据构成与分析1.内容评论数据指标分析内容评论率是指在统计时间周期里，产生过内容评论行为的用户占内容阅读点击人数的比重，即：

内容评论率=内容评论人数/内容点击人数通过这个指标，我们可以分析得到用户对于哪些内容更有讨论的欲望。一般比较有争议的话题内容评论率会较高。内容评论率内容平均评论量是在某个时间周期里的内容分享的平均水平，

即：内容平均评论量=内容评论量/内容图文量这个指标可以反映用户对内容的黏性。内容平均评论量Interactivedatacompositionandanalysis1.AnalysisofcontentcommentdataindicatorsContentcommentrateThecontentcommentratereferstotheproportionofuserswhohavegeneratedcontentcommentbehaviorstothenumberofcontentreadingclicksinthestatisticaltimeperiod,thatis:Contentcommentrate=numberofcontentcomments/numberofcontentclicksThroughthismetric,wecananalyzewhichcontentuserswanttodiscussmore.Ingeneral,thecontentofmorecontroversialtopicswillhaveahighercommentrate.TheaveragenumberofcommentsonthecontentTheaveragenumberofcontentcommentsistheaverageamountofcontentsharedoveracertaintimeperiod.Thatis:theaveragenumberofcontentcomments=thenumberofcontentcomments/theamountofcontentgraphicsandtextsThismetriccanreflecttheuser'sstickinesstothecontent.互动数据构成与分析1.内容评论数据指标分析内容平均评论量趋势内容平均评论量趋势指在同样的时间周期里用户活跃度是否有提升。内容平均评论量趋势向上，通常说明内容越来越受用户欢迎。内容最高评论量内容最高评论量指过去某段时间内，单篇内容获得的最多的评论量。除了找到最高评论数值及其对应的文章，还可以分析其内容特征，并总结出该类内容的选题和编辑技巧。Interactivedatacompositionandanalysis1.AnalysisofcontentcommentdataindicatorsTrendintheaveragenumberofcommentsoncontentThetrendofaveragecommentsoncontentreferstowhetheruseractivityhasincreasedoverthesametimeperiod.Anupwardtrendintheaveragenumberofcommentsoncontentusuallyindicatesthatcontentisbecomingmoreandmorepopularwithusers.ThecontenthasthehighestnumberofcommentsThehighestnumberofcommentsonapieceofcontentreferstothemaximumnumberofcommentsreceivedbyasinglepieceofcontentinthepastperiod.Inadditiontofindingthehighestreviewvalueanditscorrespondingarticles,youcanalsoanalyzethecharacteristicsofitscontent,andsummarizethetopicselectionandeditingskillsofthistypeofcontent.互动数据构成与分析2.内容分享转发数据指标分析内容分享量/内容分享人数内容分享量是内容被分享出去的总数量。分享量越高，通常意味着内容功能里的内容越受目标用户喜欢。分享人数则是指在统计时间周期里，产生过内容分享行为的用户总和。

内容分享率内容分享率是指在统计时间周期里，产生过内容分享行为的用户占内容阅读点击人数的比重，

即：内容分享率=内容分享人数/内容点击人数。Interactivedatacompositionandanalysis2.AnalysisofcontentsharingandforwardingdataindicatorsThenumberofcontentshares/thenumberofcontentsharesContentsharesarethetotalamountofcontentthathasbeenshared.Thehigherthenumberofshares,themorepopularthecontentinthecontentfeatureisforthetargetusers.Thenumberofuserswhosharereferstothetotalnumberofuserswhohavesharedcontentinthestatisticaltimeperiod.ContentsharingrateThecontentsharingratereferstotheproportionofuserswhohavegeneratedcontentsharingbehaviorstothenumberofcontentreadingclicksinthestatisticaltimeperiod.Thatis,contentsharingrate=numberofcontentsharing/numberofcontentclicks.互动数据构成与分析2.内容分享转发数据指标分析内容平均分享量内容平均分享量是指在某个时间周期里的内容分享的平均水平，即每篇内容平均有多少次分享：

内容平均分享量=内容分享总量/内容总量。

内容平均分享量趋势内容平均分享量趋势是指在同样的时间周期里，内容平均分享量是否有提升。趋势向上的分享量说明内容越来越被目标用户认可，文章的结构和长度也都是符合用户阅读习惯的。Interactivedatacompositionandanalysis2.AnalysisofcontentsharingandforwardingdataindicatorsTheaveragenumberofcontentsharesTheaveragenumberofcontentsharesistheaveragenumberofcontentsharesoveraperiodoftime,i.e.,howmanytimeseachpieceofcontentissharedonaverage:Averagecontentsharing=totalcontentsharing/totalcontentsharing.TrendinaveragecontentsharesThetrendofaveragecontentsharesreferstowhethertheaveragenumberofcontentshareshasincreasedoverthesametimeperiod.Theupwardtrendofsharingshowsthatthecontentismoreandmorerecognizedbythetargetusers,andthestructureandlengthofthearticlearealsoinlinewiththeuser'sreadingXi.互动数据构成与分析2.内容分享转发数据指标分析内容最高分享量内容最高分享量是指过去某段时间内，单篇内容获得的最高的分享量。建议持续分析具有高分享量的文章具有哪些特征，并在后续的写作中进行验证。Interactivedatacompositionandanalysis2.AnalysisofcontentsharingandforwardingdataindicatorsThehighestnumberofsharesofcontentThehighestnumberofcontentsharesreferstothehighestnumberofsharesthatasinglepieceofcontenthasreceivedinthepastperiod.Itisadvisabletocontinuouslyanalyzethecharacteristicsofarticleswithhighsharevolumeandverifytheminsubsequentwriting.互动数据构成与分析（三）活动互动数据的构成与分析新媒体运营活动是通过开展独立或联合的运营活动，拉动某一个或多个指标的短期提升。活动是基于与用户之间的互动开展的，根据运营活动类型不同，产生的活动互动数据和数据分析过程也不一样。拉新类活动数据指标分析促活类活动数据指标分析留存类活动数据指标分析付费类活动数据指标分析Interactivedatacompositionandanalysis(3)CompositionandanalysisofactivityinteractiondataNewmediaoperationactivitiesarecarriedoutindependentlyorjointlytodriveshort-termimprovementofoneormoreindicators.Campaignsarebasedoninteractionswithusers,andtheresultingcampaigninteractiondataanddataanalysisprocessesvarydependingonthetypeofoperationalactivity.AnalysisofnewuseracquisitiondataindicatorsAnalysisofdataindicatorsforactivatingactivitiesMetricsanalysisofretentionactivitydataMetricanalysisofpaidactivitydata互动数据构成与分析（三）活动互动数据的构成与分析拉新类活动数据指标分析拉新类活动是指通过新媒体手段新增粉丝的活动，这类活动的核心指标通常是新增用户数。例如微信公众号上常见的微课涨粉活动、微博上常见的关注并@三个好友的抽奖活动，都属于拉新类活动。促活类活动数据指标分析促活类活动是指为了促进现有粉丝活跃程度的活动，这类活动的核心指标通常是活跃用户数或活跃率。例如微信公众号发起的留言互动，就属于促活类活动。Interactivedatacompositionandanalysis(3)CompositionandanalysisofactivityinteractiondataAnalysisofnewuseracquisitiondataindicatorsNewcampaignreferstotheactivityofaddingnewfansthroughnewmediameans,andthecoreindicatorofthistypeofactivityisusuallythenumberofnewusers.Forexample,thecommonmicro-classfanincreaseactivitiesontheWeChatpublicaccount,andthecommonlotteryactivitiesonWeibothatfollowand@threefriends,allbelongtothecategoryofactivitiestoattractnewusers.AnalysisofdataindicatorsforactivatingactivitiesActivationcampaignsarecampaignsthatdriveactivityamongexistingfollowers,andthecoremetricsforthesecampaignsareusuallythenumberofactiveusersoractivityrate.Forexample,themessageinteractioninitiatedbytheWeChatpublicaccountisapromotionactivity.互动数据构成与分析（三）活动互动数据的构成与分析留存类活动数据指标分析留存类活动是指为了增加用户黏性、减少粉丝流失的活动，这类活动的核心指标通常是次日留存率、七日留存率和三十日留存率。留存类活动的特点是持续性，例如有些公众号会经常在文章底部放现金红包抽奖链接，或者在每个月举办留言送书的活动。付费类活动数据指标分析付费类活动是指引导粉丝付费的活动，核心指标即电商类平台的转化指标，包括付费人数、新增付费人数、销售总额、付费订单总量、人均客单价。例如微信公众号上卖产品、卖课的活动，即为付费类活动。Interactivedatacompositionandanalysis(3)CompositionandanalysisofactivityinteractiondataMetricsanalysisofretentionactivitydataMetricanalysisofpaidactivitydataRetentionactivitiesrefertoactivitiesthatincreaseuserstickinessandreducefanchurn,andthecoreindicatorsofsuchactivitiesareusuallythenext-dayretentionrate,seven-dayretentionrate,and30-dayretentionrate.Retentionactivitiesarecharacterizedbycontinuity,forexample,someofficialaccountswilloftenputcashredenvelopelotterylinksatthebottomofarticles,orholdmessagesandbookdeliveryactivitieseverymonth.Paidactivitiesrefertoactivitiesthatguidefanstopay,andthecoreindicatorsaretheconversionindicatorsofe-commerceplatforms,includingthenumberofpayers,thenumberofnewpayers,totalsales,totalpaidorders,andpercapitaunitprice.Forexample,theactivitiesofsellingproductsandclassesontheWeChatpublicaccountarepaidactivities.03转化数据构成与分析PARTTHREEConversiondatacompositionandanalysis转化数据构成与分析（一）交易类平台转化数据构成GMV即GrossMerchandiseVolume，指网站的成交的应付金额，而这里的成交金额包括：付款金额和未付款。GMV=销售额+取消订单金额+拒收订单金额+退货订单金额GMV指用户付款的实际流水，是用户购买后的消费金额。

成交额Conversiondatacompositionandanalysis(1)ThecompositionoftheconversiondataofthetradingplatformGMVstandsforGrossMerchandiseVolume,whichreferstotheamountpayableforthetransactiononthewebsite,andthetransactionamounthereincludes:theamountofpaymentandtheamountofunpaid.GMV=Sales+CancelledOrderAmount+RejectedOrderAmount+ReturnOrderAmountItreferstotheactualflowofuserpayment,whichistheamountofconsumptionafterpurchase.转化数据构成与分析（一）交易类平台转化数据构成指成交金额减去退款后剩余的金额。销售收入在产品里边产生过交易行为的用户总量，同活跃用户一样，交易用户也可以区分为首单用户（第一次消费）、忠诚消费用户（持续购买的用户）、流失消费用户（流失后又回来的用户）等。同时，为了研究用户的付费潜力，还可以研究下产品的付费用户比例

即：付费用户比例=付费用户÷总注册用户

付费用户量Conversiondatacompositionandanalysis(1)ThecompositionoftheconversiondataofthetradingplatformReferstotheamountofthetransactionminustheamountleftaftertherefund.Thetotalnumberofuserswhohavegeneratedtransactionbehaviorsintheproduct,likeactiveusers,canalsobedividedintofirst-orderusers(first-timeconsumption),loyalconsumers(userswhocontinuetopurchase),andchurnedconsumptionusers(userswhoreturnafterchurning).Atthesametime,inordertostudytheuser'spaymentpotential,youcanalsostudytheproportionofpayingusersoftheproductThatis:Proportionofpayingusers=Payingusers÷Totalregisteredusers转化数据构成与分析（一）交易类平台转化数据构成在单个促销活动中，指每一笔用户订单的收入，即：总收入/订单数。而在整个产品生命周期中，我们更关注用户平均付费，即：总收入/用户数ARPU可以再一步细分，当普通用户占比太多，往往还会采用每付费用户平均收入：

ARPPU=总收入÷收费用户数ARPU和新增用户一样，获得一个新付费用户的成本已经高于维护熟客的成本。复购率更多用在整体的重复购买次数统计：单位时间内，消费两次以上的用户数占购买总用户数。

复购率Conversiondatacompositionandanalysis(1)ThecompositionoftheconversiondataofthetradingplatformInasinglepromotion,itreferstotherevenueofeachuserorder,i.e.,totalrevenue/numberoforders.Throughouttheproductlifecycle,wefocusmoreonwhatuserspayperuser,i.e.,totalrevenue/numberofusersARPUcanbefurthersubdivided,whentheproportionofordinaryusersistoolarge,theaveragerevenueperpayinguserisoftenused:ARPPU=TotalRevenue÷NumberofChargedUsersAswithnewsubscribers,thecostofacquiringanewpayingsubscriberisalreadyhigherthanthecostofmaintainingaregularcustomer.Therepurchaserateismoreusedintheoverallnumberofrepeatpurchases:thenumberofuserswhoconsumemorethantwiceperunitoftimeaccountsforthetotalnumberofuserswhopurchase.转化数据构成与分析（二）社区型平台转化指标社区型平台的首要目的是将用户聚集在一起，并且引入用户生成内容，核心关键在于培养一个能够生成内容的活跃用户社区。内容生成者参与内容生存的用户总量，其中以内容生产比例（内容生产者/用户总量）来衡量一个社区的内容生成健康度。如小红书社区的核心竞争力就在于具有内容生产者数量的优势。内容互动者指平台中有生成内容、阅读内容、评论内容、分享内容、收藏内容、投票（任一）行为的用户总量。其中以内容互动比例（内容互动者/总用户量）来衡量整个产品的用户参与度。内容生成者指通过内容产生的实际收入，具体包含广告收入、分成收入、订阅付费等。Conversiondatacompositionandanalysis(2)Indicatorsforthetransformationofcommunity-basedplatformsTheprimarypurposeofacommunity-basedplatformistobringuserstogetherandintroduceuser-generatedcontent,andthecorekeyistocultivateanactivecommunityofuserswhocangeneratecontent.ContentGeneratorsThetotalnumberofusersparticipatinginthecontentsurvival,wherethecontentproductionratio(totalcontentproducers/users)isusedtomeasurethecontentgenerationhealthofacommunity.Forexample,thecorecompetitivenessoftheXiaohongshucommunityliesintheadvantageofthenumberofcontentproducers.ContentGeneratorsReferstotheactualrevenuegeneratedthroughcontent,includingadvertisingrevenue,sharingrevenue,subscriptionpayment,etc.ContentEngagersItreferstothetotalnumberofusersontheplatformwhohavegeneratedcontent,readcontent,commentedcontent,sharedcontent,collectedcontent,andvoted(anyone).Thecontentengagementratio(contentinteractors/totalusers)isusedtomeasureuserengagementacrosstheproduct.转化数据构成与分析（三）企业型平台转化指标体验用户量申请体验企业服务的用户总量，运营需要以体验率（体验用户量/用户量）来衡量产品的包装吸引力。付费用户量对于企业服务类产品来说，最需要非常关注的是有多少免费用户最终成为了付费客户，运营需要以转化率（付费用户总量/体验用户总量）来衡量产品的市场匹配度。追加销售量有多少人升级到了更贵的服务级别，运营需要以追加销售比例（总加销售用户/总付费用户）来衡量产品的售后服务能力。平均客户营收单位时间里平均每位客户带来的营收（销售额/付费用户），相应的还有客户终身价值，它指的是客户使用产品过程中的付费总额。Conversiondatacompositionandanalysis(3)Indicatorsforthetransformationofenterprise-basedplatformsExperiencethenumberofusersThetotalnumberofuserswhoapplytoexperienceenterpriseservicesneedstobemeasuredbytheexperiencerate(numberofexperienceusers/numberofusers)tomeasurethepackagingattractivenessoftheproduct.UpsellvolumeHowmanypeoplehaveupgradedtoamoreexpensiveservicelevel,operationsneedtomeasuretheproduct'safter-salesservicecapabilityintermsofupsellratio(totalplussalesusers/totalpayingusers).ThenumberofpayingusersForenterpriseserviceproducts,themostimportantthingtopayattentiontoishowmanyfreeusersendupaspayingcustomers,andtheoperationneedstomeasurethemarketfitoftheproductbytheconversionrate(totalnumberofpayingusers/totalnumberofexperienceusers).AveragecustomerrevenueTheaveragerevenuepercustomerperunitoftime(sales/payingusers)andthecorrespondingcustomerlifetimevalue,whichreferstothetotalamountpaidbycustomersoverthecourseofusingtheproduct.04典型平台数据分析PARTFOURTypicalplatformdataanalysis典型平台数据分析（一）微信公众号数据分析微信公众号是在微信公众平台上申请的应用账号，发布形式包括文字、语音、图片、视频，是商家通过微信进行互动营销及自媒体人与用户群体进行互动的平台。微信公众号有数据后台，在首页中单击“统计内容”即可查看。微信公众号后台Typicalplatformdataanalysis(1)DataanalysisofWeChatpublicaccountsWeChatofficialaccountisanapplicationaccountappliedforontheWeChatpublicplatform,andthereleaseformincludestext,voice,pictures,andvideos,whichisaplatformformerchantstoconductinteractivemarketingthroughWeChatandforself-mediapeopletointeractwithusergroups.TheWeChatofficialaccounthasadatabackground,whichcanbeviewedbyclicking"statisticalcontent"onthehomepage.WeChatpublicaccountbackground典型平台数据分析1.公众号数据分析概述0201用户分析内容分析用户分析模块包括用户增长数据和用户属性数据，分别对应微信公众号的涨粉量和用户画像。内容分析模块包括群发数据和视频数据，除了可以看到总体数据以外，还可以看到单篇群发数据和单个视频数据。对于主要发布形式为图文的公众号运营人员来说，经常分析的模块是单篇群发数据，即针对每次推送的图文进行数据分析，分析主要围绕图文阅读量展开。Typicalplatformdataanalysis1.OverviewofdataanalysisofOfficialAccountsTheuseranalysismoduleincludesusergrowthdataanduserattributedata,whichcorrespondtothenumberoffollowersanduserportraitsoftheWeChatofficialaccount,respectively.UseranalyticsContentanalysisThecontentanalysismoduleincludesmassdataandvideodata,andinadditiontotheoveralldata,youcanalsoseeindividualmassdataandindividualvideodata.Fortheoperatorsoftheofficialaccountwhosemainpublishingformisgraphicsandtexts,themodulethatisoftenanalyzedisasinglemassdata,thatis,dataanalysisiscarriedoutforeachpushofgraphicsandtexts,andtheanalysismainlyfocusesonthereadingvolumeofgraphicsandtexts.典型平台数据分析1.公众号数据分析概述03菜单分析微信公众号可以在消息界面底部设置自定义菜单，作为用户互动的入口。用户可以通过点击菜单选项，收到设定的响应，如收取消息、跳转链接。通过菜单数据分析，我们可以看出公众号用户对菜单功能的满意程度和活跃程度，点击次数越多，说明服务的覆盖人群越多；人均点击次数越大，说明用户越活跃。Typicalplatformdataanalysis1.OverviewofdataanalysisofOfficialAccountsMenuanalysisWeChatofficialaccountscansetacustommenuatthebottomofthemessageinterfaceasanentranceforuserinteraction.Userscanclickonthemenuoptionstoreceiveasetresponse,suchasreceivingamessageoraredirectlink.Throughmenudataanalysis,wecanseethesatisfactionandactivityofOfficialAccountuserswiththemenufunction,andthemoreclicks,themorepeoplecoveredbytheservice;Thehigherthenumberofclickspercapita,themoreactivetheuseris.典型平台数据分析1.公众号数据分析概述04消息分析消息分析模块包括消息分析和消息关键词，其中消息是指公众号的关注用户主动发送的消息。消息关键词是指公众号运营人员设置好一定关键词规则后，用户在公众号消息页面发送该关键词，公众号会自动回复设置好的内容。回复内容的形式包括图文消息、纯文字、图片等。在公众号后台，我们可以看到不同关键词的出现次数，并可以据此了解用户喜好。Typicalplatformdataanalysis1.OverviewofdataanalysisofOfficialAccountsThemessageanalysismoduleincludesmessageanalysisandmessagekeywords,wheremessagesrefertomessagessentbyuserswhofollowtheOfficialAccount.AmessagekeywordmeansthataftertheOfficialAccountoperatorhassetcertainkeywordrules,theusersendsthekeyw