大数据分析的速度

1、以手机网络数据获取匿名预付费用户群的基本信息及使用偏好以进行绅分营销大数据分析的速度2日程分析无处不在一切有他自己的时间分析速度的价值分析和灵活案例在中国市场热数据变现实践标注所有您的多渠道客户旅程，带有10条线路的条码, 并理解在哪和为什么您正在丢失销售一个单独的视觉化向您显示百万交易隐 藏的错误？在2天理解 10个影响您客户的事宜，他们对这些 感觉如何，您需要什么来解决他们？仅仅6周在您的分析数据投资上产生500% 回报?分析无处不在8典型案例 基于实时数据Cell0004Cell 0002Cell 0001Cell 0005Cell 0003Cell 0011Cell 0013Cell

2、0010Cell 0012Cell 0014Call into the Orange shop to see new phones and earn 10 points per 10Euro Top Up !Sorry for the problems tickets for you in the next hour !Has movie channel at homePassing movie theatreReal-time offerHigh Value CustomerJust Dropped a callHas used 90% of BundleOld model HandsetM

3、edium Value CustomerJust Dropped a callPassing Orange ShopWill Cage, TeradataLoses ValueGains ValueResolution and notification before customer knowsBusiness Event e.g., a Customer Service Incident Apology within 15 minutesApology within 3 hoursApology within 24 hoursTimeApology within 3 daysNo Apolo

4、gyPerception of Resolution by Customer典型的时间和价值:客户服务, 只是及时道歉Effectiveness of retention CampaignTime (hours)典型的时间和价值: 客户呼吁或检查竞争，然后开始制造过程3x retention and early winback campaign effectivenessAfterBefore623X24 hoursEARLY WINBACK of the customers who called the competition is only effective within 24 hour

5、s during which the call must be discovered and acted uponCustomer calls or checks competitor and churnsCustomer is given a special offer in the retention platform or called right after churnEffectiveness of retention CampaignTime (minutes)典型的时间和价值: 客户步入专项区域，此处有他感兴趣的零售提供RELEVANT LOCATION BASED OFFER

6、requires spee in reaction to predefined rules but also fast discovery of relevant eventsCustomer is checking info about the Spiderman PremierCustomer walks inside the ring fenceCustomer is within 20m of the cinemaT1T2T1: Time to discover the relevant eventT2: Time to relate it to location分析提升的类型Enha

7、nce existing classification or predictive power with new methods that require new speedFast discovery of a new piece of data that explains and allows identification of the cause ofa new problemFast trial of new data that enhances an existing predictive modelSpeed of analytic iterations makes easier

8、to find hidden patterns in data or rapidly changing patterns旅程分析: 制造& 渠道Based on the knowledge of the customer journey, we need to intervene here or we risk churnCEM attention requiredRESULT: Mofe complex problems required 4-6 weeks案例: 多重渠道行为分析The Multi Channel AnalysisProblem:Customers keep calling

9、 after they paid their billsSolution:Found wrong communication and changed itVerizon Case: http:/bcove.me/fu5d5vcb, or/customers/listening-to-100m-customers-with-unified-data-architecture/RESULT: Base Cause was found in 2 weeks and corrections saved thousands in call center路径分析 看看什么优于一个电话找到更好的销售机会RE

10、SULT: 3x Better Predictability, 3.8x Additional Leads with Desired Response Rate$8M of Incremental Profit from One CampaignEXISTING PREDICTIVE MODELNO. OF CHILDRENSALARYADDRESSDETAILS. . .NEW DIGITAL VARIABLESPAGEDURATIONNO. OFRECURRING SCORECLICKSVISITSCell #1111 RadiusXACTION:1. Dispatch Engineer

11、to this location to check for physical blockage, antennae tilt, hand- off, reducing research area from 67M m2 to 100m22. Determine if cell 1114 with 850mhz is better for this metro area3. Send Text Msg to High Value Subscriber apologizingNetwork OK during rush hour butHigh-value Subscriber has repea

12、ted dropsGPS data shows specific location of RIFCall used 3G 1900mhz networkAnalysis of all calls within 1000 radius show abnormally high RIF rate规划和优化网络工作RESULT: Symptoms can be found on a daily basisDegrees function allows analysis with very few lines of codeIMSIs of interest rapidly and easily id

13、entified from CDR dataSimbox欺骗什么的速度?Reaction SpeedSpeed to Learn那么我们需要什么来做这个？为什么？21分析和数据的价值分解$X$X$X$X$X$XRealization Of ROIWk 1Wk 2Wk 3Wk 4Wk 6Wk 5RoadmapAlignCreateEvaluateDeploy12345ExtensionAcquire DataInsight CreationPrep DataRecommendedDiscovery is a FAST process$Xm3 Releases Over 2 years$Xm$Xm

14、Release 1Release 2Release 340+ Projects over 2 yearsSuccessful ProjectFailed ProjectChange of Process only357911131517192123MARMAYJULSEPNOVJANMARMAYJULSEPNOV357911131517192123MARMAYJULSEPNOVJANMARMAYJULSEPNOV传统的分析模式vs. 大数据探索DiscoveryTraditional速度是一切: 赛跑快速的分析咨询参与程序EvaluateDeploy1RoadmapRACE2Align345E

15、xtensionAcquire DataInsight CreationCreatePrepDataRecommended1w4w1w利用数据再利用和培训实现赛跑 时间 改善AlignEvaluate234Acquire DataInsight CreationCreatePrepData1w4w1wAlignEval234CreateInsight Creation0.5w2w0.5wAlignEvaluate234CreateInsight CreationPrep1w3w1w6 weeks3 weeksHow to halve your RACE time25TheoryHypothes

16、isObservationConfirmationTheoryHypothesisPattern探索驱动方法需要灵活和速度Operationalized models lead to new observationsProvide hypothesis to validate and modelPROOF FOCUSEDObservationINNOVATION FOCUSEDDiscovery Driven analytics are a complimentary capability to Model Driven analytics Model-DrivenDiscovery-Driv

17、en有洞察力的分离Mixed skill sets combine for deep insightThink tank project space for interactionCommunal sharing and collaborationCulture of exploration and challengeRapid iteration with business sponsorsVirtual teams or “Pods”3 or 4 people for 6 weeksInsight PODsData Scientist (TD)Business Consultant (TD

18、)Subject Matter Expert (Client)Analyst (Client)Data PODData SME (Client)Source Expert (Client)Data Engineer/Developer (TD)DBA (TD/Client)在探索团队的有洞察力的分离Discovery TeamCRMPODFraud PODChurn PODClient PODDataPODexample only for illustration创造性的, 有洞察力的创新案例 (2-5 周)Intra day business workshops during the Ins

19、ight Creation phase12RoadmapCreateEvaluateDeploy1RACE2Align345ExtensionRecommended1 week4 weeks1 week29Eyeballs CountAnalytic insights improve “eyeballs” or people increaseOutside perspectives* can add intuitiveleaps in thinking速度的关键:一起工作(*) Could include using 2 hour virtual workshop to boost insig

20、hts using the TD Virtual Global Expert TeamRoadmapCreateEvaluateDeploy1RACE2Align345ExtensionRecommended1 week4 weeks1 week30为成功做准备LFEASIBILITYHLVALUEHIDENTIFY USE CASESPRIORITISATIONCRITICAL SUCCESS FACTORSWHO IS INVOLVED FROMTHE CUSTOMER?Analysts/Data ScientistsIT/ ArchitectureData OwnersBusiness

21、Subject Matter ExpertsUSE CASESDATA SOURCESData Source 1Data Source 2Use Case 1/Use Case 2DATA SCOPE & DEFINITION31对于 敏捷和成功的用能力者BUSINESS CHALLENGEALL TYPES OF DATAADVANCED ANALYTICS32使用案例:高级的分析价值框架 49 CASESMarketing & SalesConnections AnalyticsPricing OptimisationCustomer Satisfaction Through using

22、network & device data & all CRM dataB2B Corporate Offer ManagementCustomer Experience ManagementCustomer Behavior Analysis(voice, sms, data, mobility)Sentiment analysis text in social media, call center and CRM dataCEM IndexingService Efficiency operational failures impact satisfaction and ChurnEnha

23、nced Customer Profiling Enrichment using network, devices, social media analysisCustomer Journey leading to Churnchurn reason sequenceNext Best ActionCustomer behaviorCustomer Service EmpowermentBring the information to the front-lineNetwork IntelligenceCustomer Network Experience Quantify & underst

24、and the quality of customer experience when using the network & its servicesNetwork Management & PerformanceAnalysis of network & service utilisation, performance etcCustomer Services & Ops RT or NRT monitoring of customer experience for internal & external stakeholdersNetwork Ops & Maintenance Moni

25、toring & analysis of network availability for enhanced O&M capabilitiesDigital Telco & Data MonetizationLocation DataB2B Business where location based behavior/segmentation is soldMobilityTargeted advertising based upon subscriber mobility & behaviorsSegmentDirect data monetization based on segmenta

26、tionTarget Combining segmentation, location and behavior forbetter targeted actionsOperational Intelligence& Data Driven FinanceReal time Personalization Dynamic online targeting for recommendations & personalizationGolden Path Analysis Optimize web/self service portals & customer interactionsFraudU

27、sage, behavior and Customer data to detect internal/external Fraud and linkagesRisk ManagementIdentify customers at risk of payment defaultSingle view and integration of transactionsand eventsEasy inspection and analysis of new data sourcesSimple logic to tie precise transactional information of a c

28、ustomer with behavioural patternsHistory as needed to allow for a “Time Machine”用正确的工具和生态系统解除数据34AcquisitionAnalyticsAccessEMERGINGAPP FRAMEWORKData EnginesCONVENTIONALMULTI GENREDATAWAREHOUSEIN MEMORYDATA LAKENo SQLCOMPUTE CLUSTEROPERATIONA LQueryGridVIRTUAL QUERYUsersOperational SystemsCustomers P

29、artnersEngineersData ScientistsBusiness AnalystsKnowledge WorkersMarketing ExecutivesPlatform ServicesDEVELOPMENTDATAOPERATIONSPRIVATEHYBRIDCloud DeploymentPUBLICSourcesERPSCMCRMSensorsAudio and VideoMachine LogsTextWeb and SocialAppCenterREAL TIMEAster AnalyticsR, Spark,GiraphSAS, SPSS,KXENTeradata

30、 DatabaseHadoop Teradata DatabaseBusiness IntelligenceLanguagesIntegrated Development EnvironmentINGESTListener35分析以平衡分析生态系统 2016 Teradata UK&IIsolated datasets with simpler workload.RESULT: Allows a permanent rationalisation ofthe use of the analytical platformGraph analytics performed on database

31、queries results in an interactive map of the warehouse highlighting dataset usage.Datasets integral to theEnterprise Data Warehouse.New Analytics for IT36日程Analytics everywhereEverything has its own timeThe Value of the Speed ofAnalyticsAnalytics and AgilityExamplesHot Data Monetization Practice in the Chinese market37挑战：从电信1.0 to 电信2.0, CS