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Digit-Tech Analytics Workshop

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Datalicious

The document discusses how to generate insights from data and take action. It provides examples of data sources and platforms for capturing data. Generating insights requires defining appropriate metrics frameworks and combining different data sets. Various tools and techniques for attribution modeling are presented to help determine how to optimize marketing channels and increase ROI. The key is to use data and insights to inform targeted testing and drive media, targeting, and testing strategies.

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[Digital Measurement ]Analytics workshop on how to turn data into actionable insights
[ Company history ]Datalicious was founded in 2007Strong Omniture web analytics historyOne-stop data agency with specialist teamCombination of analysts and developersMaking data accessible and actionableDriving industry best practiceEvangelizing use of dataJune 2010© Datalicious Pty Ltd2
[ Challenging clients ]June 2010© Datalicious Pty Ltd3
[ Data driven marketing ]June 2010© Datalicious Pty Ltd4InsightsReportingData mining and modellingCustomised dashboardsMedia attribution modelsMarket and competitor trendsSocial media monitoringOnline surveys and pollsCustomer profilingActionApplicationsData usage and applicationMarketing automationAprimo, Traction, Inxmail, etcTargeting and merchandisingInternal search optimisationCRM strategy and executionTesting programsDataPlatformsData collection and processingWeb analytics solutionsOmniture, Google Analytics, etcTagless online data captureEnd-to-end data platformsIVR and call center reportingSingle customer view
[ Today ]Capturing dataOptions, limitations, innovationsGenerating insightsProcess, metrics, examplesTaking actionMedia, targeting, testingJune 2010© Datalicious Pty Ltd5
[ Capturing data ]101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010June 2010© Datalicious Pty Ltd6
[ Digital data is cheap ]June 2010© Datalicious Pty Ltd7Source: Omniture Summit, Matt Belkin, 2007
[ Digital data options ]June 2010© Datalicious Pty Ltd8+SocialSource: Accuracy Whitepaper for web analytics, Brian Clifton, 2008
[ On-site analytics tools ]June 2010© Datalicious Pty Ltd9Google: ”forrester wave web analytics pdf” or http://bit.ly/aTLAKTSource: Forrester Wave Web Analytics, 2009
[ What platform to use ]June 2010© Datalicious Pty Ltd10Stage 1: DataStage 2: InsightsStage 3: ActionData is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!SophisticationData is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?Third parties control most data, ad hoc reporting only, i.e. what happened?Time, Control
[ Governance and data integrity ]June 2010© Datalicious Pty Ltd11Source: Omniture Summit, Matt Belkin, 2007
© Datalicious Pty Ltd[ Free off-site analytics tools ]http://www.google.com/trends http://www.google.com/sktoolhttp://www.google.com/insights/searchhttp://www.google.com/webmastershttp://www.google.com/adplannerhttp://www.google.com/videotargetinghttp://www.keywordspy.comhttp://www.compete.comhttp://www.alexa.comhttp://wiki.kenburbary.comJune 201012
[ Search at all stages ]June 2010© Datalicious Pty Ltd13In Australia Google has a market share of almost 90% of all searches, making it a very large and reliable data sampleSource: Inside the Mind of the Searcher, Enquiro 2004
[ Search call to action for offline ]June 2010© Datalicious Pty Ltd14
[ Client side tracking process ]June 2010© Datalicious Pty Ltd15What if: Someone deletes their cookies? Or uses a device that does not support JavaScript? Or uses two computers (work vs. home)? Or two people use the same computer?Source: Google Analytics, Justin Cutroni, 2007
[ Tag-less data capture ]June 2010© Datalicious Pty Ltd16Google: “atomic labs” www.atomiclabs.com
The study examined data from two of the UK’s busiest ecommerce websites, ASDAand William Hill. Given that more than half of all page impressions on these sites are from logged-in users, they provided a robust sample to compare IP-based and cookie-based analysis against.The results were staggering, for example an IP-based approach overestimated visitors by up to 7.6 times whilst a cookie-based approach overestimated visitors by up to 2.3 times.Google: ”red eye cookie report pdf” or http://bit.ly/cszp2o[ Overestimation of unique visitors ]June 2010© Datalicious Pty Ltd17Source: White Paper, RedEye, 2007
[ Maximise identification points ]June 2010© Datalicious Pty Ltd18Campaign responseEmail subscriptionOnline purchaseRepeat purchaseConfirmation emailEmail newsletterWebsite loginOnline bill payment
June 2010© Datalicious Pty Ltd19DataliciousSuperCookiePersistent Flash cookie that cannot be deleted
[ Mobile page headers ]June 2010© Datalicious Pty Ltd20MSISDN = Mobile NumberSource: Mobile Tracking, Omniture, 2008
[ Single-sign on ]June 2010© Datalicious Pty Ltd21Facebook Connect gives your company the following data and more with just one click!ID, first name, last name, middle name, picture, affiliations, last profile update, time zone, religion, political interests, interests, sex, birthday, attracted to which sex, why they want to meet someone, home town, relationship status, current location, activities, music interests, tv show interests, education history, work history, family and email Need anything else?
[ Research online, shop offline ]June 2010© Datalicious Pty Ltd22Google: ”digital future report 2009 pdf” or http://bit.ly/ZkLvrSource: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
[ Offline sales driven by online ]June 2010© Datalicious Pty Ltd23Tying offline conversions back to online campaign and research behavior using standard cookie technology by triggering virtual online order confirmation pages for offline sales using email receipts.Credit Check FulfilmentPhone OrdersWebsite.com ResearchVirtual OrderConfirmation@Retail OrdersCredit Check FulfilmentWebsite.com ResearchVirtual OrderConfirmation@Advertising CampaignWebsite.com ResearchOnline OrdersCredit Check FulfilmentOnline Order ConfirmationVirtual OrderConfirmation@CookieCookieCookie
[ Summary: Capturing data ]Plenty of data sources and platformsEspecially search is great free data sourceMaintaining data integrity takes effortCookie technology has its limitationsNew tag-less technologies emergingMaximise identification pointsOffline can be tied to onlineJune 2010© Datalicious Pty Ltd24
[ Generating insights ]101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010June 2010© Datalicious Pty Ltd25
[ Corporate data journey ]June 2010© Datalicious Pty Ltd26Stage 1DataStage 2InsightsStage 3ActionData is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!SophisticationData is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?Third parties control most data, ad hoc reporting only, i.e. what happened?Time, Control
[ The ideal analyst ]Business mindedSetting realistic improvement goalsTechnically savvyBridging gap between business and ITStrong sales skillsRaising awareness for the value of dataSeniority and experienceNeeds to be taken serious across organisationPosition within hierarchyAble to analyse without loyalty conflict June 2010© Datalicious Pty Ltd27
[ Process is key to success ]June 2010© Datalicious Pty Ltd28Source: Omniture Summit, Matt Belkin, 2007
Website, call center and retail dataQuantitative and qualitative research data[ Defining metrics frameworks ]June 2010© Datalicious Pty Ltd29Media and search dataSocial media dataSocial media
[ Key metrics by website type ]June 2010© Datalicious Pty Ltd30Source: Omniture Summit, Matt Belkin, 2007
[ Conversion funnel 1.0 ]June 2010Campaign responsesConversion funnelProduct page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping information, order confirmation, etcConversion event© Datalicious Pty Ltd31
[ Conversion funnel 2.0 ]June 2010Campaign responses (inbound spokes)Offline campaigns, banner ads, email marketing, referrals, organic search, paid search, internal promotions, etcLanding page (hub)Success events (outbound spokes)Bounce rate, add to cart, cart checkout, confirmed order, call back request, registration, product comparison, product review, forward to friend, etc© Datalicious Pty Ltd32
[ Additional success metrics ]June 2010© Datalicious Pty Ltd33Click Through$Click ThroughAdd To Cart$Cart Checkout?Click ThroughBounce Rate$Pages Per VisitVideo ViewsClick ThroughCall back requestsStore Searches?$
June 2010© Datalicious Pty LtdExercise: Metrics framework34
[ Exercise: Metrics framework ]June 2010© Datalicious Pty Ltd35
[ Exercise: Metrics framework ]June 2010© Datalicious Pty Ltd36
Customer data[ Combining data sets ]June 2010© Datalicious Pty Ltd37Web analytics data+The whole is greater than the sum of its parts3rd party data
[ Behaviours vs. transactions ]June 2010© Datalicious Pty Ltd38CRM ProfileSite Behaviourone-off collection of demographical data age, gender, address, etccustomer lifecycle metrics and key datesprofitability, expiration, etcpredictive models based on data miningpropensity to buy, churn, etchistorical data from previous transactionsaverage order value, points, etctracking of purchase funnel stagebrowsing, checkout, etctracking of content preferencesproducts, brands, features, etctracking of external campaign responsessearch terms, referrers, etctracking of internal promotion responsesemails, internal search, etc+Updated OCCASIONALLYUpdated continuously
[ Store searches vs. actual locations ]June 2010© Datalicious Pty Ltd39
[ Enriching customer profiles ]June 2010© Datalicious Pty Ltd40All you need is an addressSource: Hitwise, 2006
[Hitwise Mosaic segment swing ]australia.com vs. newzealand.comaustralia.com vs. bulafiji.com June 2010© Datalicious Pty Ltd41Source: Hitwise, 2006
[Hitwise Mosaic segment swing ]australia.com vs. newzealand.comaustralia.com vs. newzealand.comJune 2010© Datalicious Pty Ltd42Source: Hitwise, 2006
[ Single source of truth ]June 2010© Datalicious Pty Ltd43InsightsReporting
[ De-duplication across channels ]June 2010© Datalicious Pty Ltd44Paid Search$Bid MgmtCentral AnalyticsPlatformBanner AdsAd Server$$Email BlastEmail Platform$$Organic SearchGoogle Analytics$$
June 2010© Datalicious Pty LtdThinking outside the box45
[ Search and brand strength ]June 2010© Datalicious Pty Ltd46
[ Search and the product lifecycle ]June 2010© Datalicious Pty Ltd47Nokia N-Serieswww.google.com/trendsApple iPhone
[ Search and media planning ]June 2010© Datalicious Pty Ltd48www.google.com/adplanner
June 2010© Datalicious Pty Ltd49
June 2010© Datalicious Pty Ltd50
June 2010© Datalicious Pty Ltd51Fiat 500: Online influencing offlineGoogle: “slideshare fiat 500 case study” or http://bit.ly/lh7bx
[ Search driving offline creative ]June 2010© Datalicious Pty Ltd52
June 2010© Datalicious Pty Ltd53
June 2010© Datalicious Pty Ltd54Sentiment analysis: People vs. machineGoogle: “people vs machines debate” or http://bit.ly/8VbtB
[ Social metrics and tools ]June 2010© Datalicious Pty Ltd55Google: ”slidesharealtimeter report” or http://bit.ly/c8uYXTSource: Social Marketing Analytics, Altimeter, 2010
June 2010© Datalicious Pty LtdExercise: Statistical significance56
June 2010© Datalicious Pty Ltd57How many survey responses do you need if you have 10,000 customers?How many email opens do you need to test 2 subject linesif your subscriber base is 50,000?How many orders do you need to test 6 banner executions if you serve 1,000,000 banners
June 2010© Datalicious Pty Ltd58How many survey responses do you need if you have 10,000 customers?369 for each question or 369 complete responsesHow many email opens do you need to test 2 subject linesif your subscriber base is 50,000?381 per subject line or 381 x 2 = 762 email opensHow many orders do you need to test 6 banner executions if you serve 1,000,000 banners?383 sales per banner execution or 383 x 6 = 2,298 sales
[ Summary: Generating insights ]Right resources and processes are keyDefine a flexible metrics frameworkMaintain framework to enable comparisonCombine data sets for hidden insights Establish a single (data) source of truthThink outside the box and across channelsData does not equal significanceJune 2010© Datalicious Pty Ltd59
[ Taking action ]101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010June 2010© Datalicious Pty Ltd60
[ How to drive ROI ]Increasing revenueIncreasing overall amount of sales Increasing the average revenue per saleReducing costsIncreasing media effectivenessIncreasing website conversion ratesIncreasing online self-service usageImproving customer experienceReducing steps necessary to complete a taskPerceived value or quality of the final solutionJune 2010© Datalicious Pty Ltd61
[ How to drive ROI ]June 2010© Datalicious Pty Ltd62Media or how to optimise the channel mixTargeting or how to increasing relevanceTesting or how to maximise conversion
[ Success attribution models ]Banner AdPaid SearchOrganicSearch$100Success$100Last channel gets all creditBanner Ad$100Email BlastSuccess$100First channel gets all creditPaid SearchPaid Search$100Banner Ad$100Affiliate Referral$100Success$100All channels get equal creditPrint Ad$33Social Media$33Paid Search$33Success$100All channels get partial creditJune 201063© Datalicious Pty Ltd
[ First vs. last click attribution ]June 2010© Datalicious Pty Ltd64Chart shows percentage of channel touch points that lead to a conversion.Paid/Organic SearchNeither first nor last-click measurementwould provide true picture Emails/Shopping Engines
[ Path to purchase ]Banner ClickSEM GenericPartnerSiteDirect Visit$Banner ViewJune 201065© Datalicious Pty LtdSEO Generic$TVAdSEOBrandedBanner Click$Print AdSocial MediaEmail UpdateDirect Visit$
[ Forrester media attribution ]June 2010© Datalicious Pty Ltd66Google: ”forrester attribution framework pdf” or http://bit.ly/dnbnzYSource: Forrester, 2009
[ Customer data journey ]June 2010© Datalicious Pty Ltd67To retention messagesTo transactional dataFrom suspect toTo customerprospectTimeTimeFrom behavioural dataFrom awareness messages
June 2010© Datalicious Pty Ltd68
June 2010© Datalicious Pty Ltd69
[ Matching segments are key ]June 2010© Datalicious Pty Ltd70On and off-site targeting platforms should use identical triggers to sort visitors into segments
[ Off-site targeting platforms ]Ad serversGoogle/DoubleClickEyeblasterFaciliateAtlasEtcAd NetworksGoogleYahooValueClickAdconianEtcJune 2010© Datalicious Pty Ltd71http://en.wikipedia.org/wiki/Contextual_advertising, http://hubpages.com/hub/101-Google-Adsense-Alternatives, http://en.wikipedia.org/wiki/Central_ad_server, http://www.adoperationsonline.com/2008/05/23/list-of-ad-servers/, http://lists.econsultant.com/top-10-advertising-networks.html, http://www.clickz.com/3633599, http://en.wikipedia.org/wiki/behavioural_targeting
[ On-site targeting platforms ]Test&Target (Omniture, Offermatica, TouchClarity)Memetrics (Accenture)Optimost (Autonomy)Kefta (Acxiom)AudienceScienceMaxymiserAmadesaCertonaSiteSpectBTBuckets (free)Google/DoubleClick Ad Server (free)June 2010© Datalicious Pty Ltd72
[ Prospect targeting parameters ]June 2010© Datalicious Pty Ltd73
[ Vodafone affinity targeting ]June 2010© Datalicious Pty Ltd74Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.
[ Affinity targeting ]Function of behavioural targetingGrouping of visitors into major segmentsBased on content and conversion behaviourEase of use vs. reduced targeting abilityMost common affinities usedBrand affinityImage preferencePrice sensitivityProduct affinityContent affinityJune 2010© Datalicious Pty Ltd75
[ Coordinate the experience ]June 2010© Datalicious Pty Ltd76By coordinating the consumer’s end-to-end experience, companies could enjoy revenue increases of 10-20%.Google: “get more value from digital marketing” or http://bit.ly/cAtSUNSource: McKinsey Quarterly, 2010
AvinashKaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour targeting platform tick, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […]. You feed your BT system crap and it will quickly and efficiently target crap to your customers. Faster then you could ever have yourself.”[ Quality content is key ]June 2010© Datalicious Pty Ltd77
June 2010© Datalicious Pty LtdExercise: Targeting matrix78
[ Exercise: Targeting matrix ]June 2010© Datalicious Pty Ltd79
[ Exercise: Targeting matrix ]June 2010© Datalicious Pty Ltd80
Google: “change one word double conversion” or http://bit.ly/bpyqFp[ClickTale testing case study ]June 2010© Datalicious Pty Ltd81
[ Testing platforms ]Test&Target (Omniture, Offermatica, TouchClarity)Memetrics (Accenture)Optimost (Autonomy)Kefta (Acxiom)MaxymiserAmadesaSiteSpectClickTale (cheap)Unbounce (cheap)Google Website Optimiser (free)June 2010© Datalicious Pty Ltd82
[ Summary ]There is no magic formula for ROIFocus on the entire conversion funnelMedia attribution is hard but necessaryNeither first nor last click method worksCreate a coordinated targeted experienceContent is always king no matter whatTest, learn and refine continuouslyJune 2010© Datalicious Pty Ltd83
June 2010© Datalicious Pty Ltd84Contact mecbartens@datalicious.comLearn moreblog.datalicious.comFollow ustwitter.com/datalicious

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Digit-Tech Analytics Workshop

  • 1. [Digital Measurement ]Analytics workshop on how to turn data into actionable insights
  • 2. [ Company history ]Datalicious was founded in 2007Strong Omniture web analytics historyOne-stop data agency with specialist teamCombination of analysts and developersMaking data accessible and actionableDriving industry best practiceEvangelizing use of dataJune 2010© Datalicious Pty Ltd2
  • 3. [ Challenging clients ]June 2010© Datalicious Pty Ltd3
  • 4. [ Data driven marketing ]June 2010© Datalicious Pty Ltd4InsightsReportingData mining and modellingCustomised dashboardsMedia attribution modelsMarket and competitor trendsSocial media monitoringOnline surveys and pollsCustomer profilingActionApplicationsData usage and applicationMarketing automationAprimo, Traction, Inxmail, etcTargeting and merchandisingInternal search optimisationCRM strategy and executionTesting programsDataPlatformsData collection and processingWeb analytics solutionsOmniture, Google Analytics, etcTagless online data captureEnd-to-end data platformsIVR and call center reportingSingle customer view
  • 5. [ Today ]Capturing dataOptions, limitations, innovationsGenerating insightsProcess, metrics, examplesTaking actionMedia, targeting, testingJune 2010© Datalicious Pty Ltd5
  • 6. [ Capturing data ]101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010June 2010© Datalicious Pty Ltd6
  • 7. [ Digital data is cheap ]June 2010© Datalicious Pty Ltd7Source: Omniture Summit, Matt Belkin, 2007
  • 8. [ Digital data options ]June 2010© Datalicious Pty Ltd8+SocialSource: Accuracy Whitepaper for web analytics, Brian Clifton, 2008
  • 9. [ On-site analytics tools ]June 2010© Datalicious Pty Ltd9Google: ”forrester wave web analytics pdf” or http://bit.ly/aTLAKTSource: Forrester Wave Web Analytics, 2009
  • 10. [ What platform to use ]June 2010© Datalicious Pty Ltd10Stage 1: DataStage 2: InsightsStage 3: ActionData is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!SophisticationData is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?Third parties control most data, ad hoc reporting only, i.e. what happened?Time, Control
  • 11. [ Governance and data integrity ]June 2010© Datalicious Pty Ltd11Source: Omniture Summit, Matt Belkin, 2007
  • 12. © Datalicious Pty Ltd[ Free off-site analytics tools ]http://www.google.com/trends http://www.google.com/sktoolhttp://www.google.com/insights/searchhttp://www.google.com/webmastershttp://www.google.com/adplannerhttp://www.google.com/videotargetinghttp://www.keywordspy.comhttp://www.compete.comhttp://www.alexa.comhttp://wiki.kenburbary.comJune 201012
  • 13. [ Search at all stages ]June 2010© Datalicious Pty Ltd13In Australia Google has a market share of almost 90% of all searches, making it a very large and reliable data sampleSource: Inside the Mind of the Searcher, Enquiro 2004
  • 14. [ Search call to action for offline ]June 2010© Datalicious Pty Ltd14
  • 15. [ Client side tracking process ]June 2010© Datalicious Pty Ltd15What if: Someone deletes their cookies? Or uses a device that does not support JavaScript? Or uses two computers (work vs. home)? Or two people use the same computer?Source: Google Analytics, Justin Cutroni, 2007
  • 16. [ Tag-less data capture ]June 2010© Datalicious Pty Ltd16Google: “atomic labs” www.atomiclabs.com
  • 17. The study examined data from two of the UK’s busiest ecommerce websites, ASDAand William Hill. Given that more than half of all page impressions on these sites are from logged-in users, they provided a robust sample to compare IP-based and cookie-based analysis against.The results were staggering, for example an IP-based approach overestimated visitors by up to 7.6 times whilst a cookie-based approach overestimated visitors by up to 2.3 times.Google: ”red eye cookie report pdf” or http://bit.ly/cszp2o[ Overestimation of unique visitors ]June 2010© Datalicious Pty Ltd17Source: White Paper, RedEye, 2007
  • 18. [ Maximise identification points ]June 2010© Datalicious Pty Ltd18Campaign responseEmail subscriptionOnline purchaseRepeat purchaseConfirmation emailEmail newsletterWebsite loginOnline bill payment
  • 19. June 2010© Datalicious Pty Ltd19DataliciousSuperCookiePersistent Flash cookie that cannot be deleted
  • 20. [ Mobile page headers ]June 2010© Datalicious Pty Ltd20MSISDN = Mobile NumberSource: Mobile Tracking, Omniture, 2008
  • 21. [ Single-sign on ]June 2010© Datalicious Pty Ltd21Facebook Connect gives your company the following data and more with just one click!ID, first name, last name, middle name, picture, affiliations, last profile update, time zone, religion, political interests, interests, sex, birthday, attracted to which sex, why they want to meet someone, home town, relationship status, current location, activities, music interests, tv show interests, education history, work history, family and email Need anything else?
  • 22. [ Research online, shop offline ]June 2010© Datalicious Pty Ltd22Google: ”digital future report 2009 pdf” or http://bit.ly/ZkLvrSource: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
  • 23. [ Offline sales driven by online ]June 2010© Datalicious Pty Ltd23Tying offline conversions back to online campaign and research behavior using standard cookie technology by triggering virtual online order confirmation pages for offline sales using email receipts.Credit Check FulfilmentPhone OrdersWebsite.com ResearchVirtual OrderConfirmation@Retail OrdersCredit Check FulfilmentWebsite.com ResearchVirtual OrderConfirmation@Advertising CampaignWebsite.com ResearchOnline OrdersCredit Check FulfilmentOnline Order ConfirmationVirtual OrderConfirmation@CookieCookieCookie
  • 24. [ Summary: Capturing data ]Plenty of data sources and platformsEspecially search is great free data sourceMaintaining data integrity takes effortCookie technology has its limitationsNew tag-less technologies emergingMaximise identification pointsOffline can be tied to onlineJune 2010© Datalicious Pty Ltd24
  • 25. [ Generating insights ]101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010June 2010© Datalicious Pty Ltd25
  • 26. [ Corporate data journey ]June 2010© Datalicious Pty Ltd26Stage 1DataStage 2InsightsStage 3ActionData is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!SophisticationData is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?Third parties control most data, ad hoc reporting only, i.e. what happened?Time, Control
  • 27. [ The ideal analyst ]Business mindedSetting realistic improvement goalsTechnically savvyBridging gap between business and ITStrong sales skillsRaising awareness for the value of dataSeniority and experienceNeeds to be taken serious across organisationPosition within hierarchyAble to analyse without loyalty conflict June 2010© Datalicious Pty Ltd27
  • 28. [ Process is key to success ]June 2010© Datalicious Pty Ltd28Source: Omniture Summit, Matt Belkin, 2007
  • 29. Website, call center and retail dataQuantitative and qualitative research data[ Defining metrics frameworks ]June 2010© Datalicious Pty Ltd29Media and search dataSocial media dataSocial media
  • 30. [ Key metrics by website type ]June 2010© Datalicious Pty Ltd30Source: Omniture Summit, Matt Belkin, 2007
  • 31. [ Conversion funnel 1.0 ]June 2010Campaign responsesConversion funnelProduct page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping information, order confirmation, etcConversion event© Datalicious Pty Ltd31
  • 32. [ Conversion funnel 2.0 ]June 2010Campaign responses (inbound spokes)Offline campaigns, banner ads, email marketing, referrals, organic search, paid search, internal promotions, etcLanding page (hub)Success events (outbound spokes)Bounce rate, add to cart, cart checkout, confirmed order, call back request, registration, product comparison, product review, forward to friend, etc© Datalicious Pty Ltd32
  • 33. [ Additional success metrics ]June 2010© Datalicious Pty Ltd33Click Through$Click ThroughAdd To Cart$Cart Checkout?Click ThroughBounce Rate$Pages Per VisitVideo ViewsClick ThroughCall back requestsStore Searches?$
  • 34. June 2010© Datalicious Pty LtdExercise: Metrics framework34
  • 35. [ Exercise: Metrics framework ]June 2010© Datalicious Pty Ltd35
  • 36. [ Exercise: Metrics framework ]June 2010© Datalicious Pty Ltd36
  • 37. Customer data[ Combining data sets ]June 2010© Datalicious Pty Ltd37Web analytics data+The whole is greater than the sum of its parts3rd party data
  • 38. [ Behaviours vs. transactions ]June 2010© Datalicious Pty Ltd38CRM ProfileSite Behaviourone-off collection of demographical data age, gender, address, etccustomer lifecycle metrics and key datesprofitability, expiration, etcpredictive models based on data miningpropensity to buy, churn, etchistorical data from previous transactionsaverage order value, points, etctracking of purchase funnel stagebrowsing, checkout, etctracking of content preferencesproducts, brands, features, etctracking of external campaign responsessearch terms, referrers, etctracking of internal promotion responsesemails, internal search, etc+Updated OCCASIONALLYUpdated continuously
  • 39. [ Store searches vs. actual locations ]June 2010© Datalicious Pty Ltd39
  • 40. [ Enriching customer profiles ]June 2010© Datalicious Pty Ltd40All you need is an addressSource: Hitwise, 2006
  • 41. [Hitwise Mosaic segment swing ]australia.com vs. newzealand.comaustralia.com vs. bulafiji.com June 2010© Datalicious Pty Ltd41Source: Hitwise, 2006
  • 42. [Hitwise Mosaic segment swing ]australia.com vs. newzealand.comaustralia.com vs. newzealand.comJune 2010© Datalicious Pty Ltd42Source: Hitwise, 2006
  • 43. [ Single source of truth ]June 2010© Datalicious Pty Ltd43InsightsReporting
  • 44. [ De-duplication across channels ]June 2010© Datalicious Pty Ltd44Paid Search$Bid MgmtCentral AnalyticsPlatformBanner AdsAd Server$$Email BlastEmail Platform$$Organic SearchGoogle Analytics$$
  • 45. June 2010© Datalicious Pty LtdThinking outside the box45
  • 46. [ Search and brand strength ]June 2010© Datalicious Pty Ltd46
  • 47. [ Search and the product lifecycle ]June 2010© Datalicious Pty Ltd47Nokia N-Serieswww.google.com/trendsApple iPhone
  • 48. [ Search and media planning ]June 2010© Datalicious Pty Ltd48www.google.com/adplanner
  • 51. June 2010© Datalicious Pty Ltd51Fiat 500: Online influencing offlineGoogle: “slideshare fiat 500 case study” or http://bit.ly/lh7bx
  • 52. [ Search driving offline creative ]June 2010© Datalicious Pty Ltd52
  • 54. June 2010© Datalicious Pty Ltd54Sentiment analysis: People vs. machineGoogle: “people vs machines debate” or http://bit.ly/8VbtB
  • 55. [ Social metrics and tools ]June 2010© Datalicious Pty Ltd55Google: ”slidesharealtimeter report” or http://bit.ly/c8uYXTSource: Social Marketing Analytics, Altimeter, 2010
  • 56. June 2010© Datalicious Pty LtdExercise: Statistical significance56
  • 57. June 2010© Datalicious Pty Ltd57How many survey responses do you need if you have 10,000 customers?How many email opens do you need to test 2 subject linesif your subscriber base is 50,000?How many orders do you need to test 6 banner executions if you serve 1,000,000 banners
  • 58. June 2010© Datalicious Pty Ltd58How many survey responses do you need if you have 10,000 customers?369 for each question or 369 complete responsesHow many email opens do you need to test 2 subject linesif your subscriber base is 50,000?381 per subject line or 381 x 2 = 762 email opensHow many orders do you need to test 6 banner executions if you serve 1,000,000 banners?383 sales per banner execution or 383 x 6 = 2,298 sales
  • 59. [ Summary: Generating insights ]Right resources and processes are keyDefine a flexible metrics frameworkMaintain framework to enable comparisonCombine data sets for hidden insights Establish a single (data) source of truthThink outside the box and across channelsData does not equal significanceJune 2010© Datalicious Pty Ltd59
  • 60. [ Taking action ]101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010June 2010© Datalicious Pty Ltd60
  • 61. [ How to drive ROI ]Increasing revenueIncreasing overall amount of sales Increasing the average revenue per saleReducing costsIncreasing media effectivenessIncreasing website conversion ratesIncreasing online self-service usageImproving customer experienceReducing steps necessary to complete a taskPerceived value or quality of the final solutionJune 2010© Datalicious Pty Ltd61
  • 62. [ How to drive ROI ]June 2010© Datalicious Pty Ltd62Media or how to optimise the channel mixTargeting or how to increasing relevanceTesting or how to maximise conversion
  • 63. [ Success attribution models ]Banner AdPaid SearchOrganicSearch$100Success$100Last channel gets all creditBanner Ad$100Email BlastSuccess$100First channel gets all creditPaid SearchPaid Search$100Banner Ad$100Affiliate Referral$100Success$100All channels get equal creditPrint Ad$33Social Media$33Paid Search$33Success$100All channels get partial creditJune 201063© Datalicious Pty Ltd
  • 64. [ First vs. last click attribution ]June 2010© Datalicious Pty Ltd64Chart shows percentage of channel touch points that lead to a conversion.Paid/Organic SearchNeither first nor last-click measurementwould provide true picture Emails/Shopping Engines
  • 65. [ Path to purchase ]Banner ClickSEM GenericPartnerSiteDirect Visit$Banner ViewJune 201065© Datalicious Pty LtdSEO Generic$TVAdSEOBrandedBanner Click$Print AdSocial MediaEmail UpdateDirect Visit$
  • 66. [ Forrester media attribution ]June 2010© Datalicious Pty Ltd66Google: ”forrester attribution framework pdf” or http://bit.ly/dnbnzYSource: Forrester, 2009
  • 67. [ Customer data journey ]June 2010© Datalicious Pty Ltd67To retention messagesTo transactional dataFrom suspect toTo customerprospectTimeTimeFrom behavioural dataFrom awareness messages
  • 70. [ Matching segments are key ]June 2010© Datalicious Pty Ltd70On and off-site targeting platforms should use identical triggers to sort visitors into segments
  • 71. [ Off-site targeting platforms ]Ad serversGoogle/DoubleClickEyeblasterFaciliateAtlasEtcAd NetworksGoogleYahooValueClickAdconianEtcJune 2010© Datalicious Pty Ltd71http://en.wikipedia.org/wiki/Contextual_advertising, http://hubpages.com/hub/101-Google-Adsense-Alternatives, http://en.wikipedia.org/wiki/Central_ad_server, http://www.adoperationsonline.com/2008/05/23/list-of-ad-servers/, http://lists.econsultant.com/top-10-advertising-networks.html, http://www.clickz.com/3633599, http://en.wikipedia.org/wiki/behavioural_targeting
  • 72. [ On-site targeting platforms ]Test&Target (Omniture, Offermatica, TouchClarity)Memetrics (Accenture)Optimost (Autonomy)Kefta (Acxiom)AudienceScienceMaxymiserAmadesaCertonaSiteSpectBTBuckets (free)Google/DoubleClick Ad Server (free)June 2010© Datalicious Pty Ltd72
  • 73. [ Prospect targeting parameters ]June 2010© Datalicious Pty Ltd73
  • 74. [ Vodafone affinity targeting ]June 2010© Datalicious Pty Ltd74Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.
  • 75. [ Affinity targeting ]Function of behavioural targetingGrouping of visitors into major segmentsBased on content and conversion behaviourEase of use vs. reduced targeting abilityMost common affinities usedBrand affinityImage preferencePrice sensitivityProduct affinityContent affinityJune 2010© Datalicious Pty Ltd75
  • 76. [ Coordinate the experience ]June 2010© Datalicious Pty Ltd76By coordinating the consumer’s end-to-end experience, companies could enjoy revenue increases of 10-20%.Google: “get more value from digital marketing” or http://bit.ly/cAtSUNSource: McKinsey Quarterly, 2010
  • 77. AvinashKaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour targeting platform tick, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […]. You feed your BT system crap and it will quickly and efficiently target crap to your customers. Faster then you could ever have yourself.”[ Quality content is key ]June 2010© Datalicious Pty Ltd77
  • 78. June 2010© Datalicious Pty LtdExercise: Targeting matrix78
  • 79. [ Exercise: Targeting matrix ]June 2010© Datalicious Pty Ltd79
  • 80. [ Exercise: Targeting matrix ]June 2010© Datalicious Pty Ltd80
  • 81. Google: “change one word double conversion” or http://bit.ly/bpyqFp[ClickTale testing case study ]June 2010© Datalicious Pty Ltd81
  • 82. [ Testing platforms ]Test&Target (Omniture, Offermatica, TouchClarity)Memetrics (Accenture)Optimost (Autonomy)Kefta (Acxiom)MaxymiserAmadesaSiteSpectClickTale (cheap)Unbounce (cheap)Google Website Optimiser (free)June 2010© Datalicious Pty Ltd82
  • 83. [ Summary ]There is no magic formula for ROIFocus on the entire conversion funnelMedia attribution is hard but necessaryNeither first nor last click method worksCreate a coordinated targeted experienceContent is always king no matter whatTest, learn and refine continuouslyJune 2010© Datalicious Pty Ltd83
  • 84. June 2010© Datalicious Pty Ltd84Contact mecbartens@datalicious.comLearn moreblog.datalicious.comFollow ustwitter.com/datalicious

Editor's Notes

  • #33: Customer Behavior Isn't LinearIf analysis has taught us in the online marketing, where a 10 percent visit-to-purchase conversion rate is still considered extraordinary, it's that customers don't behave in a linear fashion. Customers' goals don't always align with our direct online revenue goals. Customers change their minds. They get distracted. They lose interest. They save carts, abandon carts, add items to carts, remove items from carts, and sometimes all the above -- and in no particular order. Sometimes they navigate for products, sometimes they search for products. Sometimes they do both in the same visit. So long as customers are people, customer behavior will be dynamic and at times irrational, random, and unexplainable.So why are we trying to fit the dynamic nature of online customer behavior into a linear model? I've heard this question discussed recently in online retailing circles. It will gain momentum as a better model for analyzing customer behavior for e-commerce organizations. http://www.clickz.com/showPage.html?page=3596566
  • #77: Please insert the actual statistics into the text below the graph and point out that this is based on McKinsey research and best practiceAdmit that NDS is not there to make money and there might not be any direct competitors but point out that the above applies for leads as well And although we might have a limited amount of direct competitors we’re competing for attention with other sectorsThe smoother the overall experience is from TV ad over website content to application process the better we can competeUse the actual care careers numbers to make the connection clear