Analyze to Optimize (Part 2)
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The presentation discusses training on data, measurement and ROI.
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Analyze to Optimize (Part 2)
- 1. > Analyse to op-mise < ADMA short course on data, measurement and ROI
- 2. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Quick recap October 2010 © ADMA & Datalicious Pty Ltd 2
- 3. > Day 1: Basic Analy-cs § Defining a metrics framework – What to report on, when and why? – Matching strategic and tacHcal goals to metrics – Covering all major categories of business goals § Finding and developing the right data – Data sources across channels and goals – Meaningful trends vs. 100% accurate data – Human and technological limitaHons § Plus hands-‐on exercises October 2010 © ADMA & Datalicious Pty Ltd 3
- 4. > Day 1: Basic Analy-cs § Hands-‐on exercises and examples – Funnel breakdowns – Conversions metrics – Metrics framework – Search insights – DuplicaHon impact – StaHsHcal significance October 2010 © ADMA & Datalicious Pty Ltd 4
- 5. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Course overview October 2010 © ADMA & Datalicious Pty Ltd 5
- 6. > Day 2: Advanced Analy-cs § Campaign flow and media aWribuHon – Designing a campaign flow including metrics – Omniture vs. Google AnalyHcs capabiliHes § How to reduce media waste – TesHng and targeHng in a media world – Media vs. content and usability § Plus hands-‐on exercises October 2010 © ADMA & Datalicious Pty Ltd 6
- 7. > Get the most out of the course Category Data Metrics Insights PlaForm Why? What? How? October 2010 © ADMA & Datalicious Pty Ltd 7
- 8. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Media aJribu-on October 2010 © ADMA & Datalicious Pty Ltd 8
- 9. > Campaign flow and calls to ac-on = Paid media Organic PR, WOM, search events, etc = Viral elements = Coupons, surveys YouTube, Home pages, Paid TV, print, blog, etc portals, etc search radio, etc Direct mail, Landing pages, Display ads, email, etc offers, etc affiliates, etc C1 C2 CRM Facebook program TwiJer, etc C3 POS kiosks, Call center, loyalty cards, etc retail stores, etc October 2010 © ADMA & Datalicious Pty Ltd 9
- 11. Exercise: Calls to ac-on
- 12. > Exercise: Calls to ac-on § Unique click-‐through URLs § Unique vanity domains or URLs § Unique phone numbers § Unique search terms § Unique email addresses § Unique personal URLs (PURLs) § Unique SMS numbers, QR codes § Unique promoHonal codes, vouchers § Geographic locaHon (Facebook, FourSquare) § Regression analysis of cause and effect October 2010 © ADMA & Datalicious Pty Ltd 12
- 13. > Search call to ac-on for offline October 2010 © ADMA & Datalicious Pty Ltd 13
- 16. > Reach and channel overlap TV audience Banner Search audience audience October 2010 © ADMA & Datalicious Pty Ltd 16
- 17. > Indirect display impact October 2010 © ADMA & Datalicious Pty Ltd 17
- 18. > Indirect display impact October 2010 © ADMA & Datalicious Pty Ltd 18
- 19. > Indirect display impact October 2010 © ADMA & Datalicious Pty Ltd 19
- 20. > De-‐duplica-on across channels Paid Bid Search Mgmt $ Banner Ad Ads Server $ Central Analy-cs PlaForm Email Email Blast PlaForm $ Organic Google Search Analy-cs $ October 2010 © ADMA & Datalicious Pty Ltd 20
- 22. > Success aJribu-on models Banner Paid Organic Success Last channel Search Ad Search $100 $100 gets all credit Banner Paid Email Success First channel Ad $100 Search Blast $100 gets all credit Paid Banner Affiliate Success All channels get Search Ad Referral $100 $100 $100 $100 equal credit Print Social Paid Success All channels get Ad Media Search $33 $33 $33 $100 par-al credit October 2010 © ADMA & Datalicious Pty Ltd 22
- 23. > First and last click aJribu-on Chart shows percentage of channel touch points that lead Paid/Organic Search to a conversion. Neither first Emails/Shopping Engines nor last-‐click measurement would provide true picture October 2010 © ADMA & Datalicious Pty Ltd 23
- 24. > Paid and organic stacking October 2010 © ADMA & Datalicious Pty Ltd 24
- 25. > Full path to purchase Introducer Influencer Influencer Closer $ SEM Banner Direct SEO Generic Click Visit Branded $ Banner SEO Affiliate Social View Generic Click Media $ TV SEO Direct Email Abandon Ad Branded Visit Update October 2010 © ADMA & Datalicious Pty Ltd 25
- 26. > Where to collect the data Ad Server Web Analy-cs Banner impressions Referral visits Banner clicks Social media visits + Organic search visits Paid search clicks Paid search visits Other paid visits Email visits Paid Impressions/Clicks Paid/Organic Visits October 2010 © ADMA & Datalicious Pty Ltd 26
- 27. > Success aJribu-on models Introducer Influencer Influencer Closer $ Even 25% 25% 25% 25% AJrib. Exclusion 33% 33% 33% 0% AJrib. PaJern 30% 20% 20% 30% AJrib. October 2010 © ADMA & Datalicious Pty Ltd 27
- 29. > Exercise: AJribu-on models Introducer Influencer Influencer Closer $ Even 25% 25% 25% 25% AJrib. Exclusion 33% 33% 33% 0% AJrib. ? ? ? ? Custom AJrib. October 2010 © ADMA & Datalicious Pty Ltd 29
- 30. > Exercise: AJribu-on model § Allocate more conversion credits to more recent touch points for brands with a strong baseline to sHmulate repeat purchases § Allocate more conversion credits to more recent touch points for brands with a direct response focus § Allocate more conversion credits to iniHaHng touch points for new and expensive brands and products to insert them into the mindset October 2010 © ADMA & Datalicious Pty Ltd 30
- 31. > Understanding channel overlap October 2010 © ADMA & Datalicious Pty Ltd 31
- 32. > Website entry survey De-‐duped Campaign Report Greatest Influencer on Branded Search / STS } Channel % of Conversions Channel % of Influence Straight to Site 27% Word of Mouth 32% SEO Branded 15% Blogging & Social Media 24% SEM Branded 9% Newspaper AdverHsing 9% SEO Generic 7% Display AdverHsing 14% SEM Generic 14% Email MarkeHng 7% Display AdverHsing 7% Retail PromoHons 14% Affiliate MarkeHng 9% Referrals 5% Conversions aWributed to search terms Email MarkeHng 7% that contain brand keywords and direct website visits are most likely not the originaHng channel that generated the awareness and as such conversion credits should be re-‐allocated. October 2010 © ADMA & Datalicious Pty Ltd 32
- 33. > Ad server exposure test 1 User qualifies for the display campaign (if the user has already been tagged go to step 3) 1st impression 2 Audience Segmenta-on 10% of users in control group, 90% in exposed group User tagged with segment Measurement: Conversions per 1000 unique Control Exposed visitors (displayed non-‐branded message) (displayed branded message) User remains in segment N impressions 3 Control Exposed (displayed non-‐branded message) (displayed branded message) October 2010 © ADMA & Datalicious Pty Ltd 33
- 34. > Research online, shop offline October 2010 © ADMA & Datalicious Pty Ltd 34 Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
- 35. > Offline sales driven by online Adver-sing Phone Credit check, campaign order fulfilment Retail Confirma-on order email Website Online Online order Virtual order research order confirma-on confirma-on Cookie October 2010 © ADMA & Datalicious Pty Ltd 35
- 37. > Exercise: Offline conversions § Email click-‐through aner purchase § First online login aner purchase § Unique website phone number § Call back request or online chat § Unique website promoHon code § Unique printable vouchers § Store locator searches § Make an appointment online October 2010 © ADMA & Datalicious Pty Ltd 37
- 38. > Media aJribu-on phases § Phase 1: De-‐duplicaHon – Conversion de-‐duplicaHon across all channels – Requires one central reporHng plaoorm – Limited to first/last click aWribuHon § Phase 2: Direct response pathing – Response pathing across paid and organic channels – Only covers clicks and not mere banner views – Can be enabled in Google AnalyHcs and Omniture § Phase 3: Full purchase path – Direct response tracking including banner exposure – Cannot be done in Google AnalyHcs or Omniture – Easier to import addiHonal channels into ad server October 2010 © ADMA & Datalicious Pty Ltd 38
- 39. > Recommended resources § 200812 ComScore How Online AdverHsing Works § 200905 iProspect Research Study Search And Display § 200904 ClearSaleing American AWribuHon Index § 201003 Datalicious Tying Offline Sales To Online Media § Google: “Forrester Campaign AWribuHon Framework PDF” October 2010 © ADMA & Datalicious Pty Ltd 39
- 40. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 > Reducing waste October 2010 © ADMA & Datalicious Pty Ltd 40
- 41. > Reducing waste along funnel Media aJribu-on Op-mising channel mix Targe-ng Increasing relevance Tes-ng Improving usability $$$ October 2010 © ADMA & Datalicious Pty Ltd 41
- 42. > Increase revenue by 10-‐20% Capture internet traffic Capture 50-‐100% of fair market share of traffic Increase consumer engagement Exceed 50% of best compeHtor’s engagement rate Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% into sales Building consumer loyalty Build 60% loyalty rate and 40% sales conversion Increase online revenue Earn 10-‐20% incremental revenue online October 2010 © ADMA & Datalicious Pty Ltd 42
- 43. > The consumer data journey To transac-onal data To reten-on messages From suspect to prospect To customer Time Time From behavioural data From awareness messages October 2010 © ADMA & Datalicious Pty Ltd 43
- 44. > Coordina-on across channels Genera-ng Crea-ng Maximising awareness engagement revenue TV, radio, print, Retail stores, in-‐store Outbound calls, direct outdoor, search kiosks, call centers, mail, emails, social markeHng, display brochures, websites, media, SMS, mobile ads, performance mobile apps, online apps, etc networks, affiliates, chat, social media, etc social media, etc Off-‐site On-‐site Profile targe-ng targe-ng targe-ng October 2010 © ADMA & Datalicious Pty Ltd 44
- 45. > Combining targe-ng plaForms Off-‐site targeHng Profile On-‐site targeHng targeHng October 2010 © ADMA & Datalicious Pty Ltd 45
- 48. > Combining technology On-‐site Off-‐site segments segments October 2010 © ADMA & Datalicious Pty Ltd 48
- 49. > Extended targe-ng plaForm Publishers Partners Network Brand October 2010 © ADMA & Datalicious Pty Ltd 49
- 50. > SuperTag code architecture § Central JavaScript container tag § One tag for all sites and plaoorms § Hosted internally or externally § Faster tag implementaHon/updates § Eliminates JavaScript caching § Enables code tesHng on live site § Enables heat map implementaHon § Enables redirects for A/B tesHng § Enables network wide re-‐targeHng § Enables live chat implementaHon October 2010 © ADMA & Datalicious Pty Ltd 50
- 51. > Combining data sets Website behavioural data Campaign response data + The whole is greater than the sum of its parts Customer profile data October 2010 © ADMA & Datalicious Pty Ltd 51
- 52. > Behaviours plus transac-ons Site Behaviour CRM Profile tracking of purchase funnel stage one-‐off collecHon of demographical data + browsing, checkout, etc age, gender, address, etc tracking of content preferences customer lifecycle metrics and key dates products, brands, features, etc profitability, expira-on, etc tracking of external campaign responses predicHve models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promoHon responses historical data from previous transacHons emails, internal search, etc average order value, points, etc Updated Con-nuously Updated Occasionally October 2010 © ADMA & Datalicious Pty Ltd 52
- 53. > Maximise iden-fica-on points 160% 140% 120% 100% 80% 60% −−− Probability of idenHficaHon through Cookies 40% 20% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks October 2010 © ADMA & Datalicious Pty Ltd 53
- 54. > Sample customer level data October 2010 © ADMA & Datalicious Pty Ltd 54
- 55. > Sample site visitor composi-on 30% new visitors with no 30% repeat visitors with previous website history referral data and some aside from campaign or website history allowing referrer data of which 50% to be segmented by maybe 50% is useful content affinity 30% exis-ng customers with extensive 10% serious profile including transacHonal history of prospects which maybe 50% can actually be with limited idenHfied as individuals profile data October 2010 © ADMA & Datalicious Pty Ltd 55
- 56. > Poten-al home page layout Customise content Branded header delivery on the fly based on referrer data, past content Rule based offer Login consumpHon or profile data for exisHng customers. Targeted Targeted offer offer Popular links, FAQs October 2010 © ADMA & Datalicious Pty Ltd 56
- 57. > Prospect targe-ng parameters October 2010 © ADMA & Datalicious Pty Ltd 57
- 58. > Affinity targe-ng in ac-on Different type of visitors respond to different ads. By using category affinity targeHng, response rates are lined significantly across products. CTR By Category Affinity Message Postpay Prepay Broadb. Business Blackberry Bold - - - + Google: “vodafone 5GB Mobile Broadband - - + - omniture case study” Blackberry Storm + - + + or hJp://bit.ly/de70b7 12 Month Caps - + - + October 2010 © ADMA & Datalicious Pty Ltd 58
- 59. > Poten-al newsleJer layout Using profile data Rule based branded header enhanced with website behaviour Data verifica-on NPS data imported into the email delivery plaoorm to build Rule based offer business rules and Closest stores, customise content Profile based offer delivery. offers etc October 2010 © ADMA & Datalicious Pty Ltd 59
- 60. > Customer profiling in ac-on Using website and email responses to learn a liWle bite more about subscribers at every touch point to keep refining profiles and messages. October 2010 © ADMA & Datalicious Pty Ltd 60
- 61. > Poten-al landing page layout Passing data on user Rule based branded header preferences through to the website via parameters in email Campaign message match click-‐through URLs to customise content delivery. Targeted offer Call to ac-on October 2010 © ADMA & Datalicious Pty Ltd 61
- 63. > Exercise: Targe-ng matrix Phase Segment A/B Channels Data Points Awareness Considera-on Purchase Intent Up/Cross-‐Sell October 2010 © ADMA & Datalicious Pty Ltd 63
- 64. > Exercise: Targe-ng matrix Phase Segment A/B Channels Data Points Social, display, Awareness Seen this? Default search, etc Social, search, Download, Considera-on Great feature! website, etc product view Search, site, Cart add, Purchase Intent Great value! emails, etc checkout, etc Direct mail, Email response, Up/Cross-‐Sell Add this! emails, etc login, etc October 2010 © ADMA & Datalicious Pty Ltd 64
- 65. > Quality content is key Avinash Kaushik: “The principle of garbage in, garbage out applies here. [… what makes a behaviour targe;ng pla<orm ;ck, 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.” October 2010 © ADMA & Datalicious Pty Ltd 65
- 66. > ClickTale tes-ng case study October 2010 © ADMA & Datalicious Pty Ltd 66
- 67. > Bad campaign worse than none October 2010 © ADMA & Datalicious Pty Ltd 67
- 68. > Keys to effec-ve targe-ng 1. Define success metrics 2. Define and validate segments 3. Develop targeHng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targeHng and automate 7. Keep tesHng and refining 8. Communicate results October 2010 © ADMA & Datalicious Pty Ltd 68
- 69. > Recommended resources § 201003 McKinsey Get More Value From Digital MarkeHng § 200912 Unbounce 101 Landing Page OpHmizaHon Tips § 201008 eConsultancy TV Ad Landing Pages § 200910 eMarketer Bad Campaign Worse Than None § 201003 WebCredible 10 Unexpected User Behaviours § 200910 Myth Of The Page Fold § 201008 Sample Size Currency Of MarkeHng TesHng § 200409 Roy Taguchi Or MV TesHng For Marketers § 200702 Internet Retailer NavigaHng Depths Of MV TesHng § 201009 Six Revisions 10 Usability Tips Based On Research October 2010 © ADMA & Datalicious Pty Ltd 69
- 70. Summary
- 71. > Get the most out of the course Category Data Metrics Insights PlaForm Why? What? How? October 2010 © ADMA & Datalicious Pty Ltd 71
- 72. > Summary and ac-on items § Campaign flow and media aWribuHon – Draw campaign flow for your company – Check plaoorm cookie expiraHon periods – Enable pathing of direct campaign responses – InvesHgate how to track offline conversions § How to reduce media waste – Develop basic targeHng matrix to get started – Combine targeHng plaoorms for consistency – List all customer touch points for idenHficaHon – Check for common ID across all data sources October 2010 © ADMA & Datalicious Pty Ltd 72
- 74. > Google Analy-cs prac-ce § Describing website visitors § IdenHfying traffic sources (reach) – Campaign tracking mechanics § Analyzing content usage (engagement) § Analyzing conversion drop-‐out (conversion) § Defining custom segments (breakdowns) October 2010 © ADMA & Datalicious Pty Ltd 74
- 75. > Describing website visitors § Average connecHon speed § Plug-‐in usage (i.e. Flash, etc) § Mobile vs. normal computers § Geographic locaHon of visitors § Time of day, day of week § Repeat visitaHon § What else? October 2010 © ADMA & Datalicious Pty Ltd 75
- 76. > Iden-fying traffic sources § GeneraHng de-‐duplicated reports § Campaign tracking mechanics § Conversion goals and success events § Plus adding addiHonal metrics § Paid vs. organic traffic sources § Branded vs. generic search § Traffic quanHty vs. quality October 2010 © ADMA & Datalicious Pty Ltd 76
- 77. > Analysing content usage § Page traffic vs. engagement § Entry vs. exit pages § Popular page paths § Internal search terms October 2010 © ADMA & Datalicious Pty Ltd 77
- 78. > Analysing conversion drop-‐out § Defining conversion funnels § IdenHfying main problem pages § Pages visited aner conversion barriers § Conversion drop-‐out by segment October 2010 © ADMA & Datalicious Pty Ltd 78
- 79. > Defining custom segments § New vs. repeat visitors § By geographic locaHon § By connecHon speed § By products purchased § New vs. exisHng customers § Branded vs. generic search § By demographics, custom segments October 2010 © ADMA & Datalicious Pty Ltd 79
- 80. > Useful analy-cs tools § hWp://labs.google.com/sets § hWp://www.google.com/trends § hWp://www.google.com/insights/search § hWp://bit.ly/googlekeywordtoolexternal § hWp://www.google.com/webmasters § hWp://www.facebook.com/insights § hWp://www.google.com/adplanner § hWp://www.google.com/videotargeHng § hWp://www.keywordspy.com § hWp://www.compete.com October 2010 © ADMA & Datalicious Pty Ltd 80
- 81. > Useful analy-cs tools § hWp://bit.ly/hitwisedatacenter § hWp://www.socialmenHon.com § hWp://twiWersenHment.appspot.com § hWp://bit.ly/twiWerstreamgraphs § hWp://twitrratr.com § hWp://bit.ly/listonools1 § hWp://bit.ly/listonools2 § hWp://manyeyes.alphaworks.ibm.com § hWp://www.wordle.net § hWp://www.tagxedo.com October 2010 © ADMA & Datalicious Pty Ltd 81
- 82. Contact us cbartens@datalicious.com Follow us twiWer.com/datalicious Learn more blog.datalicious.com