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Ad Tech Campaign Measurement
The document provides an overview of Datalicious, a digital marketing agency that specializes in campaign measurement and data services. It discusses Datalicious' history and clients, as well as the wide range of data, insights, and action services it offers. Key aspects covered include metrics frameworks, conversion funnels, data sources, and the importance of calendar events for proper data interpretation.
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Ad Tech Campaign Measurement
- 1. > Campaign Measurement < Digital Campaign Measurement ad:tech 2011 Workshop
- 2. > Short but sharp history § Datalicious was founded late 2007 § Strong Omniture web analyBcs history § Now 360 data agency with specialist team § CombinaBon of analysts and developers § Carefully selected best of breed partners § Evangelizing smart data driven markeBng § Making data accessible and acBonable § Driving industry best pracBce (ADMA) March 2011 © Datalicious Pty Ltd 2
- 3. > Clients across all industries March 2011 © Datalicious Pty Ltd 3
- 4. > Wide range of data services Data Insights Ac?on Pla>orms Repor?ng Campaigns Data collec?on and processing Data mining and modelling Data usage and applica?on Web analy?cs solu?ons Customised dashboards Marke?ng automa?on Omniture, Google Analy?cs, etc Media aKribu?on models Alterian, Trac?on, Inxmail, etc Tag-‐less online data capture Market and compe?tor trends Targe?ng and merchandising End-‐to-‐end data pla>orms Social media monitoring Internal search op?misa?on IVR and call center repor?ng Online surveys and polls CRM strategy and execu?on Single customer view Customer profiling Tes?ng programs March 2011 © Datalicious Pty Ltd 4
- 5. > Smart data driven marke?ng Standardised Metrics Standardised Metrics Media AKribu?on Benchmarking and trending Benchmarking and trending Op?mise channel mix Targe?ng Increase relevance Tes?ng Improve usability $$$ March 2011 © Datalicious Pty Ltd 5
- 6.
- 7. > AIDA and AIDAS formulas Old media New media Awareness Interest Desire Ac?on Sa?sfac?on Social media March 2011 © Datalicious Pty Ltd 7
- 8. > Simplified AIDAS funnel Reach Engagement Conversion +Buzz (Awareness) (Interest & Desire) (AcBon) (SaBsfacBon) March 2011 © Datalicious Pty Ltd 8
- 9. > Marke?ng is about people People People People People reached 40% engaged 10% converted 1% delighted March 2011 © Datalicious Pty Ltd 9
- 10. > Addi?onal funnel breakdowns Brand vs. direct response campaign People People People People reached 40% engaged 10% converted 1% delighted New prospects vs. exisBng customers March 2011 © Datalicious Pty Ltd 10
- 11. New vs. returning visitors
- 12. AU/NZ vs. rest of world
- 13.
- 14. > Exercise: Funnel breakdowns § List potenBally insighXul funnel breakdowns – Brand vs. direct response campaign – New prospects vs. exisBng customers – Baseline vs. incremental conversions – CompeBBve acBvity, i.e. none, a lot, etc – Segments, i.e. age, locaBon, influence, etc – Channels, i.e. search, display, social, etc – Campaigns, i.e. this/last week, month, year, etc – Products and brands, i.e. iphone, htc, etc – Offers, i.e. free minutes, free handset, etc March 2011 © Datalicious Pty Ltd 14
- 15. > Mul?ple metrics data sources Media and search data Website, call center and retail data People People People People reached engaged converted delighted QuanBtaBve and qualitaBve research data Social media data Social media March 2011 © Datalicious Pty Ltd 15
- 16. > Importance of calendar events Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless March 2011 © Datalicious Pty Ltd 16
- 17. Calendar events to add context March 2011 © Datalicious Pty Ltd 17
- 18. > Conversion funnel 1.0 Campaign responses Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informaBon, order confirmaBon, etc Conversion event March 2011 © Datalicious Pty Ltd 18
- 19. > Conversion funnel 2.0 Campaign responses (inbound spokes) Offline campaigns, banner ads, email markeBng, referrals, organic search, paid search, internal promoBons, etc Landing page (hub) Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registraBon, product comparison, product review, forward to friend, etc March 2011 © Datalicious Pty Ltd 19
- 20. > Addi?onal success metrics Click Through $ Click Add To Cart Through Cart Checkout ? $ Click Page Page Product Through Bounce Views Views $ Click Call back Store Through request Search ? $ March 2011 © Datalicious Pty Ltd 20
- 21. Exercise: Sta?s?cal significance March 2011 © Datalicious Pty Ltd 21
- 22. How many survey responses do you need if you have 10,000 customers? How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? How many orders do you need to test 6 banner execu?ons if you serve 1,000,000 banners March 2011 © Datalicious Pty Ltd 22 Google “nss sample size calculator”
- 23. How many survey responses do you need if you have 10,000 customers? 369 for each ques?on or 369 complete responses How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? And email sends? 381 per subject line or 381 x 2 = 762 email opens How many orders do you need to test 6 banner execu?ons if you serve 1,000,000 banners? 383 sales per banner execu?on or 383 x 6 = 2,298 sales March 2011 © Datalicious Pty Ltd 23 Google “nss sample size calculator”
- 24. > Addi?onal success metrics Click Through $ Click Add To Cart Through Cart Checkout ? $ Click Page Page Product Through Bounce Views Views $ Click Call back Store Through request Search ? $ March 2011 © Datalicious Pty Ltd 24
- 25.
- 26. > Exercise: Metrics framework Level Reach Engagement Conversion +Buzz Level 1 People Level 2 Strategic Level 3 Tac?cal March 2011 © Datalicious Pty Ltd 26
- 27. > Exercise: Metrics framework Level Reach Engagement Conversion +Buzz Level 1 People People People People People reached engaged converted delighted Search Level 2 Strategic impressions, UBs, etc ? ? ? Keyword Level 3 Tac?cal rank, click-‐ through, etc ? ? ? March 2011 © Datalicious Pty Ltd 27
- 28.
- 29. > Complex campaign flows = Paid media Organic PR, WOM, search events, etc = Viral elements = Sales channels 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 CRM Facebook program TwiKer, etc POS kiosks, Call center, loyalty cards, etc retail stores, etc March 2011 © Datalicious Pty Ltd 29
- 30. > Duplica?on across channels Paid Bid Search Mgmt $ Banner Ad Ads Server $ Email Email Blast Pla>orm $ Organic Google Search Analy?cs $ March 2011 © Datalicious Pty Ltd 30
- 31. > Cookie expira?on impact Paid Bid Search Mgmt $ Banner Banner Ad Ad Click Ad View Server $ Email Email Expira?on Blast Pla>orm $ Organic Google Search Analy?cs $ March 2011 © Datalicious Pty Ltd 31
- 32. > De-‐duplica?on across channels Paid Search $ Banner Ads $ Central Analy?cs Pla>orm Email Blast $ Organic Search $ March 2011 © Datalicious Pty Ltd 32
- 33. Exercise: Duplica?on impact March 2011 © Datalicious Pty Ltd 33
- 34. > Exercise: Duplica?on impact § Double-‐counBng of conversions across channels can have a significant impact on key metrics, especially CPA § Example: Display ads and paid search – Total media budget of $10,000 of which 50% is spend on paid search and 50% on display ads – Total of 100 conversions across both channels with a channel overlap of 50%, i.e. both channels claim 100% of conversions based on their own reporBng but once de-‐duplicated they each only contributed 50% of conversions – What are the iniBal CPA values and what is the true CPA? § SoluBon: $50 iniBal CPA and $100 true CPA – $5,000 / 100 = $50 iniBal CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase) March 2011 © Datalicious Pty Ltd 34
- 35. > Reach and channel overlap TV/Print audience Banner Search audience audience March 2011 © Datalicious Pty Ltd 35
- 36. > Ad server exposure test Banner TV/Print Search Impression Response Response $ Banner Search Direct Impression Response Response $ Users are segmented before 1st ad is even Exposed group: 90% of users get branded message served Control group: 10% of users get non-‐branded message Banner Search Direct Impression Response Response $ March 2011 © Datalicious Pty Ltd 36
- 37. > Indirect display impact March 2011 © Datalicious Pty Ltd 37
- 38. > Indirect display impact March 2011 © Datalicious Pty Ltd 38
- 39. > Indirect display impact March 2011 © Datalicious Pty Ltd 39
- 40. > Success aKribu?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 March 2011 © Datalicious Pty Ltd 40
- 41. > First and last click aKribu?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 March 2011 © Datalicious Pty Ltd 41
- 42. > Full path to purchase Introducer Influencer Influencer Closer $ SEM Banner Direct SEO Online Generic Click Visit Branded Banner SEO Affiliate Social Offline View Generic Click Media TV SEO Direct Email Abandon Ad Branded Visit Update March 2011 © Datalicious Pty Ltd 42
- 43. > Search call to ac?on for offline March 2011 © Datalicious Pty Ltd 43
- 44. Offline response tracking and improved experience March 2011 © Datalicious Pty Ltd 44
- 45. March 2011 © Datalicious Pty Ltd 45
- 46. hKp://www.suncorp.com.au?campaign=workshop March 2011 © Datalicious Pty Ltd 46
- 47. > PURLs boos?ng DM response rates Text March 2011 © Datalicious Pty Ltd 47
- 48. > Poten?al 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 promoBonal codes, vouchers § Geographic locaBon (Facebook, FourSquare) § Plus regression analysis of cause and effect March 2011 © Datalicious Pty Ltd 48
- 49. > Jet Interac?ve phone call data March 2011 © Datalicious Pty Ltd 49
- 50. > Unique phone numbers § 1 unique phone number – Phone number is considered part of the brand – Media origin of calls cannot be established – Added value of website interacBon unknown § 2-‐10 unique phone numbers – Different numbers for different media channels – Exclusive number(s) reserved for website use – Call origin data more granular but not perfect – Difficult to rotate and pause numbers March 2011 © Datalicious Pty Ltd 50
- 51. > Unique phone numbers § 10+ unique phone numbers – Different numbers for different media channels – Different numbers for different product categories – Different numbers for different conversion steps – Call origin becoming useful to shape call script – Feasible to pause numbers to improve integrity § 100+ unique phone numbers – Different numbers for different website visitors – Call origin and Bme stamp enable individual match – Call conversions matched back to search terms March 2011 © Datalicious Pty Ltd 51
- 52. > Cross-‐channel impact March 2011 © Datalicious Pty Ltd 52
- 53. > 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 March 2011 © Datalicious Pty Ltd 53
- 54. > Full path to purchase Introducer Influencer Influencer Closer $ SEM Banner Direct SEO Online Generic Click Visit Branded Banner SEO Affiliate Social Offline View Generic Click Media TV SEO Direct Email Abandon Ad Branded Visit Update March 2011 © Datalicious Pty Ltd 54
- 55. > Adobe stacking/par?cipa?on Adobe can only stack direct paid and organic responses that end up on your website proper?es, mere banner impressions are missing from the stack and cannot be included via Genesis ater the fact. March 2011 © Datalicious Pty Ltd 55
- 56. > 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 Email visits, etc Lacking organic visits Lacking banner impressions More granular & complex Less granular & complex March 2011 © Datalicious Pty Ltd 56
- 57. > Combining data sources March 2011 © Datalicious Pty Ltd 57
- 58. > Single source of truth repor?ng Insights Repor?ng March 2011 © Datalicious Pty Ltd 58
- 59. > Understanding channel mix March 2011 © Datalicious Pty Ltd 59
- 61. > 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 AdverBsing 9% SEO Generic 7% Display AdverBsing 14% SEM Generic 14% Email MarkeBng 7% Display AdverBsing 7% Retail PromoBons 14% Affiliate MarkeBng 9% Referrals 5% Conversions aoributed to search terms Email MarkeBng 7% that contain brand keywords and direct website visits are most likely not the originaBng channel that generated the awareness and as such conversion credits should be re-‐allocated. March 2011 © Datalicious Pty Ltd 61
- 62. > Adjus?ng for offline impact -‐5 -‐15 -‐10 +5 +15 +10 March 2011 © Datalicious Pty Ltd 62
- 63. > Success aKribu?on models Introducer Influencer Influencer Closer $ Even 25% 25% 25% 25% AKrib. Exclusion 33% 33% 33% 0% AKrib. PaKern 30% 20% 20% 30% AKrib. March 2011 © Datalicious Pty Ltd 63
- 64. > Path across different segments Introducer Influencer Influencer Closer $ Product Channel 1 Channel 2 Channel 3 Channel 4 A vs. B New Channel 1 Channel 2 Channel 3 Channel 4 prospects Exis?ng Channel 1 Channel 2 Channel 3 Product 4 customers March 2011 © Datalicious Pty Ltd 64
- 65. Exercise: AKribu?on model March 2011 © Datalicious Pty Ltd 65
- 66. > Exercise: AKribu?on models Introducer Influencer Influencer Closer $ Even 25% 25% 25% 25% AKrib. Exclusion 33% 33% 33% 0% AKrib. ? ? ? ? Custom AKrib. March 2011 © Datalicious Pty Ltd 66
- 67. > Common aKribu?on models § Allocate more conversion credits to more recent touch points for brands with a strong baseline to sBmulate repeat purchases § Allocate more conversion credits to more recent touch points for brands with a direct response focus § Allocate more conversion credits to iniBaBng touch points for new and expensive brands and products to insert them into the mindset March 2011 © Datalicious Pty Ltd 67
- 68. > Media aKribu?on phases § Phase 1: De-‐duplicaBon – Conversion de-‐duplicaBon across all channels – Requires one central reporBng plaXorm – Limited to first/last click aoribuBon § 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 AnalyBcs and Omniture § Phase 3: Full purchase path – Direct response tracking including banner exposure – Cannot be done in Google AnalyBcs or Omniture – Easier to import addiBonal channels into ad server March 2011 © Datalicious Pty Ltd 68
- 69.
- 70. > Increase revenue by 10-‐20% Capture internet traffic Capture 50-‐100% of fair market share of traffic Increase consumer engagement Exceed 50% of best compeBtor’s engagement rate Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% to sales Building consumer loyalty Build 60% loyalty rate and 40% sales conversion Increase online revenue Earn 10-‐20% incremental revenue online March 2011 © Datalicious Pty Ltd 70
- 71. > New consumer decision journey The consumer decision process is changing from linear to circular. March 2011 © Datalicious Pty Ltd 71
- 72. > New consumer decision journey The consumer decision process is changing from linear to circular. Online research Change increases the importance of experience during research phase. March 2011 © Datalicious Pty Ltd 72
- 73. > 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 March 2011 © Datalicious Pty Ltd 73
- 74. > 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 markeBng, 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 March 2011 © Datalicious Pty Ltd 74
- 75. > Combining targe?ng pla>orms Off-‐site targeBng Profile On-‐site targeBng targeBng March 2011 © Datalicious Pty Ltd 75
- 76. March 2011 © Datalicious Pty Ltd 76
- 77. Take a closer look at our cash flow solu?ons March 2011 © Datalicious Pty Ltd 77
- 78. March 2011 © Datalicious Pty Ltd 78
- 79. + Add website behaviour to submiKed contact form data March 2011 © Datalicious Pty Ltd 79
- 80. Take a closer look at our cash flow solu?ons March 2011 © Datalicious Pty Ltd 80
- 81. Save ?me and get your business insurance March 2011 © Datalicious Pty Ltd online. 81
- 82. Our Flexi-‐Premium car insurance can help you March 2011 © Datalicious Pty Ltd save. 82
- 83. Save with our combine Our Flexi-‐Premium car car and life an help you insurance c insurance March 2011 © Datalicious Pty Ltd offer. save. 83
- 84. March 2011 © Datalicious Pty Ltd 84
- 85. March 2011 © Datalicious Pty Ltd 85
- 86. It’s no accident March 2011 © Datalicious Pty Ltd 86 we’re cheaper
- 87. > Combining technology On-‐site Off-‐site segments segments CRM March 2011 © Datalicious Pty Ltd 87
- 88. > SuperTag code architecture § Central JavaScript container tag § One tag for all sites and plaXorms § Hosted internally or externally § Faster tag implementaBon/updates § Eliminates JavaScript caching § Enables code tesBng on live site § Enables heat map implementaBon § Enables redirects for A/B tesBng § Enables network wide re-‐targeBng § Enables live chat implementaBon March 2011 © Datalicious Pty Ltd 88
- 89. > Combining data sets Website behavioural data Campaign response data + The whole is greater than the sum of its parts Customer profile data March 2011 © Datalicious Pty Ltd 89
- 90. > Behaviours plus transac?ons Site Behaviour CRM Profile tracking of purchase funnel stage one-‐off collecBon 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 predicBve models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promoBon responses historical data from previous transacBons emails, internal search, etc average order value, points, etc Updated Con?nuously Updated Occasionally March 2011 © Datalicious Pty Ltd 90
- 91. > Unique visitor overes?ma?on The study examined data from two of the UK’s busiest ecommerce websites, ASDA and 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 overesBmated visitors by up to 7.6 Bmes whilst a cookie-‐based approach overes?mated visitors by up to 2.3 ?mes. March 2011 © Datalicious Pty Ltd 91 Source: White Paper, RedEye, 2007
- 92. Datalicious SuperCookie Persistent Flash cookie that cannot be deleted March 2011 © Datalicious Pty Ltd 92
- 93. > Maximise iden?fica?on points 160% 140% 120% 100% 80% 60% −−− Probability of idenBficaBon through Cookies 40% 20% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks March 2011 © Datalicious Pty Ltd 93
- 94. > Maximise iden?fica?on points Mobile Home Work Online Phone Branch March 2011 © Datalicious Pty Ltd 94
- 95. > Sample customer level data March 2011 © Datalicious Pty Ltd 95
- 96. > 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 transacBonal history of prospects which maybe 50% can actually be with limited idenBfied as individuals profile data March 2011 © Datalicious Pty Ltd 96
- 97. > Poten?al home page layout Customise content Branded header delivery on the fly based on referrer data, past content Rule based offer Login consumpBon or profile data for exisBng customers. Targeted Targeted offer offer Popular links, FAQs March 2011 © Datalicious Pty Ltd 97
- 98. > Prospect targe?ng parameters March 2011 © Datalicious Pty Ltd 98
- 99. > Affinity re-‐targe?ng in ac?on Different type of visitors respond to different ads. By using category affinity targeBng, response rates are lited 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 hKp://bit.ly/de70b7 12 Month Caps - + - + March 2011 © Datalicious Pty Ltd 99
- 100. > Ad-‐sequencing in ac?on MarkeBng is about telling stories and stories are not staBc but evolve over Bme Ad-‐sequencing can help to evolve stories over Bme the more users engage with ads March 2011 © Datalicious Pty Ltd 100
- 101. > Poten?al newsleKer layout Using profile data Rule based branded header enhanced with website behaviour Data verifica?on NPS data imported into the email delivery plaXorm to build Rule based offer business rules and Closest stores, customise content Profile based offer delivery. offers etc March 2011 © Datalicious Pty Ltd 101
- 102. > Customer profiling in ac?on Using website and email responses to learn a liole bite more about subscribers at every touch point to keep refining profiles and messages. March 2011 © Datalicious Pty Ltd 102
- 103. > 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 March 2011 © Datalicious Pty Ltd 103
- 104. March 2011 © Datalicious Pty Ltd 104
- 105. > Poten?al call center interface Customers can also Call center menu op?ons be idenBfied offline and given most call center plaXorms are Customer contact history now web-‐based it would be possible to use online targeBng Targeted offer Call script plaXorms to shape the call experience. March 2011 © Datalicious Pty Ltd 105
- 106. Exercise: Targe?ng matrix March 2011 © Datalicious Pty Ltd 106
- 107. Segment A Segment B Purchase Media Data cycle channels points Default, awareness Research, considera?on Purchase intent Reten?on, up/Cross-‐Sell March 2011 © Datalicious Pty Ltd 107
- 108. Segment A Segment B Purchase Media Data cycle channels points Colour, price, product affinity, etc Default, Have you Have you Display, Default awareness seen A? seen B? search, etc Research, A has great B has great Search, Ad clicks, considera?on features! features! website, etc product views Purchase A delivers B delivers Website, Cart adds, intent great value! great value! emails, etc checkouts, etc Reten?on, Why not Why not Direct mails, Email clicks, up/Cross-‐Sell March 2011 buy B? buy A? © Datalicious Pty Ltd emails, etc logins, 108 etc
- 109. > 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.” March 2011 © Datalicious Pty Ltd 109
- 110. > ClickTale tes?ng case study March 2011 © Datalicious Pty Ltd 110
- 111. > Bad campaign worse than none March 2011 © Datalicious Pty Ltd 111
- 112. > Keys to effec?ve targe?ng 1. Define success metrics 2. Define and validate segments 3. Develop targeBng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targeBng and automate 7. Keep tesBng and refining 8. Communicate results March 2011 © Datalicious Pty Ltd 112
- 113. Contact us cbartens@datalicious.com Learn more blog.datalicious.com Follow us twiKer.com/datalicious March 2011 © Datalicious Pty Ltd 113
- 114. Data > Insights > Ac?on