Analytics workshop on how to turn data into actionable insights.

1. [Digital Measurement ] Analytics workshop on how to turn data into actionable insights

2. [ Company history ] Datalicious was founded in 2007 Strong Omniture web analytics history One-stop data agency with specialist team Combination of analysts and developers Making data accessible and actionable Driving industry best practice Evangelizing use of data June 2010 © Datalicious Pty Ltd 2

3. [ Challenging clients ] June 2010 © Datalicious Pty Ltd 3

4. [ Data driven marketing ] June 2010 © Datalicious Pty Ltd 4 Insights Reporting Data mining and modelling Customised dashboards Media attribution models Market and competitor trends Social media monitoring Online surveys and polls Customer profiling Action Applications Data usage and application Marketing automation Aprimo, Traction, Inxmail, etc Targeting and merchandising Internal search optimisation CRM strategy and execution Testing programs Data Platforms Data collection and processing Web analytics solutions Omniture, Google Analytics, etc Tagless online data capture End-to-end data platforms IVR and call center reporting Single customer view

5. [ Today ] Capturing data Options, limitations, innovations Generating insights Process, metrics, examples Taking action Media, targeting, testing June 2010 © Datalicious Pty Ltd 5

6. [ Capturing data ] 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010 June 2010 © Datalicious Pty Ltd 6

7. [ Digital data is cheap ] June 2010 © Datalicious Pty Ltd 7 Source: Omniture Summit, Matt Belkin, 2007

8. [ Digital data options ] June 2010 © Datalicious Pty Ltd 8 +Social Source: Accuracy Whitepaper for web analytics, Brian Clifton, 2008

9. [ On-site analytics tools ] June 2010 © Datalicious Pty Ltd 9 Google: ”forrester wave web analytics pdf” or http://bit.ly/aTLAKT Source: Forrester Wave Web Analytics, 2009

10. [ What platform to use ] June 2010 © Datalicious Pty Ltd 10 Stage 1: Data Stage 2: Insights Stage 3: Action Data is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen! Sophistication Data 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 Ltd 11 Source: Omniture Summit, Matt Belkin, 2007

12. © Datalicious Pty Ltd [ Free off-site analytics tools ] http://www.google.com/trends http://www.google.com/sktool http://www.google.com/insights/search http://www.google.com/webmasters http://www.google.com/adplanner http://www.google.com/videotargeting http://www.keywordspy.com http://www.compete.com http://www.alexa.com http://wiki.kenburbary.com June 2010 12

13. [ Search at all stages ] June 2010 © Datalicious Pty Ltd 13 In Australia Google has a market share of almost 90% of all searches, making it a very large and reliable data sample Source: Inside the Mind of the Searcher, Enquiro 2004

14. [ Search call to action for offline ] June 2010 © Datalicious Pty Ltd 14

15. [ Client side tracking process ] June 2010 © Datalicious Pty Ltd 15 What 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 Ltd 16 Google: “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 Ltd 17 Source: White Paper, RedEye, 2007

18. [ Maximise identification points ] June 2010 © Datalicious Pty Ltd 18 Campaign response Email subscription Online purchase Repeat purchase Confirmation email Email newsletter Website login Online bill payment

19. June 2010 © Datalicious Pty Ltd 19 DataliciousSuperCookie Persistent Flash cookie that cannot be deleted

20. [ Mobile page headers ] June 2010 © Datalicious Pty Ltd 20 MSISDN = Mobile Number Source: Mobile Tracking, Omniture, 2008

21. [ Single-sign on ] June 2010 © Datalicious Pty Ltd 21 Facebook 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 Ltd 22 Google: ”digital future report 2009 pdf” or http://bit.ly/ZkLvr Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School

23. [ Offline sales driven by online ] June 2010 © Datalicious Pty Ltd 23 Tying 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 Fulfilment Phone Orders Website.com Research Virtual OrderConfirmation @ Retail Orders Credit Check Fulfilment Website.com Research Virtual OrderConfirmation @ Advertising Campaign Website.com Research Online Orders Credit Check Fulfilment Online Order Confirmation Virtual OrderConfirmation @ Cookie Cookie Cookie

24. [ Summary: Capturing data ] Plenty of data sources and platforms Especially search is great free data source Maintaining data integrity takes effort Cookie technology has its limitations New tag-less technologies emerging Maximise identification points Offline can be tied to online June 2010 © Datalicious Pty Ltd 24

25. [ Generating insights ] 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010 June 2010 © Datalicious Pty Ltd 25

26. [ Corporate data journey ] June 2010 © Datalicious Pty Ltd 26 Stage 1Data Stage 2Insights Stage 3Action Data is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen! Sophistication Data 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 minded Setting realistic improvement goals Technically savvy Bridging gap between business and IT Strong sales skills Raising awareness for the value of data Seniority and experience Needs to be taken serious across organisation Position within hierarchy Able to analyse without loyalty conflict June 2010 © Datalicious Pty Ltd 27

28. [ Process is key to success ] June 2010 © Datalicious Pty Ltd 28 Source: Omniture Summit, Matt Belkin, 2007

29. Website, call center and retail data Quantitative and qualitative research data [ Defining metrics frameworks ] June 2010 © Datalicious Pty Ltd 29 Media and search data Social media data Social media

30. [ Key metrics by website type ] June 2010 © Datalicious Pty Ltd 30 Source: Omniture Summit, Matt Belkin, 2007

31. [ Conversion funnel 1.0 ] June 2010 Campaign responses Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping information, order confirmation, etc Conversion event © Datalicious Pty Ltd 31

32. [ Conversion funnel 2.0 ] June 2010 Campaign responses (inbound spokes) Offline campaigns, banner ads, email marketing, referrals, organic search, paid search, internal promotions, etc Landing 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 Ltd 32

33. [ Additional success metrics ] June 2010 © Datalicious Pty Ltd 33 Click Through $ Click Through Add To Cart $ Cart Checkout ? Click Through Bounce Rate $ Pages Per Visit Video Views Click Through Call back requests Store Searches ? $

34. June 2010 © Datalicious Pty Ltd Exercise: Metrics framework 34

35. [ Exercise: Metrics framework ] June 2010 © Datalicious Pty Ltd 35

36. [ Exercise: Metrics framework ] June 2010 © Datalicious Pty Ltd 36

37. Customer data [ Combining data sets ] June 2010 © Datalicious Pty Ltd 37 Web analytics data + The whole is greater than the sum of its parts 3rd party data

38. [ Behaviours vs. transactions ] June 2010 © Datalicious Pty Ltd 38 CRM Profile Site Behaviour one-off collection of demographical data age, gender, address, etc customer lifecycle metrics and key datesprofitability, expiration, etc predictive models based on data miningpropensity to buy, churn, etc historical data from previous transactionsaverage order value, points, etc tracking of purchase funnel stagebrowsing, checkout, etc tracking of content preferencesproducts, brands, features, etc tracking of external campaign responses search terms, referrers, etc tracking of internal promotion responses emails, internal search, etc + Updated OCCASIONALLY Updated continuously

39. [ Store searches vs. actual locations ] June 2010 © Datalicious Pty Ltd 39

40. [ Enriching customer profiles ] June 2010 © Datalicious Pty Ltd 40 All you need is an address Source: Hitwise, 2006

41. [Hitwise Mosaic segment swing ] australia.com vs. newzealand.com australia.com vs. bulafiji.com June 2010 © Datalicious Pty Ltd 41 Source: Hitwise, 2006

42. [Hitwise Mosaic segment swing ] australia.com vs. newzealand.com australia.com vs. newzealand.com June 2010 © Datalicious Pty Ltd 42 Source: Hitwise, 2006

43. [ Single source of truth ] June 2010 © Datalicious Pty Ltd 43 Insights Reporting

44. [ De-duplication across channels ] June 2010 © Datalicious Pty Ltd 44 Paid Search $ Bid Mgmt Central AnalyticsPlatform Banner Ads Ad Server $ $ Email Blast Email Platform $ $ Organic Search Google Analytics $ $

45. June 2010 © Datalicious Pty Ltd Thinking outside the box 45

46. [ Search and brand strength ] June 2010 © Datalicious Pty Ltd 46

47. [ Search and the product lifecycle ] June 2010 © Datalicious Pty Ltd 47 Nokia N-Series www.google.com/trends Apple iPhone

48. [ Search and media planning ] June 2010 © Datalicious Pty Ltd 48 www.google.com/adplanner

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51. June 2010 © Datalicious Pty Ltd 51 Fiat 500: Online influencing offline Google: “slideshare fiat 500 case study” or http://bit.ly/lh7bx

52. [ Search driving offline creative ] June 2010 © Datalicious Pty Ltd 52

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54. June 2010 © Datalicious Pty Ltd 54 Sentiment analysis: People vs. machine Google: “people vs machines debate” or http://bit.ly/8VbtB

55. [ Social metrics and tools ] June 2010 © Datalicious Pty Ltd 55 Google: ”slidesharealtimeter report” or http://bit.ly/c8uYXT Source: Social Marketing Analytics, Altimeter, 2010

56. June 2010 © Datalicious Pty Ltd Exercise: Statistical significance 56

57. June 2010 © Datalicious Pty Ltd 57 How 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 Ltd 58 How many survey responses do you need if you have 10,000 customers? 369 for each question or 369 complete responses How 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 opens How 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 key Define a flexible metrics framework Maintain framework to enable comparison Combine data sets for hidden insights Establish a single (data) source of truth Think outside the box and across channels Data does not equal significance June 2010 © Datalicious Pty Ltd 59

60. [ Taking action ] 101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010 June 2010 © Datalicious Pty Ltd 60

61. [ How to drive ROI ] Increasing revenue Increasing overall amount of sales Increasing the average revenue per sale Reducing costs Increasing media effectiveness Increasing website conversion rates Increasing online self-service usage Improving customer experience Reducing steps necessary to complete a task Perceived value or quality of the final solution June 2010 © Datalicious Pty Ltd 61

62. [ How to drive ROI ] June 2010 © Datalicious Pty Ltd 62 Media or how to optimise the channel mix Targeting or how to increasing relevance Testing or how to maximise conversion

63. [ Success attribution models ] Banner Ad Paid Search OrganicSearch$100 Success $100 Last channel gets all credit Banner Ad$100 Email Blast Success$100 First channel gets all credit Paid Search Paid Search$100 Banner Ad$100 Affiliate Referral$100 Success$100 All channels get equal credit Print Ad$33 Social Media$33 Paid Search$33 Success$100 All channels get partial credit June 2010 63 © Datalicious Pty Ltd

64. [ First vs. last click attribution ] June 2010 © Datalicious Pty Ltd 64 Chart shows percentage of channel touch points that lead to a conversion. Paid/Organic Search Neither first nor last-click measurementwould provide true picture Emails/Shopping Engines

65. [ Path to purchase ] Banner Click SEM Generic PartnerSite Direct Visit $ Banner View June 2010 65 © Datalicious Pty Ltd SEO Generic $ TVAd SEOBranded Banner Click $ Print Ad Social Media Email Update Direct Visit $

66. [ Forrester media attribution ] June 2010 © Datalicious Pty Ltd 66 Google: ”forrester attribution framework pdf” or http://bit.ly/dnbnzY Source: Forrester, 2009

67. [ Customer data journey ] June 2010 © Datalicious Pty Ltd 67 To retention messages To transactional data From suspect to To customer prospect Time Time From behavioural data From awareness messages

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70. [ Matching segments are key ] June 2010 © Datalicious Pty Ltd 70 On and off-site targeting platforms should use identical triggers to sort visitors into segments

71. [ Off-site targeting platforms ] Ad servers Google/DoubleClick Eyeblaster Faciliate Atlas Etc Ad Networks Google Yahoo ValueClick Adconian Etc June 2010 © Datalicious Pty Ltd 71 http://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) AudienceScience Maxymiser Amadesa Certona SiteSpect BTBuckets (free) Google/DoubleClick Ad Server (free) June 2010 © Datalicious Pty Ltd 72

73. [ Prospect targeting parameters ] June 2010 © Datalicious Pty Ltd 73

74. [ Vodafone affinity targeting ] June 2010 © Datalicious Pty Ltd 74 Different 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 targeting Grouping of visitors into major segments Based on content and conversion behaviour Ease of use vs. reduced targeting ability Most common affinities used Brand affinity Image preference Price sensitivity Product affinity Content affinity June 2010 © Datalicious Pty Ltd 75

76. [ Coordinate the experience ] June 2010 © Datalicious Pty Ltd 76 By 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/cAtSUN Source: 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 Ltd 77

78. June 2010 © Datalicious Pty Ltd Exercise: Targeting matrix 78

79. [ Exercise: Targeting matrix ] June 2010 © Datalicious Pty Ltd 79

80. [ Exercise: Targeting matrix ] June 2010 © Datalicious Pty Ltd 80

81. Google: “change one word double conversion” or http://bit.ly/bpyqFp [ClickTale testing case study ] June 2010 © Datalicious Pty Ltd 81

82. [ Testing platforms ] Test&Target (Omniture, Offermatica, TouchClarity) Memetrics (Accenture) Optimost (Autonomy) Kefta (Acxiom) Maxymiser Amadesa SiteSpect ClickTale (cheap) Unbounce (cheap) Google Website Optimiser (free) June 2010 © Datalicious Pty Ltd 82

83. [ Summary ] There is no magic formula for ROI Focus on the entire conversion funnel Media attribution is hard but necessary Neither first nor last click method works Create a coordinated targeted experience Content is always king no matter what Test, learn and refine continuously June 2010 © Datalicious Pty Ltd 83

84. June 2010 © Datalicious Pty Ltd 84 Contact mecbartens@datalicious.com Learn moreblog.datalicious.com Follow ustwitter.com/datalicious