Webanalyticscongres.nl (29th May 2008): Presentation Neil Mason

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Keynote by Neil Mason at the (Dutch) webanalyticscongres.nl

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Webanalyticscongres.nl (29th May 2008): Presentation Neil Mason

  1. 1. “From Web Analytics to Marketing Optimisation and Beyond!” Neil Mason Applied Insights
  2. 2. “From Web Analytics to Marketing Optimisation and Beyond!” Neil Mason Applied Insights
  3. 3. Forces at work Aggressive competition
  4. 4. Forces at work Aggressive competition
  5. 5. Internet Adoption & Usage Curve Activity y Propensity to Transact Wanderlust Wanderl st Assessment Orientation Event Driven Usage Confidence Trepidation Maturity Scepticism Online One Month Three Months Six Months Nine Months One Year Two Years Time Source: Net Poll/QXL
  6. 6. Internet Adoption & Usage Curve Activity y Propensity to Transact Wanderlust Wanderl st Assessment Orientation Event Driven Usage Confidence Trepidation Maturity Scepticism Online One Month Three Months Six Months Nine Months One Year Two Years Time Source: Net Poll/QXL
  7. 7. Internet User E-commerce Development Auctions ce On-line e/Confidenc Retail Transactions +ve Rare/usual items Fun/excitement Experience +ve Information (Easy) Good deals & Research Efficient -ve: Exciting Safety Smart Confidence about product delivery -ve: Whether it’s a good deal Safety Lack of relevance Anonymity of seller Up to 12 to 24 months
  8. 8. Internet User E-commerce Development Auctions ce On-line e/Confidenc Retail Transactions +ve Rare/usual items Fun/excitement Experience +ve Information (Easy) Good deals & Research Efficient -ve: Exciting Safety Smart Confidence about product delivery -ve: Whether it’s a good deal Safety Lack of relevance Anonymity of seller Up to 12 to 24 months
  9. 9. Yesterday…. Sales Reports Transactional Board Reports Systems Site Reports
  10. 10. Yesterday…. Sales Reports Transactional Board Reports Systems Site Reports
  11. 11. Today…. Country Management Group Information Transactional Management Extracts Factory Systems Functional Management g
  12. 12. Today…. Country Management Group Information Transactional Management Extracts Factory Systems Functional Management g
  13. 13. Tomorrow…. Country Management Rules Group Knowledge Transactional Management Repository Systems Extracts Functional Management g
  14. 14. Tomorrow…. Country Management Rules Group Knowledge Transactional Management Repository Systems Extracts Functional Management g
  15. 15. Business and Customer Intelligence Strategy Phase 1- Integrated Reporting Site Analysi Tools Analysts is Live Trading Managers g Data Warehouse Reporting Environment Customer Service g Partners Financials Third Party Data
  16. 16. Business and Customer Intelligence Strategy Phase 1- Integrated Reporting Site Analysi Tools Analysts is Live Trading Managers g Data Warehouse Reporting Environment Customer Service g Partners Financials Third Party Data
  17. 17. Implementation Approach Localised Documents Local Ad-hoc Queries Analysts Support Training Best Practice Users Users Users Centre Development Corporate Documents Administration Security Expertise “Centralise D “C t li Development and Technology - L l t dT h l Localise Usage and Support” li U dS t”
  18. 18. Implementation Approach Localised Documents Local Ad-hoc Queries Analysts Support Training Best Practice Users Users Users Centre Development Corporate Documents Administration Security Expertise “Centralise D “C t li Development and Technology - L l t dT h l Localise Usage and Support” li U dS t”
  19. 19. CRM Technology Framework Information & Analysis Systems Marketing Data Warehouse Systems Transaction Systems
  20. 20. CRM Technology Framework Information & Analysis Systems Marketing Data Warehouse Systems Transaction Systems
  21. 21. I Wish It Was As Easy As This….
  22. 22. I Wish It Was As Easy As This….
  23. 23. The journey to online marketing optimisation Investment User Centricity Process Analysis y & Optimisation Web Performance Tracking Insight
  24. 24. The journey to online marketing optimisation Investment User Centricity Process Analysis y & Optimisation Web Performance Tracking Insight
  25. 25. It’s about getting…. …the right numbers right the
  26. 26. It’s about getting…. …the right numbers right the
  27. 27. As Albert said…
  28. 28. As Albert said…
  29. 29. The not‐so secret approach to getting performance tracking right… f k h Organisational goals and objectives Online goals and objectives Online KPIs
  30. 30. The not‐so secret approach to getting performance tracking right… f k h Organisational goals and objectives Online goals and objectives Online KPIs
  31. 31. Some simple questions to help you get your KPIs right h • State the online marketing objective • What does that really mean? • What d h does good l k lik ? What will b d look like? h ill be happening differently if you are meeting this objective? bj i ? • Can these behaviours or outcomes be measured? How? • Are these metrics good enough to be KPIs? g g
  32. 32. Some simple questions to help you get your KPIs right h • State the online marketing objective • What does that really mean? • What d h does good l k lik ? What will b d look like? h ill be happening differently if you are meeting this objective? bj i ? • Can these behaviours or outcomes be measured? How? • Are these metrics good enough to be KPIs? g g
  33. 33. Good KPIs are “Übermetrics”… Übermetrics … Easy to Actionable understand Strategic Based on measures of valid data success Good KPI
  34. 34. Good KPIs are “Übermetrics”… Übermetrics … Easy to Actionable understand Strategic Based on measures of valid data success Good KPI
  35. 35. Organisations need to have the desire f change… d for h
  36. 36. Organisations need to have the desire f change… d for h
  37. 37. The organisational analytical hierarchy Analytical competitors Analytical companies Analytical aspirations Localised analytics Analytically impaired Source: Tom Davenport “Competing on Analytics”
  38. 38. The organisational analytical hierarchy Analytical competitors Analytical companies Analytical aspirations Localised analytics Analytically impaired Source: Tom Davenport “Competing on Analytics”
  39. 39. Organisations need to have the desire f change… d for h
  40. 40. Organisations need to have the desire f change… d for h
  41. 41. …to have the ability to execute…
  42. 42. …to have the ability to execute…
  43. 43. ..and to remain focussed
  44. 44. ..and to remain focussed
  45. 45. The ingredients for optimisation Philosophy Marketing Technology Processes Optimisation Data
  46. 46. The ingredients for optimisation Philosophy Marketing Technology Processes Optimisation Data
  47. 47. The principle of optimisation Adjust Test Learn
  48. 48. The principle of optimisation Adjust Test Learn
  49. 49. Defeat the HIPPO! Highly Important p Person’s Personal Opinion
  50. 50. Defeat the HIPPO! Highly Important p Person’s Personal Opinion
  51. 51. Get the data out of the silos… Web Survey Campaign Customer analytics data data data data
  52. 52. Get the data out of the silos… Web Survey Campaign Customer analytics data data data data
  53. 53. Campaign optimisation Cross‐ Multi‐ In‐channel channel channel optimisation optimisation optimisation
  54. 54. Campaign optimisation Cross‐ Multi‐ In‐channel channel channel optimisation optimisation optimisation
  55. 55. Optimising site processes… Browse Land Transact and Search
  56. 56. Optimising site processes… Browse Land Transact and Search
  57. 57. Defining page purpose and measurement d Landing zones Landing zone Category Product list Product Home page Basket Checkout page page page Engage Engage Route Process Process Route Convince Visitor numbers Exit rate Cross‐sell Exit rate Bounce rate rate Conversion Conversion Click to rate rate Promotion space CTR Add to product rate basket rate Tool usage
  58. 58. Defining page purpose and measurement d Landing zones Landing zone Category Product list Product Home page Basket Checkout page page page Engage Engage Route Process Process Route Convince Visitor numbers Exit rate Cross‐sell Exit rate Bounce rate rate Conversion Conversion Click to rate rate Promotion space CTR Add to product rate basket rate Tool usage
  59. 59. Multi variate Multi‐variate testing (MVT) • 6 banners • 4 boxes • 5 pictures p • 4 sets of links • 3 product lists • 7 copy variations 6 x 4 x 5 x 4 x 3 x 7 = 10,800 permutations!! 10 800
  60. 60. Multi variate Multi‐variate testing (MVT) • 6 banners • 4 boxes • 5 pictures p • 4 sets of links • 3 product lists • 7 copy variations 6 x 4 x 5 x 4 x 3 x 7 = 10,800 permutations!! 10 800
  61. 61. Create meaningful segments
  62. 62. Create meaningful segments
  63. 63. A user centric framework… Who visits the site? Why do they visit the What do they do on the site? site? ? ? ? ?
  64. 64. A user centric framework… Who visits the site? Why do they visit the What do they do on the site? site? ? ? ? ?
  65. 65. Happy Trackers ( ) ppy (6%) Happy Trackers mainly use the site for Track and Trace and little else In terms of profile they tend to have a stronger business slant and be slightly older than on average They are not heavy users of the site and their visits are relatively light and narrow – all they do is use Track and Trace However they are happy with what they do, they rate the site functionality the best out of all the segments
  66. 66. Happy Trackers ( ) ppy (6%) Happy Trackers mainly use the site for Track and Trace and little else In terms of profile they tend to have a stronger business slant and be slightly older than on average They are not heavy users of the site and their visits are relatively light and narrow – all they do is use Track and Trace However they are happy with what they do, they rate the site functionality the best out of all the segments
  67. 67. Cottage Industrialists (2%) g () Cottage Industrialists are frequent users of the site and they mainly come looking for information on postal prices, delivery services, parcel information and the like. Half of this segment are involved in some type of online auction related activity and over the course of their lifetime they tend to look at the broadest amount of content on the site. Quite often they will be using the search function to do this They are reasonably happy with the customer experience on the site and are more likely than on average to recommend the site to others
  68. 68. Cottage Industrialists (2%) g () Cottage Industrialists are frequent users of the site and they mainly come looking for information on postal prices, delivery services, parcel information and the like. Half of this segment are involved in some type of online auction related activity and over the course of their lifetime they tend to look at the broadest amount of content on the site. Quite often they will be using the search function to do this They are reasonably happy with the customer experience on the site and are more likely than on average to recommend the site to others
  69. 69. The user centric framework…in action Who visits the site? Why do they visit the What do they do on the site and what do they hat the site? think of it?
  70. 70. The user centric framework…in action Who visits the site? Why do they visit the What do they do on the site and what do they hat the site? think of it?
  71. 71. Most people who visit your website… Only visit once once… Generally only look at one page… Most lik l only stay f one minute… likely l for i And probably only transact with you once
  72. 72. Most people who visit your website… Only visit once once… Generally only look at one page… Most lik l only stay f one minute… likely l for i And probably only transact with you once
  73. 73. Look forward… Reflection Anticipation
  74. 74. Look forward… Reflection Anticipation
  75. 75. Look forward… Reflection Anticipation
  76. 76. Look forward… Reflection Anticipation
  77. 77. Predicting segment membership 75% shoppers 25% shoppers 43% items sold 57% items sold “1 to 2” conversion Repeat Visitor Single order Repeat Only y shoppers pp shoppers pp Events and triggers Probable segment membership 11 product based segments Profiled on shopping Profiled on category behaviour, purchasing Profiled on category purchasing, category behaviour, category browsing browsing, lifecycles, mos browsing, plus opt‐in aic, opt‐in
  78. 78. Predicting segment membership 75% shoppers 25% shoppers 43% items sold 57% items sold “1 to 2” conversion Repeat Visitor Single order Repeat Only y shoppers pp shoppers pp Events and triggers Probable segment membership 11 product based segments Profiled on shopping Profiled on category behaviour, purchasing Profiled on category purchasing, category behaviour, category browsing browsing, lifecycles, mos browsing, plus opt‐in aic, opt‐in
  79. 79. Thank you! Neil Mason neil@applied‐insights.co.uk neil@applied insights co uk
  80. 80. Thank you! Neil Mason neil@applied‐insights.co.uk neil@applied insights co uk

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