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ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
ADMA Marketing Data Strategy Workshop
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ADMA Marketing Data Strategy Workshop

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  • ProsConsumers multi-taskIncreased recollection levelsAbility to track offline channelsConsPaid search competitionDifficult to get natural rankings
  • Transcript

    • 1. > Marketing Data Strategy <
      Smart data driven marketing
    • 2. > Short but sharp history
      • Datalicious was founded late 2007
      • 3. Strong Omniture web analytics history
      • 4. Now 360 data agency with specialist team
      • 5. Combination of analysts and developers
      • 6. Carefully selected best of breed partners
      • 7. Driving industry best practice (ADMA)
      • 8. Turning data into actionable insights
      • 9. Executing smart data driven campaigns
      May 2011
      © Datalicious Pty Ltd
      2
    • 10. > Smart data driven marketing
      May 2011
      © Datalicious Pty Ltd
      3
      Media Attribution & ModelingOptimise channel mix, predict sales
      Targeted Direct Marketing Increase relevance, reduce churn
      Testing & OptimisationRemove barriers, drive sales
      Boost ROAS
    • 11. > Wide range of data services
      May 2011
      © Datalicious Pty Ltd
      4
      Insights
      Analytics
      Data mining and modelling
      Customised dashboards
      Tableau, Spotfire, SPSS, etc
      Media attribution models
      Market and competitor trends
      Social media monitoring
      Customer profiling
      Action
      Campaigns
      Data usage and application
      Marketing automation
      Alterian, SiteCore, 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
      Tag-less online data capture
      End-to-end data platforms
      IVR and call center reporting
      Single customer view
    • 12. > Clients across all industries
      May 2011
      © Datalicious Pty Ltd
      5
    • 13. > Data driven marketing
      What is data driven marketing?
      Self assessment: Your capabilities
      Strategies for effective data collection
      Campaign development and data integrity
      Effective multi-channel campaign execution
      Analysis and performance measurement
      In-sourcing or outsourcing
      May 2011
      © Datalicious Pty Ltd
      6
    • 14. May 2011
      © Datalicious Pty Ltd
      7
      Clive Humby: Data is the new oil
    • 15. > Major data categories
      May 2011
      © Datalicious Pty Ltd
      8
      Campaign dataTV, print, call center, search, web analytics, ad serving, etc
      Customer data
      Direct mail, call center, web analytics, emails, surveys, etc
      Consumer data
      Geo-demographics, search, social, 3rd party research, etc
      Competitor data
      Search, social, ad spend, 3rd party research, news, etc
      Campaigns
      Customers
      Competitors
      Consumers
    • 16. >Corporate data journey
      May 2011
      © Datalicious Pty Ltd
      9
      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
    • 17. May 2011
      © Datalicious Pty Ltd
      10
    • 18. May 2011
      © Datalicious Pty Ltd
      11
      Oil and data come at a price
    • 19. > Google Ngram: Privacy
      May 2011
      © Datalicious Pty Ltd
      12
    • 20. May 2011
      © Datalicious Pty Ltd
      Collecting data for the sake of itor to add valueto customers?
      13
    • 21. > Privacy vs. data benefits policy
      • Do not hide behind small print
      • 22. Use plain English in your privacy policy
      • 23. Explain exactly what data you are recording
      • 24. Explain why you are recording the data
      • 25. Explain the benefits for the consumer
      • 26. Provide opt-out and feedback options
      • 27. Make opt-outs a KPI not just opt-ins
      = Data benefits and privacy policy
      May 2011
      © Datalicious Pty Ltd
      14
    • 28. Exercise: Marketing mix
      May 2011
      © Datalicious Pty Ltd
      15
    • 29.
    • 30. Targeting
      The right message
      Via the right channel
      To the right person
      At the right time
      May 2011
      © Datalicious Pty Ltd
      17
    • 31. > Increase revenue by 10-20%
      May 2011
      © Datalicious Pty Ltd
      18
    • 32. > New consumer decision journey
      May 2011
      © Datalicious Pty Ltd
      19
      The consumer decision process is changing from linearto circular.
    • 33. > New consumer decision journey
      May 2011
      © Datalicious Pty Ltd
      20
      The consumer decision process is changing from linear to circular.
      Online research
      Change increases the importance of experience during research phase.
    • 34. May 2011
      © Datalicious Pty Ltd
      21
    • 35. > Coordination across channels
      May 2011
      © Datalicious Pty Ltd
      22
      TV, radio, print, outdoor, search marketing, display ads, performance networks, affiliates, social media, etc
      Retail stores, in-store kiosks, call centers, brochures, websites, mobile apps, online chat, social media, etc
      Outbound calls, direct mail, emails, social media, SMS, mobile apps, etc
    • 36. > Combining targeting platforms
      May 2011
      © Datalicious Pty Ltd
      23
    • 37. November 2010
      © Datalicious Pty Ltd
      24
    • 38. November 2010
      © Datalicious Pty Ltd
      25
      Take a closer look at our cash flow solutions
    • 39. > Affinity re-targeting in action
      May 2011
      © Datalicious Pty Ltd
      26
      Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.
      Google: “vodafone omniture case study”or http://bit.ly/de70b7
    • 40. > Ad-sequencing in action
      May 2011
      © Datalicious Pty Ltd
      27
      Marketing is about telling stories and stories are not static but evolve over time
      Ad-sequencing can help to evolve stories over time the more users engage with ads
    • 41. > Prospect targeting parameters
      May 2011
      © Datalicious Pty Ltd
      28
    • 42. November 2010
      © Datalicious Pty Ltd
      29
    • 43. > Sample site visitor composition
      May 2011
      © Datalicious Pty Ltd
      30
      30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful
      30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity
      10% serious prospects with limited profile data
      30% existing customers with extensive profile including transactional history of which maybe 50% can actually be identified as individuals
    • 44. > Search call to action for offline
      May 2011
      © Datalicious Pty Ltd
      31
    • 45. May 2011
      © Datalicious Pty Ltd
      32
    • 46. > PURLs boosting DM response rates
      May 2011
      © Datalicious Pty Ltd
      33
      Text
    • 47. > Unique phone numbers
      • 1 unique phone number
      • 48. Phone number is considered part of the brand
      • 49. Media origin of calls cannot be established
      • 50. Added value of website interaction unknown
      • 51. 2-10 unique phone numbers
      • 52. Different numbers for different media channels
      • 53. Exclusive number(s) reserved for website use
      • 54. Call origin data more granular but not perfect
      • 55. Difficult to rotate and pause numbers
      May 2011
      © Datalicious Pty Ltd
      34
    • 56. > Unique phone numbers
      • 10+ unique phone numbers
      • 57. Different numbers for different media channels
      • 58. Different numbers for different product categories
      • 59. Different numbers for different conversion steps
      • 60. Call origin becoming useful to shape call script
      • 61. Feasible to pause numbers to improve integrity
      • 62. 100+ unique phone numbers
      • 63. Different numbers for different website visitors
      • 64. Call origin and time stamp enable individual match
      • 65. Call conversions matched back to search terms
      May 2011
      © Datalicious Pty Ltd
      35
    • 66. > Jet Interactive phone call data
      May 2011
      © Datalicious Pty Ltd
      36
    • 67. > Potential calls to action
      • Unique click-through URLs
      • 68. Unique vanity domains or URLs
      • 69. Unique phone numbers
      • 70. Unique search terms
      • 71. Unique email addresses
      • 72. Unique personal URLs (PURLs)
      • 73. Unique SMS numbers, QR codes
      • 74. Unique promotional codes, vouchers
      • 75. Geographic location (Facebook, FourSquare)
      • 76. Plus regression analysis of cause and effect
      May 2011
      © Datalicious Pty Ltd
      37
      Calls to action can help shape the customer experience not just evaluate responses
    • 77. > The consumer data journey
      May 2011
      © Datalicious Pty Ltd
      38
      To retention messages
      To transactional data
      From suspect to
      To customer
      prospect
      Time
      Time
      From behavioural data
      From awareness messages
    • 78. Campaign response data
      > Combining data sources
      May 2011
      © Datalicious Pty Ltd
      39
      Website behavioural data
      +
      The whole is greater than the sum of its parts
      Customer profile data
    • 79. > Transactions plus behaviours
      May 2011
      © Datalicious Pty Ltd
      40
      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
    • 80. > Customer profiling in action
      May 2011
      © Datalicious Pty Ltd
      41
      Using website and email responses to learn a little bite more about subscribers at every
      touch point to keep
      refining profiles
      and messages.
    • 81. > Online form best practice
      May 2011
      © Datalicious Pty Ltd
      42
      Maximise data integrity
      Age vs. year of birth
      Free text vs. options
      Use auto-complete
      wherever possible
    • 82. Exercise: Enriching profiles
      May 2011
      © Datalicious Pty Ltd
      43
    • 83. > Exercise: Enriching profiles
      May 2011
      © Datalicious Pty Ltd
      44
      CRM Profile
      Site Behaviour
      +
      ?
      ?
    • 84. Exercise: Customer IDs
      May 2011
      © Datalicious Pty Ltd
      45
    • 85. >Exercise: Customer IDs
      May 2011
      © Datalicious Pty Ltd
      46
      To retention messages
      To transactional data
      From suspect to
      To customer
      prospect
      Time
      Time
      From behavioural data
      From awareness messages
    • 86. Geo-demographic data
      > Enhancing data sources
      May 2011
      © Datalicious Pty Ltd
      47
      Customer profile data
      +
      The whole is greater than the sum of its parts
      3rd party data
    • 87. > Geo-demographic segments
      May 2011
      © Datalicious Pty Ltd
      48
    • 88. May 2011
      © Datalicious Pty Ltd
      49
    • 89. May 2011
      © Datalicious Pty Ltd
      50
      Event sponsor presentation
    • 90. transcape
      data solutions
    • 91. Magazine Subscribers
      Mail Order Catalog Buyers
      E-commerce customers
    • 92. transcape
      Buyer File
      1
      Buyer File
      2
      Buyer File
      7
      Buyer File
      3
      Buyer File
      6
      Buyer File
      4
      Buyer File
      5
      "IMP have been working with Alliance Data ever since they launched and have using their Australian & NZ datawith great success across a range of products"
      Victoria Coleman
      Media Manager
      International Masters Publishers
    • 93. transcape
      Selectable by:
      Recency
      Money
      Frequency
    • 94. transcape
      Gender
      Age
      Income
      Selectable by:
      Female
      Male
    • 95. RFM Segmentation (house file)
      0-6 mo.
      7-12 mo.
      13-24 mo.
      25-36 mo.
      37mo.+
      <$10
      0.10%
      1.20%
      0.30%
      0.50%
      0.70%
      $10-$24
      1.50%
      0.90%
      0.70%
      0.40%
      0.20%
      $25-$49
      1.80%
      1.20%
      1.00%
      0.50%
      0.30%
      $50-$99
      2.00%
      1.70%
      1.20%
      0.80%
      0.40%
      2.50%
      2.10%
      1.50%
      1.10%
      0.50%
      $100-$249
      $250+
      3.00%+
      2.20%
      2.00%
      1.40%
      0.70%
      450,000 Buyers
      50,000 Buyers
    • 96. Last bought from YOU
      25-36 mo., $25-$49
      Response Rate = 0.50%
      transcape
      35,000 matches
      50,000 Buyers
      1 .4 million names
    • 97. 0.50%
      0.90%
      Response Rate =
      Last bought from you
      25-36 mo., $25-$49
      50,000
      35,000
      20,000
      Universe =
      Have also bought elsewhere
      1x
      2x
      3x
      1+
      Frequency =
      Recency
      Value
      0-12 mo.
      25+ mo.
      12-24 mo.
      0.30%
      <$25
      0.10%
      0.50%
      $25-49
      0.70%
      0.50%
      0.30%
      0.70%
      0.90%
      $50-$99
      0.50%
      $100+
      0.90%
      1.10%
      0.70%
      Further optimise your house file segments
    • 98. Transactional Data
      Demographic Data
      Geographic Data
    • 99.
    • 100. transcape
      data solutions
      Thank you!
    • 101. Exercise: Targeting matrix
      May 2011
      © Datalicious Pty Ltd
      62
    • 102. > Exercise: Targeting matrix
      May 2011
      © Datalicious Pty Ltd
      63
    • 103. > Exercise: Targeting matrix
      May 2011
      © Datalicious Pty Ltd
      64
    • 104. May 2011
      © Datalicious Pty Ltd
      65
    • 105. May 2011
      © Datalicious Pty Ltd
      66
    • 106. May 2011
      © Datalicious Pty Ltd
      67
    • 107. May 2011
      © Datalicious Pty Ltd
      68
    • 108. Exercise: Marketing automation
      May 2011
      © Datalicious Pty Ltd
      69
    • 109. May 2011
      © Datalicious Pty Ltd
      70
    • 110. > Quality content is key
      Avinash Kaushik: “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.”
      May 2011
      © Datalicious Pty Ltd
      71
    • 111. Plan to fail …
      May 2011
      © Datalicious Pty Ltd
      72
    • 112. > Develop a testing matrix
      May 2011
      © Datalicious Pty Ltd
      73
    • 113. > Develop a testing matrix
      May 2011
      © Datalicious Pty Ltd
      74
    • 114. > AIDA and AIDAS formulas
      May 2011
      © Datalicious Pty Ltd
      75
      Old media
      New media
      Social media
    • 115. > Simplified AIDAS funnel
      May 2011
      © Datalicious Pty Ltd
      76
    • 116. > Marketing is about people
      May 2011
      © Datalicious Pty Ltd
      77
      40%
      10%
      1%
    • 117. > Additional funnel breakdowns
      May 2011
      © Datalicious Pty Ltd
      78
      Brand vs. direct response campaign
      40%
      10%
      1%
      New prospects vs. existing customers
    • 118. May 2011
      © Datalicious Pty Ltd
      79
      New vs. returning visitors
    • 119. May 2011
      © Datalicious Pty Ltd
      80
      AU/NZ vs. rest of world
    • 120. > Potential funnel breakdowns
      • Brand vs. direct response campaign
      • 121. New prospects vs. existing customers
      • 122. Baseline vs. incremental conversions
      • 123. Competitive activity, i.e. none, a lot, etc
      • 124. Segments, i.e. age, location, influence, etc
      • 125. Channels, i.e. search, display, social, etc
      • 126. Campaigns, i.e. this/last week, month, year, etc
      • 127. Products and brands, i.e. iphone, htc, etc
      • 128. Offers, i.e. free minutes, free handset, etc
      • 129. Devices, i.e. home, office, mobile, tablet, etc
      May 2011
      © Datalicious Pty Ltd
      81
    • 130. > Developing a metrics framework
      May 2011
      © Datalicious Pty Ltd
      82
    • 131. > Developing a metrics framework
      May 2011
      © Datalicious Pty Ltd
      83
    • 132. > Establishing a baseline
      May 2011
      © Datalicious Pty Ltd
      84
      Switch all advertising off for a period of time (unlikely) or establish a smaller control group that is representative of the entire population (i.e. search term, geography, etc) and switch off selected channels one at a time to minimise impact on overall conversions.
    • 133. > Importance of calendar events
      May 2011
      © Datalicious Pty Ltd
      85
      Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
    • 134. >Out-sourcing or in-sourcing?
      May 2011
      © Datalicious Pty Ltd
      86
      Year 1Platforms
      Year 2Training
      Year 3Support
      Reduce vendor reliance to absolute minimum but consider the value of support agreements for both maintenance as well as updates on market innovations and new features.
      Degree of in-house control and sophistication
      Start taking control of technology and data, shift vendor focus to enhancements and the provision of training
      for internal resources
      Engage third parties with more experience to get started and to implement technology
      Time, Control
    • 135. May 2011
      © Datalicious Pty Ltd
      87
      Contact mecbartens@datalicious.com
      Learn moreblog.datalicious.com
      Follow metwitter.com/datalicious
    • 136. Data > Insights > Action

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