ADMA Digital Council: Digital Direct Marketing

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May 2009, read more on our blog http://blog.datalicious.com/adma-digital-council-direct-marketing-in-the

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ADMA Digital Council: Digital Direct Marketing

  1. 1. [ Digital Direct Marketing ] From prospect to customer – smart targeting at different stages of the customer lifecycle
  2. 2. 30/01/2015 © Datalicious Pty Ltd 2 Everyone has preferences. That is human nature. Users inform us of their preferences through online behaviour. The ability to make these insights actionable and to deliver more relevant content creates a better experience for users as well as better results for businesses.
  3. 3. [ Overview ]  Targeting basics – Targeting applications – Targeting approaches – Affinity vs. one-to-one – Targeting options – Attributing success  Targeting technology – Off-site providers – On-site providers – Technology limitations – Integration options  Targeting management – Strategy development – Internal processes – Potential segments 30/01/2015 © Datalicious Pty Ltd 3
  4. 4. 30/01/2015 © Datalicious Pty Ltd 4
  5. 5. 30/01/2015 © Datalicious Pty Ltd 5
  6. 6. [ Targeting basics ] 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 30/01/2015 © Datalicious Pty Ltd 6
  7. 7. [ Targeting applications ]  Acquisition – Convert prospects  Retention – Up-sell and cross-sell – Reduce churn  Branding – Convert prospects – Build customer loyalty 30/01/2015 © Datalicious Pty Ltd 7
  8. 8. [ Targeting approaches ]  Contextual targeting – Ads based on viewed content – Anonymous prospects (and customers)  Behavioural targeting – Ads based on past behaviour – Anonymous prospects (and customers)  Profile targeting – Ads based on user profile database – Identified customers 30/01/2015 © Datalicious Pty Ltd 8
  9. 9. 30/01/2015 © Datalicious Pty Ltd 9
  10. 10. [ 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 30/01/2015 © Datalicious Pty Ltd 10
  11. 11. [ Affinity targeting ] 30/01/2015 © Datalicious Pty Ltd 11 Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products. Message CTR By Category Affinity Postpay Prepay Broadb. Business Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +
  12. 12. [ Targeting options ]  Off-site – Contextual targeting – behavioural targeting  Based on generic online behaviour  Based on specific site behaviour  On-site – Contextual targeting – behavioural targeting  Based on specific site behaviour – Profile targeting 30/01/2015 © Datalicious Pty Ltd 12
  13. 13. [ Attributing success ]  View-through conversion – Ad exposure sufficient  All ads (or last) user was exposed to receive conversion credit  Use in combination with click-through conversion tracking  Cookie expiration settings should be sensible  Click-through conversion – Ad click-through required  Only ads user responded to can receive conversion credit  Define what ad response receives credit – First, last, all equally, all partially  Cookie expiration – Define duration in days ads can claim conversion credit  Survey research can help examine ad recollection rate  Usually different for on-site vs. off-site ads 30/01/2015 © Datalicious Pty Ltd 13
  14. 14. [ Success attribution models ] 30/01/2015 © Datalicious Pty Ltd, www.datalicious.com 14 AD 1 $100 AD 3 AD 1 $100 AD 2 $100 AD 3 $100 $100 $100 AD 1 AD 2 AD 3 $100 $100 Last ad gets all credit First ad gets all credit All ads get equal credit AD 1 $33 AD 2 $33 AD 3 $33 $100 All ads get partial credit AD 2
  15. 15. [ Targeting technology ] 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 30/01/2015 © Datalicious Pty Ltd 15
  16. 16. [ Off-site targeting platforms ]  Ad servers – Eyeblaster – DoubleClick – Faciliate – Atlas – Etc  Ad Networks – Google – Yahoo – ValueClick – Adconian – Etc 30/01/2015 © Datalicious Pty Ltd 16 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
  17. 17. 30/01/2015 © Datalicious Pty Ltd 17
  18. 18. [ On-site targeting platforms ]  Test&Target (Omniture, Offermatica, TouchClarity)  Memetrics (Accenture)  Optimost (Autonomy)  Kefta (Acxiom)  AudienceScience  Maxymiser  Amadesa  Certona  SiteSpect  BTBuckets (free, targeting only)  Google Website Optimizer (free, testing only) 30/01/2015 © Datalicious Pty Ltd 18
  19. 19. On-site segments Off-site segments [ Matching segments are key ] 30/01/2015 © Datalicious Pty Ltd 19 On and off-site targeting platforms should use identical triggers to sort visitors into segments
  20. 20. [ Technology limitations ]  JavaScript – Relies on JavaScript to be enabled  Cookies – Relies on cookies for identification  http://blogs.omniture.com/2006/04/08/15-reasons-why-all- unique-visitors-are-not-created-equal/  Multiple users per computer  Multiple computers  Cookie deletion  Segments – Can’t find profitable segments  Content – Can’t produce quality content 30/01/2015 © Datalicious Pty Ltd 20
  21. 21. [ Integration options ]  Web analytics – Record behavioural segments allocated through on-site targeting platform in web analytics platform as well for each visitor – Example: break down site traffic and campaign responses by product category affinity  Ad serving – Replicate behavioural segments allocated through on-site targeting platform in off-site ad serving environment – Example: use on-site targeting platform to dynamically write ad server tags into each page if visitor is in specific segment  Affiliates – Implement on-site targeting platform tags on affiliate sites in order to grow targeting cookie pool faster – Example: display customized ads to first time site visitors although they have only visited affiliate sites so far 30/01/2015 © Datalicious Pty Ltd 21
  22. 22. [ Integration options ]  Email – Adjust email content for customers based on behavioural segments allocated through on-site targeting platform – Example: email customers product suggestions based on their current content affinity and position in purchase funnel  CRM – Add customer profile data to on-site behavioural parameters – Example: record customer’s profitability in on-site targeting platform upon login on email click-through  Offline – Adjust on-site content based on unique offline call to action – Example: visitors using a specific call to action see on-site ads matching the offline ads to guarantee consistency 30/01/2015 © Datalicious Pty Ltd 22
  23. 23. website data customer data campaign data [ Maximise profiling data ] 30/01/2015 © Datalicious Pty Ltd 23
  24. 24. [ Targeting management ] 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 30/01/2015 © Datalicious Pty Ltd 24
  25. 25. 1. Define success 2. Conduct research 3. Define segments 4. Validate segments 5. Define content 6. Test content 7. Business rules 8. Start targeting 9. Communicate results  [ Keys to effective targeting ] 30/01/2015 © Datalicious Pty Ltd 25
  26. 26. [ Strategy and execution ] 30/01/2015 © Datalicious Pty Ltd 26 Ongoing Targeting Success Content Segments Resources Process Resource training Content production Platform maintenance Campaign integration Ongoing reporting Agency processes Success definition Consumer research Segment definition Segment validation Content testing Business rules
  27. 27. [ Prospect targeting parameters ] 30/01/2015 © Datalicious Pty Ltd 27
  28. 28. -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 Weeks [ Customer targeting journey ] 30/01/2015 © Datalicious Pty Ltd 28 Retention Consideration Customer Profile Prospect Visitor Behaviour Customer Prospect sees banner ad, no response Prospect sees print ad, executes unique search, sees customized offers on site Prospect visits retail store for demonstration, receives personalized voucher Referral from affiliate site, prospect sees customized offers on site Prospects clicks on paid search, starts checkout using voucher but leaves Prospect receives reminder email, finishes online purchase Customer frequently visits specific product pages Customer reads news online, sees banner for special customer offer Receives welcome email with product FAQ Customer visits online help site instead of calling call center Customer receives email with customized content, upgrades online Customer visits website, sees messaging emphasising upgrade benefits
  29. 29. [ Add customer parameters ] 30/01/2015 © Datalicious Pty Ltd 29 one-off collection of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expiration, etc predictive models based on data mining propensity to buy, churn, etc historical data from previous transactions average order value, points, etc CRM Profile UPDATED OCCASIONALLY + tracking of purchase funnel stage browsing, checkout, etc tracking of content preferences products, brands, features, etc tracking of external campaign responses search terms, referrers, etc tracking of internal promotion responses emails, internal search, etc Site Behaviour UPDATED CONTINUOUSLY
  30. 30. [ Multiply identification points ] 30/01/2015 © Datalicious Pty Ltd 30 0% 20% 40% 60% 80% 100% 120% 140% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks Probability of identification through cookie
  31. 31. [ Email identification points ] 30/01/2015 © Datalicious Pty Ltd & Omniture Inc 31 Advertising Campaign Cookie ID Website research Fulfilment Phone Conversion Retail Conversion Online Conversion Fulfilment Fulfilment Website research Website research Online Order Confirmation Online Receipt Confirmation Online Receipt Confirmation Online Receipt Confirmation @ @ @
  32. 32. 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.” [ Quality content is key ] 30/01/2015 © Datalicious Pty Ltd 32
  33. 33. [ About us ] 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 30/01/2015 © Datalicious Pty Ltd 33
  34. 34. [ Datalicious services ] 30/01/2015 Web Analytics Solutions © Datalicious Pty Ltd Data Marketing System Integration Cross Channel Media Tracking Insights Action Online Surveys/Panels Omniture Specialists Google Analytics Specialists Campaign Reporting Competitor Analysis Keyword Research Segmentation/Data Mining Market/Consumer Trends Search Lead Media A/B, Multivariate Testing Internal Search Optimisation Campaign Optimisation Targeting/Merchandizing Staff Training/Workshops Quantitative Research 34
  35. 35. [ Datalicious clients ] 30/01/2015 © Datalicious Pty Ltd 35
  36. 36. 30/01/2015 © Datalicious Pty Ltd insights@datalicious.com 36

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