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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

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

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  • 1. [ Digital Direct Marketing ]
    From prospect to customer – smart targeting at different stages of the customer lifecycle
  • 2. 5/26/09
    © 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 asbetter results for businesses.
  • 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
    5/26/09
    © Datalicious Pty Ltd
    3
  • 4. 5/26/09
    © Datalicious Pty Ltd
    4
  • 5. 5/26/09
    © Datalicious Pty Ltd
    5
  • 6. [ Targeting basics ]
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    5/26/09
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  • 7. [ Targeting applications ]
    Acquisition
    Convert prospects
    Retention
    Up-sell and cross-sell
    Reduce churn
    Branding
    Convert prospects
    Build customer loyalty
    5/26/09
    © Datalicious Pty Ltd
    7
  • 8. [ Targeting approaches ]
    Contextual targeting
    Ads based on viewed content
    Anonymous prospects (and customers)
    Behaviouraltargeting
    Ads based on past behaviour
    Anonymous prospects (and customers)
    Profile targeting
    Ads based on user profile database
    Identified customers
    5/26/09
    © Datalicious Pty Ltd
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  • 9. 5/26/09
    © Datalicious Pty Ltd
    9
  • 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
    5/26/09
    © Datalicious Pty Ltd
    10
  • 11. [ Affinity targeting ]
    5/26/09
    © Datalicious Pty Ltd
    11
    Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.
  • 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
    5/26/09
    © Datalicious Pty Ltd
    12
  • 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
    5/26/09
    © Datalicious Pty Ltd
    13
  • 14. [ Success attribution models ]
    5/26/09
    © Datalicious Pty Ltd, www.datalicious.com
    14
    AD 1
    AD 2
    AD 3$100
    $100
    Last ad gets all credit
    AD 1$100
    AD 3
    $100
    First ad gets all credit
    AD 2
    AD 1$100
    AD 2$100
    AD 3$100
    $100
    All ads get equal credit
    AD 1$33
    AD 2$33
    AD 3$33
    $100
    All ads get partial credit
  • 15. [ Targeting technology ]
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    5/26/09
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    15
  • 16. [ Off-site targeting platforms ]
    Ad servers
    Eyeblaster
    DoubleClick
    Faciliate
    Atlas
    Etc
    Ad Networks
    Google
    Yahoo
    ValueClick
    Adconian
    Etc
    5/26/09
    © 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. 5/26/09
    © Datalicious Pty Ltd
    17
  • 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)
    5/26/09
    © Datalicious Pty Ltd
    18
  • 19. [ Matching segments are key ]
    5/26/09
    © Datalicious Pty Ltd
    19
    On and off-site targeting platforms should use identical triggers to sort visitors into segments
  • 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
    5/26/09
    © Datalicious Pty Ltd
    20
  • 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
    5/26/09
    © Datalicious Pty Ltd
    21
  • 22. [ Integration options ]
    Email
    Adjust email content for customers based on behaviouralsegments 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
    5/26/09
    © Datalicious Pty Ltd
    22
  • 23. [ Maximise profiling data ]
    5/26/09
    © Datalicious Pty Ltd
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  • 24. [ Targeting management ]
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  • 25. Define success
    Conduct research
    Define segments
    Validate segments
    Define content
    Test content
    Business rules
    Start targeting
    Communicate results
    [ Keys to effective targeting ]
    5/26/09
    © Datalicious Pty Ltd
    25
  • 26. [ Strategy and execution ]
    5/26/09
    © Datalicious Pty Ltd
    26
    Content
    Process
    Ongoing Targeting Success
    Resource training
    Content production
    Platform maintenance
    Campaign integration
    Ongoing reporting
    Agency processes
    Success definition
    Consumer research
    Segment definition
    Segment validation
    Content testing
    Business rules
    Segments
    Resources
  • 27. [ Prospect targeting parameters ]
    5/26/09
    © Datalicious Pty Ltd
    27
  • 28. [ Customer targeting journey ]
    5/26/09
    © Datalicious Pty Ltd
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    Retention
    Customer Profile
    Customer receives email with customized content, upgrades online
    Customer visits website, sees messaging emphasising upgrade benefits
    Customer frequently visits specific product pages
    Customer reads news online, sees banner for special customer offer
    Customer visits online help site instead of calling call center
    Prospect
    Customer
    Receives welcome email with product FAQ
    Prospect receives reminder email, finishes online purchase
    Prospects clicks on paid search, starts checkout using voucher but leaves
    Prospect visits retail store for demonstration, receives personalized voucher
    Referral from affiliate site, prospect sees customized offers on site
    Prospect sees print ad, executes unique search, sees customized offers on site
    Prospect sees banner ad, no response
    Consideration
    Visitor Behaviour
  • 29. [ Add customer parameters ]
    5/26/09
    © Datalicious Pty Ltd
    29
    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
  • 30. [ Multiply identification points ]
    5/26/09
    © Datalicious Pty Ltd
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    Campaign response
    Online purchase
    Confirmation email
    Repeat purchase
    Email newsletter
    Website login
  • 31. [ Email identification points ]
    5/26/09
    © Datalicious Pty Ltd & Omniture Inc
    31
    Fulfilment
    Phone Conversion
    Website research
    Online ReceiptConfirmation
    @
    Advertising Campaign
    Retail Conversion
    Fulfilment
    Website research
    Online ReceiptConfirmation
    @
    Website research
    Online Conversion
    Fulfilment
    Online Order Confirmation
    Online ReceiptConfirmation
    @
    Cookie ID
  • 32. 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 ]
    5/26/09
    © Datalicious Pty Ltd
    32
  • 33. [ About us ]
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  • 34. [ Datalicious services ]
    5/26/09
    © Datalicious Pty Ltd
    Data
    Insights
    Action
    Web Analytics Solutions
    Keyword Research
    Search Lead Media
    Marketing System Integration
    Campaign Reporting
    Campaign Optimisation
    Cross Channel Media Tracking
    Segmentation/Data Mining
    Internal Search Optimisation
    Online Surveys/Panels
    Targeting/Merchandizing
    Quantitative Research
    Omniture Specialists
    Market/Consumer Trends
    A/B, Multivariate Testing
    Google Analytics Specialists
    Competitor Analysis
    Staff Training/Workshops
    34
  • 35. [ Datalicious clients ]
    5/26/09
    © Datalicious Pty Ltd
    35
  • 36. 5/26/09
    © Datalicious Pty Ltd
    insights@datalicious.com
    36