ADMA Forum: Eliminating Waste & Increasing Relevance through Targeting

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  • Please insert the actual statistics into the text below the graph and point out that this is based on McKinsey research and best practiceAdmit that NDS is not there to make money and there might not be any direct competitors but point out that the above applies for leads as well And although we might have a limited amount of direct competitors we’re competing for attention with other sectorsThe smoother the overall experience is from TV ad over website content to application process the better we can competeUse the actual care careers numbers to make the connection clear

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  • 1. [ Data driven marketing ]
    Reducing waste and increasing relevance through targeting
  • 2. [ Using data to reduce waste ]
    August 2010
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    Media attributionOptimising channel mix
    TargetingIncreasing relevance
    TestingImproving usability
    $$$
  • 3. [ Increase revenue by 10-20% ]
    August 2010
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    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
  • 4. [The consumer data journey ]
    August 2010
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    To retention messages
    To transactional data
    From suspect to
    To customer
    prospect
    Time
    Time
    From behavioural data
    From awareness messages
  • 5. [ Coordination across channels ]
    August 2010
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    TV, radio, print, outdoor, search marketing, display ads, performance networks, affiliates, social media, etc
    Retail stores, call centers, brochures, websites, landing pages, mobile apps, online chat, etc
    Outbound calls, direct mail, emails, SMS, etc
  • 6. [ Combining targeting platforms ]
    August 2010
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  • 7. [ Targeting platforms ]
    Off-site targeting
    Ad networks: Google, Yahoo, ValueClick, etc
    Ad servers: DoubleClick, Eyeblaster, Atlas, etc
    On-site targeting
    Paid: OmnitureTest&Target(Offermatica, TouchClarity), Memetrics(Accenture), Optimost(Autonomy), Kefta(Acxiom), AudienceScience, Maxymiser, Amadesa, etc
    Free: BTBuckets, Google Analytics, etc
    Profile targeting
    Email platforms: Inxmail, Traction, Returnity, etc
    Marketing automation: Aprimo, Unica, Eloqua, etc
    August 2010
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  • 8. [ Combining technology platforms ]
    August 2010
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    On and off-site targeting platforms should use identical triggers to sort visitors into segments
  • 9. August 2010
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  • 10. August 2010
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  • 11. Customer data
    [ Combining data sets ]
    August 2010
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    Web analytics data
    +
    The whole is greater than the sum of its parts
    3rd party data
  • 12. [ Behavioursplustransactions ]
    August 2010
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    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
  • 13. 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
    [Overestimating unique visitors ]
    Source: White Paper, RedEye, 2007
  • 14. [ Maximise identification points ]
    Campaign response
    Online purchase
    Confirmation email
    Email subscription
    Email newsletter
    Online bill payment
    Repeat purchase
    Website login
    −−− Probability of identification through Cookies
  • 15. August 2010
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    DataliciousSuperCookie
    Persistent Flash cookie that cannot be deleted
  • 16. August 2010
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  • 17. [ Sample site visitor composition ]
    August 2010
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    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
  • 18. [Developing a targeting matrix ]
  • 19. [Developing a targeting matrix ]
  • 20. 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 ]
  • 21. Define success metrics
    Define and validate segments
    Develop targeting and message matrix
    Transform matrix into business rules
    Develop and test content
    Start targeting and automate
    Keep testing and refining
    Communicate results
    [ Keys to effective targeting ]
    August 2010
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  • 22. Google: “change one word double conversion” or http://bit.ly/bpyqFp
    [ClickTale testing case study ]
    August 2010
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  • 23. August 2010
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    ADMA short course
    “Analyse to optimise” In Melbourne & Sydney
    October/November
    By Datalicious
  • 24. August 2010
    © Datalicious Pty Ltd
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    Email mecbartens@datalicious.com
    Talk to usADMA Forum Stand 347
    Learn morewww.datalicious.com