[ Data driven marketing ]<br />Reducing waste and increasing relevance through targeting<br />
[ Using data to reduce waste ]<br />August 2010<br />© Datalicious Pty Ltd<br />2<br />Media attributionOptimising channel...
[ Increase revenue by 10-20% ]<br />August 2010<br />© Datalicious Pty Ltd<br />3<br />By coordinating the consumer’s end-...
[The consumer data journey ]<br />August 2010<br />© Datalicious Pty Ltd<br />4<br />To retention messages<br />To transac...
[ Coordination across channels ]<br />August 2010<br />© Datalicious Pty Ltd<br />5<br />TV, radio, print, outdoor, search...
[ Combining targeting platforms ]<br />August 2010<br />© Datalicious Pty Ltd<br />6<br />
[ Targeting platforms ]<br />Off-site targeting<br />Ad networks: Google, Yahoo, ValueClick, etc<br />Ad servers: DoubleCl...
[ Combining technology platforms ]<br />August 2010<br />© Datalicious Pty Ltd<br />8<br />On and off-site targeting platf...
August 2010<br />© Datalicious Pty Ltd<br />9<br />
August 2010<br />© Datalicious Pty Ltd<br />10<br />
Customer data<br />[ Combining data sets ]<br />August 2010<br />© Datalicious Pty Ltd<br />11<br />Web analytics data<br ...
[ Behavioursplustransactions ]<br />August 2010<br />© Datalicious Pty Ltd<br />12<br />CRM Profile<br />Site Behaviour<br...
The study examined data from two of the UK’s busiest ecommerce websites, ASDAand William Hill. <br />Given that more than ...
[ Maximise identification points ]<br />Campaign response<br />Online purchase<br />Confirmation email<br />Email subscrip...
August 2010<br />© Datalicious Pty Ltd<br />15<br />DataliciousSuperCookie<br />Persistent Flash cookie that cannot be del...
August 2010<br />© Datalicious Pty Ltd<br />16<br />
[ Sample site visitor composition ]<br />August 2010<br />© Datalicious Pty Ltd<br />17<br />30% new visitors with no prev...
[Developing a targeting matrix ]<br />
[Developing a targeting matrix ]<br />
AvinashKaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour targeting platform tick...
Define success metrics<br />Define and validate segments<br />Develop targeting and message matrix <br />Transform matrix ...
Google: “change one word double conversion” or http://bit.ly/bpyqFp<br />[ClickTale testing case study ]<br />August 2010<...
August 2010<br />© Datalicious Pty Ltd<br />23<br />ADMA short course<br />“Analyse to optimise” In Melbourne & Sydney<br ...
August 2010<br />© Datalicious Pty Ltd<br />24<br />Email mecbartens@datalicious.com<br />Talk to usADMA Forum Stand 347<b...
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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
  • ADMA Forum: Eliminating Waste & Increasing Relevance through Targeting

    1. 1. [ Data driven marketing ]<br />Reducing waste and increasing relevance through targeting<br />
    2. 2. [ Using data to reduce waste ]<br />August 2010<br />© Datalicious Pty Ltd<br />2<br />Media attributionOptimising channel mix<br />TargetingIncreasing relevance<br />TestingImproving usability<br />$$$<br />
    3. 3. [ Increase revenue by 10-20% ]<br />August 2010<br />© Datalicious Pty Ltd<br />3<br />By coordinating the consumer’s end-to-end experience, companies could enjoy revenue increases of 10-20%.<br />Google: “get more value from digital marketing” or http://bit.ly/cAtSUN<br />Source: McKinsey Quarterly, 2010<br />
    4. 4. [The consumer data journey ]<br />August 2010<br />© Datalicious Pty Ltd<br />4<br />To retention messages<br />To transactional data<br />From suspect to<br />To customer<br />prospect<br />Time<br />Time<br />From behavioural data<br />From awareness messages<br />
    5. 5. [ Coordination across channels ]<br />August 2010<br />© Datalicious Pty Ltd<br />5<br />TV, radio, print, outdoor, search marketing, display ads, performance networks, affiliates, social media, etc<br />Retail stores, call centers, brochures, websites, landing pages, mobile apps, online chat, etc<br />Outbound calls, direct mail, emails, SMS, etc<br />
    6. 6. [ Combining targeting platforms ]<br />August 2010<br />© Datalicious Pty Ltd<br />6<br />
    7. 7. [ Targeting platforms ]<br />Off-site targeting<br />Ad networks: Google, Yahoo, ValueClick, etc<br />Ad servers: DoubleClick, Eyeblaster, Atlas, etc<br />On-site targeting<br />Paid: OmnitureTest&Target(Offermatica, TouchClarity), Memetrics(Accenture), Optimost(Autonomy), Kefta(Acxiom), AudienceScience, Maxymiser, Amadesa, etc<br />Free: BTBuckets, Google Analytics, etc<br />Profile targeting<br />Email platforms: Inxmail, Traction, Returnity, etc<br />Marketing automation: Aprimo, Unica, Eloqua, etc<br />August 2010<br />© Datalicious Pty Ltd<br />7<br />
    8. 8. [ Combining technology platforms ]<br />August 2010<br />© Datalicious Pty Ltd<br />8<br />On and off-site targeting platforms should use identical triggers to sort visitors into segments<br />
    9. 9. August 2010<br />© Datalicious Pty Ltd<br />9<br />
    10. 10. August 2010<br />© Datalicious Pty Ltd<br />10<br />
    11. 11. Customer data<br />[ Combining data sets ]<br />August 2010<br />© Datalicious Pty Ltd<br />11<br />Web analytics data<br />+<br />The whole is greater than the sum of its parts<br />3rd party data<br />
    12. 12. [ Behavioursplustransactions ]<br />August 2010<br />© Datalicious Pty Ltd<br />12<br />CRM Profile<br />Site Behaviour<br />one-off collection of demographical data age, gender, address, etc<br />customer lifecycle metrics and key datesprofitability, expiration, etc<br />predictive models based on data miningpropensity to buy, churn, etc<br />historical data from previous transactionsaverage order value, points, etc<br />tracking of purchase funnel stagebrowsing, checkout, etc<br />tracking of content preferencesproducts, brands, features, etc<br />tracking of external campaign responses<br />search terms, referrers, etc<br />tracking of internal promotion responses<br />emails, internal search, etc<br />+<br />Updated OCCASIONALLY<br />Updated continuously<br />
    13. 13. The study examined data from two of the UK’s busiest ecommerce websites, ASDAand William Hill. <br />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.<br />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.<br />Google: ”red eye cookie report pdf” or http://bit.ly/cszp2o<br />[Overestimating unique visitors ]<br />Source: White Paper, RedEye, 2007<br />
    14. 14. [ Maximise identification points ]<br />Campaign response<br />Online purchase<br />Confirmation email<br />Email subscription<br />Email newsletter<br />Online bill payment<br />Repeat purchase<br />Website login<br />−−− Probability of identification through Cookies<br />
    15. 15. August 2010<br />© Datalicious Pty Ltd<br />15<br />DataliciousSuperCookie<br />Persistent Flash cookie that cannot be deleted<br />
    16. 16. August 2010<br />© Datalicious Pty Ltd<br />16<br />
    17. 17. [ Sample site visitor composition ]<br />August 2010<br />© Datalicious Pty Ltd<br />17<br />30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful<br />30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity<br />10% serious prospects with limited profile data<br />30% existing customers with extensive profile including transactional history of which maybe 50% can actually be identified as individuals <br />
    18. 18. [Developing a targeting matrix ]<br />
    19. 19. [Developing a targeting matrix ]<br />
    20. 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.”<br />[ Quality content is key ]<br />
    21. 21. Define success metrics<br />Define and validate segments<br />Develop targeting and message matrix <br />Transform matrix into business rules<br />Develop and test content<br />Start targeting and automate<br />Keep testing and refining<br />Communicate results<br />[ Keys to effective targeting ]<br />August 2010<br />© Datalicious Pty Ltd<br />21<br />
    22. 22. Google: “change one word double conversion” or http://bit.ly/bpyqFp<br />[ClickTale testing case study ]<br />August 2010<br />© Datalicious Pty Ltd<br />22<br />
    23. 23. August 2010<br />© Datalicious Pty Ltd<br />23<br />ADMA short course<br />“Analyse to optimise” In Melbourne & Sydney<br />October/November<br />By Datalicious<br />
    24. 24. August 2010<br />© Datalicious Pty Ltd<br />24<br />Email mecbartens@datalicious.com<br />Talk to usADMA Forum Stand 347<br />Learn morewww.datalicious.com<br />

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