Data driven marketing<br />Increasing campaign response rates through data driven targeting<br />
Datalicious company history<br />Datalicious was founded in 2007<br />Strong Omniture web analytics history, now<br />One-...
Data driven marketing<br />August 2010<br />© Datalicious Pty Ltd<br />3<br />Media attributionOptimising channel mix<br /...
Increase revenue by 10-20%<br />August 2010<br />© Datalicious Pty Ltd<br />4<br />By coordinating the consumer’s end-to-e...
The consumer data journey<br />August 2010<br />© Datalicious Pty Ltd<br />5<br />To retention messages<br />To transactio...
Coordination across channels  <br />August 2010<br />© Datalicious Pty Ltd<br />6<br />TV, radio, print, outdoor, search m...
Combining targeting platforms<br />August 2010<br />© Datalicious Pty Ltd<br />7<br />
Combining technology platforms<br />August 2010<br />© Datalicious Pty Ltd<br />8<br />On and off-site targeting platforms...
August 2010<br />© Datalicious Pty Ltd<br />9<br />
August 2010<br />© Datalicious Pty Ltd<br />10<br />
Campaign response data<br />Combining data sets<br />August 2010<br />© Datalicious Pty Ltd<br />11<br />Website behaviour...
Behaviours plus transactions<br />August 2010<br />© Datalicious Pty Ltd<br />12<br />CRM Profile<br />Site Behaviour<br /...
Facebook as subscription option<br />August 2010<br />© Datalicious Pty Ltd<br />13<br />Facebook Connect gives your compa...
August 2010<br />© Datalicious Pty Ltd<br />14<br />Flowtown social profiling<br />Name, age, gender, occupation, location...
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 subscription...
Sample site visitor composition<br />August 2010<br />© Datalicious Pty Ltd<br />17<br />30% new visitors with no previous...
Developing a targeting matrix<br />
Developing a targeting matrix<br />
Affinity targeting in action<br />June 2010<br />© Datalicious Pty Ltd<br />20<br />Different type of visitors respond to ...
Potential newsletter layout<br />August 2010<br />© Datalicious Pty Ltd<br />21<br />Using data on website behaviour impor...
Potential landing page layout<br />August 2010<br />© Datalicious Pty Ltd<br />22<br />Passing data on user preferences th...
AvinashKaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour targeting platform tick...
Google: “change one word double conversion” or http://bit.ly/bpyqFp<br />Testing case study<br />August 2010<br />© Datali...
Define success metrics<br />Define and validate segments<br />Develop targeting and message matrix <br />Transform matrix ...
August 2010<br />© Datalicious Pty Ltd<br />26<br />ADMA short course<br />“Analyse to optimise” In Melbourne & Sydney<br ...
August 2010<br />© Datalicious Pty Ltd<br />27<br />Email mecbartens@datalicious.com<br />Follow ustwitter.com/datalicious...
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Aprimo/Omniture breakfast seminar on data driven marketing and effective cross channel 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
  • Aprimo/Omniture breakfast seminar on data driven marketing and effective cross channel targeting

    1. 1. Data driven marketing<br />Increasing campaign response rates through data driven targeting<br />
    2. 2. Datalicious company history<br />Datalicious was founded in 2007<br />Strong Omniture web analytics history, now<br />One-stop data agency with specialist team<br />Combination of analysts and developers<br />Making data accessible and actionable<br />Driving industry best practice<br />Evangelizing use of data<br />August 2010<br />© Datalicious Pty Ltd<br />2<br />
    3. 3. Data driven marketing<br />August 2010<br />© Datalicious Pty Ltd<br />3<br />Media attributionOptimising channel mix<br />TargetingIncreasing relevance<br />TestingImproving usability<br />$$$<br />
    4. 4. Increase revenue by 10-20%<br />August 2010<br />© Datalicious Pty Ltd<br />4<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 />
    5. 5. The consumer data journey<br />August 2010<br />© Datalicious Pty Ltd<br />5<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 />
    6. 6. Coordination across channels <br />August 2010<br />© Datalicious Pty Ltd<br />6<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 />
    7. 7. Combining targeting platforms<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. Campaign response data<br />Combining data sets<br />August 2010<br />© Datalicious Pty Ltd<br />11<br />Website behavioural data<br />+<br />The whole is greater than the sum of its parts<br />Customer profile data<br />
    12. 12. Behaviours plus transactions<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. Facebook as subscription option<br />August 2010<br />© Datalicious Pty Ltd<br />13<br />Facebook Connect gives your company the following data and more with just one click!<br />Email address, first name, last name, middle name, picture, affiliations, last profile update, time zone, religion, political interests, interests, sex, birthday, attracted to which sex, why they want to meet someone, home town, relationship status, current location, activities, music interests, tv show interests, education history, work history, family and ID<br />
    14. 14. August 2010<br />© Datalicious Pty Ltd<br />14<br />Flowtown social profiling<br />Name, age, gender, occupation, location, social profiles and influencer ranking based on email<br />(influencers only)<br />(all contacts)<br />
    15. 15. 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 />
    16. 16. 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 />
    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. Affinity targeting in action<br />June 2010<br />© Datalicious Pty Ltd<br />20<br />Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.<br />Google: “vodafoneomniture case study” or http://bit.ly/de70b7<br />
    21. 21. Potential newsletter layout<br />August 2010<br />© Datalicious Pty Ltd<br />21<br />Using data on website behaviour imported into the email delivery platform to build business rules to customise content delivery.<br />Rule based header theme<br />Data verification<br />NPS<br />Closest stores, offers etc<br />Rule based offer<br />Profile based offer<br />
    22. 22. Potential landing page layout<br />August 2010<br />© Datalicious Pty Ltd<br />22<br />Passing data on user preferences through to the website via parameters in email click-through URLs to customise content delivery.<br />Branded header<br />Email or campaign message match<br />Targeted offers<br />Call to action<br />
    23. 23. 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 />
    24. 24. Google: “change one word double conversion” or http://bit.ly/bpyqFp<br />Testing case study<br />August 2010<br />© Datalicious Pty Ltd<br />24<br />
    25. 25. 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 />25<br />
    26. 26. August 2010<br />© Datalicious Pty Ltd<br />26<br />ADMA short course<br />“Analyse to optimise” In Melbourne & Sydney<br />October/November<br />By Datalicious<br />
    27. 27. August 2010<br />© Datalicious Pty Ltd<br />27<br />Email mecbartens@datalicious.com<br />Follow ustwitter.com/datalicious<br />Learn moreblog.datalicious.com<br />

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