1. [ Data driven marketing ] Reducing waste and increasing relevance through targeting

2. [ Using data to reduce waste ] August 2010 © Datalicious Pty Ltd 2 Media attributionOptimising channel mix TargetingIncreasing relevance TestingImproving usability $$$

3. [ Increase revenue by 10-20% ] August 2010 © Datalicious Pty Ltd 3 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 © Datalicious Pty Ltd 4 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 © Datalicious Pty Ltd 5 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 © Datalicious Pty Ltd 6

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 © Datalicious Pty Ltd 7

8. [ Combining technology platforms ] August 2010 © Datalicious Pty Ltd 8 On and off-site targeting platforms should use identical triggers to sort visitors into segments

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11. Customer data [ Combining data sets ] August 2010 © Datalicious Pty Ltd 11 Web analytics data + The whole is greater than the sum of its parts 3rd party data

12. [ Behavioursplustransactions ] August 2010 © Datalicious Pty Ltd 12 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 © Datalicious Pty Ltd 15 DataliciousSuperCookie Persistent Flash cookie that cannot be deleted

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17. [ Sample site visitor composition ] August 2010 © Datalicious Pty Ltd 17 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 © Datalicious Pty Ltd 21

22. Google: “change one word double conversion” or http://bit.ly/bpyqFp [ClickTale testing case study ] August 2010 © Datalicious Pty Ltd 22

23. August 2010 © Datalicious Pty Ltd 23 ADMA short course “Analyse to optimise” In Melbourne & Sydney October/November By Datalicious

24. August 2010 © Datalicious Pty Ltd 24 Email mecbartens@datalicious.com Talk to usADMA Forum Stand 347 Learn morewww.datalicious.com