This document discusses how data-driven marketing can increase campaign response rates through targeted digital advertising. It provides examples of how combining different data sources, such as website behavior data and customer profile information, allows for more precise targeting of customers. Integrating data from online and offline activities across channels is also recommended to improve coordination of the consumer experience. Case studies demonstrate how affinity targeting based on interests can significantly boost response rates.

1. Data driven marketing Increasing campaign response rates through data driven targeting

2. Datalicious company history Datalicious was founded in 2007 Strong Omniture web analytics history, now One-stop data agency with specialist team Combination of analysts and developers Making data accessible and actionable Driving industry best practice Evangelizing use of data August 2010 © Datalicious Pty Ltd 2

3. Data driven marketing August 2010 © Datalicious Pty Ltd 3 Media attributionOptimising channel mix TargetingIncreasing relevance TestingImproving usability $$$

4. Increase revenue by 10-20% August 2010 © Datalicious Pty Ltd 4 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

5. The consumer data journey August 2010 © Datalicious Pty Ltd 5 To retention messages To transactional data From suspect to To customer prospect Time Time From behavioural data From awareness messages

6. Coordination across channels August 2010 © Datalicious Pty Ltd 6 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

7. Combining targeting platforms 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. Campaign response data Combining data sets August 2010 © Datalicious Pty Ltd 11 Website behavioural data + The whole is greater than the sum of its parts Customer profile data

12. Behaviours plus transactions 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. Facebook as subscription option August 2010 © Datalicious Pty Ltd 13 Facebook Connect gives your company the following data and more with just one click! 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

14. August 2010 © Datalicious Pty Ltd 14 Flowtown social profiling Name, age, gender, occupation, location, social profiles and influencer ranking based on email (influencers only) (all contacts)

15. 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

16. 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

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. Affinity targeting in action June 2010 © Datalicious Pty Ltd 20 Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products. Google: “vodafoneomniture case study” or http://bit.ly/de70b7

21. Potential newsletter layout August 2010 © Datalicious Pty Ltd 21 Using data on website behaviour imported into the email delivery platform to build business rules to customise content delivery. Rule based header theme Data verification NPS Closest stores, offers etc Rule based offer Profile based offer

22. Potential landing page layout August 2010 © Datalicious Pty Ltd 22 Passing data on user preferences through to the website via parameters in email click-through URLs to customise content delivery. Branded header Email or campaign message match Targeted offers Call to action

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.” Quality content is key

24. Google: “change one word double conversion” or http://bit.ly/bpyqFp Testing case study August 2010 © Datalicious Pty Ltd 24

25. 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 25

26. August 2010 © Datalicious Pty Ltd 26 ADMA short course “Analyse to optimise” In Melbourne & Sydney October/November By Datalicious

27. August 2010 © Datalicious Pty Ltd 27 Email mecbartens@datalicious.com Follow ustwitter.com/datalicious Learn moreblog.datalicious.com