Aprimo Omniture Webex: Data Driven Marketing

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Increasing campaign response rates through data driven 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 practice
    Admit 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 sectors
    The smoother the overall experience is from TV ad over website content to application process the better we can compete
    Use the actual care careers numbers to make the connection clear
  • Aprimo Omniture Webex: Data Driven Marketing

    1. 1. Data driven marketing Increasing campaign response rates through data driven targeting
    2. 2. Datalicious company history • Datalicious was founded in late 2007 • Strong Omniture web analytics history • 1 of 4 Omniture Service Partners globally • Now 360 data agency with specialist team • Combination of analysts and developers • Making data accessible and actionable • Evangelizing smart data driven marketing • Driving industry best practice (ADMA) October 2010 © Datalicious Pty Ltd 2
    3. 3. Data driven marketing October 2010 © Datalicious Pty Ltd 3 Media attribution Optimising channel mix Testing Improving usability $$$ Targeting Increasing relevance
    4. 4. Increase revenue by 10-20% October 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
    5. 5. The consumer data journey October 2010 © Datalicious Pty Ltd 5 To retention messagesTo transactional data From suspect to To customer From behavioural data From awareness messages TimeTime prospect
    6. 6. Coordination across channels October 2010 © Datalicious Pty Ltd 6 Off-site targeting On-site targeting Profile targeting Generating awareness Creating engagement Maximising revenue 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. 7. Off-site targeting On-site targeting Profile targeting Combining targeting platforms October 2010 © Datalicious Pty Ltd 7
    8. 8. October 2010 © Datalicious Pty Ltd 8
    9. 9. October 2010 © Datalicious Pty Ltd 9
    10. 10. On-site segments Off-site segments Combining technology October 2010 © Datalicious Pty Ltd 10
    11. 11. Campaign response data Combining data sets October 2010 © Datalicious Pty Ltd 11 Customer profile data + The whole is greater than the sum of its parts Website behavioural data
    12. 12. Behaviours plus transactions October 2010 © Datalicious Pty Ltd 12 one-off collection of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expiration, etc predictive models based on data mining propensity to buy, churn, etc historical data from previous transactions average order value, points, etc CRM Profile Updated Occasionally + tracking of purchase funnel stage browsing, checkout, etc tracking of content preferences products, brands, features, etc tracking of external campaign responses search terms, referrers, etc tracking of internal promotion responses emails, internal search, etc Site Behaviour Updated Continuously
    13. 13. Facebook as subscription option October 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. 14. (influencers only) (all contacts) Appending social data to customer profiles Name, age, gender, occupation, location, social profiles and influencer ranking based on email October 2010 14© Datalicious Pty Ltd
    15. 15. The study examined data from two of the UK’s busiest ecommerce websites, ASDA and 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 October 2010 15© Datalicious Pty Ltd
    16. 16. Maximise identification points 20% 40% 60% 80% 100% 120% 140% 160% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks −−− Probability of identification through Cookies October 2010 16© Datalicious Pty Ltd
    17. 17. Sample site visitor composition October 2010 © Datalicious Pty Ltd 17 30% existing customers with extensive profile including transactional history of which maybe 50% can actually be identified as individuals 30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful 10% serious prospects with limited profile data 30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity
    18. 18. Phase Segment A/B Channels Data Points Awareness Consideration Purchase Intent Up/Cross-Sell Developing a targeting matrix October 2010 18© Datalicious Pty Ltd
    19. 19. Phase Segment A/B Channels Data Points Awareness Seen this? Social, display, search, etc Default Consideration Great feature! Social, search, website, etc Download, product view Purchase Intent Great value! Search, site, emails, etc Cart add, checkout, etc Up/Cross-Sell Add this! Direct mail, emails, etc Email response, login, etc Developing a targeting matrix October 2010 19© Datalicious Pty Ltd
    20. 20. Potential home page layout October 2010 © Datalicious Pty Ltd 20 Branded header Rule based offer Customise content delivery on the fly based on referrer data, past content consumption or profile data for existing customers. Targeted offer Popular links, FAQs Targeted offer Login
    21. 21. Prospect targeting parameters October 2010 © Datalicious Pty Ltd 21
    22. 22. Affinity targeting in action October 2010 © Datalicious Pty Ltd 22 Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products. Message CTR By Category Affinity Postpay Prepay Broadb. Business Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - + Google: “vodafone omniture case study” or http://bit.ly/de70b7
    23. 23. Potential newsletter layout October 2010 © Datalicious Pty Ltd 23 Closest stores, offers etc Rule based branded header Data verification Rule based offer Profile based offer Using profile data enhanced with website behaviour data imported into the email delivery platform to build business rules and customise content delivery. NPS
    24. 24. Customer profiling in action October 2010 © Datalicious Pty Ltd 24 Using website and email responses to learn a little bite more about subscribers at every touch point to keep refining profiles and messages.
    25. 25. Potential landing page layout October 2010 © Datalicious Pty Ltd 25 Rule based branded header Campaign message match Targeted offer Passing data on user preferences through to the website via parameters in email click-through URLs to customise content delivery. Call to action
    26. 26. Avinash Kaushik: “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 October 2010 26© Datalicious Pty Ltd
    27. 27. ClickTale testing case study October 2010 © Datalicious Pty Ltd 27
    28. 28. 1. Define success metrics 2. Define and validate segments 3. Develop targeting and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targeting and automate 7. Keep testing and refining 8. Communicate results Keys to effective targeting October 2010 © Datalicious Pty Ltd 28
    29. 29. October 2010 © Datalicious Pty Ltd 29 ADMA short course “Analyse to optimise” In Melbourne & Sydney October/November By Datalicious
    30. 30. October 2010 © Datalicious Pty Ltd 30 Email me cbartens@datalicious.com Follow us twitter.com/datalicious Learn more blog.datalicious.com

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