ADMA Digital Council Targeting

[ Digital Direct Marketing ] From prospect to customer – smart targeting at different stages of the customer lifecycle

Everyone has preferences. That is human nature. Users inform us of their preferences through online behaviour. The ability to make these insights actionable and to deliver more relevant content creates a better experience for users as well as better results for businesses. 26/05/2009 © Datalicious Pty Ltd 2

[ Overview ]  Targeting basics – Targeting applications – Targeting approaches – Affinity vs. one-to-one – Targeting options – Attributing success  Targeting technology – Off-site providers – On-site providers – Technology limitations – Integration options  Targeting management – Strategy development – Internal processes – Potential segments 26/05/2009 © Datalicious Pty Ltd 3

26/05/2009 © Datalicious Pty Ltd 4

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ Targeting basics ] 26/05/2009 © Datalicious Pty Ltd 6

[ Targeting applications ]  Acquisition – Convert prospects  Retention – Up-sell and cross-sell – Reduce churn  Branding – Convert prospects – Build customer loyalty 26/05/2009 © Datalicious Pty Ltd 7

[ Targeting approaches ]  Contextual targeting – Ads based on viewed content – Anonymous prospects (and customers)  Behaviouraltargeting – Ads based on past behaviour – Anonymous prospects (and customers)  Profile targeting – Ads based on user profile database – Identified customers 26/05/2009 © Datalicious Pty Ltd 8

[ Affinity targeting ]  Function of behavioural targeting – Grouping of visitors into major segments – Based on content and conversion behaviour – Ease of use vs. reduced targeting ability  Most common affinities used – Brand affinity – Image preference – Price sensitivity – Product affinity – Content affinity 26/05/2009 © Datalicious Pty Ltd 10

[ Affinity targeting ] Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products. CTR By Category Affinity Message Postpay Prepay Broadb. Business Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - + 26/05/2009 © Datalicious Pty Ltd 11

[ Targeting options ]  Off-site – Contextual targeting – behavioural targeting  Based on generic online behaviour  Based on specific site behaviour  On-site – Contextual targeting – behavioural targeting  Based on specific site behaviour – Profile targeting 26/05/2009 © Datalicious Pty Ltd 12

[ Attributing success ]  View-through conversion – Ad exposure sufficient  All ads (or last) user was exposed to receive conversion credit  Use in combination with click-through conversion tracking  Cookie expiration settings should be sensible  Click-through conversion – Ad click-through required  Only ads user responded to can receive conversion credit  Define what ad response receives credit – First, last, all equally, all partially  Cookie expiration – Define duration in days ads can claim conversion credit  Survey research can help examine ad recollection rate  Usually different for on-site vs. off-site ads 26/05/2009 © Datalicious Pty Ltd 13

[ Success attribution models ] AD 3 Last ad gets AD 1 AD 2 $100 $100 all credit AD 1 First ad gets $100 AD 2 AD 3 $100 all credit AD 1 AD 2 AD 3 All ads get $100 $100 $100 $100 equal credit AD 1 AD 2 AD 3 All ads get $33 $33 $33 $100 partial credit 26/05/2009 © Datalicious Pty Ltd, www.datalicious.com 14

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ Targeting technology ] 26/05/2009 © Datalicious Pty Ltd 15

[ Off-site targeting platforms ]  Ad servers  Ad Networks – Eyeblaster – Google – DoubleClick – Yahoo – Faciliate – ValueClick – Atlas – Adconian – Etc – Etc http://en.wikipedia.org/wiki/Contextual_advertising, http://hubpages.com/hub/101-Google-Adsense-Alternatives, http://en.wikipedia.org/wiki/Central_ad_server, http://www.adoperationsonline.com/2008/05/23/list-of-ad-servers/, http://lists.econsultant.com/top-10-advertising- networks.html, http://www.clickz.com/3633599, http://en.wikipedia.org/wiki/behavioural_targeting 26/05/2009 © Datalicious Pty Ltd 16

[ On-site targeting platforms ]  Test&Target (Omniture, Offermatica, TouchClarity)  Memetrics (Accenture)  Optimost (Autonomy)  Kefta (Acxiom)  AudienceScience  Maxymiser  Amadesa  Certona  SiteSpect  BTBuckets (free, targeting only)  Google Website Optimizer (free, testing only) 26/05/2009 © Datalicious Pty Ltd 18

[ Matching segments are key ] On-site Off-site segments segments On and off-site targeting platforms should use identical triggers to sort visitors into segments 26/05/2009 © Datalicious Pty Ltd 19

[ Technology limitations ]  JavaScript – Relies on JavaScript to be enabled  Cookies – Relies on cookies for identification  http://blogs.omniture.com/2006/04/08/15-reasons-why-all- unique-visitors-are-not-created-equal/  Multiple users per computer  Multiple computers  Cookie deletion  Segments – Can’t find profitable segments  Content – Can’t produce quality content 26/05/2009 © Datalicious Pty Ltd 20

[ Integration options ]  Web analytics – Record behavioural segments allocated through on-site targeting platform in web analytics platform as well for each visitor – Example: break down site traffic and campaign responses by product category affinity  Ad serving – Replicate behavioural segments allocated through on-site targeting platform in off-site ad serving environment – Example: use on-site targeting platform to dynamically write ad server tags into each page if visitor is in specific segment  Affiliates – Implement on-site targeting platform tags on affiliate sites in order to grow targeting cookie pool faster – Example: display customized ads to first time site visitors although they have only visited affiliate sites so far 26/05/2009 © Datalicious Pty Ltd 21

[ Integration options ]  Email – Adjust email content for customers based on behavioural segments allocated through on-site targeting platform – Example: email customers product suggestions based on their current content affinity and position in purchase funnel  CRM – Add customer profile data to on-site behavioural parameters – Example: record customer’s profitability in on-site targeting platform upon login on email click-through  Offline – Adjust on-site content based on unique offline call to action – Example: visitors using a specific call to action see on-site ads matching the offline ads to guarantee consistency 26/05/2009 © Datalicious Pty Ltd 22

[ Maximise profiling data ] website data campaign customer data data 26/05/2009 © Datalicious Pty Ltd 23

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ Targeting management ] 26/05/2009 © Datalicious Pty Ltd 24

[ Keys to effective targeting ] 1. Define success 2. Conduct research 3. Define segments 4. Validate segments 5. Define content 6. Test content 7. Business rules 8. Start targeting 9. Communicate results 26/05/2009 © Datalicious Pty Ltd 25

[ Strategy and execution ] Content Process Success definition Resource training Consumer research Content production Segment definition Ongoing Platform maintenance Segment validation Targeting Campaign integration Content testing Success Ongoing reporting Business rules Agency processes Segments Resources 26/05/2009 © Datalicious Pty Ltd 26

[ Customer targeting journey ] Retention Customer Profile Customer receives email with customized content, upgrades online Customer visits website, sees messaging emphasising upgrade benefits Customer frequently visits specific product pages Customer reads news online, sees banner for special customer offer Customer visits online help site instead of calling call center Prospect Receives welcome email with product FAQ Customer Prospect receives reminder email, finishes online purchase -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 Prospects clicks on paid search, starts checkout using voucher but leaves Prospect visits retail store for demonstration, receives personalized voucher Referral from affiliate site, prospect sees customized offers on site Prospect sees print ad, executes unique search, sees customized offers on site Prospect sees banner ad, no response Consideration Visitor Behaviour Weeks 26/05/2009 © Datalicious Pty Ltd 28

[ Add customer parameters ] Site Behaviour CRM Profile tracking of purchase funnel stage one-off collection of demographical data browsing, checkout, etc age, gender, address, etc + tracking of content preferences customer lifecycle metrics and key dates products, brands, features, etc profitability, expiration, etc tracking of external campaign responses predictive models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promotion responses historical data from previous transactions emails, internal search, etc average order value, points, etc UPDATED CONTINUOUSLY UPDATED OCCASIONALLY 26/05/2009 © Datalicious Pty Ltd 29

[ Multiply identification points ] Probability of identification through cookie 140% 120% 100% 80% 60% 40% 20% 0% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks 26/05/2009 © Datalicious Pty Ltd 30

[ Email identification points ] Website Online Receipt research Phone Conversion Fulfilment @ Confirmation Website Online Receipt Advertising Campaign research Retail Conversion Fulfilment @ Confirmation Website Online Order Online Receipt research Online Conversion Confirmation Fulfilment @ Confirmation Cookie ID 26/05/2009 © Datalicious Pty Ltd & Omniture Inc 31

[ Quality content is key ] 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.” 26/05/2009 © Datalicious Pty Ltd 32

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ About us ] 26/05/2009 © Datalicious Pty Ltd 33

[ Datalicious services ] Data Insights Action Web Analytics Solutions Keyword Research Search Lead Media Marketing System Integration Campaign Reporting Campaign Optimisation Cross Channel Media Tracking Segmentation/Data Mining Internal Search Optimisation Online Surveys/Panels Quantitative Research Targeting/Merchandizing Omniture Specialists Market/Consumer Trends A/B, Multivariate Testing Google Analytics Specialists Competitor Analysis Staff Training/Workshops 26/05/2009 © Datalicious Pty Ltd 34

ADMA Digital Council Targeting

1 comments

Notes on slide 1

2 Favorites