Digital Direct Marketing
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The presentation discusses the application of smart targeting at different stages of the customer life cycle.
![[
Digital
Direct
Marke.ng
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From
prospect
to
customer
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smart
targe/ng
at
different
stages
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customer
lifecycle](https://image.slidesharecdn.com/200905admadirectmarketingindigitalagev3-121117120706-phpapp02/85/digital-direct-marketing-1-320.jpg)
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Digital Direct Marketing
- 1. [ Digital Direct Marke.ng ] From prospect to customer – smart targe/ng at different stages of the customer lifecycle
- 2. Everyone has preferences. That is human nature. Users inform us of their preferences through online behaviour. The ability to make these insights ac.onable and to deliver more relevant content creates a be@er experience for users as well as be@er results for businesses. 14/11/12 © Datalicious Pty Ltd 2
- 3. [ Overview ] § Targe/ng basics – Targe/ng applica/ons – Targe/ng approaches – Affinity vs. one-‐to-‐one – Targe/ng op/ons – AGribu/ng success § Targe/ng technology – Off-‐site providers – On-‐site providers – Technology limita/ons – Integra/on op/ons § Targe/ng management – Strategy development – Internal processes – Poten/al segments 14/11/12 © Datalicious Pty Ltd 3
- 4. 14/11/12 © Datalicious Pty Ltd 4
- 5. 14/11/12 © Datalicious Pty Ltd 5
- 6. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ Targe.ng basics ] 14/11/12 © Datalicious Pty Ltd 6
- 7. [ Targe.ng applica.ons ] § Acquisi/on – Convert prospects § Reten/on – Up-‐sell and cross-‐sell – Reduce churn § Branding – Convert prospects – Build customer loyalty 14/11/12 © Datalicious Pty Ltd 7
- 8. [ Targe.ng approaches ] § Contextual targe/ng – Ads based on viewed content – Anonymous prospects (and customers) § Behavioural targe/ng – Ads based on past behaviour – Anonymous prospects (and customers) § Profile targe/ng – Ads based on user profile database – Iden/fied customers 14/11/12 © Datalicious Pty Ltd 8
- 9. 14/11/12 © Datalicious Pty Ltd 9
- 10. [ Affinity targe.ng ] § Func/on of behavioural targe/ng – Grouping of visitors into major segments – Based on content and conversion behaviour – Ease of use vs. reduced targe/ng ability § Most common affini/es used – Brand affinity – Image preference – Price sensi/vity – Product affinity – Content affinity 14/11/12 © Datalicious Pty Ltd 10
- 11. [ Affinity targe.ng ] Different type of visitors respond to different ads. By using category affinity targe/ng, response rates are liaed significantly across products. CTR By Category Affinity Message Postpay Prepay Broadb. Business Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - + 14/11/12 © Datalicious Pty Ltd 11
- 12. [ Targe.ng op.ons ] § Off-‐site – Contextual targe/ng – behavioural targe/ng § Based on generic online behaviour § Based on specific site behaviour § On-‐site – Contextual targe/ng – behavioural targe/ng § Based on specific site behaviour – Profile targe/ng 14/11/12 © Datalicious Pty Ltd 12
- 13. [ A@ribu.ng success ] § View-‐through conversion – Ad exposure sufficient § All ads (or last) user was exposed to receive conversion credit § Use in combina/on with click-‐through conversion tracking § Cookie expira/on sedngs 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 par/ally § Cookie expira/on – Define dura/on in days ads can claim conversion credit § Survey research can help examine ad recollec/on rate § Usually different for on-‐site vs. off-‐site ads 14/11/12 © Datalicious Pty Ltd 13
- 14. [ Success a@ribu.on 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 par.al credit 14/11/12 © Datalicious Pty Ltd, www.datalicious.com 14
- 15. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ Targe.ng technology ] 14/11/12 © Datalicious Pty Ltd 15
- 16. [ Off-‐site targe.ng plaTorms ] § Ad servers § Ad Networks – Eyeblaster – Google – DoubleClick – Yahoo – Faciliate – ValueClick – Atlas – Adconian – Etc – Etc hGp://en.wikipedia.org/wiki/Contextual_adver/sing, hGp://hubpages.com/hub/101-‐Google-‐Adsense-‐Alterna/ves, hGp://en.wikipedia.org/wiki/Central_ad_server, hGp://www.adopera/onsonline.com/2008/05/23/list-‐of-‐ad-‐servers/, hGp://lists.econsultant.com/top-‐10-‐adver/sing-‐networks.html, hGp://www.clickz.com/3633599, hGp://en.wikipedia.org/wiki/ behavioural_targe/ng 14/11/12 © Datalicious Pty Ltd 16
- 17. 14/11/12 © Datalicious Pty Ltd 17
- 18. [ On-‐site targe.ng plaTorms ] § Test&Target (Omniture, Offerma/ca, TouchClarity) § Memetrics (Accenture) § Op/most (Autonomy) § Keaa (Acxiom) § AudienceScience § Maxymiser § Amadesa § Certona § SiteSpect § BTBuckets (free, targe/ng only) § Google Website Op/mizer (free, tes/ng only) 14/11/12 © Datalicious Pty Ltd 18
- 19. [ Matching segments are key ] On-‐site Off-‐site segments segments On and off-‐site targe/ng plamorms should use iden/cal triggers to sort visitors into segments 14/11/12 © Datalicious Pty Ltd 19
- 20. [ Technology limita.ons ] § JavaScript – Relies on JavaScript to be enabled § Cookies – Relies on cookies for iden/fica/on § hGp://blogs.omniture.com/2006/04/08/15-‐reasons-‐why-‐all-‐ unique-‐visitors-‐are-‐not-‐created-‐equal/ § Mul/ple users per computer § Mul/ple computers § Cookie dele/on § Segments – Can’t find profitable segments § Content – Can’t produce quality content 14/11/12 © Datalicious Pty Ltd 20
- 21. [ Integra.on op.ons ] § Web analy/cs – Record behavioural segments allocated through on-‐site targe/ng plamorm in web analy/cs plamorm 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 targe/ng plamorm in off-‐site ad serving environment – Example: use on-‐site targe/ng plamorm to dynamically write ad server tags into each page if visitor is in specific segment § Affiliates – Implement on-‐site targe/ng plamorm tags on affiliate sites in order to grow targe/ng cookie pool faster – Example: display customized ads to first /me site visitors although they have only visited affiliate sites so far 14/11/12 © Datalicious Pty Ltd 21
- 22. [ Integra.on op.ons ] § Email – Adjust email content for customers based on behavioural segments allocated through on-‐site targe/ng plamorm – Example: email customers product sugges/ons based on their current content affinity and posi/on in purchase funnel § CRM – Add customer profile data to on-‐site behavioural parameters – Example: record customer’s profitability in on-‐site targe/ng plamorm upon login on email click-‐through § Offline – Adjust on-‐site content based on unique offline call to ac/on – Example: visitors using a specific call to ac/on see on-‐site ads matching the offline ads to guarantee consistency 14/11/12 © Datalicious Pty Ltd 22
- 23. [ Maximise profiling data ] website data campaign customer data data 14/11/12 © Datalicious Pty Ltd 23
- 24. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ Targe.ng management ] 14/11/12 © Datalicious Pty Ltd 24
- 25. [ Keys to effec.ve targe.ng ] 1. Define success 2. Conduct research 3. Define segments 4. Validate segments 5. Define content 6. Test content 7. Business rules 8. Start targe/ng 9. Communicate results § 14/11/12 © Datalicious Pty Ltd 25
- 26. [ Strategy and execu.on ] Content Process Success defini/on Resource training Consumer research Content produc/on Segment defini/on Ongoing Plamorm maintenance Segment valida/on Targe.ng Campaign integra/on Content tes/ng Success Ongoing repor/ng Business rules Agency processes Segments Resources 14/11/12 © Datalicious Pty Ltd 26
- 27. [ Prospect targe.ng parameters ] 14/11/12 © Datalicious Pty Ltd 27
- 28. [ Customer targe.ng journey ] Reten/on 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 Receives welcome email with product FAQ Prospect Customer -‐12 -‐11 -‐10 -‐9 -‐8 -‐7 -‐6 -‐5 -‐4 -‐3 -‐2 -‐1 0 Prospect receives reminder email, finishes online purchase 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 demonstra/on, 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 Considera/on Visitor Behaviour Weeks 14/11/12 © Datalicious Pty Ltd 28
- 29. [ Add customer parameters ] Site Behaviour CRM Profile tracking of purchase funnel stage one-‐off collec/on 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, expira.on, etc tracking of external campaign responses predic/ve models based on data mining search terms, referrers, etc propensity to buy, churn, etc tracking of internal promo/on responses historical data from previous transac/ons emails, internal search, etc average order value, points, etc UPDATED CONTINUOUSLY UPDATED OCCASIONALLY 14/11/12 © Datalicious Pty Ltd 29
- 30. [ Mul.ply iden.fica.on points ] Probability of iden/fica/on through cookie 140% 120% 100% 80% 60% 40% 20% 0% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks 14/11/12 © Datalicious Pty Ltd 30
- 31. [ Email iden.fica.on points ] @ Website Phone Online Receipt research Conversion Fulfilment Confirma/on @ Adver/sing Website Retail Online Receipt Campaign research Conversion Fulfilment Confirma/on @ Website Online Online Order Online Receipt research Conversion Confirma/on Fulfilment Confirma/on Cookie ID 14/11/12 © Datalicious Pty Ltd & Omniture Inc 31
- 32. [ Quality content is key ] Avinash Kaushik: “The principle of garbage in, garbage out applies here. […] what makes a behaviour targe<ng pla=orm <ck, 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.” 14/11/12 © Datalicious Pty Ltd 32
- 33. 101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010 [ About us ] 14/11/12 © Datalicious Pty Ltd 33
- 34. [ Datalicious services ] Data Insights Ac.on Web Analy.cs Solu.ons Keyword Research Search Lead Media Marke.ng System Integra.on Campaign Repor.ng Campaign Op.misa.on Cross Channel Media Tracking Segmenta.on/Data Mining Internal Search Op.misa.on Online Surveys/Panels Quan.ta.ve Research Targe.ng/Merchandizing Omniture Specialists Market/Consumer Trends A/B, Mul.variate Tes.ng Google Analy.cs Specialists Compe.tor Analysis Staff Training/Workshops 14/11/12 © Datalicious Pty Ltd 34
- 35. [ Datalicious clients ] 14/11/12 © Datalicious Pty Ltd 35
- 36. insights@datalicious.com 14/11/12 © Datalicious Pty Ltd 36