Date post: | 29-Nov-2014 |
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[ Digital Direct Marke.ng ] From prospect to customer – smart targe/ng at different stages of the
customer lifecycle
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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.
[ 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
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[ Targe.ng basics ]
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[ Targe.ng applica.ons ]
§ Acquisi/on – Convert prospects
§ Reten/on – Up-‐sell and cross-‐sell – Reduce churn
§ Branding – Convert prospects – Build customer loyalty
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[ 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
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[ 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
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[ Affinity targe.ng ]
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Different type of visitors respond to different ads. By using category affinity targe/ng, response rates are liaed significantly across products.
Message CTR By Category Affinity
Postpay Prepay Broadb. Business
Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +
[ 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
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[ [email protected] 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
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[ Success [email protected] models ]
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AD 1 $100 AD 3
AD 1 $100
AD 2 $100
AD 3 $100
$100
$100
AD 1 AD 2 AD 3 $100 $100 Last ad gets
all credit
First ad gets all credit
All ads get equal credit
AD 1 $33
AD 2 $33
AD 3 $33 $100 All ads get
par.al credit
AD 2
[ Targe.ng technology ]
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[ Off-‐site targe.ng plaTorms ]
§ Ad servers – Eyeblaster – DoubleClick – Faciliate – Atlas – Etc
§ Ad Networks – Google – Yahoo – ValueClick – Adconian – Etc
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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
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[ 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)
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On-‐site segments
Off-‐site segments
[ Matching segments are key ]
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On and off-‐site targe/ng plamorms should use iden/cal triggers to sort visitors into segments
[ 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
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[ 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
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[ 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
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website data
customer data
campaign data
[ Maximise profiling data ]
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[ Targe.ng management ]
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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 §
[ Keys to effec.ve targe.ng ]
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[ Strategy and execu.on ]
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Ongoing Targe.ng Success
Content
Segments Resources
Process
Resource training Content produc/on
Plamorm maintenance Campaign integra/on
Ongoing repor/ng Agency processes
Success defini/on Consumer research Segment defini/on Segment valida/on Content tes/ng Business rules
[ Prospect targe.ng parameters ]
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-‐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
Weeks
[ Customer targe.ng journey ]
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Reten/on
Considera/on
Customer Profile
Prospect
Visitor Behaviour
Customer
Prospect sees banner ad, no response Prospect sees print ad, executes unique search, sees customized offers on site
Prospect visits retail store for demonstra/on, receives personalized voucher
Referral from affiliate site, prospect sees customized offers on site
Prospects clicks on paid search, starts checkout using voucher but leaves
Prospect receives reminder email, finishes online purchase
Customer frequently visits specific product pages
Customer reads news online, sees banner for special customer offer
Receives welcome email with product FAQ Customer visits online help site instead of calling call center
Customer receives email with customized content, upgrades online
Customer visits website, sees messaging emphasising upgrade benefits
[ Add customer parameters ]
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one-‐off collec/on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira.on, etc predic/ve models based on data mining
propensity to buy, churn, etc historical data from previous transac/ons
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 promo/on responses
emails, internal search, etc
Site Behaviour
UPDATED CONTINUOUSLY
[ Mul.ply iden.fica.on points ]
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0%
20%
40%
60%
80%
100%
120%
140%
0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks
Probability of iden/fica/on through cookie
[ Email iden.fica.on points ]
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Adver/sing Campaign
Cookie ID
Website research
Fulfilment Phone
Conversion
Retail Conversion
Online Conversion
Fulfilment
Fulfilment
Website research
Website research
Online Order Confirma/on
Online Receipt Confirma/on
Online Receipt Confirma/on
Online Receipt Confirma/on
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@
@
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.”
[ Quality content is key ]
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[ About us ]
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[ Datalicious services ]
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Web Analy.cs Solu.ons
© Datalicious Pty Ltd
Data
Marke.ng System Integra.on
Cross Channel Media Tracking
Insights Ac.on
Online Surveys/Panels
Omniture Specialists
Google Analy.cs Specialists
Campaign Repor.ng
Compe.tor Analysis
Keyword Research
Segmenta.on/Data Mining
Market/Consumer Trends
Search Lead Media
A/B, Mul.variate Tes.ng
Internal Search Op.misa.on
Campaign Op.misa.on
Targe.ng/Merchandizing
Staff Training/Workshops
Quan.ta.ve Research
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[ Datalicious clients ]
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