Digital Direct Marketing

[ Digital Direct Marke.ng ] From prospect to customer – smart targe/ng at different stages of the

customer lifecycle

14/11/12 © Datalicious Pty Ltd 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.

[ 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




[ 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


[ 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



[ 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


[ Affinity targe.ng ]


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


[ [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


[ 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


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


[ 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)


On-‐site segments

Off-‐site segments

[ Matching segments are key ]


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


[ 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


[ 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


website data

customer data

campaign data

[ Maximise profiling data ]


[ 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 ]


[ Strategy and execu.on ]


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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Weeks

[ Customer targe.ng journey ]


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 ]


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 ]


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



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@

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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 ]


[ About us ]

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[ Datalicious services ]

14/11/12

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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[email protected]

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