Westfield Shopper Data

[ Wes&ield shopper data ] From email database to core asset,

the brains of the virtual mall

Capture internet traffic Capture 50-‐100% of fair market share of traffic

Increase consumer engagement Exceed 50% of best compe@tor’s engagement rate

Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% into sales

Building consumer loyalty Build 60% loyalty rate and 40% sales conversion

Increase online revenue Earn 10-‐20% incremental revenue online

[ Increase revenue by 10-‐20% ]

September 2010 © Datalicious Pty Ltd 2

[ The consumer data journey ]


To retenFon messages To transacFonal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

Anonymous data 3rd party data

Individual data Wes&ield data

[ CoordinaFon across channels ]


Off-‐site targeFng

On-‐site targeFng

Profile targeFng

GeneraFng awareness

CreaFng engagement

Maximising revenue

TV, radio, print, outdoor, search marke@ng, display ads, performance networks, affiliates, social media, etc

Retail stores, in-‐store kiosks, call centers, brochures, websites, mobile apps, online chat, social media, etc

Outbound calls, direct mail, emails, social media, SMS, mobile apps, etc

Off-‐site targe@ng

On-‐site targe@ng

Profile targe@ng

[ Combining targeFng pla&orms ]


September 2010 © Datalicious Pty Ltd 6 hPp://ww.wes&ield.com?data=zimbio,promoFon

September 2010 © Datalicious Pty Ltd 7 cookie: zimbio, promoFon, chrisFne, fashion

September 2010 © Datalicious Pty Ltd 8 hPp://ww.wes&ield.com?data=chrisFne,promoFon

[ Extended targeFng pla&orm ]


Brand

Network

Partners

Publishers

September 2010 10 © Datalicious Pty Ltd


Campaign response data

[ Combining data sets ]


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

[ Combining Wes&ield data sets ]


Wes&ield transacFons 3rd party segmentaFon

Geo-‐demographics Website behaviour

Campaign responses

Survey responses

Wes&ield profiles

Reviews/raFngs

Social sharing/likes

Social profiles/comments Combine into single database

for analysis, modelling and to ID targe@ng variables most likely to influence behaviour

[ Behaviours plus transacFons ]


one-‐off collec@on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expiraFon, 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 ConFnuously

[ Sample customer level data ]


[ Maximise idenFficaFon 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 iden@fica@on through Cookies






September 2010 © Datalicious Pty Ltd 21 hPp://www.wes&ield.com?data=digitalforum

[ Profiling at every touch point ]


Using website and email responses to learn a li]le bite more about

subscribers at every touch point to keep

refining profiles and messages.

[ Social media as data source ]


Facebook Connect gives your company the following data and more with just one click Email address, first name, last name, gender, birthday, interests, picture, affilia@ons, last profile update, @me zone, religion, poli@cal interests, a]racted to which sex, why they want to meet someone, home town, rela@onship status, current loca@on, ac@vi@es, music interests, tv show interests, educa@on history, work history, family, etc Need anything else?




Data from

[ Overall volume and influence ]


Data from

[ Influence and media value ]


US

UK

AU/NZ

Data from

(influencers only)

(all contacts)


Appending social data to customer profiles Name, age, gender, occupaFon, locaFon, social profiles and influencer ranking based on email

[ Social media data potenFal ]

§  Large Australian consumer brand §  20% of customer emails had social profiles §  Each profile had an average of 8 friends §  2% of profiles had an influencer score §  0.5% of social had a score of over 10 §  For a database of 500,000 that would mean §  Poten@al addi@onal reach of 100,000 friends §  Includes 2,500 influen@al individuals September 2010 © Datalicious Pty Ltd 30




[ MulFple stores with sales data ]


One backend with mulFple store fronts

[ UK Wes&ield online audience ]


[ US Wes&ield online audience ]


[ Track offline sales driven by online ]

August 2010 © Datalicious Pty Ltd 37

Website research

Phone order

Retail order

Online order

Cookie

AdverFsing campaign

Credit check, fulfilment

Online order confirmaFon

Virtual order confirmaFon

ConfirmaFon email



Phase Fashion Channels Data Points

Awareness

ConsideraFon

Purchase Intent

Up/Cross-‐Sell

[ Developing a targeFng matrix ]


Phase Fashion Channels Data Points

Awareness Seen this? Social, display, search, etc Default

ConsideraFon Great feature! Social, search, website, etc

Download, product view

Purchase Intent Great value! Search, site, email, etc

Cart add, checkout, etc

Up/Cross-‐Sell Add this! Mail, mobile, email, etc

Email click, login, etc

[ Developing a targeFng matrix ]


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.”

[ But quality content is sFll key ]


[ ImplicaFons for Wes&ield ]

§  Collect data to drive value for customers – Not just for the sake of collec@ng data

§  Use data to coordinate customer experience – Mul@ple data sources and targe@ng plaiorms

§  Iden@fy customers wherever possible – Be crea@ve about real world transac@on data

§  KISS principle applies: Keep it simple stupid



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Westfield Shopper Data

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