westfield shopper data
Post on 29-Nov-2014
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[ 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 ]
September 2010 © Datalicious Pty Ltd 3
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 ]
September 2010 © Datalicious Pty Ltd 4
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 5
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 ]
September 2010 © Datalicious Pty Ltd 9
Brand
Network
Partners
Publishers
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Campaign response data
[ Combining data sets ]
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
[ Combining Wes&ield data sets ]
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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 ]
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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 ]
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[ 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
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September 2010 © Datalicious Pty Ltd 21 hPp://www.wes&ield.com?data=digitalforum
[ Profiling at every touch point ]
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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 ]
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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?
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Data from
[ Overall volume and influence ]
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Data from
[ Influence and media value ]
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US
UK
AU/NZ
Data from
(influencers only)
(all contacts)
September 2010 29 © Datalicious Pty Ltd
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
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[ MulFple stores with sales data ]
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One backend with mulFple store fronts
[ UK Wes&ield online audience ]
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[ US Wes&ield online audience ]
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[ 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
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Phase Fashion Channels Data Points
Awareness
ConsideraFon
Purchase Intent
Up/Cross-‐Sell
[ Developing a targeFng matrix ]
September 2010 40 © Datalicious Pty Ltd
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 ]
September 2010 41 © Datalicious Pty Ltd
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 ]
September 2010 42 © Datalicious Pty Ltd
[ 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
September 2010 © Datalicious Pty Ltd 43
September 2010 © Datalicious Pty Ltd 44
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