The presentation illustrates Westfield consumer data journey.

1. [
Wes&ield
shopper
data
]
From
email
database
to
core
asset,
the
brains
of
the
virtual
mall

2. [
Increase
revenue
by
10-­‐20%
]
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
September
2010
©
Datalicious
Pty
Ltd
2

3. [
The
consumer
data
journey
]
To
transacFonal
data
To
retenFon
messages
Individual
data
Wes&ield
data
From
suspect
to
prospect
To
customer
Time
Time
Anonymous
data
3rd
party
data
From
behavioural
data
From
awareness
messages
September
2010
©
Datalicious
Pty
Ltd
3

4. [
CoordinaFon
across
channels
]
GeneraFng
CreaFng
Maximising
awareness
engagement
revenue
TV,
radio,
print,
Retail
stores,
in-­‐store
Outbound
calls,
direct
outdoor,
search
kiosks,
call
centers,
mail,
emails,
social
marke@ng,
display
brochures,
websites,
media,
SMS,
mobile
ads,
performance
mobile
apps,
online
apps,
etc
networks,
affiliates,
chat,
social
media,
etc
social
media,
etc
Off-­‐site
On-­‐site
Profile
targeFng
targeFng
targeFng
September
2010
©
Datalicious
Pty
Ltd
4

5. [
Combining
targeFng
pla&orms
]
Off-­‐site
targe@ng
Profile
On-­‐site
targe@ng
targe@ng
September
2010
©
Datalicious
Pty
Ltd
5

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

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

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

9. [
Extended
targeFng
pla&orm
]
Publishers
Partners
Network
Brand
September
2010
©
Datalicious
Pty
Ltd
9

10. September
2010
©
Datalicious
Pty
Ltd
10

11. September
2010
©
Datalicious
Pty
Ltd
11

12. [
Combining
data
sets
]
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
September
2010
©
Datalicious
Pty
Ltd
12

13. [
Combining
Wes&ield
data
sets
]
Wes&ield
profiles
Social
profiles/comments
Combine
into
single
Survey
responses
database
Social
sharing/likes
for
analysis,
modelling
Campaign
responses
and
to
ID
Reviews/raFngs
targe@ng
variables
Website
behaviour
most
likely
Geo-­‐demographics
to
influence
behaviour
Wes&ield
transacFons
3rd
party
segmentaFon
September
2010
©
Datalicious
Pty
Ltd
13

14. [
Behaviours
plus
transacFons
]
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,
expiraFon,
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
ConFnuously
Updated
Occasionally
September
2010
©
Datalicious
Pty
Ltd
14

15. [
Sample
customer
level
data
]
September
2010
©
Datalicious
Pty
Ltd
15

16. [
Maximise
idenFficaFon
points
]
160%
140%
120%
100%
80%
60%
−−−
Probability
of
iden@fica@on
through
Cookies
40%
20%
0
4
8
12
16
20
24
28
32
36
40
44
48
Weeks
September
2010
©
Datalicious
Pty
Ltd
16

17. September
2010
©
Datalicious
Pty
Ltd
17

18. September
2010
©
Datalicious
Pty
Ltd
18

19. September
2010
©
Datalicious
Pty
Ltd
19

20. September
2010
©
Datalicious
Pty
Ltd
20

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

22. [
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.
September
2010
©
Datalicious
Pty
Ltd
22

23. [
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?
September
2010
©
Datalicious
Pty
Ltd
23

24. September
2010
©
Datalicious
Pty
Ltd
24

25. September
2010
©
Datalicious
Pty
Ltd
25

26. Data
from
September
2010
©
Datalicious
Pty
Ltd
26

27. [
Overall
volume
and
influence
]
Data
from
September
2010
©
Datalicious
Pty
Ltd
27

28. [
Influence
and
media
value
]
US
Data
from
UK
AU/NZ
September
2010
©
Datalicious
Pty
Ltd
28

29. Appending
social
data
to
customer
profiles
Name,
age,
gender,
occupaFon,
locaFon,
social
profiles
and
influencer
ranking
based
on
email
(influencers
only)
(all
contacts)
September
2010
©
Datalicious
Pty
Ltd
29

30. [
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

31. September
2010
©
Datalicious
Pty
Ltd
31

32. September
2010
©
Datalicious
Pty
Ltd
32

33. September
2010
©
Datalicious
Pty
Ltd
33

34. [
MulFple
stores
with
sales
data
]
One
backend
with
mulFple
store
fronts
September
2010
©
Datalicious
Pty
Ltd
34

35. [
UK
Wes&ield
online
audience
]
September
2010
©
Datalicious
Pty
Ltd
35

36. [
US
Wes&ield
online
audience
]
September
2010
©
Datalicious
Pty
Ltd
36

37. [
Track
offline
sales
driven
by
online
]
AdverFsing
Phone
Credit
check,
campaign
order
fulfilment
Retail
ConfirmaFon
order
email
Website
Online
Online
order
Virtual
order
research
order
confirmaFon
confirmaFon
Cookie
August
2010
©
Datalicious
Pty
Ltd
37

38. September
2010
©
Datalicious
Pty
Ltd
38

39. September
2010
©
Datalicious
Pty
Ltd
39

40. [
Developing
a
targeFng
matrix
]
Phase
Fashion
Channels
Data
Points
Awareness
ConsideraFon
Purchase
Intent
Up/Cross-­‐Sell
September
2010
©
Datalicious
Pty
Ltd
40

41. [
Developing
a
targeFng
matrix
]
Phase
Fashion
Channels
Data
Points
Social,
display,
Awareness
Seen
this?
Default
search,
etc
Social,
search,
Download,
ConsideraFon
Great
feature!
website,
etc
product
view
Search,
site,
Cart
add,
Purchase
Intent
Great
value!
email,
etc
checkout,
etc
Mail,
mobile,
Email
click,
Up/Cross-­‐Sell
Add
this!
email,
etc
login,
etc
September
2010
©
Datalicious
Pty
Ltd
41

42. [
But
quality
content
is
sFll
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.”
September
2010
©
Datalicious
Pty
Ltd
42

43. [
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

44. Email
us
cbartens@datalicious.com
Follow
us
twiPer.com/datalicious
Learn
more
blog.datalicious.com
September
2010
©
Datalicious
Pty
Ltd
44