The presentation discusses the significance of data in marketing through targeting.

1. [
Data
driven
marke.ng
]
Reducing
waste
and
increasing
relevance
through
targe3ng

2. [
Using
data
to
reduce
waste
]
Media
a8ribu.on
Op.mising
channel
mix
Targe.ng
Increasing
relevance
Tes.ng
Improving
usability
$$$
August
2010
©
Datalicious
Pty
Ltd
2

3. [
Increase
revenue
by
10-­‐20%
]
By
coordina.ng
the
consumer’s
end-­‐to-­‐end
experience,
companies
could
enjoy
revenue
increases
of
10-­‐20%.
Google:
“get
more
value
from
digital
marke.ng”
or
h8p://bit.ly/cAtSUN
August
2010
©
Datalicious
Pty
Ltd
3
Source:
McKinsey
Quarterly,
2010

4. [
The
consumer
data
journey
]
To
transac.onal
data
To
reten.on
messages
From
suspect
to
prospect
To
customer
Time
Time
From
behavioural
data
From
awareness
messages
August
2010
©
Datalicious
Pty
Ltd
4

5. [
Coordina.on
across
channels
]
Genera.ng
Crea.ng
Maximising
awareness
engagement
revenue
TV,
radio,
print,
Retail
stores,
call
Outbound
calls,
direct
outdoor,
search
centers,
brochures,
mail,
emails,
SMS,
etc
marke3ng,
display
websites,
landing
ads,
performance
pages,
mobile
apps,
networks,
affiliates,
online
chat,
etc
social
media,
etc
Off-­‐site
On-­‐site
Profile
targe.ng
targe.ng
targe.ng
August
2010
©
Datalicious
Pty
Ltd
5

6. [
Combining
targe.ng
plaZorms
]
Off-­‐site
targe3ng
Profile
On-­‐site
targe3ng
targe3ng
August
2010
©
Datalicious
Pty
Ltd
6

7. [
Targe.ng
plaZorms
]
§ Off-­‐site
targe3ng
– Ad
networks:
Google,
Yahoo,
ValueClick,
etc
– Ad
servers:
DoubleClick,
Eyeblaster,
Atlas,
etc
§ On-­‐site
targe3ng
– Paid:
Omniture
Test&Target
(Offerma3ca,
TouchClarity),
Memetrics
(Accenture),
Op3most
(Autonomy),
Ke[a
(Acxiom),
AudienceScience,
Maxymiser,
Amadesa,
etc
– Free:
BTBuckets,
Google
Analy3cs,
etc
§ Profile
targe3ng
– Email
pla^orms:
Inxmail,
Trac3on,
Returnity,
etc
– Marke3ng
automa3on:
Aprimo,
Unica,
Eloqua,
etc
August
2010
©
Datalicious
Pty
Ltd
7

8. [
Combining
technology
plaZorms
]
On-­‐site
Off-­‐site
segments
segments
On
and
off-­‐site
targe.ng
plaZorms
should
use
iden.cal
triggers
to
sort
visitors
into
segments
August
2010
©
Datalicious
Pty
Ltd
8

9. August
2010
©
Datalicious
Pty
Ltd
9

10. August
2010
©
Datalicious
Pty
Ltd
10

11. [
Combining
data
sets
]
Web
analy.cs
data
Customer
data
+
The
whole
is
greater
than
the
sum
of
its
parts
3rd
party
data
August
2010
©
Datalicious
Pty
Ltd
11

12. [
Behaviours
plus
transac.ons
]
Site
Behaviour
CRM
Profile
tracking
of
purchase
funnel
stage
one-­‐off
collec3on
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,
expira.on,
etc
tracking
of
external
campaign
responses
predic3ve
models
based
on
data
mining
search
terms,
referrers,
etc
propensity
to
buy,
churn,
etc
tracking
of
internal
promo3on
responses
historical
data
from
previous
transac3ons
emails,
internal
search,
etc
average
order
value,
points,
etc
UPDATED
CONTINUOUSLY
UPDATED
OCCASIONALLY
August
2010
©
Datalicious
Pty
Ltd
12

13. [
Overes.ma.ng
unique
visitors
]
The
study
examined
data
from
two
of
the
UK’s
busiest
ecommerce
websites,
ASDA
and
William
Hill.
Given
that
more
than
half
of
all
page
impressions
on
these
sites
are
from
logged-­‐in
users,
they
provided
a
robust
sample
to
compare
IP-­‐based
and
cookie-­‐based
analysis
against.
The
results
were
staggering,
for
example
an
IP-­‐based
approach
overes3mated
visitors
by
up
to
7.6
3mes
whilst
a
cookie-­‐based
approach
overes.mated
visitors
by
up
to
2.3
.mes.
Google:
”red
eye
cookie
report
pdf”
or
h8p://bit.ly/cszp2o
Source:
White
Paper,
RedEye,
2007

14. [
Maximise
iden.fica.on
points
]
160%
140%
120%
100%
80%
60%
−−−
Probability
of
iden3fica3on
through
Cookies
40%
20%
0
4
8
12
16
20
24
28
32
36
40
44
48
Weeks

15. Datalicious
SuperCookie
Persistent
Flash
cookie
that
cannot
be
deleted
August
2010
©
Datalicious
Pty
Ltd
15

16. August
2010
©
Datalicious
Pty
Ltd
16

17. [
Sample
site
visitor
composi.on
]
30%
new
visitors
with
no
30%
repeat
visitors
with
previous
website
history
referral
data
and
some
aside
from
campaign
or
website
history
allowing
referrer
data
of
which
50%
to
be
segmented
by
maybe
50%
is
useful
content
affinity
30%
exis.ng
customers
with
extensive
10%
serious
profile
including
transac3onal
history
of
prospects
which
maybe
50%
can
actually
be
with
limited
iden3fied
as
individuals
profile
data
August
2010
©
Datalicious
Pty
Ltd
17

18. [
Developing
a
targe.ng
matrix
]
Phase
Segment
A
Segment
B
Channels
Awareness
Considera.on
Purchase
Intent
Up/Cross-­‐Sell

19. [
Developing
a
targe.ng
matrix
]
Phase
Segment
A
Segment
B
Channels
Social,
display,
Awareness
Seen
this?
search,
etc
Social,
search,
Considera.on
Great
feature!
website,
etc
Search,
site,
Purchase
Intent
Great
value!
emails,
etc
Direct
mail,
Up/Cross-­‐Sell
Add
this!
emails,
etc

20. [
Quality
content
is
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.”

21. [
Keys
to
effec.ve
targe.ng
]
1. Define
success
metrics
2. Define
and
validate
segments
3. Develop
targe3ng
and
message
matrix
4. Transform
matrix
into
business
rules
5. Develop
and
test
content
6. Start
targe3ng
and
automate
7. Keep
tes3ng
and
refining
8. Communicate
results
August
2010
©
Datalicious
Pty
Ltd
21

22. [
ClickTale
tes.ng
case
study
]
Google:
“change
one
word
double
conversion”
or
h8p://bit.ly/bpyqFp
August
2010
©
Datalicious
Pty
Ltd
22

23. ADMA
short
course
“Analyse
to
op.mise”
In
Melbourne
&
Sydney
October/November
By
Datalicious
August
2010
©
Datalicious
Pty
Ltd
23

24. Email
me
cbartens@datalicious.com
Talk
to
us
ADMA
Forum
Stand
347
Learn
more
www.datalicious.com
August
2010
©
Datalicious
Pty
Ltd
24