The presentation discusses the impact of data driven targeting in marketing campaigns.

1. Data
driven
marke-ng
Increasing
campaign
response
rates
through
data
driven
targe3ng

2. Datalicious
company
history
• Datalicious
was
founded
in
late
2007
• Strong
Omniture
web
analy3cs
history
• 1
of
4
Omniture
Service
Partners
globally
• Now
360
data
agency
with
specialist
team
• Combina3on
of
analysts
and
developers
• Making
data
accessible
and
ac3onable
• Evangelizing
smart
data
driven
marke3ng
• Driving
industry
best
prac3ce
(ADMA)
October
2010
©
Datalicious
Pty
Ltd
2

3. Data
driven
marke-ng
Media
a8ribu-on
Op-mising
channel
mix
Targe-ng
Increasing
relevance
Tes-ng
Improving
usability
$$$
October
2010
©
Datalicious
Pty
Ltd
3

4. 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
October
2010
©
Datalicious
Pty
Ltd
4

5. 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
October
2010
©
Datalicious
Pty
Ltd
5

6. 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
October
2010
©
Datalicious
Pty
Ltd
6

7. Combining
targe-ng
plaXorms
Off-­‐site
targe3ng
Profile
On-­‐site
targe3ng
targe3ng
October
2010
©
Datalicious
Pty
Ltd
7

8. October
2010
©
Datalicious
Pty
Ltd
8

9. October
2010
©
Datalicious
Pty
Ltd
9

10. Combining
technology
On-­‐site
Off-­‐site
segments
segments
October
2010
©
Datalicious
Pty
Ltd
10

11. Combining
data
sets
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
October
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
Con-nuously
Updated
Occasionally
October
2010
©
Datalicious
Pty
Ltd
12

13. Facebook
as
subscrip-on
op-on
Facebook
Connect
gives
your
company
the
following
data
and
more
with
just
one
click!
Email
address,
first
name,
last
name,
middle
name,
picture,
affilia3ons,
last
profile
update,
3me
zone,
religion,
poli3cal
interests,
interests,
sex,
birthday,
aracted
to
which
sex,
why
they
want
to
meet
someone,
home
town,
rela3onship
status,
current
loca3on,
ac3vi3es,
music
interests,
tv
show
interests,
educa3on
history,
work
history,
family
and
ID
October
2010
©
Datalicious
Pty
Ltd
13

14. Appending
social
data
to
customer
profiles
Name,
age,
gender,
occupa-on,
loca-on,
social
profiles
and
influencer
ranking
based
on
email
(influencers
only)
(all
contacts)
October
2010
©
Datalicious
Pty
Ltd
14

15. 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
October
2010
©
Datalicious
Pty
Ltd
15

16. 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
October
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
October
2010
©
Datalicious
Pty
Ltd
17

18. Developing
a
targe-ng
matrix
Phase
Segment
A/B
Channels
Data
Points
Awareness
Considera-on
Purchase
Intent
Up/Cross-­‐Sell
October
2010
©
Datalicious
Pty
Ltd
18

19. Developing
a
targe-ng
matrix
Phase
Segment
A/B
Channels
Data
Points
Social,
display,
Awareness
Seen
this?
Default
search,
etc
Social,
search,
Download,
Considera-on
Great
feature!
website,
etc
product
view
Search,
site,
Cart
add,
Purchase
Intent
Great
value!
emails,
etc
checkout,
etc
Direct
mail,
Email
response,
Up/Cross-­‐Sell
Add
this!
emails,
etc
login,
etc
October
2010
©
Datalicious
Pty
Ltd
19

20. Poten-al
home
page
layout
Customise
content
Branded
header
delivery
on
the
fly
based
on
referrer
data,
past
content
Rule
based
offer
Login
consump3on
or
profile
data
for
exis3ng
customers.
Targeted
Targeted
offer
offer
Popular
links,
FAQs
October
2010
©
Datalicious
Pty
Ltd
20

21. Prospect
targe-ng
parameters
October
2010
©
Datalicious
Pty
Ltd
21

22. Affinity
targe-ng
in
ac-on
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe3ng,
response
rates
are
liged
significantly
across
products.
CTR
By
Category
Affinity
Message
Postpay
Prepay
Broadb.
Business
Blackberry
Bold
- - - +
Google:
“vodafone
5GB
Mobile
Broadband
- - + -
omniture
case
study”
Blackberry
Storm
+ - + +
or
h8p://bit.ly/de70b7
12
Month
Caps
- + - +
October
2010
©
Datalicious
Pty
Ltd
22

23. Poten-al
newsle8er
layout
Using
profile
data
Rule
based
branded
header
enhanced
with
website
behaviour
Data
verifica-on
NPS
data
imported
into
the
email
delivery
plahorm
to
build
Rule
based
offer
business
rules
and
Closest
stores,
customise
content
Profile
based
offer
delivery.
offers
etc
October
2010
©
Datalicious
Pty
Ltd
23

24. Customer
profiling
in
ac-on
Using
website
and
email
responses
to
learn
a
lile
bite
more
about
subscribers
at
every
touch
point
to
keep
refining
profiles
and
messages.
October
2010
©
Datalicious
Pty
Ltd
24

25. Poten-al
landing
page
layout
Passing
data
on
user
Rule
based
branded
header
preferences
through
to
the
website
via
parameters
in
email
Campaign
message
match
click-­‐through
URLs
to
customise
content
delivery.
Targeted
offer
Call
to
ac-on
October
2010
©
Datalicious
Pty
Ltd
25

26. 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.”
October
2010
©
Datalicious
Pty
Ltd
26

27. ClickTale
tes-ng
case
study
October
2010
©
Datalicious
Pty
Ltd
27

28. 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
October
2010
©
Datalicious
Pty
Ltd
28

29. ADMA
short
course
“Analyse
to
op-mise”
In
Melbourne
&
Sydney
October/November
By
Datalicious
October
2010
©
Datalicious
Pty
Ltd
29

30. Email
me
cbartens@datalicious.com
Follow
us
twi8er.com/datalicious
Learn
more
blog.datalicious.com
October
2010
©
Datalicious
Pty
Ltd
30