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Data Driven Marketing - the Key to an Effective Marketing Campaign
- 1. [
Data
driven
marke.ng
]
Data
to
help
create
highly
targeted
and
engaging
campaigns
- 2. [
Quick
company
history
]
§ Datalicious
was
founded
in
2007
§ Strong
Omniture
web
analy<cs
history
§ 1
of
4
global
Omniture
Preferred
Partners
§ Now
360
data
agency
with
specialist
team
§ Combina<on
of
analysts
and
developers
§ Evangelizing
smart
data
driven
marke<ng
§ Making
data
accessible
and
ac<onable
§ Driving
industry
best
prac<ce
(ADMA)
September
2010
©
Datalicious
Pty
Ltd
2
- 4. [
Using
data
to
reduce
waste
]
Media
a>ribu.on
Op.mising
channel
mix
Targe.ng
Increasing
relevance
Tes.ng
Improving
usability
$$$
September
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
September
2010
©
Datalicious
Pty
Ltd
5
- 6. [
Coordina.on
across
channels
]
Genera.ng
Crea.ng
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
targe.ng
targe.ng
targe.ng
September
2010
©
Datalicious
Pty
Ltd
6
- 7. [
Combining
targe.ng
plaKorms
]
Off-‐site
targe<ng
Profile
On-‐site
targe<ng
targe<ng
September
2010
©
Datalicious
Pty
Ltd
7
- 9. [
Search
and
media
planning
]
September
2010
©
Datalicious
Pty
Ltd
9
- 11. [
Affinity
targe.ng
in
ac.on
]
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe<ng,
response
rates
are
lied
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
h>p://bit.ly/de70b7
12
Month
Caps
- + - +
September
2010
©
Datalicious
Pty
Ltd
11
- 13. [
Customer
profiling
in
ac.on
]
Using
website
and
email
responses
to
learn
a
li_le
bite
more
about
customers
at
every
touch
point
in
order
to
keep
refining
customer
profiles
and
customising
future
communica<ons.
September
2010
©
Datalicious
Pty
Ltd
13
- 14. [
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
September
2010
©
Datalicious
Pty
Ltd
14
- 15. [
Quality
content
key
to
success
]
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
15
- 16. [
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
16
- 17. [
Behaviours
plus
transac.ons
]
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,
expira.on,
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
Con.nuously
Updated
Occasionally
September
2010
©
Datalicious
Pty
Ltd
17
- 19. [
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
19
- 21. 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)
September
2010
©
Datalicious
Pty
Ltd
21
- 22. [
Social
media
data
poten.al
]
§ 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
22
- 23. [
Overall
volume
and
influence
]
Data
from
September
2010
©
Datalicious
Pty
Ltd
23
- 24. [
Influence
and
media
value
]
US
Data
from
UK
AU/NZ
September
2010
©
Datalicious
Pty
Ltd
24
- 25. [
Google
data
in
Australia
]
Source:
h_p://www.hitwise.com/au/datacentre
September
2010
©
Datalicious
Pty
Ltd
25
- 26. [
Search
at
all
stages
]
September
2010
©
Datalicious
Pty
Ltd
26
Source:
Inside
the
Mind
of
the
Searcher,
Enquiro
2004
- 27. [
Search
and
brand
strength
]
September
2010
©
Datalicious
Pty
Ltd
27
- 28. [
Search
and
the
product
lifecycle
]
Nokia
N-‐Series
Apple
iPhone
September
2010
©
Datalicious
Pty
Ltd
28
- 30. [
Mapping
out
campaign
flows
]
=
Paid
media
Organic
PR,
WOM,
search
events,
etc
=
Viral
elements
=
Coupons,
surveys
YouTube,
Home
pages,
Paid
TV,
print,
blog,
etc
portals,
etc
search
radio,
etc
Direct
mail,
Landing
pages,
Display
ads,
email,
etc
offers,
etc
affiliates,
etc
C1
C2
CRM
Facebook
program
Twi>er,
etc
C3
POS
kiosks,
Call
center,
loyalty
cards,
etc
retail
stores,
etc
September
2010
©
Datalicious
Pty
Ltd
30
- 31. [
Developing
a
metrics
framework
]
Media
and
search
data
Website,
call
center
and
retail
data
People
People
People
People
Reached
40%
Engaged
10%
Converted
1%
Delighted
Quan<ta<ve
and
qualita<ve
research
data
Social
media
data
Social
media
September
2010
©
Datalicious
Pty
Ltd
31
- 32. [
De-‐duplica.on
across
channels
]
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Central
Analy.cs
PlaKorm
Email
Email
Blast
PlaKorm
$
Organic
Google
Search
Analy.cs
$
September
2010
©
Datalicious
Pty
Ltd
32
- 33. [
Success
a>ribu.on
models
]
Banner
Paid
Organic
Success
Last
channel
Search
Ad
Search
$100
$100
gets
all
credit
Banner
Paid
Email
Success
First
channel
Ad
$100
Search
Blast
$100
gets
all
credit
Paid
Banner
Affiliate
Success
All
channels
get
Search
Ad
Referral
$100
$100
$100
$100
equal
credit
Print
Social
Paid
Success
All
channels
get
Ad
Media
Search
$33
$33
$33
$100
par.al
credit
September
2010
©
Datalicious
Pty
Ltd
33
- 34. [
Search
call
to
ac.on
for
offline
]
September
2010
©
Datalicious
Pty
Ltd
34
- 37. [
Target
Denim
]
§ 51,737
Visitors
§ 521,857
Unique
Page
Views
§ 11,402
people
shared
on
Facebook
(Most
from
emails
or
Facebook)
§ 6,821
TVC
Views
§ 82%
New
Visits
(Target
average
73%)
§ 2,005
Wins
§ Average
Time
on
site
is
2.25
minutes
(Target
average
1.07
minutes)
September
2010
©
Datalicious
Pty
Ltd
37
- 38. [
Key
traffic
drivers
]
§ The
campaign
had
a
huge
first
day
before
paid
media
began
which
built
momentum
early
NB:
Removed
data
from
Friday
Feb
11th
as
due
to
extreme
skew
September
2010
©
Datalicious
Pty
Ltd
38
- 39. [
YouTube
]
§ 13,084
YouTube
views,
70
comments,
636
ra<ngs
(490
bad,
136
good)
– Silvia
Pfeiffer
from
Vquence
found
that
males
aged
15
–
25
were
more
likely
to
comment
than
any
other
demographic
Engagement
compared
to
videos
of
similar
length
§ Higher
than
average
engagement
from
viewers
compared
to
videos
of
a
similar
length
§ Honourable
men<ons
for
the
week
ending
the
Feb
21st
September
2010
©
Datalicious
Pty
Ltd
39
- 40. [
Campaign
comparison
]
§ Campaign
traffic
was
almost
that
of
Christmas
and
much
higher
than
the
very
successful
‘Colours’
campaign
Christmas
=
Nov
20
to
Dec
30,
2009
Colours
=
Aug
7
–
Sep
16,
2009
§ Looking
only
at
campaign
Site
Visits
incep<on,
it
did
drive
56%
of
Visits
occur
in
the
higher
daily
average
traffic;
first
4
days
34k
to
32k
respec<vely
September
2010
©
Datalicious
Pty
Ltd
40
- 41. Email
me
cbartens@datalicious.com
Follow
us
twi>er.com/datalicious
Learn
more
blog.datalicious.com
September
2010
©
Datalicious
Pty
Ltd
41