Contenu connexe Similaire à SunCorp Analytics (20) SunCorp Analytics2. >
Short
but
sharp
history
§ Datalicious
was
founded
late
2007
§ Strong
Omniture
web
analy-cs
history
§ Now
360
data
agency
with
specialist
team
§ Combina-on
of
analysts
and
developers
§ Carefully
selected
best
of
breed
partners
§ Evangelizing
smart
data
driven
marke-ng
§ Making
data
accessible
and
ac-onable
§ Driving
industry
best
prac-ce
(ADMA)
November
2010
©
Datalicious
Pty
Ltd
2
4. >
Wide
range
of
data
services
Data
Insights
Ac.on
Pla>orms
Repor.ng
Applica.ons
Data
collec.on
and
processing
Data
mining
and
modelling
Data
usage
and
applica.on
Web
analy.cs
solu.ons
Customised
dashboards
Marke.ng
automa.on
Omniture,
Google
Analy.cs,
etc
Media
aKribu.on
models
Aprimo,
Trac.on,
Inxmail,
etc
Tag-‐less
online
data
capture
Market
and
compe.tor
trends
Targe.ng
and
merchandising
End-‐to-‐end
data
pla>orms
Social
media
monitoring
Internal
search
op.misa.on
IVR
and
call
center
repor.ng
Online
surveys
and
polls
CRM
strategy
and
execu.on
Single
customer
view
Customer
profiling
Tes.ng
programs
November
2010
©
Datalicious
Pty
Ltd
4
5. >
Smart
data
driven
marke.ng
Media
AKribu.on
Op.mise
channel
mix
Targe.ng
Increase
relevance
Tes.ng
Improve
usability
$$$
November
2010
©
Datalicious
Pty
Ltd
5
6. 15 tools are proposed largely centred on Adobe
Once implemented these tools will deliver the capability for each LOB to leverage online data to optimise the
customer's journey across any channel or brand within the Suncorp group
CUSTOMER JOURNEY CAPABILITY
CAPABILITY TOOL AQUIRE CONVERT GROW EVOLUTION
Multi-Media 1. Digital campaign tracking & attribution ü ü ü
Measurement
& Attribution 2. Full digital pathway tracking & attribution ü ü ü
3. Onsite promotion tracking ü
Increased Personalisation
4. Fallout & conversion analysis ü
5. Adv site navigation & form analysis ü
Site
Anonymous
Optimisation 6. Internal search analysis ü
7. Brand portfolio level measurement ü ü ü
8. Site surveys ü
9. Advanced visitor segmentation ü ü ü
Advanced 10. A/B & content testing ü ü ü
Segmentation
& Targeting 11. Campaign retargeting ü ü
12. Behavioural targeting ü ü Semi-Identified
Personalised 13. Syndicate personalised content ü
Targeting
14. Digital identification & CRM integration ü ü ü
Identified
Multi-Channel 15. Online to offline conversion ü ü
Optimisation
Optimised Optimised
Optimised
channel mix customer Optimised
STRATEGIC GOAL DELIVERED marketing mix
RIGHT OFFER
RIGHT
CHANNEL
RIGHT
CUSTOMER
Customer Journey
6
8. >
Campaign
flow
and
calls
to
ac.on
=
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
TwiKer,
etc
C3
POS
kiosks,
Call
center,
loyalty
cards,
etc
retail
stores,
etc
November
2010
©
Datalicious
Pty
Ltd
8
10. >
Duplica.on
across
channels
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Email
Email
Blast
Pla>orm
$
Organic
Google
Search
Analy.cs
$
November
2010
©
Datalicious
Pty
Ltd
10
11. >
Cookie
expira.on
impact
Paid
Bid
Search
Mgmt
$
Banner
Banner
Ad
Ad
Click
Ad
View
Server
$
Email
Email
Expira.on
Blast
Pla>orm
$
Organic
Google
Search
Analy.cs
$
November
2010
©
Datalicious
Pty
Ltd
11
12. >
De-‐duplica.on
across
channels
Paid
Search
$
Banner
Ads
$
Central
Analy.cs
Pla>orm
Email
Blast
$
Organic
Search
$
November
2010
©
Datalicious
Pty
Ltd
12
14. >
Exercise:
Duplica.on
impact
§ Double-‐coun-ng
of
conversions
across
channels
can
have
a
significant
impact
on
key
metrics,
especially
CPA
§ Example:
Display
ads
and
paid
search
– Total
media
budget
of
$10,000
of
which
50%
is
spend
on
paid
search
and
50%
on
display
ads
– Total
of
100
conversions
across
both
channels
with
a
channel
overlap
of
50%,
i.e.
both
channels
claim
100%
of
conversions
based
on
their
own
repor-ng
but
once
de-‐duplicated
they
each
only
contributed
50%
of
conversions
– What
are
the
ini-al
CPA
values
and
what
is
the
true
CPA?
§ Solu-on:
$50
ini-al
CPA
and
$100
true
CPA
– $5,000
/
100
=
$50
ini-al
CPA
and
$5,000
/
50
=
$100
true
CPA
(which
represents
a
100%
increase)
November
2010
©
Datalicious
Pty
Ltd
14
16. >
Reach
and
channel
overlap
TV/Print
audience
Banner
Search
audience
audience
November
2010
©
Datalicious
Pty
Ltd
16
20. >
Success
aKribu.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
November
2010
©
Datalicious
Pty
Ltd
20
21. >
First
and
last
click
aKribu.on
Chart
shows
percentage
of
channel
touch
points
that
lead
Paid/Organic
Search
to
a
conversion.
Neither
first
Emails/Shopping
Engines
nor
last-‐click
measurement
would
provide
true
picture
November
2010
©
Datalicious
Pty
Ltd
21
22. >
Adobe
stacking/par.cipa.on
Adobe
can
only
stack
direct
paid
and
organic
responses
that
end
up
on
your
website
proper.es,
mere
banner
impressions
are
missing
from
the
stack
and
cannot
be
included
via
Genesis
a`er
the
fact.
November
2010
©
Datalicious
Pty
Ltd
22
23. >
Full
path
to
purchase
Introducer
Influencer
Influencer
Closer
$
SEM
Banner
Direct
SEO
Online
Generic
Click
Visit
Branded
Banner
SEO
Affiliate
Social
Offline
View
Generic
Click
Media
TV
SEO
Direct
Email
Abandon
Ad
Branded
Visit
Update
November
2010
©
Datalicious
Pty
Ltd
23
24. >
Where
to
collect
the
data
Ad
Server
Web
Analy.cs
Banner
impressions
Referral
visits
Banner
clicks
Social
media
visits
+
Organic
search
visits
Paid
search
clicks
Paid
search
visits
Email
visits,
etc
Lacking
organic
visits
Lacking
banner
impressions
More
granular
&
complex
Less
granular
&
complex
November
2010
©
Datalicious
Pty
Ltd
24
26. >
Search
call
to
ac.on
for
offline
November
2010
©
Datalicious
Pty
Ltd
26
31. >
Poten.al
calls
to
ac.on
§ Unique
click-‐through
URLs
§ Unique
vanity
domains
or
URLs
§ Unique
phone
numbers
§ Unique
search
terms
§ Unique
email
addresses
§ Unique
personal
URLs
(PURLs)
§ Unique
SMS
numbers,
QR
codes
§ Unique
promo-onal
codes,
vouchers
§ Geographic
loca-on
(Facebook,
FourSquare)
§ Plus
regression
analysis
of
cause
and
effect
November
2010
©
Datalicious
Pty
Ltd
31
32. >
Unique
phone
numbers
§ 1
unique
phone
number
– Phone
number
is
considered
part
of
the
brand
– Media
origin
of
calls
cannot
be
established
– Added
value
of
website
interac-on
unknown
§ 2-‐10
unique
phone
numbers
– Different
numbers
for
different
media
channels
– Exclusive
number(s)
reserved
for
website
use
– Call
origin
data
more
granular
but
not
perfect
– Difficult
to
rotate
and
pause
numbers
November
2010
©
Datalicious
Pty
Ltd
32
33. >
Unique
phone
numbers
§ 10+
unique
phone
numbers
– Different
numbers
for
different
media
channels
– Different
numbers
for
different
product
categories
– Different
numbers
for
different
conversion
steps
– Call
origin
becoming
useful
to
shape
call
script
– Feasible
to
pause
numbers
to
improve
integrity
§ 100+
unique
phone
numbers
– Different
numbers
for
different
website
visitors
– Call
origin
and
-me
stamp
enable
individual
match
– Call
conversions
matched
back
to
search
terms
November
2010
©
Datalicious
Pty
Ltd
33
36. >
PURLs
boos.ng
DM
response
rates
Text
November
2010
©
Datalicious
Pty
Ltd
36
37. >
Media
aKribu.on
phases
§ Phase
1:
De-‐duplica-on
– Conversion
de-‐duplica-on
across
all
channels
– Requires
one
central
repor-ng
plaiorm
– Limited
to
first/last
click
ajribu-on
§ Phase
2:
Direct
response
pathing
– Response
pathing
across
paid
and
organic
channels
– Only
covers
clicks
and
not
mere
banner
views
– Can
be
enabled
in
Google
Analy-cs
and
Omniture
§ Phase
3:
Full
purchase
path
– Direct
response
tracking
including
banner
exposure
– Cannot
be
done
in
Google
Analy-cs
or
Omniture
– Easier
to
import
addi-onal
channels
into
ad
server
November
2010
©
Datalicious
Pty
Ltd
37
39. >
Single
source
of
truth
repor.ng
Insights
Repor.ng
November
2010
©
Datalicious
Pty
Ltd
39
41. >
Website
entry
survey
De-‐duped
Campaign
Report
Greatest
Influencer
on
Branded
Search
/
STS
}
Channel
%
of
Conversions
Channel
%
of
Influence
Straight
to
Site
27%
Word
of
Mouth
32%
SEO
Branded
15%
Blogging
&
Social
Media
24%
SEM
Branded
9%
Newspaper
Adver-sing
9%
SEO
Generic
7%
Display
Adver-sing
14%
SEM
Generic
14%
Email
Marke-ng
7%
Display
Adver-sing
7%
Retail
Promo-ons
14%
Affiliate
Marke-ng
9%
Referrals
5%
Conversions
ajributed
to
search
terms
Email
Marke-ng
7%
that
contain
brand
keywords
and
direct
website
visits
are
most
likely
not
the
origina-ng
channel
that
generated
the
awareness
and
as
such
conversion
credits
should
be
re-‐allocated.
November
2010
©
Datalicious
Pty
Ltd
41
42. >
Adjus.ng
for
offline
impact
-‐5
-‐15
-‐10
+5
+15
+10
November
2010
©
Datalicious
Pty
Ltd
42
43. >
Ad
server
exposure
test
Banner
TV/Print
Search
Impression
Response
Response
$
Banner
Search
Direct
Impression
Response
Response
$
Users
are
segmented
before
1st
ad
is
even
Exposed
group:
90%
of
users
get
branded
message
served
Control
group:
10%
of
users
get
non-‐branded
message
Banner
Search
Direct
Impression
Response
Response
$
November
2010
©
Datalicious
Pty
Ltd
43
44. >
Full
path
to
purchase
Introducer
Influencer
Influencer
Closer
$
SEM
Banner
Direct
SEO
Online
Generic
Click
Visit
Branded
Banner
SEO
Affiliate
Social
Offline
View
Generic
Click
Media
TV
SEO
Direct
Email
Abandon
Ad
Branded
Visit
Update
November
2010
©
Datalicious
Pty
Ltd
44
46. >
Offline
sales
driven
by
online
Adver.sing
Phone
Credit
check,
campaign
order
fulfilment
Retail
Confirma.on
order
email
Website
Online
Online
order
Virtual
order
research
order
confirma.on
confirma.on
Cookie
November
2010
©
Datalicious
Pty
Ltd
46
47. >
Tracking
offline
conversions
§ Email
click-‐through
aoer
purchase
§ First
online
login
aoer
purchase
§ Unique
website
or
visitor
phone
number
§ Call
back
request
or
online
chat
§ Unique
website
promo-on
code
§ Unique
printable
vouchers
§ Store
locator
searches
§ Make
an
appointment
online
November
2010
©
Datalicious
Pty
Ltd
47
48. >
Success
aKribu.on
models
Introducer
Influencer
Influencer
Closer
$
Even
25%
25%
25%
25%
AKrib.
Exclusion
33%
33%
33%
0%
AKrib.
PaKern
30%
20%
20%
30%
AKrib.
November
2010
©
Datalicious
Pty
Ltd
48
49. >
Path
across
different
segments
Introducer
Influencer
Influencer
Closer
$
Product
Channel
1
Channel
2
Channel
3
Channel
4
A
vs.
B
New
Channel
1
Channel
2
Channel
3
Channel
4
prospects
Exis.ng
Channel
1
Channel
2
Channel
3
Product
4
customers
November
2010
©
Datalicious
Pty
Ltd
49
50. >
Paths
across
business
units
Introducer
Influencer
Influencer
Closer
$
Brand
1
Brand
2
Brand
3
Brand
4
$
Brand
1
Brand
2
Brand
3
Brand
4
$
Brand
1
Brand
2
Brand
3
Brand
4
$
November
2010
©
Datalicious
Pty
Ltd
50
52. >
Exercise:
AKribu.on
models
Introducer
Influencer
Influencer
Closer
$
Even
25%
25%
25%
25%
AKrib.
Exclusion
33%
33%
33%
0%
AKrib.
?
?
?
?
Custom
AKrib.
November
2010
©
Datalicious
Pty
Ltd
52
53. >
Common
aKribu.on
models
§ Allocate
more
conversion
credits
to
more
recent
touch
points
for
brands
with
a
strong
baseline
to
s-mulate
repeat
purchases
§ Allocate
more
conversion
credits
to
more
recent
touch
points
for
brands
with
a
direct
response
focus
§ Allocate
more
conversion
credits
to
ini-a-ng
touch
points
for
new
and
expensive
brands
and
products
to
insert
them
into
the
mindset
November
2010
©
Datalicious
Pty
Ltd
53
55. How
many
survey
responses
do
you
need
if
you
have
10,000
customers?
How
many
email
opens
do
you
need
to
test
2
subject
lines
if
your
subscriber
base
is
50,000?
How
many
orders
do
you
need
to
test
6
banner
execu.ons
if
you
serve
1,000,000
banners
November
2010
©
Datalicious
Pty
Ltd
55
Google
“nss
sample
size
calculator”
56. How
many
survey
responses
do
you
need
if
you
have
10,000
customers?
369
for
each
ques.on
or
369
complete
responses
How
many
email
opens
do
you
need
to
test
2
subject
lines
if
your
subscriber
base
is
50,000?
And
email
sends?
381
per
subject
line
or
381
x
2
=
762
email
opens
How
many
orders
do
you
need
to
test
6
banner
execu.ons
if
you
serve
1,000,000
banners?
383
sales
per
banner
execu.on
or
383
x
6
=
2,298
sales
November
2010
©
Datalicious
Pty
Ltd
56
Google
“nss
sample
size
calculator”
57. >
Addi.onal
success
metrics
Click
Through
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Page
Page
Product
Through
Bounce
Views
Views
$
Click
Call
back
Store
Through
request
Search
?
$
November
2010
©
Datalicious
Pty
Ltd
57
59. >
Importance
of
calendar
events
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
November
2010
©
Datalicious
Pty
Ltd
59
62. >
Quick
wins
and
geung
started
§ Central
analy-cs
plaiorm
§ Addi-onal
data
into
ad
server
§ Unique
phone
numbers
§ Search
call
to
ac-on
§ Addi-onal
metrics
§ Event
calendar
November
2010
©
Datalicious
Pty
Ltd
62
64. >
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%
to
sales
Building
consumer
loyalty
Build
60%
loyalty
rate
and
40%
sales
conversion
Increase
online
revenue
Earn
10-‐20%
incremental
revenue
online
November
2010
©
Datalicious
Pty
Ltd
64
65. >
New
consumer
decision
journey
The
consumer
decision
process
is
changing
from
linear
to
circular.
November
2010
©
Datalicious
Pty
Ltd
65
66. >
New
consumer
decision
journey
The
consumer
decision
process
is
changing
from
linear
to
circular.
Online
research
Change
increases
the
importance
of
experience
during
research
phase.
November
2010
©
Datalicious
Pty
Ltd
66
67. >
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
November
2010
©
Datalicious
Pty
Ltd
67
68. >
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
November
2010
©
Datalicious
Pty
Ltd
68
69. >
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
November
2010
©
Datalicious
Pty
Ltd
69
70. >
Combining
targe.ng
pla>orms
Off-‐site
targe-ng
Profile
On-‐site
targe-ng
targe-ng
November
2010
©
Datalicious
Pty
Ltd
70
72. Take
a
closer
look
at
our
cash
flow
solu.ons
November
2010
©
Datalicious
Pty
Ltd
72
74. +
Add
website
behaviour
to
submiKed
contact
form
data
November
2010
©
Datalicious
Pty
Ltd
74
75. Take
a
closer
look
at
our
cash
flow
solu.ons
November
2010
©
Datalicious
Pty
Ltd
75
76. Save
.me
and
get
your
business
insurance
November
2010
©
Datalicious
Pty
Ltd
online.
76
78. Save
with
our
combine
Our
Flexi-‐Premium
car
car
and
life
an
help
you
insurance
c insurance
November
2010
©
Datalicious
Pty
Ltd
offer.
save.
78
83. >
Extended
targe.ng
pla>orm
Publishers
Partners
Network
Brand
November
2010
©
Datalicious
Pty
Ltd
83
84. >
SuperTag
code
architecture
§ Central
JavaScript
container
tag
§ One
tag
for
all
sites
and
plaiorms
§ Hosted
internally
or
externally
§ Faster
tag
implementa-on/updates
§ Eliminates
JavaScript
caching
§ Enables
code
tes-ng
on
live
site
§ Enables
heat
map
implementa-on
§ Enables
redirects
for
A/B
tes-ng
§ Enables
network
wide
re-‐targe-ng
§ Enables
live
chat
implementa-on
November
2010
©
Datalicious
Pty
Ltd
84
85. >
Combining
data
sets
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
November
2010
©
Datalicious
Pty
Ltd
85
86. >
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
November
2010
©
Datalicious
Pty
Ltd
86
87. >
Unique
visitor
overes.ma.on
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
overes-mated
visitors
by
up
to
7.6
-mes
whilst
a
cookie-‐based
approach
overes.mated
visitors
by
up
to
2.3
.mes.
November
2010
©
Datalicious
Pty
Ltd
87
Source:
White
Paper,
RedEye,
2007
89. >
Maximise
iden.fica.on
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
November
2010
©
Datalicious
Pty
Ltd
89
93. >
Facebook
Connect
single
sign
on
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,
ajracted
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?
November
2010
©
Datalicious
Pty
Ltd
93
94. 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)
November
2010
©
Datalicious
Pty
Ltd
94
95. >
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
transac-onal
history
of
prospects
which
maybe
50%
can
actually
be
with
limited
iden-fied
as
individuals
profile
data
November
2010
©
Datalicious
Pty
Ltd
95
96. >
Poten.al
home
page
layout
Customise
content
Branded
header
delivery
on
the
fly
based
on
referrer
data,
past
content
Rule
based
offer
Login
consump-on
or
profile
data
for
exis-ng
customers.
Targeted
Targeted
offer
offer
Popular
links,
FAQs
November
2010
©
Datalicious
Pty
Ltd
96
98. >
Affinity
re-‐targe.ng
in
ac.on
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe-ng,
response
rates
are
lioed
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
hKp://bit.ly/de70b7
12
Month
Caps
- + - +
November
2010
©
Datalicious
Pty
Ltd
98
99. >
Ad-‐sequencing
in
ac.on
Marke-ng
is
about
telling
stories
and
stories
are
not
sta-c
but
evolve
over
-me
Ad-‐sequencing
can
help
to
evolve
stories
over
-me
the
more
users
engage
with
ads
November
2010
©
Datalicious
Pty
Ltd
99
101. >
Poten.al
newsleKer
layout
Using
profile
data
Rule
based
branded
header
enhanced
with
website
behaviour
Data
verifica.on
NPS
data
imported
into
the
email
delivery
plaiorm
to
build
Rule
based
offer
business
rules
and
Closest
stores,
customise
content
Profile
based
offer
delivery.
offers
etc
November
2010
©
Datalicious
Pty
Ltd
101
102. >
Customer
profiling
in
ac.on
Using
website
and
email
responses
to
learn
a
lijle
bite
more
about
subscribers
at
every
touch
point
to
keep
refining
profiles
and
messages.
November
2010
©
Datalicious
Pty
Ltd
102
103. >
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
November
2010
©
Datalicious
Pty
Ltd
103
105. >
Poten.al
call
center
interface
Customers
can
also
Call
center
menu
op.ons
be
iden-fied
offline
and
given
most
call
center
plaiorms
are
Customer
contact
history
now
web-‐based
it
would
be
possible
to
use
online
targe-ng
Targeted
offer
Call
script
plaiorms
to
shape
the
call
experience.
November
2010
©
Datalicious
Pty
Ltd
105
107. Segment
A
Segment
B
Purchase
Media
Data
cycle
channels
points
Default,
awareness
Research,
considera.on
Purchase
intent
Reten.on,
up/Cross-‐Sell
November
2010
©
Datalicious
Pty
Ltd
107
108. Segment
A
Segment
B
Purchase
Media
Data
cycle
channels
points
Colour,
price,
product
affinity,
etc
Default,
Have
you
Have
you
Display,
Default
awareness
seen
A?
seen
B?
search,
etc
Research,
A
has
great
B
has
great
Search,
Ad
clicks,
considera.on
features!
features!
website,
etc
product
views
Purchase
A
delivers
B
delivers
Website,
Cart
adds,
intent
great
value!
great
value!
emails,
etc
checkouts,
etc
Reten.on,
Why
not
Why
not
Direct
mails,
Email
clicks,
up/Cross-‐Sell
November
2010
buy
B?
buy
A?
©
Datalicious
Pty
Ltd
emails,
etc
logins,
108
etc
109. >
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.”
November
2010
©
Datalicious
Pty
Ltd
109
111. >
Bad
campaign
worse
than
none
November
2010
©
Datalicious
Pty
Ltd
111
112. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac.on
Sa.sfac.on
Social
media
November
2010
©
Datalicious
Pty
Ltd
112
113. >
Simplified
AIDA
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac-on)
(Sa-sfac-on)
November
2010
©
Datalicious
Pty
Ltd
113
114. >
Standardised
global
metrics
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
November
2010
©
Datalicious
Pty
Ltd
114
116. >
Keys
to
effec.ve
targe.ng
1. Define
success
metrics
2. Define
and
validate
segments
3. Develop
targe-ng
and
message
matrix
4. Transform
matrix
into
business
rules
5. Develop
and
test
content
6. Start
targe-ng
and
automate
7. Keep
tes-ng
and
refining
8. Communicate
results
November
2010
©
Datalicious
Pty
Ltd
116
117. >
Quick
wins
and
geung
started
§ Iden-fica-on
of
individual
users
§ Simple
home
page
re-‐targe-ng
§ Simple
ad
server
re-‐targe-ng
§ Global
targe-ng
matrix
§ Standardised
metrics
November
2010
©
Datalicious
Pty
Ltd
117
119. >
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
November
2010
©
Datalicious
Pty
Ltd
119
120. >
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
November
2010
©
Datalicious
Pty
Ltd
120
121. >
Forrester
on
web
analy.cs
November
2010
©
Datalicious
Pty
Ltd
121
122. >
Forrester
on
tes.ng/targe.ng
November
2010
©
Datalicious
Pty
Ltd
122
123. >
Forrester
on
media
aKribu.on
November
2010
©
Datalicious
Pty
Ltd
123
126. Contact
us
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
us
twiKer.com/datalicious
November
2010
©
Datalicious
Pty
Ltd
126