The presentation discusses the concepts, principles and significance of data driven marketing.

1. >
Marke(ng
Data
Strategy
<
Smart
data
driven
marke-ng

2. >
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
§ Driving
industry
best
prac-ce
(ADMA)
§ Turning
data
into
ac-onable
insights
§ Execu-ng
smart
data
driven
campaigns
June
2010
©
Datalicious
Pty
Ltd
2

3. >
Smart
data
driven
marke(ng
“Using
data
to
widen
the
funnel”
Media
A;ribu(on
&
Modeling
Op(mise
channel
mix,
predict
sales
Targeted
Direct
Marke(ng
Increase
relevance,
reduce
churn
Tes(ng
&
Op(misa(on
Remove
barriers,
drive
sales
Boos(ng
ROI
June
2010
©
Datalicious
Pty
Ltd
3

4. >
Wide
range
of
data
services
Data
Insights
Ac(on
PlaIorms
Analy(cs
Campaigns
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
Tableau,
SpoIire,
SPSS,
etc
Alterian,
SiteCore,
Inxmail,
etc
Tag-­‐less
online
data
capture
Media
a;ribu(on
models
Targe(ng
and
merchandising
End-­‐to-­‐end
data
plaIorms
Market
and
compe(tor
trends
Internal
search
op(misa(on
IVR
and
call
center
repor(ng
Social
media
monitoring
CRM
strategy
and
execu(on
Single
customer
view
Customer
profiling
Tes(ng
programs
June
2010
©
Datalicious
Pty
Ltd
4

5. >
Clients
across
all
industries
June
2010
©
Datalicious
Pty
Ltd
5

6. >
Today
§ Capturing
data
– Op-ons,
limita-ons,
innova-ons
§ Genera-ng
insights
– Process,
metrics,
examples
§ Taking
ac-on
– Media,
targe-ng,
tes-ng
June
2010
©
Datalicious
Pty
Ltd
6

7. Ques(ons?
Yell
out
or
tweet
@datalicious
June
2010
©
Datalicious
Pty
Ltd
7

8. Clive
Humby:
Data
is
the
new
oil
June
2010
©
Datalicious
Pty
Ltd
8

9. Oil
and
data
come
at
a
price
June
2010
©
Datalicious
Pty
Ltd
9

10. >
Google
Ngram:
Privacy
June
2010
©
Datalicious
Pty
Ltd
10

11. Collec(ng
data
for
the
sake
of
it
or
to
add
value
to
customers?
June
2010
©
Datalicious
Pty
Ltd
11

12. Product
Partners
Price
Marke(ng
Process
Mix
Place
People
Promo(on
Physical
Evidence
June
2010
©
Datalicious
Pty
Ltd
12

13. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Capturing
data
June
2010
©
Datalicious
Pty
Ltd
13

14. >
Digital
data
is
plen(ful
and
cheap
June
2010
©
Datalicious
Pty
Ltd
14
Source:
Omniture
Summit,
MaV
Belkin,
2007

15. >
Digital
metric
categories
+Social
June
2010
©
Datalicious
Pty
Ltd
15
Source:
Accuracy
Whitepaper
for
web
analy-cs,
Brian
CliYon,
2008

16. >
What
plaIorm
to
use
Stage
1:
Data
Stage
2:
Insights
Stage
3:
Ac(on
Data
is
fully
owned
Sophis-ca-on
in-­‐house,
advanced
Data
is
being
brought
predic-ve
modelling
in-­‐house,
shiY
towards
and
trigger
based
Third
par-es
control
insights
genera-on
and
marke-ng,
i.e.
what
data
mining,
i.e.
why
will
happen
and
most
data,
ad
hoc
did
it
happen?
making
it
happen!
repor-ng
only,
i.e.
what
happened?
Time,
Control
June
2010
©
Datalicious
Pty
Ltd
16

17. >
Governance
and
data
integrity
June
2010
©
Datalicious
Pty
Ltd
17
Source:
Omniture
Summit,
MaV
Belkin,
2007

18. >
Tag-­‐less
data
capture
Google:
“atomic
labs”
www.atomiclabs.com
June
2010
©
Datalicious
Pty
Ltd
18

19. >
Google
data
in
Australia
Source:
hVp://www.hitwise.com/au/resources/data-­‐centre
June
2010
©
Datalicious
Pty
Ltd
19

20. >
Search
at
all
stages
June
2010
©
Datalicious
Pty
Ltd
20
Source:
Inside
the
Mind
of
the
Searcher,
Enquiro
2004

21. >
Search
call
to
ac(on
for
offline
June
2010
©
Datalicious
Pty
Ltd
21

22. June
2010
©
Datalicious
Pty
Ltd
22

23. >
PURLs
boos(ng
DM
response
rates
Text
June
2010
©
Datalicious
Pty
Ltd
23

24. >
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
June
2010
©
Datalicious
Pty
Ltd
24

25. >
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
June
2010
©
Datalicious
Pty
Ltd
25

26. >
Jet
Interac(ve
phone
call
data
June
2010
©
Datalicious
Pty
Ltd
26

27. >
Bad
experience:
67%
hang
up
2/3
of
callers
hang
up
the
phone
as
they
cannot
get
what
they
want
fast
enough.
June
2010
©
Datalicious
Pty
Ltd
27

28. >
Poten(al
calls
to
ac(on
§ Unique
click-­‐through
URLs
Calls
to
ac(on
§ Unique
vanity
domains
or
URLs
can
help
shape
§ Unique
phone
numbers
the
customer
§ Unique
search
terms
experience
not
just
evaluate
§ Unique
email
addresses
responses
§ 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
June
2010
©
Datalicious
Pty
Ltd
28

29. >
Cookie
based
tracking
process
What
if:
Someone
deletes
their
cookies?
Or
uses
a
device
that
does
not
support
JavaScript?
Or
uses
two
computers
(work
vs.
home)?
Or
two
people
use
the
same
computer?
June
2010
©
Datalicious
Pty
Ltd
29
Source:
Google
Analy-cs,
Jus-n
Cutroni,
2007

30. >
Duplica(on
across
channels
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Email
Email
Blast
PlaIorm
$
Organic
Google
Search
Analy(cs
$
June
2010
©
Datalicious
Pty
Ltd
30

31. >
De-­‐duplica(on
across
channels
Paid
Search
$
Banner
Ads
$
Central
Analy(cs
PlaIorm
Email
Blast
$
Organic
Search
$
June
2010
©
Datalicious
Pty
Ltd
31

32. >
Datalicious
SuperTag
Ad
Sever,
Web
SuperTag
Paid
Search
Analy-cs
Use
the
same
business
rules
to
trigger
conversions
across
all
plaIorms
to
reduce
discrepancies
June
2010
©
Datalicious
Pty
Ltd
32

33. >
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.
June
2010
©
Datalicious
Pty
Ltd
33
Source:
White
Paper,
RedEye,
2007

34. >
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
June
2010
©
Datalicious
Pty
Ltd
34

35. >
Customer
profiling
in
ac(on
Using
website
and
email
responses
to
learn
a
liVle
bite
more
about
subscribers
at
every
touch
point
to
keep
refining
profiles
and
messages.
June
2010
©
Datalicious
Pty
Ltd
35

36. >
Online
form
best
prac(ce
Maximise
data
integrity
Age
vs.
year
of
birth
Free
text
vs.
op-ons
Use
auto-­‐complete
wherever
possible
June
2010
©
Datalicious
Pty
Ltd
36

37. >
Research
online,
shop
offline
June
2010
©
Datalicious
Pty
Ltd
37
Source:
2008
Digital
Future
Report,
Surveying
The
Digital
Future,
Year
Seven,
USC
Annenberg
School

38. >
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
June
2010
©
Datalicious
Pty
Ltd
38

39. >
Summary:
Capturing
data
§ Plenty
of
data
sources
and
planorms
§ Especially
search
is
great
free
data
source
§ Maintaining
data
integrity
takes
effort
§ Cookie
technology
has
its
limita-ons
§ New
tag-­‐less
technologies
emerging
§ Maximise
iden-fica-on
points
§ Offline
can
be
-ed
to
online
June
2010
©
Datalicious
Pty
Ltd
39

40. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Genera(ng
insights
June
2010
©
Datalicious
Pty
Ltd
40

41. >
Corporate
data
journey
Stage
1
Stage
2
Stage
3
Data
Insights
Ac(on
“Leaders”
Data
is
fully
owned
“Followers”
Sophis-ca-on
in-­‐house,
advanced
Data
is
being
brought
predic-ve
modelling
“Laggards”
in-­‐house,
shiY
towards
and
trigger
based
Third
par-es
control
insights
genera-on
and
marke-ng,
i.e.
what
data
mining,
i.e.
why
will
happen
and
most
data,
ad
hoc
did
it
happen?
making
it
happen!
repor-ng
only,
i.e.
what
happened?
Time,
Control
June
2010
©
Datalicious
Pty
Ltd
41

42. >
Process
is
key
to
success
June
2010
©
Datalicious
Pty
Ltd
42
Source:
Omniture
Summit,
MaV
Belkin,
2007

43. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac(on
Sa(sfac(on
Social
media
June
2010
©
Datalicious
Pty
Ltd
43

44. >
Simplified
AIDAS
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac-on)
(Sa-sfac-on)
June
2010
©
Datalicious
Pty
Ltd
44

45. >
Marke(ng
is
about
people
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
June
2010
©
Datalicious
Pty
Ltd
45

46. >
Addi(onal
funnel
breakdowns
Brand
vs.
direct
response
campaign
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
New
prospects
vs.
exis-ng
customers
June
2010
©
Datalicious
Pty
Ltd
46

47. New
vs.
returning
visitors
June
2010
©
Datalicious
Pty
Ltd
47

48. AU/NZ
vs.
rest
of
world
June
2010
©
Datalicious
Pty
Ltd
48

49. >
Poten(al
funnel
breakdowns
§ Brand
vs.
direct
response
campaign
§ New
prospects
vs.
exis-ng
customers
§ Baseline
vs.
incremental
conversions
§ Compe--ve
ac-vity,
i.e.
none,
a
lot,
etc
§ Segments,
i.e.
age,
loca-on,
influence,
etc
§ Channels,
i.e.
search,
display,
social,
etc
§ Campaigns,
i.e.
this/last
week,
month,
year,
etc
§ Products
and
brands,
i.e.
iphone,
htc,
etc
§ Offers,
i.e.
free
minutes,
free
handset,
etc
§ Devices,
i.e.
home,
office,
mobile,
tablet,
etc
June
2010
©
Datalicious
Pty
Ltd
49

50. >
Conversion
funnel
1.0
Campaign
responses
Conversion
funnel
Product
page,
add
to
shopping
cart,
view
shopping
cart,
cart
checkout,
payment
details,
shipping
informa-on,
order
confirma-on,
etc
Conversion
event
June
2010
©
Datalicious
Pty
Ltd
50

51. >
Conversion
funnel
2.0
Campaign
responses
(inbound
spokes)
Offline
campaigns,
banner
ads,
email
marke-ng,
referrals,
organic
search,
paid
search,
internal
promo-ons,
etc
Landing
page
(hub)
Success
events
(outbound
spokes)
Bounce
rate,
add
to
cart,
cart
checkout,
confirmed
order,
call
back
request,
registra-on,
product
comparison,
product
review,
forward
to
friend,
etc
June
2010
©
Datalicious
Pty
Ltd
51

52. >
Addi(onal
success
metrics
Click
Through
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Bounce
Pages
Per
Avg
Cart
Through
Rate
Visit
Value
$
Click
Call
back
Store
Through
requests
Searches
>
...
$
June
2010
©
Datalicious
Pty
Ltd
52

53. Exercise:
Sta(s(cal
significance
June
2010
©
Datalicious
Pty
Ltd
53

54. 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
June
2010
©
Datalicious
Pty
Ltd
54
Google
“nss
sample
size
calculator”

55. 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
June
2010
©
Datalicious
Pty
Ltd
55
Google
“nss
sample
size
calculator”

56. Exercise:
Metrics
framework
June
2010
©
Datalicious
Pty
Ltd
56

57. >
Exercise:
Metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1,
people
Level
2,
strategic
Level
3,
tac(cal
Funnel
breakdowns
June
2010
©
Datalicious
Pty
Ltd
57

58. >
Exercise:
Metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1,
People
People
People
People
people
reached
engaged
converted
delighted
Level
2,
Display
strategic
impressions
?
?
?
Level
3,
Interac(on
tac(cal
rate,
etc
?
?
?
Funnel
Exis(ng
customers
vs.
new
prospects,
products,
etc
breakdowns
June
2010
©
Datalicious
Pty
Ltd
58

59. >
Establishing
a
baseline
Switch
all
adver-sing
off
for
a
period
of
-me
(unlikely)
or
establish
a
smaller
control
group
that
is
representa-ve
of
the
en-re
popula-on
(i.e.
search
term,
geography,
etc)
and
switch
off
selected
channels
one
at
a
-me
to
minimise
impact
on
overall
conversions.
June
2010
©
Datalicious
Pty
Ltd
59

60. >
Combining
data
sources
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
June
2010
©
Datalicious
Pty
Ltd
60

61. >
Transac(ons
plus
behaviours
CRM
Profile
Site
Behaviour
one-­‐off
collec-on
of
demographical
data
tracking
of
purchase
funnel
stage
+
age,
gender,
address,
etc
browsing,
checkout,
etc
customer
lifecycle
metrics
and
key
dates
tracking
of
content
preferences
profitability,
expira(on,
etc
products,
brands,
features,
etc
predic-ve
models
based
on
data
mining
tracking
of
external
campaign
responses
propensity
to
buy,
churn,
etc
search
terms,
referrers,
etc
historical
data
from
previous
transac-ons
tracking
of
internal
promo-on
responses
average
order
value,
points,
etc
emails,
internal
search,
etc
Updated
Occasionally
Updated
Con(nuously
June
2010
©
Datalicious
Pty
Ltd
61

62. >
Sample
customer
level
data
June
2010
©
Datalicious
Pty
Ltd
62

63. >
Enhancing
data
sources
Customer
profile
data
Geo-­‐demographic
data
+
The
whole
is
greater
than
the
sum
of
its
parts
3rd
party
data
June
2010
©
Datalicious
Pty
Ltd
63

64. >
Geo-­‐demographic
segments
June
2010
©
Datalicious
Pty
Ltd
64

65. June
2010
©
Datalicious
Pty
Ltd
65

66. >
Hitwise
Mosaic
segment
swing
australia.com
vs.
newzealand.com
australia.com
vs.
bulafiji.com
June
2010
©
Datalicious
Pty
Ltd
66
Source:
Hitwise,
2006

67. >
Single
source
of
truth
repor(ng
Insights
Repor(ng
June
2010
©
Datalicious
Pty
Ltd
67

68. June
2010
©
Datalicious
Pty
Ltd
68

69. Thinking
outside
the
box
June
2010
©
Datalicious
Pty
Ltd
69

70. >
Store
locator
searches
June
2010
©
Datalicious
Pty
Ltd
70

71. >
Search
and
brand
strength
June
2010
©
Datalicious
Pty
Ltd
71

72. >
Search
and
media
planning
June
2010
©
Datalicious
Pty
Ltd
72

73. >
Search
driving
offline
crea(ve
June
2010
©
Datalicious
Pty
Ltd
73

74. >
Importance
of
calendar
events
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
June
2010
©
Datalicious
Pty
Ltd
74

75. >
Summary:
Genera(ng
insights
§ Right
resources
and
processes
are
key
§ Define
a
standardised
metrics
framework
§ Maintain
framework
to
enable
comparison
§ Combine
data
sets
for
hidden
insights
§ Establish
a
single
(data)
source
of
truth
§ Think
outside
the
box
and
across
channels
§ Data
does
not
equal
significance
June
2010
©
Datalicious
Pty
Ltd
75

76. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Taking
ac(on
June
2010
©
Datalicious
Pty
Ltd
76

77. >
Smart
data
driven
marke(ng
“Using
data
to
widen
the
funnel”
Media
A;ribu(on
&
Modeling
Op(mise
channel
mix,
predict
sales
Targeted
Direct
Marke(ng
Increase
relevance,
reduce
churn
Tes(ng
&
Op(misa(on
Remove
barriers,
drive
sales
Boos(ng
ROI
June
2010
©
Datalicious
Pty
Ltd
77

78. >
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
Twi;er,
etc
C3
POS
kiosks,
Call
center,
loyalty
cards,
etc
retail
stores,
etc
June
2010
©
Datalicious
Pty
Ltd
78

79. >
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
June
2010
©
Datalicious
Pty
Ltd
79

80. >
First
and
last
click
a;ribu(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
June
2010
©
Datalicious
Pty
Ltd
80

81. >
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
June
2010
©
Datalicious
Pty
Ltd
81

82. >
Understanding
channel
mix
June
2010
©
Datalicious
Pty
Ltd
82

83. >
ClearSaleing
media
a;ribu(on
June
2010
©
Datalicious
Pty
Ltd
83

84. June
2010
©
Datalicious
Pty
Ltd
84

85. Targe(ng
The
right
message
Via
the
right
channel
To
the
right
person
At
the
right
-me
June
2010
©
Datalicious
Pty
Ltd
85

86. >
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
June
2010
©
Datalicious
Pty
Ltd
86

87. >
New
consumer
decision
journey
The
consumer
decision
process
is
changing
from
linear
to
circular.
June
2010
©
Datalicious
Pty
Ltd
87

88. >
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.
June
2010
©
Datalicious
Pty
Ltd
88

89. June
2010
©
Datalicious
Pty
Ltd
89

90. >
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
June
2010
©
Datalicious
Pty
Ltd
90

91. >
Combining
targe(ng
plaIorms
Off-­‐site
targe-ng
Profile
On-­‐site
targe-ng
targe-ng
June
2010
©
Datalicious
Pty
Ltd
91

92. June
2010
©
Datalicious
Pty
Ltd
92

93. Take
a
closer
look
at
our
cash
flow
solu(ons
June
2010
©
Datalicious
Pty
Ltd
93

94. June
2010
©
Datalicious
Pty
Ltd
94

95. >
Combining
technology
On-­‐site
Off-­‐site
segments
segments
CRM
June
2010
©
Datalicious
Pty
Ltd
95

96. >
Datalicious
SuperTag
§ One
tag
for
all
sites
and
planorms
§ Hosted
internally
or
externally
§ Fast
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
§ Plus
mul--­‐channel
media
aVribu-on
June
2010
©
Datalicious
Pty
Ltd
96

97. >
Affinity
re-­‐targe(ng
in
ac(on
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe-ng,
response
rates
are
liYed
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
- + - +
June
2010
©
Datalicious
Pty
Ltd
97

98. >
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
June
2010
©
Datalicious
Pty
Ltd
98

99. >
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
June
2010
©
Datalicious
Pty
Ltd
99

100. Exercise:
Targe(ng
matrix
June
2010
©
Datalicious
Pty
Ltd
100

101. >
Exercise:
Targe(ng
matrix
Purchase
Segments:
Colour,
price,
Media
Data
Cycle
product
affinity,
etc
Channels
Points
Default,
awareness
Research,
considera(on
Purchase
intent
Reten(on,
up/cross-­‐sell
June
2010
©
Datalicious
Pty
Ltd
101

102. >
Exercise:
Targe(ng
matrix
Purchase
Segments:
Colour,
price,
Media
Data
Cycle
product
affinity,
etc
Channels
Points
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
prod
views
Purchase
A
delivers
B
delivers
Website,
Cart
adds,
intent
great
value!
great
value!
emails,
etc
checkouts
Reten(on,
Why
not
Why
not
Direct
mails,
Email
clicks,
up/cross-­‐sell
buy
B?
buy
A?
emails,
etc
logins,
etc
June
2010
©
Datalicious
Pty
Ltd
102

103. >
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.”
June
2010
©
Datalicious
Pty
Ltd
103

104. >
ClickTale
tes(ng
case
study
June
2010
©
Datalicious
Pty
Ltd
104

105. >
Developing
a
tes(ng
matrix
Test
Segment
Content
KPIs
Poten(al
Results
New
Conversion
Next
step,
Test
#1A
prospects
form
A
order,
etc
?
?
New
Conversion
Next
step,
Test
#1B
prospects
form
B
order,
etc
?
?
New
Conversion
Next
step,
Test
#1N
prospects
form
N
order,
etc
?
?
?
?
?
?
?
?
June
2010
©
Datalicious
Pty
Ltd
105

106. >
Summary
§ There
is
no
magic
formula
for
ROI
§ Focus
on
the
en-re
conversion
funnel
§ Media
aVribu-on
is
hard
but
necessary
§ Neither
first
nor
last
click
method
works
§ Create
a
coordinated
targeted
experience
§ Content
is
always
king
no
maVer
what
§ Test,
learn
and
refine
con-nuously
June
2010
©
Datalicious
Pty
Ltd
106

107. Don’t
wait
for
be;er
data,
get
started
now.
June
2010
©
Datalicious
Pty
Ltd
107

108. Contact
me
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
me
twi;er.com/datalicious
June
2010
©
Datalicious
Pty
Ltd
108

109. Data
>
Insights
>
Ac(on
June
2010
©
Datalicious
Pty
Ltd
109