The presentation discusses the concepts and principles of digital measurement in tracking and measuring marketing performance.

1. [
Digital
Measurement
]
Analy&cs
workshop
on
how
to
turn
data
into
ac&onable
insights

2. [
Company
history
]
§ Datalicious
was
founded
in
2007
§ Strong
Omniture
web
analy&cs
history
§ One-­‐stop
data
agency
with
specialist
team
§ Combina&on
of
analysts
and
developers
§ Making
data
accessible
and
ac&onable
§ Driving
industry
best
prac&ce
§ Evangelizing
use
of
data
June
2010
©
Datalicious
Pty
Ltd
2

3. [
Challenging
clients
]
June
2010
©
Datalicious
Pty
Ltd
3

4. [
Data
driven
marke:ng
]
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
Tagless
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
June
2010
©
Datalicious
Pty
Ltd
4

5. [
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
5

6. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Capturing
data
]
June
2010
©
Datalicious
Pty
Ltd
6

7. [
Digital
data
is
cheap
]
June
2010
©
Datalicious
Pty
Ltd
7
Source:
Omniture
Summit,
MaS
Belkin,
2007

8. [
Digital
data
op:ons
]
+Social
June
2010
©
Datalicious
Pty
Ltd
8
Source:
Accuracy
Whitepaper
for
web
analy&cs,
Brian
CliWon,
2008

9. [
On-­‐site
analy:cs
tools
]
Google:
”forrester
wave
web
analy:cs
pdf”
or
hKp://bit.ly/aTLAKT
June
2010
©
Datalicious
Pty
Ltd
9
Source:
Forrester
Wave
Web
Analy&cs,
2009

10. [
What
pla<orm
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,
shiW
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
10

11. [
Governance
and
data
integrity
]
June
2010
©
Datalicious
Pty
Ltd
11
Source:
Omniture
Summit,
MaS
Belkin,
2007

12. [
Free
off-­‐site
analy:cs
tools
]
§ hSp://www.google.com/trends
§ hSp://www.google.com/sktool
§ hSp://www.google.com/insights/search
§ hSp://www.google.com/webmasters
§ hSp://www.google.com/adplanner
§ hSp://www.google.com/videotarge&ng
§ hSp://www.keywordspy.com
§ hSp://www.compete.com
§ hSp://www.alexa.com
§ hSp://wiki.kenburbary.com
June
2010
©
Datalicious
Pty
Ltd
12

13. [
Search
at
all
stages
]
In
Australia
Google
has
a
market
share
of
almost
90%
of
all
searches,
making
it
a
very
large
and
reliable
data
sample
June
2010
©
Datalicious
Pty
Ltd
13
Source:
Inside
the
Mind
of
the
Searcher,
Enquiro
2004

14. [
Search
call
to
ac:on
for
offline
]
June
2010
©
Datalicious
Pty
Ltd
14

15. [
Client
side
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
15
Source:
Google
Analy&cs,
Jus&n
Cutroni,
2007

16. [
Tag-­‐less
data
capture
]
Google:
“atomic
labs”
www.atomiclabs.com
June
2010
©
Datalicious
Pty
Ltd
16

17. [
Overes:ma:on
of
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
overes&mated
visitors
by
up
to
7.6
&mes
whilst
a
cookie-­‐based
approach
overes:mated
visitors
by
up
to
2.3
:mes.
Google:
”red
eye
cookie
report
pdf”
or
hKp://bit.ly/cszp2o
2010
June
©
Datalicious
Pty
Ltd
17
Source:
White
Paper,
RedEye,
2007

18. [
Maximise
iden:fica:on
points
]
Probability
of
iden&fica&on
through
cookie
140%
120%
100%
80%
60%
40%
20%
0%
0
4
8
12
16
20
24
28
32
36
40
44
48
Weeks
June
2010
©
Datalicious
Pty
Ltd
18

19. Datalicious
SuperCookie
Persistent
Flash
cookie
that
cannot
be
deleted
June
2010
©
Datalicious
Pty
Ltd
19

20. [
Mobile
page
headers
]
MSISDN
=
Mobile
Number
June
2010
©
Datalicious
Pty
Ltd
20
Source:
Mobile
Tracking,
Omniture,
2008

21. [
Single-­‐sign
on
]
Facebook
Connect
gives
your
company
the
following
data
and
more
with
just
one
click!
ID,
first
name,
last
name,
middle
name,
picture,
affilia&ons,
last
profile
update,
&me
zone,
religion,
poli&cal
interests,
interests,
sex,
birthday,
aSracted
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
and
email
Need
anything
else?
June
2010
©
Datalicious
Pty
Ltd
21

22. [
Research
online,
shop
offline
]
Google:
”digital
future
report
2009
pdf”
or
hKp://bit.ly/ZkLvr
June
2010
©
Datalicious
Pty
Ltd
22
Source:
2008
Digital
Future
Report,
Surveying
The
Digital
Future,
Year
Seven,
USC
Annenberg
School

23. [
Offline
sales
driven
by
online
]
Tying
offline
conversions
back
to
online
campaign
and
research
behavior
using
standard
cookie
technology
by
triggering
virtual
online
order
confirma&on
pages
for
offline
sales
using
email
receipts.
Website.com
Phone
Virtual
Order
Research
Orders
Credit
Check
Fulfilment
@
Confirma:on
Adver:sing
Website.com
Retail
Virtual
Order
Campaign
Research
Orders
Credit
Check
Fulfilment
@
Confirma:on
Website.com
Online
Online
Order
Virtual
Order
Research
Orders
Confirma:on
Credit
Check
Fulfilment
@
Confirma:on
Cookie
Cookie
Cookie
June
2010
©
Datalicious
Pty
Ltd
23

24. [
Summary:
Capturing
data
]
§ Plenty
of
data
sources
and
plajorms
§ 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
24

25. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Genera:ng
insights
]
June
2010
©
Datalicious
Pty
Ltd
25

26. [
Corporate
data
journey
]
Stage
1
Stage
2
Stage
3
Data
Insights
Ac:on
Data
is
fully
owned
Sophis&ca&on
in-­‐house,
advanced
Data
is
being
brought
predic&ve
modelling
in-­‐house,
shiW
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
26

27. [
The
ideal
analyst
]
§ Business
minded
– Semng
realis&c
improvement
goals
§ Technically
savvy
– Bridging
gap
between
business
and
IT
§ Strong
sales
skills
– Raising
awareness
for
the
value
of
data
§ Seniority
and
experience
– Needs
to
be
taken
serious
across
organisa&on
§ Posi&on
within
hierarchy
– Able
to
analyse
without
loyalty
conflict
June
2010
©
Datalicious
Pty
Ltd
27

28. [
Process
is
key
to
success
]
June
2010
©
Datalicious
Pty
Ltd
28
Source:
Omniture
Summit,
MaS
Belkin,
2007

29. [
Defining
metrics
frameworks
]
Media
and
search
data
Website,
call
center
and
retail
data
Reach
Engagement
Ac:on
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac&on)
(Sa&sfac&on)
Quan&ta&ve
and
qualita&ve
research
data
Social
media
data
Social
media
June
2010
©
Datalicious
Pty
Ltd
29

30. [
Key
metrics
by
website
type
]
June
2010
©
Datalicious
Pty
Ltd
30
Source:
Omniture
Summit,
MaS
Belkin,
2007

31. [
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
31

32. [
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
32

33. [
Addi:onal
success
metrics
]
Click
Through
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Bounce
Pages
Per
Video
Through
Rate
Visit
Views
$
Click
Call
back
Store
Through
requests
Searches
?
$
June
2010
©
Datalicious
Pty
Ltd
33

34. Exercise:
Metrics
framework
June
2010
©
Datalicious
Pty
Ltd
34

35. [
Exercise:
Metrics
framework
]
Stage
Metrics
Data
Sources
Reach
Engagement
Ac:on
+Buzz
June
2010
©
Datalicious
Pty
Ltd
35

36. [
Exercise:
Metrics
framework
]
Stage
Metrics
Data
Sources
Impressions,
Ad
Server,
Reach
Searches
Google
Video
Views,
Web
Analy:cs
Engagement
Product
Views
Pla<orm
Orders,
Web
Analy:cs,
Ac:on
Store
Searches
Call
Center
Comments,
Social
Analy:cs
+Buzz
Men:ons
Pla<orm
June
2010
©
Datalicious
Pty
Ltd
36

37. [
Combining
data
sets
]
Web
analy:cs
data
Customer
data
+
The
whole
is
greater
than
the
sum
of
its
parts
3rd
party
data
June
2010
©
Datalicious
Pty
Ltd
37

38. [
Behaviours
vs.
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
CONTINUOUSLY
UPDATED
OCCASIONALLY
June
2010
©
Datalicious
Pty
Ltd
38

39. [
Store
searches
vs.
actual
loca:ons
]
June
2010
©
Datalicious
Pty
Ltd
39

40. [
Enriching
customer
profiles
]
All
you
need
is
an
address
June
2010
©
Datalicious
Pty
Ltd
40
Source:
Hitwise,
2006

41. [
Hitwise
Mosaic
segment
swing
]
australia.com
vs.
newzealand.com
australia.com
vs.
bulafiji.com
June
2010
©
Datalicious
Pty
Ltd
41
Source:
Hitwise,
2006

42. [
Hitwise
Mosaic
segment
swing
]
australia.com
vs.
newzealand.com
australia.com
vs.
newzealand.com
June
2010
©
Datalicious
Pty
Ltd
42
Source:
Hitwise,
2006

43. [
Single
source
of
truth
]
Insights
Repor:ng
June
2010
©
Datalicious
Pty
Ltd
43

44. [
De-­‐duplica:on
across
channels
]
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Central
Analy:cs
Pla<orm
Email
Email
Blast
Pla<orm
$
Organic
Google
Search
Analy:cs
$
June
2010
©
Datalicious
Pty
Ltd
44

45. Thinking
outside
the
box
June
2010
©
Datalicious
Pty
Ltd
45

46. [
Search
and
brand
strength
]
June
2010
©
Datalicious
Pty
Ltd
46

47. [
Search
and
the
product
lifecycle
]
Nokia
N-­‐Series
www.google.com/trends
Apple
iPhone
June
2010
©
Datalicious
Pty
Ltd
47

48. [
Search
and
media
planning
]
www.google.com/adplanner
June
2010
©
Datalicious
Pty
Ltd
48

49. June
2010
©
Datalicious
Pty
Ltd
49

50. June
2010
©
Datalicious
Pty
Ltd
50

51. Fiat
500:
Online
influencing
offline
June
2010
©
Datalicious
Pty
Ltd
51
Google:
“slideshare
fiat
500
case
study”
or
hKp://bit.ly/lh7bx

52. [
Search
driving
offline
crea:ve
]
June
2010
©
Datalicious
Pty
Ltd
52

53. June
2010
©
Datalicious
Pty
Ltd
53

54. Sen:ment
analysis:
People
vs.
machine
June
2010
©
Datalicious
Pty
Ltd
54
Google:
“people
vs
machines
debate”
or
hKp://bit.ly/8VbtB

55. [
Social
metrics
and
tools
]
Google:
”slideshare
al:meter
report”
or
hKp://bit.ly/c8uYXT
June
2010
©
Datalicious
Pty
Ltd
55
Source:
Social
Marke&ng
Analy&cs,
Al&meter,
2010

56. Exercise:
Sta:s:cal
significance
June
2010
©
Datalicious
Pty
Ltd
56

57. 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
57

58. 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?
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
58

59. [
Summary:
Genera:ng
insights
]
§ Right
resources
and
processes
are
key
§ Define
a
flexible
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
59

60. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Taking
ac:on
]
June
2010
©
Datalicious
Pty
Ltd
60

61. [
How
to
drive
ROI
]
§ Increasing
revenue
– Increasing
overall
amount
of
sales
– Increasing
the
average
revenue
per
sale
§ Reducing
costs
– Increasing
media
effec&veness
– Increasing
website
conversion
rates
– Increasing
online
self-­‐service
usage
§ Improving
customer
experience
– Reducing
steps
necessary
to
complete
a
task
– Perceived
value
or
quality
of
the
final
solu&on
June
2010
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Datalicious
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61

62. [
How
to
drive
ROI
]
Media
or
how
to
op:mise
the
channel
mix
Targe:ng
or
how
to
increasing
relevance
Tes:ng
or
how
to
maximise
conversion
June
2010
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Datalicious
Pty
Ltd
62

63. [
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
June
2010
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Datalicious
Pty
Ltd
63

64. [
First
vs.
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
June
2010
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Datalicious
Pty
Ltd
64

65. [
Path
to
purchase
]
Banner
SEM
Partner
Direct
Click
Generic
Site
Visit
$
Banner
SEO
View
Generic
$
TV
SEO
Banner
Ad
Branded
Click
$
Print
Social
Email
Direct
Ad
Media
Update
Visit
$
June
2010
©
Datalicious
Pty
Ltd
65

66. [
Forrester
media
aKribu:on
]
Google:
”forrester
aKribu:on
framework
pdf”
or
hKp://bit.ly/
dnbnzY
June
2010
©
Datalicious
Pty
Ltd
66
Source:
Forrester,
2009

67. [
Customer
data
journey
]
To
transac:onal
data
To
reten:on
messages
From
suspect
to
prospect
To
customer
Time
Time
From
behavioural
data
From
awareness
messages
June
2010
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Datalicious
Pty
Ltd
67

68. June
2010
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Datalicious
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68

69. June
2010
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Datalicious
Pty
Ltd
69

70. [
Matching
segments
are
key
]
On-­‐site
Off-­‐site
segments
segments
On
and
off-­‐site
targe:ng
pla<orms
should
use
iden:cal
triggers
to
sort
visitors
into
segments
June
2010
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Datalicious
Pty
Ltd
70

71. [
Off-­‐site
targe:ng
pla<orms
]
§ Ad
servers
§ Ad
Networks
– Google/DoubleClick
– Google
– Eyeblaster
– Yahoo
– Faciliate
– ValueClick
– Atlas
– Adconian
– Etc
– Etc
hSp://en.wikipedia.org/wiki/Contextual_adver&sing,
hSp://hubpages.com/hub/101-­‐Google-­‐Adsense-­‐Alterna&ves,
hSp://en.wikipedia.org/wiki/Central_ad_server,
hSp://www.adopera&onsonline.com/2008/05/23/list-­‐of-­‐ad-­‐servers/,
hSp://lists.econsultant.com/top-­‐10-­‐adver&sing-­‐networks.html,
hSp://www.clickz.com/3633599,
hSp://en.wikipedia.org/wiki/
behavioural_targe&ng
June
2010
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Datalicious
Pty
Ltd
71

72. [
On-­‐site
targe:ng
pla<orms
]
§ Test&Target
(Omniture,
Offerma&ca,
TouchClarity)
§ Memetrics
(Accenture)
§ Op&most
(Autonomy)
§ KeWa
(Acxiom)
§ AudienceScience
§ Maxymiser
§ Amadesa
§ Certona
§ SiteSpect
§ BTBuckets
(free)
§ Google/DoubleClick
Ad
Server
(free)
June
2010
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Datalicious
Pty
Ltd
72

73. [
Prospect
targe:ng
parameters
]
June
2010
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Datalicious
Pty
Ltd
73

74. [
Vodafone
affinity
targe:ng
]
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe&ng,
response
rates
are
liWed
significantly
across
products.
CTR
By
Category
Affinity
Message
Postpay
Prepay
Broadb.
Business
Blackberry
Bold
- - - +
5GB
Mobile
Broadband
- - + -
Blackberry
Storm
+ - + +
12
Month
Caps
- + - +
June
2010
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74

75. [
Affinity
targe:ng
]
§ Func&on
of
behavioural
targe&ng
– Grouping
of
visitors
into
major
segments
– Based
on
content
and
conversion
behaviour
– Ease
of
use
vs.
reduced
targe&ng
ability
§ Most
common
affini&es
used
– Brand
affinity
– Image
preference
– Price
sensi&vity
– Product
affinity
– Content
affinity
June
2010
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Datalicious
Pty
Ltd
75

76. [
Coordinate
the
experience
]
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
hKp://bit.ly/cAtSUN
June
2010
©
Datalicious
Pty
Ltd
76
Source:
McKinsey
Quarterly,
2010

77. [
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
77

78. Exercise:
Targe:ng
matrix
June
2010
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Datalicious
Pty
Ltd
78

79. [
Exercise:
Targe:ng
matrix
]
Phase
Segment
A
Segment
B
Awareness
Considera:on
Purchase
Intent
Up/Cross-­‐Sell
Reten:on
June
2010
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Datalicious
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Ltd
79

80. [
Exercise:
Targe:ng
matrix
]
Phase
Segment
A
Segment
B
Awareness
Seen
this?
Considera:on
Great
feature!
Purchase
Intent
Great
value!
Up/Cross-­‐Sell
Add
this!
Reten:on
Discount?
June
2010
©
Datalicious
Pty
Ltd
80

81. [
ClickTale
tes:ng
case
study
]
Google:
“change
one
word
double
conversion”
or
hKp://bit.ly/bpyqFp
June
2010
©
Datalicious
Pty
Ltd
81

82. [
Tes:ng
pla<orms
]
§ Test&Target
(Omniture,
Offerma&ca,
TouchClarity)
§ Memetrics
(Accenture)
§ Op&most
(Autonomy)
§ KeWa
(Acxiom)
§ Maxymiser
§ Amadesa
§ SiteSpect
§ ClickTale
(cheap)
§ Unbounce
(cheap)
§ Google
Website
Op&miser
(free)
June
2010
©
Datalicious
Pty
Ltd
82

83. [
Summary
]
§ There
is
no
magic
formula
for
ROI
§ Focus
on
the
en&re
conversion
funnel
§ Media
aSribu&on
is
hard
but
necessary
§ Neither
first
nor
last
click
method
works
§ Create
a
coordinated
targeted
experience
§ Content
is
always
king
no
maSer
what
§ Test,
learn
and
refine
con&nuously
June
2010
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Datalicious
Pty
Ltd
83

84. Contact
me
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
us
twiSer.com/datalicious
June
2010
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Datalicious
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Ltd
84