At Datalicious, we don’t just pick a model. We calculate a custom weighted model for every client based on their own data using regression analysis. By tracking all paths, successful and unsuccessful ones, and comparing the two, we can determine whether one channel is more likely to influence conversions over another. And this can make a huge difference in your investment return. What you end up with is a media attribution model that is more accurate and flexible than any out of the box media attribution model could ever hope for. Whilst we’ve only touched on a few of the pitfalls of using a simplified, out of the box attribution model, below are 10 pitfalls you can avoid by choosing to customise your media attribution.

1. >
Media
a(ribu,on
<
Media
a'ribu+on
or
when
tracking
the
last
click
is
just
not
enough

2. >
About
Datalicious
§ Datalicious
was
founded
in
November
2007
§ Strong
web
analy+cs
background
&
experience
§ 360
data
agency
with
team
of
data
specialists
§ Combina+on
of
analysts
and
developers
§ Blue
chip
clients
across
all
industry
ver+cals
§ Carefully
selected
best
of
breed
technology
§ Lobbying
&
defining
data
best
prac+ce
ADMA
§ Execu+ng
smart
data
driven
campaigns
§ Turning
data
into
ac+onable
insights
May
2013
©
Datalicious
Pty
Ltd
2

3. >
Smart
data
driven
marke,ng
Media
A(ribu,on
&
Modeling
Op,mise
channel
mix,
predict
sales
Tes,ng
&
Op,misa,on
Remove
barriers,
drive
sales
Boos,ng
ROMI
Targe,ng
&
Merchandising
Increase
relevance,
reduce
churn
“Using
data
to
widen
the
funnel”
May
2013
©
Datalicious
Pty
Ltd
3

4. >
Wide
range
of
data
services
Data
PlaHorms
Data
collec,on
and
processing
Adobe,
Google
Analy,cs,
etc
Web
and
mobile
analy,cs
Tag-­‐less
online
data
capture
Retail
and
call
center
analy,cs
Big
data
&
data
warehousing
Single
customer
view
Insights
Analy,cs
Data
mining
and
modelling
Tableau,
Splunk,
SPSS,
R,
etc
Customised
dashboards
Media
a(ribu,on
analysis
Marke,ng
mix
modelling
Social
media
monitoring
Customer
segmenta,on
Ac,on
Campaigns
Data
usage
and
applica,on
SiteCore,
ExactTarget,
etc
Targe,ng
and
merchandising
Marke,ng
automa,on
CRM
strategy
and
execu,on
Data
driven
websites
Tes,ng
programs
May
2013
©
Datalicious
Pty
Ltd
4

5. >
50+
years
of
team
experience
May
2013
©
Datalicious
Pty
Ltd
5
Chris+an
Bartens
Founder
&
Director
§ Bachelor
of
Business
Management
with
marke+ng
focus
§ Web
analy+cs
and
digital
marke+ng
work
experience
§ Space2go,
E-­‐LoV,
Tourism
Australia
§ SuperTag
founder,
ADMA
Analy+cs
Chair,
I-­‐COM
EMR
Board
LinkedIn
profile
Elly
Gillis
General
Manager
§ Bachelor
of
Communica+ons
with
print
and
digital
focus
§ Digital
marke+ng
and
project
management
work
experience
§ M&C
Saatchi,
Mark,
Holler,
Tequila,
IAG,
OneDigital,
Telstra
§ Australian
gold
medal
in
surf
boat
rowing
LinkedIn
profile
Michael
Savio
Head
of
Insights
§ Bachelor
of
Arts
&
Science
with
applied
mathema+cs
focus
§ CRM
and
marke+ng
research
and
analy+cs
work
experience
§ ANZ
Bank,
Australian
Bureau
of
Sta+s+c,
DBM
Consultants
§ ADMA
lecturer
on
marke+ng
tes+ng
LinkedIn
profile
Juan
Delard
Head
of
Data
§ Engineering
Diploma
&
Bachelor
of
Science
in
Electrical
Engineering
§ IT
architecure,
ERP,
web
analy+cs,
big
data,
telecommunica+ons
work
experience
§ Quo+fy,
Binaria,
Codelco
§ Mathema+cs
fan
and
avid
scuba
diver
LinkedIn
profile

6. >
Unique
combina,on
of
skills
May
2013
©
Datalicious
Pty
Ltd
6
Data
visualisa,on/repor,ng
Data
mining/analysis
Data
modelling
Fast
analy,cs
Data
processing/enhancing
Big
data
Data
collec,on
The
Datalicious
team
§ Data
scien+sts
§ Business
analysts
§ Data
engineers
§ Web
engineers
§ Pla`orm
admins
§ Project
managers
§ Data
strategists
Data
strategy

7. >
Best
of
breed
technologies
May
2013
©
Datalicious
Pty
Ltd
7

8. >
Datalicious
product
development
SCV2
Surveys
Display
ads
Internal
ads
Engage
Social
media
Mobile
push
eDMs/DMs
MIS1
1
Marke+ng
informa+on
system
containing
all
data
necessary
to
analyse
and
report
on
campaigns
2
Single
customer
view
pla`orm
containing
all
data
across
all
(customer)
touch
points
Mass
media
Social
media
Digital
media
Measure
Demographics
Transac+ons
Campaigns
May
2013
©
Datalicious
Pty
Ltd
8
Report
Analyse

9. >
Clients
across
all
industries
May
2013
©
Datalicious
Pty
Ltd
9

10. Direct
mail,
email,
etc
Facebook
Twi(er,
etc
>
Channels
influence
each
other
May
2013
©
Datalicious
Pty
Ltd
10
POS
kiosks,
loyalty
cards,
etc
CRM
program
Home
pages,
portals,
etc
YouTube,
blog,
etc
Paid
search
Organic
search
Landing
pages,
offers,
etc
PR,
WOM,
events,
etc
TV,
print,
radio,
etc
=
Paid
media
=
Viral
elements
Website,
call
center,
retail
=
Sales
channels
Display
ads,
affiliates,
etc

11. >
First
and
last
click
a(ribu,on
May
2013
©
Datalicious
Pty
Ltd
11
Chart
shows
percentage
of
channel
touch
points
that
lead
to
a
conversion.
Neither
first
nor
last-­‐click
measurement
would
provide
true
picture
Paid/Organic
Search
Emails/Shopping
Engines

12. >
The
ideal
media
dashboard
Channel
Investment
ROMI
Return
Brand
equity
Baseline
($100)
n/a
$40
Offline
TV,
print,
outdoor,
etc
$7
330%
$30
Direct
Direct
mail,
email,
etc
$1
400%
$5
Online
Search,
display,
social,
etc
$2
1150%
$25
May
2013
©
Datalicious
Pty
Ltd
12

13. ©
Datalicious
Pty
Ltd
13
May
2013
A(ribu,on
piHall
#1
Excluding
brand
equity

14. >
ROMI
as
compe,,ve
advantage
May
2013
©
Datalicious
Pty
Ltd
14
74%
of
marketers
do
not
engage
in
any
form
of
media
a'ribu+on
aside
from
the
last
click
leaving
26%
of
marketers
with
a
serious
compe++ve
advantage
as
their
media
investment
is
likely
to
generate
a
much
higher
ROMI.

15. >
Media
a(ribu,on
approaches
May
2013
©
Datalicious
Pty
Ltd
15
Success
$100
Success
$100
Display
Affiliate
Search
$100
Success
$100
Last
channel
gets
all
credit
First
channel
gets
all
credit
All
channels
get
equal
credit
Success
$100
All
channels
get
custom
credit
Display
$100
Affiliate
Search
Display
$33
Affiliate
$33
Search
$33
Display
$15
Affiliate
$35
Search
$50

16. >
Duplica,on
across
channels
May
2013
©
Datalicious
Pty
Ltd
16
Display
ads
Email
blasts
Paid
search
Organic
search
$
Bid
mgmt
Ad
server
Email
plaHorm
Google
Analy,cs
$
$
$

17. >
Duplica,on
across
channels
May
2013
©
Datalicious
Pty
Ltd
17
Display
impression
Paid
search
$
Ad
Server
Bid
mgmt.
Web
analy,cs
Display
click
Ad
server
cookie
Organic
search
Analy,cs
cookie
Analy,cs
cookie
Analy,cs
cookie
Bid
mgmt.
cookie
Ad
server
cookie

18. Central
analy,cs
plaHorm
$
$
$
>
De-­‐duplica,on
across
channels
May
2013
©
Datalicious
Pty
Ltd
18
Display
ads
Email
blasts
Paid
search
Organic
search
$

19. ©
Datalicious
Pty
Ltd
19
May
2013
A(ribu,on
piHall
#2
Mul,ple
data
sources

20. >
Ad
clicks
inadequate
measure
May
2013
©
Datalicious
Pty
Ltd
20
Only
a
small
minority
of
people
actually
click
on
ads,
the
majority
merely
processes
them
(if
at
all)
like
any
other
adver+sing
without
an
immediate
response
so
adver+sers
cannot
rely
on
clicks
as
the
sole
success
measure
but
should
instead
focus
on
impressions
delivered

21. >
Indirect
display
impact
May
2013
©
Datalicious
Pty
Ltd
21

22. ©
Datalicious
Pty
Ltd
22
May
2013
A(ribu,on
piHall
#3
No
ad
impression
data

23. >
Full
vs.
par,al
purchase
path
data
May
2013
©
Datalicious
Pty
Ltd
23
Display
impression
Display
impression
Display
impression
$
Display
impression
$
Display
impression
Display
impression
$
Display
impression
Search
response
Search
response
$
Display
impression
Display
response
Direct
visit
✖
✔
✔
✖
Display
impression
Display
impression
Email
response
Search
response
✖
✔
✔
✔
✖
✖
✔
✔
✖
✔
✔
✔

24. >
Full
vs.
par,al
purchase
path
data
May
2013
©
Datalicious
Pty
Ltd
24
Display
impression
Display
impression
Display
impression
$
Display
impression
$
Display
impression
Display
impression
$
Display
impression
Search
response
Search
response
$
Display
impression
Display
response
Direct
visit
✖
✔
✔
✖
Display
impression
Display
impression
Email
response
Search
response
✖
✔
✔
✔
✖
✖
✔
✔
✖
✔
✔
✔
5%
to
65%
variance
in
conversion
a(ribu,on
for
different
channels
due
to
par,al
purchase
path
data

25. ©
Datalicious
Pty
Ltd
25
May
2013
A(ribu,on
piHall
#4
Par,al
purchase
path
data

26. Closer
Paid
search
Display
ad
views
TV/print
responses
>
Full
purchase
path
tracking
May
2013
©
Datalicious
Pty
Ltd
26
Influencer
Influencer
$
Display
ad
clicks
Online
sales
Affiliate
clicks
Social
referrals
Offline
sales
Organic
search
Social
buzz
Retail
visits
Life,me
profit
Organic
search
Emails,
direct
mail
Direct
site
visits
Introducer

27. Closer
Paid
search
Display
ad
views
TV/print
responses
>
Full
purchase
path
tracking
May
2013
©
Datalicious
Pty
Ltd
27
Influencer
Influencer
$
Display
ad
clicks
Online
leads
Affiliate
clicks
Social
referrals
Offline
sales
Organic
search
Social
buzz
Retail
visits
Life,me
profit
Organic
search
Emails,
direct
mail
Direct
site
visits
Introducer

28. >
Purchase
path
data
example
May
2013
©
Datalicious
Pty
Ltd
28

29. >
Purchase
path
data
example
U123
1/1/12
12:00
RED
AD
YAHOO
NEWS
$20
U123
1/1/12
12:05
RED
AD
SMH
FINANCE
$20
U123
1/1/12
12:10
GOOGLE
BRAND
TERM
-­‐
U123
1/1/12
12:11
WEBSITE
VISIT
-­‐
U123
1/1/12
12:12
WEBSITE
EVENT
-­‐
U123
3/1/12
14:00
GOOGLE
GENERIC
TERM
$20
U123
3/1/12
14:01
WEBSITE
VISIT
-­‐
U123
7/1/12
17:00
EMAIL
OPEN
$20
U123
8/1/12
15:00
GOOGLE
BRAND
TERM
$20
U123
8/1/12
15:01
WEBSITE
CONVERSION
$100
May
2013
©
Datalicious
Pty
Ltd
29

30. ©
Datalicious
Pty
Ltd
30
May
2013
A(ribu,on
piHall
#5
No
,me
stamp
data

31. >
Taking
credit
for
offline
sales
May
2013
©
Datalicious
Pty
Ltd
31

32. >
Tracking
offline
sales
online
§ Email
click-­‐through
– Include
offline
sales
flag
in
1st
email
click-­‐through
URL
aVer
offline
sale
to
track
an
‘assisted
offline
sales’
conversion
§ First
login
aVer
purchase
– Similar
to
the
above
method,
however
offline
sales
flag
happens
via
JavaScript
parameter
defined
on
1st
login
§ Unique
phone
numbers
– Assign
unique
website
numbers
to
responses
from
specific
channels,
search
terms
or
even
individual
visitors
to
match
offline
call
center
results
back
to
online
ac+vity
§ Website
entry
survey
for
purchase
intent
– Survey
website
visitors
to
at
least
measure
purchase
intent
in
case
actual
offline
sales
cannot
be
tracked
May
2013
©
Datalicious
Pty
Ltd
32

33. Confirma,on
email,
login
>
Offline
sales
driven
by
online
May
2013
©
Datalicious
Pty
Ltd
33
Website
research
Phone
sales
Retail
sales
Online
sales
Cookie
Adver,sing
campaign
Fulfilment,
CRM,
etc
Online
sales
confirma,on
Virtual
sales
confirma,on

34. ©
Datalicious
Pty
Ltd
34
May
2013
A(ribu,on
piHall
#6
No
offline
conversion
data

35. >
Purchase
path
for
each
cookie
May
2013
©
Datalicious
Pty
Ltd
35
Mobile
Home
Work
Tablet
Media
Etc

36. >
Purchase
path
for
each
cookie
May
2013
©
Datalicious
Pty
Ltd
36

37. Device
path
2
Device
path
1+2
>
Combining
purchase
paths
May
2013
©
Datalicious
Pty
Ltd
37
Touch
point
1
Email,
login,
etc
Touch
point
1
Touch
point
2
Touch
point
3
Individual
transac,on
Device
path
1
Individual
transac,on
Touch
point
2
Touch
point
1

38. ©
Datalicious
Pty
Ltd
38
May
2013
A(ribu,on
piHall
#7
No
cross-­‐device
tracking

39. >
Filling
purchase
path
data
gaps
May
2013
©
Datalicious
Pty
Ltd
39

40. >
Filling
purchase
path
data
gaps
May
2013
©
Datalicious
Pty
Ltd
40
+15
+5
+10
-­‐15
-­‐5
-­‐10

41. >
Tracking
offline
responses
online
§ Search
calls
to
ac+on
for
TV,
radio,
print
– Unique
search
term
only
adver+sed
in
print
so
all
responses
from
that
term
must
have
come
from
print
§ PURLs
(personalised
URLs)
for
direct
mail
– Brand.com/customer-­‐name
redirects
to
new
URL
that
includes
tracking
parameter
iden+fying
response
as
DM
§ Website
entry
survey
for
direct/branded
visits
– Survey
website
visitors
that
have
come
to
site
directly
or
via
branded
search
about
their
media
habits,
etc
§ Combine
data
sets
into
media
a'ribu+on
model
– Combine
raw
data
from
online
purchase
path,
website
entry
survey
and
offline
sales
with
offline
media
placement
data
in
tradi+onal
(econometric)
media
a'ribu+on
model
May
2013
©
Datalicious
Pty
Ltd
41

42. >
Search
call
to
ac,on
for
offline
May
2013
©
Datalicious
Pty
Ltd
42

43. >
Search
call
to
ac,on
for
TV
May
2013
©
Datalicious
Pty
Ltd
43
Consumers
are
now
experts
at
mul+-­‐tasking,
especially
while
watching
TV.
They
are
constantly
online
and
ready
to
search
so
a
unique
search
call
to
ac+on
is
ideal
to
track
responses
from
TV
ads.
In
addi+on,
consumers
also
remember
search
terms
be'er
than
phone
numbers
or
vanity
URLs
which
increases
overall
response
rates
and
it
is
easier
to
control
the
user
experience
from
a
search
response
(i.e.
what
landing
page
to
send
people
to).

44. domain.com/chris,anbartens
>
redirect
to
>
domain.com?
CampaignID=DM:123&
Demographics=M|35&
CustomerSegment=A1&
CustomerValue=High&
CustomerSince=2001&
ProductHistory=A6&
NextBestOffer=A7&
ChurnRisk=Low
[...]
>
Personalised
URLs
for
direct
mail
May
2013
©
Datalicious
Pty
Ltd
44

45. May
2013
©
Datalicious
Pty
Ltd
45

46. May
2013
©
Datalicious
Pty
Ltd
46
What
promoted
your
visit
today?
q Recent
branch
visit
q Saw
an
ad
on
television
q Saw
an
ad
in
the
newspaper
q Recommenda+on
from
family/friends
q […]

47. >
Website
entry
survey
May
2013
©
Datalicious
Pty
Ltd
47
Channel
%
of
Conversions
Straight
to
Site
27%
SEO
Branded
15%
SEM
Branded
9%
SEO
Generic
7%
SEM
Generic
14%
Display
Adver+sing
7%
Affiliate
Marke+ng
9%
Referrals
5%
Email
Marke+ng
7%
De-­‐duped
Campaign
Report
}
Channel
%
of
Influence
Word
of
Mouth
32%
Blogging
&
Social
Media
24%
Newspaper
Adver+sing
9%
Display
Adver+sing
14%
Email
Marke+ng
7%
Retail
Promo+ons
14%
Greatest
Influencer
on
Branded
Search
/
STS
Conversions
a'ributed
to
search
terms
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.

48. >
Website
entry
survey
example
May
2013
©
Datalicious
Pty
Ltd
48
In
this
retail
example,
the
exposure
to
retail
display
ads
was
the
biggest
website
traffic
driver
for
direct
visits
as
well
as
visits
origina+ng
from
search
terms
that
included
branded
keywords
–
before
TV,
word
of
mouth
and
print
ads.

49. ©
Datalicious
Pty
Ltd
49
May
2013
A(ribu,on
piHall
#8
No
offline
media
data

50. >
Econometric
media
mix
modelling
May
2013
©
Datalicious
Pty
Ltd
50
Use
of
tradi+onal
econometric
modelling
to
measure
the
impact
of
communica+ons
on
sales
for
offline
channels
where
it
cannot
be
measured
directly
through
smart
calls
to
ac+on
online
(and
thus
cookie
level
purchase
path
data).

51. >
Econometric
media
mix
modelling
May
2013
©
Datalicious
Pty
Ltd
51
Total
revenue
Total
revenue
Total
revenue
Total
revenue
Spend
channel
1
Spend
channel
1
Spend
channel
1
Totals
week
N
Spend
channel
N
Spend
channel
N
Total
revenue
Totals
week
1
Totals
week
2
Totals
week
3
Totals
week
4
Spend
channel
2
Spend
channel
2
Spend
channel
2

52. Individual
path
1
Individual
path
1
Individual
path
N
>
Individual
purchase
path
tracking
May
2013
©
Datalicious
Pty
Ltd
52
Touch
point
1
Touch
point
2
Individual
transac,on
Touch
point
1
Individual
transac,on
Touch
point
2
Touch
point
1
Touch
point
2
Touch
point
N
Individual
transac,on
Touch
point
N
Touch
point
N
Individual
path
1
Touch
point
N

53. >
Pathing
&
modelling
combined
May
2013
©
Datalicious
Pty
Ltd
53
Touch
point
1
Touch
point
2
Individual
transac,on
Spend
channel
2
Spend
channel
N
Spend
channel
1
Individual
path
1
Touch
point
N
Influencing
factors
§ Offline
media
spend
§ Compe++ve
ac+vity
§ Geo-­‐demographics
§ Transac+on
history
§ Client
sa+sfac+on
§ Social
sen+ment
§ Interest
rates
§ Weather
§ Pricing
Influence
factor
N
Influence
factor
N
Influence
factor
N

54. ©
Datalicious
Pty
Ltd
54
May
2013
A(ribu,on
piHall
#9
Excluding
custom
data

55. >
Purchase
path
vs.
a(ribu,on
§ Important
to
make
a
dis+nc+on
between
media
a'ribu+on
and
purchase
path
tracking
– Not
the
same,
one
is
necessary
to
enable
the
other
§ Tracking
the
complete
purchase
path,
i.e.
every
paid
and
organic
campaign
touch
point
leading
up
to
a
conversion
is
a
necessary
requirement
to
be
able
to
actually
do
media
a'ribu+on
or
the
alloca+on
or
conversion
credits
back
to
campaign
touch
points
– Purchase
path
tracking
is
the
data
collec+on
and
media
a'ribu+on
is
the
actual
analysis
or
modelling
May
2013
©
Datalicious
Pty
Ltd
55

56. >
Standard
a(ribu,on
models
§ The
First/Last
Interac,on
model
plus
…
§ The
Linear
model
might
be
used
if
your
campaigns
are
designed
to
maintain
awareness
with
the
customer
throughout
the
en+re
sales
cycle.
§ The
Posi,on
Based
model
can
be
used
to
adjust
credit
for
different
parts
of
the
customer
journey,
such
as
early
interac+ons
that
create
awareness
and
late
interac+ons
that
close
sales.
§ The
Time
Decay
model
assigns
the
most
credit
to
touch
points
that
occurred
nearest
to
the
+me
of
conversion.
It
can
be
useful
for
campaigns
with
short
sales
cycles,
such
as
promo+ons.
May
2013
©
Datalicious
Pty
Ltd
56

57. >
Media
a(ribu,on
models
May
2013
©
Datalicious
Pty
Ltd
57
$100
Even/linear
a(ribu,on
Time
decay
a(ribu,on
Custom
a(ribu,on
10%
15%
25%
50%
Display
impression
Display
impression
Display
click
Search
click
10%
10%
50%
30%
25%
25%
25%
25%

58. 10%
30%
10%
50%
10%
50%
30%
10%
>
Custom
(weighted)
a(ribu,on
May
2013
©
Datalicious
Pty
Ltd
58
$100
Weighted
a(ribu,on
$100
Weighted
a(ribu,on
Display
impression
Display
impression
Display
click
Search
click
Display
impression
Search
click
Display
impression
Display
click

59. >
Custom
models
most
effec,ve
May
2013
©
Datalicious
Pty
Ltd
59
56%
of
marketers
consider
a
unique
or
custom
(weighted)
media
a'ribu+on
approach
that
does
not
use
a
standard
out-­‐of-­‐the-­‐box
methodology
as
most
effec+ve.

60. Touch
point
1
>
Analy,cs
to
pick
the
best
model
May
2013
©
Datalicious
Pty
Ltd
60
Touch
point
2
Touch
point
3
Touch
point
N
Closer
Influencer
Influencer
$
Introducer
Touch
point
1
Touch
point
2
Touch
point
3
Touch
point
N
Touch
point
1
Touch
point
2
Touch
point
3
Touch
point
N
✖
✔
✖

61. Closer
Touch
point
1
Touch
point
1
Touch
point
1
>
Path
across
different
segments
May
2013
©
Datalicious
Pty
Ltd
61
Influencer
Influencer
$
Touch
point
2
Touch
point
2
Touch
point
3
Touch
point
2
Touch
point
3
Touch
point
N
Touch
point
3
Touch
point
N
Touch
point
N
Introducer
Product
A
vs.
B
Clients
vs.
prospects
Segment
A
vs.
B

62. ©
Datalicious
Pty
Ltd
62
May
2013
A(ribu,on
piHall
#10
Selec,ng
the
right
model

63. >
A(ribu,on
models
compared
May
2013
©
Datalicious
Pty
Ltd
63
COST
PER
CONVERSION
Last
click
a'ribu+on
Custom
(weighted)
a'ribu+on

64. >
Insights
to
maximise
media
ROI
May
2013
©
Datalicious
Pty
Ltd
64
COST
PER
CONVERSION
Last
click
a'ribu+on
Even/weighted
a'ribu+on
?
Email
?
Direct
mail
?
Internal
ads
?
Website
content
?
TV/Print

65. >
Generic
paid
search
overvalued
May
2013
©
Datalicious
Pty
Ltd
65
Last
click
a(ribu,on
Generic
search
terms
should
deliver
more
ROI
in
a
weighted
a(ribu,on
model
assuming
branded
search
terms
usually
make
up
the
majority
of
last
clicks,
correct?

66. >
Generic
paid
search
overvalued
May
2013
©
Datalicious
Pty
Ltd
66
Full
path
a(ribu,on
Incorrect!
ROI
is
based
on
revenue
and
cost
and
generic
search
terms
have
historically
received
too
much
credit,
hence
high
CPCs
were
ok
but
in
reality
they
are
too
high
thus
leading
to
an
overall
nega,ve
ROI!

67. >
Redistribu,ng
media
spend
May
2013
©
Datalicious
Pty
Ltd
67
ROI
FULL
PURCHASE
PATH
TOTAL
CONVERSION
VALUE
Maintain
spend
Increase
spend
Reduce
spend
Publisher
1
Publisher
2
Publisher
3
[…]
Publisher
N

68. >
ROI
&
revenue
target
simulator
May
2013
©
Datalicious
Pty
Ltd
68

69. >
Media
a(ribu,on
May
2013
©
Datalicious
Pty
Ltd
69
Aussie
purchase
path
tracking
and
media
a'ribu+on
modelling
in
close
coopera+on
with
Amnesia
designed
to
op+mise
the
overall
Aussie
budget
mix
across
paid
and
earned
media
resul+ng
in
an
overall
project
ROI
of
910%.

70. >
Media
a(ribu,on
May
2013
©
Datalicious
Pty
Ltd
70
Suncorp
purchase
path
tracking
and
media
a'ribu+on
modelling
in
order
to
op+mise
the
overall
Suncorp
insurance
budget
mix
across
paid
and
earned
media
resul+ng
in
an
overall
project
ROI
of
2,078%.

71. >
Poten,al
next
steps
§ Phase
1:
Purchase
path
tracking
– Requires
(SuperTag)
container
tag
§ Phase
2:
Data
collec+on
§ Phase
3:
One-­‐off
a'ribu+on
– Ini+al
a'ribu+on
model
(one
product
or
segment
only)
– Ac+onable
recommenda+ons
(i.e.
shiV
spend,
etc)
§ Phase
4:
Ongoing
a'ribu+on
(op+onal)
– A'ribu+on
model
maintenance
– Addi+onal
a'ribu+on
models
(products,
segments)
– Model
enhancements
(i.e.
add
interest
rate,
offline,
etc)
– Report
automa+on
(daily
reports)
May
2013
©
Datalicious
Pty
Ltd
71

72. May
2013
©
Datalicious
Pty
Ltd
72
Contact
us
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
us
twi(er.com/datalicious

73. Data
>
Insights
>
Ac,on
May
2013
©
Datalicious
Pty
Ltd
73