1. >
Media
A)ribu-on
<
Cross-­‐channel
media
a0ribu3on
best
prac3ce
and
technology
op3ons

2. >
Short
but
sharp
history
 Datalicious
was
founded
in
late
2007
 Strong
Omniture
web
analy3cs
history
 1
of
4
preferred
Omniture
partners
globally
 Now
360
data
agency
with
specialist
team
 Combina3on
of
analysts
and
developers
 Carefully
selected
best
of
breed
partners
 Driving
industry
best
prac3ce
(ADMA)
 Turning
data
into
ac3onable
insights
 Execu3ng
smart
data
driven
campaigns
January
2012
©
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
ROMI
January
2012
©
Datalicious
Pty
Ltd
3

4. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Media
a)ribu-on
January
2012
©
Datalicious
Pty
Ltd
4

5. >
The
ideal
media
dashboard
Channel
Investment
ROMI
Return
Brand
equity
($100)
n/a
$40
Baseline
Offline
$7
330%
$30
TV,
print,
outdoor,
etc
Direct
$1
400%
$5
Direct
mail,
email,
etc
Online
$2
1150%
$25
Search,
display,
social,
etc
January
2012
©
Datalicious
Pty
Ltd
5

6. >
Duplica-on
across
channels
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Email
Email
Blast
PlaNorm
$
Organic
Google
Search
Analy-cs
$
January
2012
©
Datalicious
Pty
Ltd
6

7. >
De-­‐duplica-on
across
channels
Paid
Search
$
Banner
Ads
$
Central
Analy-cs
PlaNorm
Email
Blast
$
Organic
Search
$
January
2012
©
Datalicious
Pty
Ltd
7

8. >
Campaign
flows
are
complex
=
Paid
media
Organic
PR,
WOM,
search
events,
etc
=
Viral
elements
=
Sales
channels
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
CRM
Facebook
program
Twi)er,
etc
POS
kiosks,
Call
center,
loyalty
cards,
etc
retail
stores,
etc
January
2012
©
Datalicious
Pty
Ltd
8

9. >
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
January
2012
©
Datalicious
Pty
Ltd
9

10. >
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
January
2012
©
Datalicious
Pty
Ltd
10

11. >
Ad
clicks
inadequate
measure
Only
a
small
minority
of
people
actually
click
on
ads,
the
majority
merely
processes
them
(if
at
all)
like
any
other
adver3sing
without
an
immediate
response
so
adver3sers
cannot
rely
on
clicks
as
the
sole
success
measure
but
should
instead
focus
on
impressions
delivered
February
2012
©
Datalicious
Pty
Ltd
11

12. >
Full
purchase
path
tracking
Introducer
Influencer
Influencer
Closer
$
Paid
Display
Social
Direct
Online
search
ad
clicks
referrals
site
visits
sales
Display
Affiliate
Social
Retail
Offline
ad
views
clicks
buzz
store
visits
sales
TV/print
Organic
Website
Direct
mail,
Life-me
responses
search
events
emails
profit
January
2012
©
Datalicious
Pty
Ltd
12

13. >
Tracking
offline
responses
online
 Search
calls
to
ac3on
for
TV,
radio,
print
– Unique
search
term
only
adver3sed
in
print
so
all
responses
from
that
term
must
have
come
from
print
 PURLs
(personalised
URLs)
for
direct
mail
– Brand.com/clientname
redirects
to
new
URL
that
includes
tracking
parameter
iden3fying
response
as
direct
mail
 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
a0ribu3on
model
– Combine
raw
data
from
online
purchase
path,
website
entry
survey
and
offline
sales
with
offline
media
placement
informa3on
in
tradi3onal
media
a0ribu3on
model
January
2012
©
Datalicious
Pty
Ltd
13

14. >
Search
call
to
ac-on
for
offline
December
2011
©
Datalicious
Pty
Ltd
14

15. >
Personalised
URLs
for
direct
mail
VickyCarroll.myspaday.com
>
redirect
to
>
myspaday.com?
CampaignID=DM:123&
Demographics=F|35&
CustomerSegment=A1&
CustomerValue=High&
CustomerSince=2001&
ProductHistory=P1|P2&
NextBestOffer=P3&
ChurnRisk=Low
[...]
December
2011
©
Datalicious
Pty
Ltd
15

16. >
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
Adver3sing
9%
SEO
Generic
7%
Display
Adver3sing
14%
SEM
Generic
14%
Email
Marke3ng
7%
Display
Adver3sing
7%
Retail
Promo3ons
14%
Affiliate
Marke3ng
9%
Referrals
5%
Conversions
a0ributed
to
search
terms
Email
Marke3ng
7%
that
contain
brand
keywords
and
direct
website
visits
are
most
likely
not
the
origina3ng
channel
that
generated
the
awareness
and
as
such
conversion
credits
should
be
re-­‐allocated.
December
2011
©
Datalicious
Pty
Ltd
16

17. >
Tracking
offline
sales
online
 Email
click-­‐through
– Include
offline
sales
flag
in
URL
parameter
in
welcome
email
click-­‐through
URLs
(or
1st
email
newsle0er
arer
offline
sale)
to
trigger
a
custom
‘assisted
offline
sales’
conversion
event
 First
login
arer
purchase
– Similar
to
the
above
method,
however
offline
sales
flag
happens
via
JavaScript
parameter
defined
on
login
rather
than
URL
parameter
January
2012
©
Datalicious
Pty
Ltd
17

18. >
Offline
sales
driven
by
online
Adver-sing
Phone
Credit
check,
campaign
order
fulfilment
Retail
Confirma-on
order
email,
1st
login
Website
Online
Online
order
Virtual
order
research
order
confirma-on
confirma-on
Cookie
December
2011
©
Datalicious
Pty
Ltd
18

19. >
Understanding
channel
mix
January
2012
©
Datalicious
Pty
Ltd
19

20. >
Media
a)ribu-on
models
Introducer
Influencer
Influencer
Closer
$100
Even
25%
25%
25%
25%
A)rib.
Exclusion
33%
33%
33%
0%
A)rib.
?
?
?
?
Custom
A)rib.
January
2012
©
Datalicious
Pty
Ltd
20

21. >
Purchase
path
vs.
a)ribu-on
 Important
to
make
a
dis3nc3on
between
media
a0ribu3on
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
a0ribu3on
or
the
alloca3on
or
conversion
credits
back
to
campaign
touch
points
– Purchase
path
tracking
is
the
data
collec3on
and
media
a0ribu3on
is
the
actual
analysis
or
modelling
January
2012
©
Datalicious
Pty
Ltd
21

22. >
Single
source
of
truth
repor-ng
Insights
Repor-ng
January
2012
©
Datalicious
Pty
Ltd
22

23. >
Where
to
track
purchase
path
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
ad
impressions
More
granular
&
complex
Less
granular
&
complex
January
2012
©
Datalicious
Pty
Ltd
23

24. >
Purchase
path
data
samples
Web
Analy-cs
data
sample
(AD
IMPRESSION
>)
AFFILIATE
>
SEARCH
>
$$$
SEARCH
>
SOCIAL
>
DIRECT
>
$$$
Ad
Server
data
sample
01/01/2012
11:45
AD
IMPRESSION
01/01/2012
12:00
AD
IMPRESSION
01/01/2012
12:05
SEARCH
07/01/2012
17:00
DIRECT
08/01/2012
15:00
$$$
January
2012
©
Datalicious
Pty
Ltd
24

25. >
Purchase
path
for
each
cookie
Mobile
Home
Work
Tablet
Media
Etc
January
2012
©
Datalicious
Pty
Ltd
25

26. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
PlaNorm
op-ons
January
2012
©
Datalicious
Pty
Ltd
26

27. >
Purchase
path
op-ons
 Google
Analy3cs
– Mul3-­‐channel
funnels
 Adobe
SiteCatalyst
– Cross-­‐visit
par3cipa3on
JavaScript
plugin
– DoubleClick/Mediamind
Genesis
integra3on
– Light
Server
Calls
 Atlas,
Mediamind,
DoubleClick,
etc
– Campaign
touch
point
report
(Mediamind)
– Raw
cookie
level
data
&
manual
analysis
 ClearSaleing
January
2012
©
Datalicious
Pty
Ltd
27

28. >
Google
Analy-cs
 Mul3-­‐channel
funnels
– Concatenates
and
groups
campaign
codes
across
visits
 Pros
– Free
of
charge
as
part
of
main
tool
– Out
of
the
box
reports
that
require
no
addi3onal
tags
– Does
not
require
data
warehousing
and
custom
analy3cs
 Cons
– Raw
data
cannot
be
exported
for
advanced
modelling
– Cannot
combine
purchase
paths
across
devices
– Doesn’t
include
ad
impressions
(maybe
DoubleClick
in
the
future)
– Channels
can
be
grouped
but
not
re-­‐classified
in
the
interface
 Offline
responses
via
search
call
to
ac3on
show
up
as
search,
etc
– Plauorm
cannot
accommodate
offline
data
(surveys,
CRM,
etc)
– Purchase
path
data
only,
no
a0ribu3on
modeling
or
ROMI
January
2012
©
Datalicious
Pty
Ltd
28

29. January
2012
©
Datalicious
Pty
Ltd
29

30. >
Adobe
SiteCatalyst
 Cross-­‐visit
par3cipa3on
&
light
server
calls
– Concatenates
campaign
codes
across
visits
into
single
string
– Light
Server
Call
adds
missing
ad
impressions
into
campaign
code
string
 DoubleClick/Mediamind
Genesis
– Imports
last
ad
impression,
click
and
ad
costs
into
SiteCatalyst
as
events
 Pros
– Easy
add-­‐on
for
SiteCatalyst
clients,
requires
JavaScript
change
only
– Doesn’t
require
addi3onal
data
warehousing
as
stored
in
SiteCatalyst
– Campaign
responses
can
be
re-­‐classified
arer
the
fact
(SAINT)
 Cons
– Conflic3ng
un-­‐integrated
solu3ons
(Light
Server
Call
vs.
Genesis)
– Addi3onal
costs
due
to
a
light
server
call
for
each
ad
impression
– Light
Server
Call
tracks
ad
impression
but
lacks
more
granular
ad
data
– Cannot
concatenate
campaign
codes
across
different
top
level
domains
– Data
can
only
be
exported
as
concatenated
strings
without
3me
stamps
– Plauorm
cannot
integrate
offline
data
into
modelling
(surveys,
CRM,
etc)
– Basic
a0ribu3on
modelling
only
that
cannot
be
changed
in
retrospect
– Purchase
path
data
only,
no
flexible
a0ribu3on
modeling
or
ROMI
January
2012
©
Datalicious
Pty
Ltd
30

31. January
2012
©
Datalicious
Pty
Ltd
31

32. >
Purchase
path
data
samples
Web
Analy-cs
data
sample
LAST
AD
IMPRESSION
>
SEARCH
>
$$$|
PV
$$$
AD
IMPRESSION
>
AD
IMPRESSION
>
SEARCH
>
$$$
Ad
Server
data
sample
01/01/2012
11:45
AD
IMP
YAHOO
HOME
$33
01/01/2012
12:00
AD
IMP
SMH
FINANCE
$33
01/01/2012
12:05
SEARCH
KEYWORD
-­‐
07/01/2012
17:00
DIRECT
$33
08/01/2012
15:00
$$$
$100
January
2012
©
Datalicious
Pty
Ltd
32

33. >
Atlas,
Mediamind,
DoubleClick
 Campaign
touch
points
(Mediamind
only)
– Last
10
touch
points
before
conversion
aggregated
across
users
 Raw
cookie
level
data
(all
ad
servers)
– Full
list
of
all
ad
touch
points
for
each
cookie
ID
 Pros
– Low
to
very
low
cost
for
raw
data
– Complete
raw
data
available
(cookie
level
op3on
only)
– Increased
flexibility
due
to
complete
and
very
granular
data
– Campaign
responses
can
be
re-­‐classified
arer
the
fact
– Solu3on
can
accommodate
offline
data
(surveys,
CRM,
etc)
– Ad
server
can
track
across
different
domains
due
to
3rd
party
cookie
– Enables
advanced
ROMI
and
a0ribu3on
modelling
with
offline
data
– A0ribu3on
model
can
be
changed
in
retrospect
and
recalculated
 Cons
– Requires
custom
JavaScript
tags
to
capture
organic
touch
points
– Requires
addi3onal
data
warehousing
and
custom
analy3cs
January
2012
©
Datalicious
Pty
Ltd
33

34. January
2012
©
Datalicious
Pty
Ltd
34

35. >
Purchase
path
data
samples
Ad
Server
summary
data
sample
AD
IMPRESSION
>
DIRECT
>
SEARCH
>
$$$
10x
AD
IMPRESSION
>
AFFILIATE
>
SEARCH
>
$$$
5x
Ad
Server
data
sample
UID123
01/01/2012
11:45
AD
IMP
YAHOO
$33
UID123
01/01/2012
12:00
AD
IMP
SMH
$33
UID123
01/01/2012
12:05
SEARCH
-­‐
UID123
07/01/2012
17:00
DIRECT
$33
UID123
08/01/2012
15:00
$$$
$100
January
2012
©
Datalicious
Pty
Ltd
35

36. January
2012
©
Datalicious
Pty
Ltd
36

37. >
ClearSaleing
 Pros
– Fully
managed
media
a0ribu3on
plauorm
– Extensive
out
of
the
box
repor3ng
func3onality
– Can
customise
a0ribu3on
model
(not
black
box)
– Supports
ROMI
calcula3on
and
a0ribu3on
modeling
 Cons
– Priced
on
percentage
of
media
spend
– Requires
website
tagging
and
backend
integra3ons
– Only
pre-­‐processed
data
export,
not
raw
data
January
2012
©
Datalicious
Pty
Ltd
37

38. January
2012
©
Datalicious
Pty
Ltd
38

39. >
Forrester:
ClearSaleing
ROI/TEI
 Improved
decision
making
and
media
buying
 Labor
savings
and
improved
produc3vity
 Accurate
media
repor3ng
and
improved
visibility
 Improved
flexibility
and
faster
market
response
 Transparency
and
consistency
of
metrics
 Increase
in
online
adver3sing
budget
September
2011
©
Datalicious
Pty
Ltd
39

40. Contact
me
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
me
twi)er.com/datalicious
January
2012
©
Datalicious
Pty
Ltd
40

41. Data
>
Insights
>
Ac-on
January
2012
©
Datalicious
Pty
Ltd
41