The presentation discusses the significance of multi-channel marketing as an efficient and effective communication strategy to customers.

1. >
Mul&-­‐Channel
Marke&ng
<
Effec%ve
marke%ng
measurement
in
a
mul%-­‐channel
environment

2. >
About
Datalicious
§ Datalicious
was
founded
in
November
2007
§ Official
Adobe
&
Google
Analy%cs
partner
§ 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
partners
§ Driving
industry
best
prac%ce
with
ADMA
§ Turning
data
into
ac%onable
insights
§ Execu%ng
smart
data
driven
campaigns
September
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
Targe&ng
&
Merchandising
Increase
relevance,
reduce
churn
Tes&ng
&
Op&misa&on
Remove
barriers,
drive
sales
Boos&ng
ROMI
September
2012
©
Datalicious
Pty
Ltd
3

4. >
Mul&-­‐channel
measurement
§ Metrics
standardisa%on
§ Media
aRribu%on
§ Channel
integra%on
September
2012
©
Datalicious
Pty
Ltd
4

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>
Metrics
standardisa&on
September
2012
©
Datalicious
Pty
Ltd
5

6. August
2011
©
Datalicious
Pty
Ltd
6

7. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac&on
Sa&sfac&on
Social
media
September
2012
©
Datalicious
Pty
Ltd
7

8. >
Simplified
AIDAS
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac%on)
(Delight)
September
2012
©
Datalicious
Pty
Ltd
8

9. >
Marke&ng
is
about
people
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
September
2012
©
Datalicious
Pty
Ltd
9

10. >
Standardised
roll-­‐up
metrics
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
Unique
browsers,
Unique
visitors,
Online
sales,
Facebook
search
impressions,
site
engagements,
online
leads,
store
comments,
Tweets,
TV
circula&on,
etc
video
views,
etc
locator
searches,
etc
ra&ngs,
support
calls,
etc
Response
rate,
Conversion
rate,
Review
rate,
Search
response
rate,
engagement
rate,
ra&ng
rate,
comment
TV
response
rate,
etc
checkout
rate,
etc
rate,
NPS
rate,
etc
September
2012
©
Datalicious
Pty
Ltd
10

11. >
Provide
context
with
figures
Brand
vs.
direct
response
campaign
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
New
prospects
vs.
exis%ng
customers
September
2012
©
Datalicious
Pty
Ltd
11

12. September
2012
©
Datalicious
Pty
Ltd
12

13. >
Provide
context
with
figures
§ Brand
vs.
direct
response
campaign
§ New
prospects
vs.
exis%ng
customers
§ Compe%%ve
ac%vity,
i.e.
none,
a
lot,
etc
§ Market
share,
i.e.
small,
medium,
large,
et
§ 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
September
2012
©
Datalicious
Pty
Ltd
13

14. >
Addi&onal
success
metrics
Click
Through
Use
addi&onal
metrics
closer
to
the
campaign
origin
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Page
Page
Product
Through
Bounce
Views
Views
$
Click
Call
back
Store
Through
request
Search
?
$
September
2012
©
Datalicious
Pty
Ltd
14

15. 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
September
2012
©
Datalicious
Pty
Ltd
15
Google
“nss
sample
size
calculator”

16. 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
September
2012
©
Datalicious
Pty
Ltd
16
Google
“nss
sample
size
calculator”

17. >
Rela&ve
or
calculated
metrics
§ Bounce
rate
§ Conversion
rate
§ Cost
per
acquisi%on
§ Pages
views
per
visit
§ Product
views
per
visit
§ Cart
abandonment
rate
§ Average
order
value
September
2012
©
Datalicious
Pty
Ltd
17

18. >
Align
metrics
across
channels
§ Paid
search
response
rate
=
website
visits
/
paid
search
impressions
§ Organic
search
response
rate
=
website
visits
/
organic
search
impressions
§ Display
response
rate
=
website
visits
/
display
ad
impressions
§ Email
response
rate
=
website
visits
/
emails
sent
§ Direct
mail
response
rate
=
(website
visits
+
phone
calls)
/
direct
mail
pieces
sent
§ TV
response
rate
=
(website
visits
+
phone
calls)
/
(TV
ad
reach
x
frequency)
September
2012
©
Datalicious
Pty
Ltd
18

19. >
Develop
a
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
September
2012
©
Datalicious
Pty
Ltd
19

20. >
Importance
of
calendar
events
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
September
2012
©
Datalicious
Pty
Ltd
20

21. September
2012
©
Datalicious
Pty
Ltd
21

22. >
Poten&al
calendar
events
§ Press
releases
§ Sponsored
events
§ Campaign
launches
§ Campaign
changes
§ Crea%ve
changes
§ Price
changes
§ Website
changes
§ Technical
difficul%es
September
2012
©
Datalicious
Pty
Ltd
22

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>
Media
a=ribu&on
September
2012
©
Datalicious
Pty
Ltd
23

24. >
Duplica&on
across
channels
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Email
Email
Blast
Plajorm
$
Organic
Google
Search
Analy&cs
$
September
2012
©
Datalicious
Pty
Ltd
24

25. >
Duplica&on
across
channels
Display
Paid
Display
Organic
impression
search
click
search
$
Ad
server
Ad
server
Ad
cookie
cookie
Server
Bid
mgmt.
Bid
cookie
mgmt.
Analy&cs
Analy&cs
Analy&cs
Web
cookie
cookie
cookie
analy&cs
September
2012
©
Datalicious
Pty
Ltd
25

26. >
De-­‐duplica&on
across
channels
Paid
Search
$
Banner
Ads
$
Central
Analy&cs
Plajorm
Email
Blast
$
Organic
Search
$
September
2012
©
Datalicious
Pty
Ltd
26

27. >
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
September
2012
©
Datalicious
Pty
Ltd
27

28. >
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
September
2012
©
Datalicious
Pty
Ltd
28

29. >
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
September
2012
©
Datalicious
Pty
Ltd
29

30. >
Indirect
display
impact
September
2012
©
Datalicious
Pty
Ltd
30

31. >
Full
purchase
path
tracking
Introducer
Influencer
Influencer
Closer
$
Paid
Display
Organic
Direct
Online
search
ad
clicks
search
site
visits
leads
Display
Affiliate
Social
Emails,
Offline
ad
views
clicks
referrals
direct
mail
sales
TV/print
Organic
Social
Retail
Life&me
responses
search
buzz
visits
profit
September
2012
©
Datalicious
Pty
Ltd
31

32. >
Full
purchase
path
tracking
Introducer
Influencer
Influencer
Closer
$
Paid
Display
Organic
Direct
Online
search
ad
clicks
search
site
visits
leads
Display
Affiliate
Social
Emails,
Offline
ad
views
clicks
referrals
direct
mail
sales
TV/print
Organic
Social
Retail
Life&me
responses
search
buzz
visits
profit
September
2012
©
Datalicious
Pty
Ltd
32

33. >
Purchase
path
example
September
2012
©
Datalicious
Pty
Ltd
33

34. >
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
September
2012
©
Datalicious
Pty
Ltd
34

35. >
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
September
2012
©
Datalicious
Pty
Ltd
35

36. >
Understanding
channel
mix
September
2012
©
Datalicious
Pty
Ltd
36

37. September
2012
©
Datalicious
Pty
Ltd
37

38. 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 […]
How
likely
are
you
to
apply
for
a
loan?
q Within
the
next
few
weeks
q Within
the
next
few
months
q I
am
a
customer
already
September
2012
q […]
©
Datalicious
Pty
Ltd
38

39. >
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
aRributed
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.
September
2012
©
Datalicious
Pty
Ltd
39

40. >
Adjus&ng
for
offline
impact
-­‐5
-­‐15
-­‐10
+5
+15
+10
September
2012
©
Datalicious
Pty
Ltd
40

41. >
Media
a=ribu&on
models
Display
Display
Display
Search
impression
impression
response
response
$100
Last
click
0%
0%
0%
100%
a=ribu&on
Even
25%
25%
25%
25%
a=ribu&on
Weighted
X%
X%
Y%
Z%
a=ribu&on
September
2012
©
Datalicious
Pty
Ltd
41

42. >
Media
a=ribu&on
example
Even/weighted
aRribu%on
Last
click
aRribu%on
COST
PER
CONVERSION
September
2012
©
Datalicious
Pty
Ltd
42

43. >
Media
a=ribu&on
example
?
TV/Print
Even/weighted
aRribu%on
?
Internal
?
ads
Website
content
?
Email
?
Last
click
aRribu%on
Direct
mail
COST
PER
CONVERSION
September
2012
©
Datalicious
Pty
Ltd
43

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010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Channel
integra&on
September
2012
©
Datalicious
Pty
Ltd
44

45. >
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
aRribu%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
aRribu%on
model
September
2012
©
Datalicious
Pty
Ltd
45

46. >
Search
call
to
ac&on
for
offline
September
2012
©
Datalicious
Pty
Ltd
46

47. >
Personalised
URLs
for
direct
mail
ChrisBartens.company.com
>
redirect
to
>
company.com?
CampaignID=DM:123&
Demographics=M|35&
CustomerSegment=A1&
CustomerValue=High&
CustomerSince=2001&
ProductHistory=A6&
NextBestOffer=A7&
ChurnRisk=Low
[...]
September
2012
©
Datalicious
Pty
Ltd
47

48. >
Econometric
media
modelling
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).
September
2012
©
Datalicious
Pty
Ltd
48

49. >
Cross-­‐channel
impact
September
2012
©
Datalicious
Pty
Ltd
49

50. >
Tracking
offline
sales
online
§ Email
click-­‐through
– Include
offline
sales
flag
in
1st
email
click-­‐through
URL
auer
offline
sale
to
track
an
‘assisted
offline
sales’
conversion
§ First
login
auer
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
September
2012
©
Datalicious
Pty
Ltd
50

51. >
Offline
sales
driven
by
online
Adver&sing
Phone
Fulfilment,
campaign
sales
CRM,
etc
Retail
Confirma&on
sales
email,
1st
login
Website
Online
Online
sales
Virtual
sales
research
sales
confirma&on
confirma&on
Cookie
September
2012
©
Datalicious
Pty
Ltd
51

52. >
Email
click-­‐through
iden&fica&on
hRp://www.company.com/email-­‐landing-­‐page.html?
CampaignID=DM:123&
CustomerID=12345&
Demographics=M|35&
CustomerSegment=A1&
CustomerValue=High&
ProductHistory=A6&
NextBestOffer=A7&
ChurnRisk=Low
[...]
September
2012
©
Datalicious
Pty
Ltd
52

53. >
Login
landing
and
exit
pages
Customer
data
exposed
in
page
or
URL
on
login
or
logout
CustomerID=12345&
Demographics=M|35&
CustomerSegment=A1&
CustomerValue=High&
ProductHistory=A6&
NextBestOffer=A7&
ChurnRisk=Low
[...]
September
2012
©
Datalicious
Pty
Ltd
53

54. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
About
Datalicious
September
2012
©
Datalicious
Pty
Ltd
54

55. >
Short
but
sharp
history
§ Datalicious
was
founded
in
November
2007
§ Official
Adobe
&
Google
Analy%cs
partner
§ 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
partners
§ Driving
industry
best
prac%ce
with
ADMA
§ Turning
data
into
ac%onable
insights
§ Execu%ng
smart
data
driven
campaigns
September
2012
©
Datalicious
Pty
Ltd
55

56. >
Smart
data
driven
marke&ng
“Using
data
to
widen
the
funnel”
Media
A=ribu&on
&
Modeling
Op&mise
channel
mix,
predict
sales
Targe&ng
&
Merchandising
Increase
relevance,
reduce
churn
Tes&ng
&
Op&misa&on
Remove
barriers,
drive
sales
Boos&ng
ROMI
September
2012
©
Datalicious
Pty
Ltd
56

57. >
Wide
range
of
data
services
Data
Insights
Ac&on
Plajorms
Analy&cs
Campaigns
Data
collec&on
and
processing
Data
mining
and
modelling
Data
usage
and
applica&on
Adobe,
Google
Analy&cs,
etc
Tableau,
Splunk,
SPSS,
etc
Alterian,
SiteCore,
Inxmail,
etc
Web
and
mobile
analy&cs
Customised
dashboards
Targe&ng
and
merchandising
Tag-­‐less
online
data
capture
Media
a=ribu&on
analysis
Marke&ng
automa&on
Retail
and
call
center
analy&cs
Media
mix
modelling
CRM
strategy
and
execu&on
Data
warehouse
solu&ons
Social
media
monitoring
Data
driven
websites
Single
customer
view
Customer
segmenta&on
Tes&ng
programs
September
2012
©
Datalicious
Pty
Ltd
57

58. >
Over
50
years
of
experience
Chris%an
Bartens
Elly
Gillis
Michael
Savio
Chaoming
Li
Founder
&
Director
General
Manager
Head
of
Insights
Head
of
Data
§ Bachelor
of
Business
§ Bachelor
of
§ Bachelor
of
Arts
&
§ Bachelor
of
Management
with
Communica%ons
with
Science
with
applied
Technology
with
marke%ng
focus
print
and
digital
focus
mathema%cs
focus
microelectronics
focus
§ Web
analy%cs
and
§ Digital
marke%ng
and
§ CRM
and
marke%ng
§ Souware
and
website
digital
marke%ng
project
management
research
and
analy%cs
development
work
work
experience
work
experience
work
experience
experience
§ Space2go,
E-­‐Lou,
§ M&C
Saatchi,
Mark,
§ ANZ
Bank,
Australian
§ Standards
Australia,
Tourism
Australia
Holler,
Tequila,
IAG,
Bureau
of
Sta%s%c,
DF
Securi%es,
Globiz,
§ SuperTag
founder,
OneDigital,
Telstra
DBM
Consultants
Etang
ADMA
Analy%cs
Chair,
§ Australian
gold
medal
§ ADMA
lecturer
on
§ Developing
his
own
I-­‐COM
Board
Member
in
surf
boat
rowing
marke%ng
tes%ng
CMS
pla|orm
LinkedIn
profile
LinkedIn
profile
LinkedIn
profile
LinkedIn
profile
September
2012
©
Datalicious
Pty
Ltd
58

59. >
Best
of
breed
partners
September
2012
©
Datalicious
Pty
Ltd
59

60. >
Clients
across
all
industries
September
2012
©
Datalicious
Pty
Ltd
60

61. >
Great
customer
feedback
“[…]
Datalicious
quickly
earned
our
respect
and
confidence
[…]
understand
our
business
needs,
deliver
value,
push
our
thinking
[…].
Likeable,
transparent
and
trustworthy.
I
would
be
happy
to
recommend
Datalicious
to
anyone.”
Murray
Howe,
Execu%ve
Manager,
Suncorp
Group
"[…]
Datalicious
brought
with
them
best
prac>ce
analy>cs
to
demonstrate
the
true
value
of
our
marke>ng
dollars
[…]
have
become
a
criBcal
business
partner
[…]
provided
great
insights
which
have
driven
key
business
decisions.”
Trang
Young,
Senior
Marke%ng
Manager,
E*Trade
Australia
“The
Datalicious
guys
are
great
to
work
along
side
[…]
'no
stone
unturned'
approach
to
finding
solu>ons
to
challenges
[…]
knowledge
and
passion
for
web
analy>cs
and
best
of
breed
web
opBmizaBon
was
second
to
none”
Steve
Brown,
Senior
Business
Analyst,
Vodafone
“[…]
The
Vodafone
implementa>on
of
SiteCatalyst
is
one
of
the
most
impressive
I
have
seen
and
ranks
in
the
top
10
[…].
It
is
an
amazing
founda>on
for
taking
ac>on
on
the
data
and
improving
ROI.”
Adam
Greco,
Consul%ng
Lead,
Omniture
September
2012
©
Datalicious
Pty
Ltd
61

62. >
Great
customer
feedback
"[…]
Datalicious
understand
the
value
of
informa>on
and
how
to
leverage
it
using
best
of
breed
soEware.
I
would
recommend
the
team
without
hesita>on
[...]."
James
Fleet,
Marke%ng
Director,
Appliances
Online
"[...]
Datalicious
have
been
inBmately
involved
in
building
our
analyBcs
soluBon.
Most
importantly
their
knowledge
of
best
prac>ce
combined
with
innova>ve
solu>ons
has
allowed
our
business
to
remain
nimble
and
current.
They
are
also
nice
guys."
Tzvi
Balbin,
Group
Digital
Marke%ng
Lead,
Catch
of
the
Day
"[...]
Datalicious
are
helping
us
to
move
from
a
last
click
campaign
measurement
model
to
a
more
accurate
media
aFribu>on
approach.
[...]
potenBal
to
significantly
change
our
media
planning
[...].
Highly
recommended."
Keith
Mirgis,
Senior
Digital
&
Social
Media
Marke%ng
Manager,
Telstra
"We
engaged
Datalicious
to
support
a
strategic
change
in
our
business
[...]
understand
our
customers
[and
their
transac>ons]
beFer
to
ensure
we
retained
as
many
as
possible
[...]"
Natalie
Farrell,
Direct
Marke%ng
Manager,
Luxo~ca
September
2012
©
Datalicious
Pty
Ltd
62

63. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
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>
About
SuperTag
September
2012
©
Datalicious
Pty
Ltd
63

64. >
SuperTag
tag
management
Easily
implement
and
update
Conversion
any
JavaScript
based
tag
on
any
Tracking
site
without
IT
involvement
Any
Conversion
JavaScript
De-­‐duping
De-­‐duplicate
conversions
for
CPA
deals
and
align
repor%ng
figures
across
pla|orms
Web
Media
Analy&cs
SuperTag
A=ribu&on
Collect
accurate
mul%-­‐channel
media
aRribu%on
data
to
provide
advanced
insights
Live
Behavioral
Chat
Targe&ng
A/B
Tes&ng
Enable
advanced
features
such
Heat
Maps
as
targe%ng,
tes%ng
and
chat
to
op%mise
user
experience
September
2012
©
Datalicious
Pty
Ltd
64

65. >
Unique
SuperTag
architecture
T
Injec%ng
JavaScript
tags
into
the
page
based
on
business
rules
using
SuperTag
the
SuperTag
top
and
boRom
containers.
The
SuperTag
top
and
boRom
containers
are
JavaScript
func%ons
called
in
the
page
code
just
auer
the
opening
<body>
tag
and
just
before
the
closing
</body>
tag
on
all
page
across
all
domains.
§ One
tag
for
all
sites
&
pla|orms
B
§ Hosted
internally
or
externally
§ Easy
tag
implementa%on/updates
§ Tag/business
rule
tes%ng
on
live
site
superT.t()
T
T
§ Facilitates
A/B
&
MV
test
execu%on
§ Implementa%on
of
heat-­‐maps
§ Re-­‐targe%ng
across
channels
&
tools
§ Live
chat
&
phone
number
targe%ng
superT.b()
B
B
§ Collect
raw
page
&
impression
data
September
2012
©
Datalicious
Pty
Ltd
65

66. >
Overcoming
team
barriers
Marke&ng
SuperTag
Technology
Easy
to
use
online
user
interface
enabling
marketers
to
manage
tags
without
intensive
technology
support
September
2012
©
Datalicious
Pty
Ltd
66

67. >
Cross-­‐plajorm
integra&on
Web
Analy&cs
CRM/eDMs
Heat
Maps
Paid
Search
Targe&ng
SuperTag
Ad
Servers
Live
Chat
Affiliates
Tes&ng
DFPs
Centralised
uniform
business
rules
to
trigger
conversions
and
segment
visitors
across
mul%ple
marke%ng
pla|orms
September
2012
©
Datalicious
Pty
Ltd
67

68. Same
code
on
all
pages,
no
customisa&on
required
2012
©
Supertag
Pty
Ltd
68

69. § Adobe
SiteCatalyst
§ Adobe
Test&Target
§ Google
Analy&cs
§ Google
DoubleClick
§ Microsot
Atlas
§ MediaMind
§ ClearSaleing
§ ClickTale
§ Olark
§ Etc
Easy
to
use
drag
&
drop
interface
to
manage
tags
September
2012
©
Datalicious
Pty
Ltd
69

70. Turn
any
page
element
into
tests
or
data
variables
September
2012
©
Datalicious
Pty
Ltd
70

71. >
SuperTag
deployment
op&ons
Manual
Email/FTP
JavaScript
SuperTag
JS
Tags
Client
JavaScript
SuperTag
JS
hos&ng
on
website
management
publishing
client
server
Dedicated
SuperTag
JS
Tags
CDN
=
Content
Client
Github
client
delivery
network
hos&ng
on
website
JS
archive
client
CDN
SuperTag
JS
Real-­‐&me
JavaScript
SuperTag
JS
Tags
Client
management
SuperTag
JS
hos&ng
on
CDN
JavaScript
website
app.supert.ag
publishing
c.supert.ag
September
2012
©
Datalicious
Pty
Ltd
71

72. >
CookieBouncer
extension
Domain
A
Domain
A
Domain
B
Domain
A
Search
Prod
view
Checkout
Payment
Conversion
Cookie
Cookie
Cookie
Campaign
Campaign
Campaign
=
Search
=
Domain
A
=
Domain
B
Domain
A
Domain
A
Domain
A
Domain
A
Search
Prod
view
Checkout
Payment
Conversion
Cookie
Cookie
Campaign
Cookie
Campaign
=
Search
Bouncer
=
search
September
2012
©
Datalicious
Pty
Ltd
72

73. >
DataCollector
extension
SuperTag
app
JavaScript
Full
SuperTag
JS
JavaScript
Client
pages
Tags
Browser
genera&ng
JS
hosted
by
client
referencing
execu&ng
app.supert.ag
or
on
c.supert.ag
SuperTag
JS
SuperTag
JS
Lookup
Requests
tables
Dedicated
Logs
SuperTag
client
Splunk
DataCollector(s)
server(s)
d.supert.ag
Databases,
files,
etc
Requests
3rd
party
data
Splunk
regex
Splunk
saved
Display
ads
plajorms
(i.e.
builder
and
searches
and
loading
sta&c
CRM,
POS,
IP)
data
extracts
dashboards
1x1
pixel
August
2012
©
Datalicious
Pty
Ltd
73

74. Detailed
Splunk
dashboards
for
web
analy&cs
September
2012
©
Datalicious
Pty
Ltd
74

75. Detailed
Splunk
dashboards
for
media
a=ribu&on
September
2012
©
Datalicious
Pty
Ltd
75

76. >
ExactTarget
email
extension
SuperTag
app
JavaScript
Full
SuperTag
JS
Tags
Client
pages
genera&ng
JS
hosted
by
client
referencing
app.supert.ag
or
on
c.supert.ag
SuperTag
JS
Requests
Databases,
Addi&onal
files,
etc
Dedicated
Data
SuperTag
client
data
ExactTarget
ExactTarget
(i.e.
CRM,
POS)
DataExtension
integra&on
Data
Clicks,
data
ExactTarget
List
Targeted
Emails
Relevant
business
rules
ExactTarget
ExactTarget
and
condi&ons
campaign
list
email
campaign
September
2012
©
Datalicious
Pty
Ltd
76

77. >
Unique
selling
points
(USPs)
§ Superior
pla|orm
architecture
for
more
flexibility
– Turn
any
page
element
into
variables
for
data
collec%on
or
business
rules
for
tag
execu%on
– Cross-­‐pla|orm
integra%on
and
data
exchange
– Splunk
integra%on
for
advanced
data
mining
§ Superior
tes%ng,
deployment
and
audit
features
– Tes%ng
of
tags
&
business
rules
on
the
live
website
– Complete
audit
trail
of
all
tag
changes
and
tests
§ No
lock-­‐in,
stop
using
the
SuperTag
at
any
%me
– External
and
internal
JavaScript
hos%ng
available
– Perpetual
JavaScript
usage
rights
&
Github
archive
§ All
inclusive
pricing
structure
incen%vizing
use
September
2012
©
Datalicious
Pty
Ltd
77

78. >
Blue
chip
SuperTag
clients
September
2012
©
Datalicious
Pty
Ltd
78

79. >
Great
customer
feedback
"Managing
third
party
tags
has
never
been
easier
[...]
simplicity
of
seMng
business
rules
[...]
reducBon
in
CPA
[...].“
Jason
Lima,
Online
Marke%ng,
IMB
"[...]
SuperTag
tool
is
so
easy
to
use
[...].
Live
tes>ng
is
par>cularly
useful
[...]
highly
recommended
[...]."
Helene
Cameron-­‐Heslop,
Analyst,
Appliances
Online
"SuperTag
speeds
up
tag
implementa>on
and
gives
us
increased
flexibility
[...]
manage
media
and
website
analyBcs
[...].”
Alex
Crompton,
Head
of
Digital,
Aussie
September
2012
©
Datalicious
Pty
Ltd
79

80. Contact
us
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
us
twi=er.com/datalicious
September
2012
©
Datalicious
Pty
Ltd
80

81. Data
>
Insights
>
Ac&on
September
2012
©
Datalicious
Pty
Ltd
81