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

1. >
Omniture
Training
<
Smart
Data
Driven
Marke.ng

2. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Background
March
2011
©
Datalicious
Pty
Ltd
2

3. >
What
we
do…
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
aMribu=on
models
Alterian,
Trac=on,
Inxmail,
etc
Tag-­‐less
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
March
2011
©
Datalicious
Pty
Ltd
3

4. >
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,
shiI
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
May
2011
©
Datalicious
Pty
Ltd
4

5. >
Smart
data
driven
marke=ng
Metrics
Framework
Metrics
Framework
Media
AMribu=on
Benchmarking
and
trending
Benchmarking
and
trending
Op=mise
channel
mix
Targe=ng
Increase
relevance
Tes=ng
Improve
usability
$$$
March
2011
©
Datalicious
Pty
Ltd
5

6. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Web
Analy=cs
March
2011
©
Datalicious
Pty
Ltd
6

7. >
Measuring
Online
Success
Campaign
spend
($$$)
Conversion
funnel
Product
page,
start
a
form,
download
content,
watch
%
content,
add
to
shopping
cart,
view
shopping
cart,
cart
checkout,
payment
details,
shipping
informa.on,
order
confirma.on,
applica.on
submiOed,
etc
Conversion
income
($$$$$)
March
2011
©
Datalicious
Pty
Ltd
7

8. >
Addi=onal
success
metrics
Click
Through
?
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Page
Page
Product
Through
Bounce
Views
Views
$
Click
Product
Call
back
Phone
Through
View
request
Sale
$
March
2011
©
Datalicious
Pty
Ltd
8

9. >
Conversion
Funnel
Maps
March
2011
©
Datalicious
Pty
Ltd
9

10. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac=on
Sa=sfac=on
Social
media
March
2011
©
Datalicious
Pty
Ltd
10

11. >
Simplified
AIDAS
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac.on)
(Sa.sfac.on)
March
2011
©
Datalicious
Pty
Ltd
11

12. >
It’s
all
about
people
numbers
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
March
2011
©
Datalicious
Pty
Ltd
12

13. >
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.
March
2011
©
Datalicious
Pty
Ltd
13

14. >
The
consumer
data
journey
To
transac=onal
data
To
reten=on
messages
From
suspect
to
prospect
To
customer
Time
Time
From
behavioural
data
From
awareness
messages
March
2011
©
Datalicious
Pty
Ltd
14

15. >
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
March
2011
©
Datalicious
Pty
Ltd
15

16. >
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
May
2011
©
Datalicious
Pty
Ltd
16

17. New
vs.
returning
visitors

18. AU/NZ
vs.
rest
of
world

19. >
Automa=c
Affinity
Segmenta=on
Skiing
Rugby
Content
Content
Affinity
“Skiing”
March
2011
©
Datalicious
Pty
Ltd
19

20. March
2011
©
Datalicious
Pty
Ltd
20

21. What
is
likely
to
maximise
conversion?

22. >
Targe=ng
matrixes
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
March
2011
©
Datalicious
Pty
Ltd
22

23. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
What
is
Omniture?
March
2011
©
Datalicious
Pty
Ltd
23

24. >
Omniture
is
a
BEAST!
March
2011
©
Datalicious
Pty
Ltd
24

25. Omniture
SiteCatalyst

26. >
Omniture
SiteCatalyst
Outline
§ How
it
works
§ The
data
structure
§ Key
Variables
§ Classifica.ons
§ Examples
March
2011
©
Datalicious
Pty
Ltd
26

27. >
How
it
works
§ Collect
info
from
the
page
(.tle,
url,
referrer,
sec.on,
content,
.me,
day,
etc)
§ Collect
info
on
the
user
(new/repeat,
segments,
customer/prospect,
interests,
etc)
§ Take
all
the
above
and
request
it
as
a
URL
§ Collect
the
info
above
into
a
database
March
2011
©
Datalicious
Pty
Ltd
27

28. >
Basic
Data
Structure
.me
Visitor
ID
Events
campaign
products
pageName
12:30
Sat…
12345567
event1,event2
p:sem
102,103
travel:why:skiiing
12:31
Sat…
13323222
event3
direct
travel:home
12:32
Sat…
13323222
event5
travel:why:skiiing
• Every
request
(pageview)
to
Omniture
becomes
a
new
row
• Think
of
it
as
a
giant
spreadsheet
• Events
are
grouped
together
• Products
are
grouped
together
• Custom
variables
are
also
available
March
2011
©
Datalicious
Pty
Ltd
28

29. >
Events
become
counters
pageName
event1
event2
event3
event4
event5
travel:why:skiiing
1
1
0
0
1
travel:home
0
0
1
0
0
• This
is
how
reports
show
actual
numbers
• Events
are
counted
against
each
variable
value
in
the
same
row
• When
thinking
of
a
visit,
all
rows
of
a
par.cular
visitorID
in
a
session
.me
window
are
added
together
March
2011
©
Datalicious
Pty
Ltd
29

30. >
Types
of
Variables
§ PageName
à
think
site
structure
§ Products
à
what
are
you
selling
§ Campaign
à
where
did
people
come
from
§ Props
à
What
happened
on
a
page
§ Evars
à
Something
you
learned
about
the
visitor
March
2011
©
Datalicious
Pty
Ltd
30

31. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
The
PageName
Variable
March
2011
©
Datalicious
Pty
Ltd
31

32. >
KPIs
Depend
On
Solid
Founda=on
Business
Objec.ves
1
KPIs Web
2
Analy.c
3 Data
Founda.on
Key
Performance
Indicators
also
depend
on
a
solid
founda.on
of
well-­‐
defined
page
names,
content
hierarchy,
and
report
suite
architecture.
Without
these
building
blocks
in
place,
making
decisions
and
taking
ac.on
on
the
data
will
prove
difficult.

33. >
User-­‐friendly
Page
Names
§ Defining
a
good
page
naming
strategy
is
one
of
the
most
important
steps
in
Three
C’s
of
Effec.ve
Page
Naming
maximizing
Web
analy.cs
success.
Context:
Include
directory
structure
or
content
hierarchy
in
the
page
name
to
help
users
orient
§ In
order
to
help
people
understand
the
the
page
within
the
site
and
simplify
report
filtering.
performance
of
site
content,
TA
should
consider
crea.ng
more
user-­‐friendly
Clarity:
Ensure
the
page
name
is
clear
and
easily
iden.fiable
for
infrequent
users.
page
names.
Conciseness:
Keep
the
page
name
as
short
as
§ You
will
need
to
create
page
names
possible
to
maximize
limited
character
space.
that
are
contextual,
clear,
and
concise.

34. >
Structure
of
a
Quality
Page
Name
Will
users
know
what
page
this
is?
Clarity
is
an
overarching
concern
for
the
en.re
page
name.
Clarity
Which
Context
neighborhood?
A
Friendly
Page
n
the
URL
Context
focuses
o Name
has
two
parts:
URL
structure
structure
stem,
which
helps
directory:subdirectory:sub-­‐subdirectory:specific
page
name
stem
and
where
a
page
to
iden.fy
specific
page
resides.
name.
URL
structure
stem
Conciseness
Use
“US”
instead
of
“United
States”?
Conciseness
primarily
focuses
on
making
the
URL
structure
stem
as
short
as
possible.
The
specific
page
name
part
should
also
be
as
concise
as
possible
but
most
of
the
emphasis
will
be
on
the
stem.

35. >
PageNames
should
be
like
Pyramids

36. >
Content
Hierarchy
Site
Sec.on
Level
Page
Type
Different
aggrega.ons
of
content
data
will
allow
you
to
iden.fy
Sub
Sec.on
Level
key
paOerns
at
higher
levels
and
then
drill
into
specific
details
at
lower
levels
Page
Level

37. >
Page
Naming
Examples
Good
Page
Naming
vs.
Bad
Page
Naming
§ Clear and user-friendly § Unclear and confusing
§ More concise § Too long (URLs)
§ Easy to filter and search § Awkward to filter and search
§ Consistent format § Inconsistent format

38. >
Sample
Page
Naming
Sample
page
name:
Travel:
things
to
do:
snowboarding
site
sec.on
Sub
Sec.on
Page
names
could
leverage
the
bread
crumbs
on
each
page.
The
bread
crumb
captures
the
page
CONTEXT
as
it
reveals
the
loca.on
of
each
page.
The
sec.on,
department,
and
category
of
each
page
should
go
into
each
page
name
for
page
filtering
purposes.
NOTE:
Page
names
should
not
be
exact
copies
of
the
bread
crumb
because
for
example
“Home
>”
is
unnecessary
and
other
text
in
the
bread
crumb
may
need
to
be
abbreviated
for
CONCISE
page
names.
hOp://www.newzealand.com/things_to_do/snowboarding/index.html
©
2007
Omniture
Inc.,
Confiden.al
&
Proprietary

39. >
Page
Naming
Op=ons
Recommended
1. Server-­‐side:
Use
server-­‐side
logic
to
populate
page
name
for
each
web
page.
2. Hard-­‐code:
Manually
set
page
name
on
each
web
page.
Use
With
Cau2on
3. PageName
plug-­‐in:
JavaScript
plug-­‐in
strips
“hOp://
www.domain.com/”
from
URL
page
names.
Not
Recommended
4. Leave
blank:
SiteCatalyst
defaults
to
page
URL.
5. Document.=tle:
Uses
the
.tle
of
each
page
instead
of
URL.

40. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
The
Campaign
Variable
March
2011
©
Datalicious
Pty
Ltd
40

41. >
External
Campaign
Tracking
§ Campaigns
demand
very
special
aOen.on
within
a
global
type
structure.
– Dont
duplicate
tracking
codes
for
disparate
campaigns
– Use
structured
tracking
codes:
cid=e:tar:0003
§ As
a
best
prac.ce,
we
recommend
crea.ng
uniform
tracking
codes.
Examples:
All
Marke.ng
– cid=a:033007
à
“a” flags
affiliate
campaign
– cid=e:033007
à
“e” flags
email
campaign
– cid=sem:g:033007
à”sem:g” signifies
Google
and
is
a
paid
search
campaign
Affiliates
Other
§ U.lize
SAINT
to
upload
valuable
meta
Emails
Redirects
data
for
analysis
Online
Marke.ng

42. >
TNZ
Campaign
Setup
March
2011
©
Datalicious
Pty
Ltd
42

43. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
The
Products
Variable
March
2011
©
Datalicious
Pty
Ltd
43

44. >
The
Products
variable
§ Is
for
things
that
directly
generate
$$
§ Can
store
mul.ple
values
§ Can
store
$
value,
quan..es
§ Can
increment
other
events
(tax,
etc)
§ Is
commonly
used
for
SKU
codes,
product
codes,
product
names,
etc
March
2011
©
Datalicious
Pty
Ltd
44

45. Product
Classifica=ons
§ Product
pages
are
a
key
focus
of
repor.ng
and
analysis.
12345
§ Populate
the
s.products
variable
with
all
product
IDs
viewed
on
a
page.
§ In
order
to
gain
more
insights
into
these
important
pages,
you
can
leverage
SiteCatalyst’s
SAINT
classifica.on
tool
to
upload
metadata
with
67891
different
product
aOributes
into
SiteCatalyst.
§ Able
to
analyze
the
conversion
performance
of
its
product
pages
by
aggregated
product
aOributes
such
as
category,
subcategory,
region,
facili.es,
23456
accommoda.on
type,
star
ra.ng,
etc.
§ All
meta
data
is
.ed
to
the
specific
product
id
s.products=";
12345,;
67891,;
23456"

46. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Props
March
2011
©
Datalicious
Pty
Ltd
46

47. >
Props
(custom
traffic)
• Informa.on
related
to
a
par.cular
page
load.
i.e.
not
relevant
outside
the
scope
of
that
pageview
• You
get
up
to
75
to
customise.
• They
can
contain
lists
of
mul.ple
items
• Are
essen.ally
counters
for
things
that
have
names
(as
opposed
to
“events”,
which
are
counters
for
specific
events)
• Example
usage:
Internal
Search
Keywords,
Tags,
etc
March
2011
©
Datalicious
Pty
Ltd
47

48. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Evars
March
2011
©
Datalicious
Pty
Ltd
48

49. >
Evars
(custom
conversion)
• Evars
are
like
props
except
the
value
remains
with
the
user
un.l
it
is
set
with
a
different
value
• Think
of
these
as
labelling
the
user
• User
them
to
create
segments,
such
as
new/repeat
visitor,
category
affinity,
customer/non-­‐customer,
etc
• Once
you
set
an
evar
for
a
user,
custom
events
will
con.nue
to
register
against
the
evars
value
March
2011
©
Datalicious
Pty
Ltd
49

50. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Classifica=ons
March
2011
©
Datalicious
Pty
Ltd
50

51. >
Classifica=ons
are
labels
Q.
Show
me
revenue
by
brand?
Q.
Show
me
which
product
categories
had
the
most
views?
Q.
Did
we
see
increased
traffic
for
products
we
recently
adver.sed
on
TV?
product
Category
Name
Brand
Profit
Band
TV
Ad
-­‐
7
days
001
TV
Samsung
plasma
Samsung
High
No
002
Internet
NetGear
N50
Netgear
Low
No
003
Internet
Netgear
G800
Netgear
Medium
No
004
Mobile
Phone
Samsung
Galaxy
Samsung
Low
Yes
005
Accessory
Headphones
Sony
Low
No
006
TV
Sony
LCD
Sony
High
Yes
007
Internet
Wireless
modem
Alcatel
Low
No
March
2011
©
Datalicious
Pty
Ltd
51

52. >
SAINT:
Campaigns
Classifica=ons
XID
§ By
u.lizing
the
SAINT
classifica.on
template,
you
can
AOributes
con.nue
to
upload
meta
data
for
each
variable
§ Meta
data
examples:
SAINT
– Campaign:
Name,
Channel,
Owner,
Paid
vs
Nonpaid,
Branded
keywords
vs
non-­‐branded,
etc
– Product:
Name,
Category,
Brand,
etc
– Customer
ID:
Profitability,
segment,
churn
risk,
demographics,
loca=on,
etc
§ The
process
of
assigning
aOributes
through
SAINT
can
be
automated
via
FTP
§ Classifica.ons
can
be
updated
at
any
.me
and
will
change
all
data
retrospec.vely,
because
they
are
a
label
and
do
not
change
the
underlying
variable
value
and
the
data
recorded
against
it.

53. March
2011
©
Datalicious
Pty
Ltd
53

54. Things
to
think
about

55. >
Cookie
expira=on
impact
Paid
Bid
Search
Mgmt
$
Banner
Banner
Ad
Ad
Click
Ad
View
Server
$
Email
Email
Expira=on
Blast
Pla;orm
$
Organic
Google
Search
Analy=cs
$
March
2011
©
Datalicious
Pty
Ltd
55

56. >
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.
March
2011
©
Datalicious
Pty
Ltd
56
Source:
White
Paper,
RedEye,
2007

57. >
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
March
2011
©
Datalicious
Pty
Ltd
57

58. Omniture
Test
and
Target

59. >
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
March
2011
©
Datalicious
Pty
Ltd
59

60. >
Prospect
targe=ng
parameters
March
2011
©
Datalicious
Pty
Ltd
60

61. >
Affinity
re-­‐targe=ng
in
ac=on
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe.ng,
response
rates
are
liIed
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
hMp://bit.ly/de70b7
12
Month
Caps
- + - +
March
2011
©
Datalicious
Pty
Ltd
61

62. >
Customer
profiling
in
ac=on
Using
website
and
email
responses
to
learn
a
liOle
bite
more
about
subscribers
at
every
touch
point
to
keep
refining
profiles
and
messages.
May
2011
©
Datalicious
Pty
Ltd
62

63. ©
Datalicious
Pty
Ltd
63

64. >
Tes=ng
matrixes
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
?
?
?
?
?
?
?
?
March
2011
©
Datalicious
Pty
Ltd
64

65. >
Keys
to
effec=ve
targe=ng
1. Define
success
metrics
2. Define
and
validate
segments
3. Develop
targe.ng
and
message
matrix
4. Transform
matrix
into
business
rules
5. Develop
and
test
content
6. Start
targe.ng
and
automate
7. Keep
tes.ng
and
refining
8. Communicate
results
March
2011
©
Datalicious
Pty
Ltd
65

66. SuperTagging

67. >
SuperTag
code
architecture
§ Central
JavaScript
container
tag
§ One
tag
for
all
sites
and
pla|orms
§ Hosted
internally
or
externally
§ Faster
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
March
2011
©
Datalicious
Pty
Ltd
67

68. Appendix

69. PageNaming
Op=ons
–
Pros
/
Cons
Pros
Cons
Server-side — Provides flexibility to create — Only applies to dynamic pages
friendly page names — Flexibility depends on platform
— Logic automates page — Ongoing diligence to ensure logic doesn’t break in future
naming process
Hard-code — Free to create friendly page — Can be labor-intensive
name that isn’t bound by — Doesn’t apply to dynamic pages
URL or page title
— Must be careful when using pages as boilerplates
Page Name — Requires less effort to create — Only recommended for sites with well-defined URL
page names structures
Plug-in — Shortens URL page names — Can still lead to long page names
and makes them more — Won’t aggregate a page with different URL variations
manageable
— Problematic with dynamic pages
Leave Blank — Requires no effort — URL page names can be long and unwieldy
— Limited character space is wasted on domain root
(defaults to URL)
— Won’t aggregate a page with different URL variations
— Problematic with dynamic pages
Document.title — Requires little effort to set — Page titles aren’t always unique to a page
up — Can be long and contain unnecessary keywords (SEO)
— May change frequently (SEO)
— Problems caused by translation tools (e.g., Babel Fish)