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ADMA Digital Certificate: Why Data Is Sexy
- 2. [
Main
data
categories
]
Campaign
data
Consumer
Customer
data
data
Compe5tor
data
9/10/09
©
Datalicious
Pty
Ltd
2
- 4. [
Defining
analy:cs
strategy
]
Search,
display
ads
Web
analy5cs
Awareness
Interest
Desire
Ac5on
Sa5sfac5on
Online
surveys,
site
polls
Social
media
Social
media
9/10/09
©
Datalicious
Pty
Ltd
4
- 6. [
De-‐duplica:on
across
channels
]
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Omniture
PlaGorm
Email
Email
Blast
PlaGorm
$
Organic
Google
Search
Analy:cs
$
9/10/09
©
Datalicious
Pty
Ltd
6
- 8. [
Success
aIribu: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
9/10/09
©
Datalicious
Pty
Ltd
8
- 9. [
Forrester
media
aIribu:on
]
Chart
shows
an
example
only,
aQribu5on
model
needs
to
be
defined
for
each
company
separately
based
on
their
individual
success
metrics
(and
cookie
expira5on
policies).
9/10/09
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Datalicious
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9
- 10. [
Campaign
stacking
by
channel
]
Chart
shows
percentage
of
channel
touch
points
that
lead
to
a
conversion.
Neither
first
nor
last-‐click
measurement
would
provide
true
picture
9/10/09
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Datalicious
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10
- 12. [
Analyse
channel
overlap
]
Call
Centre
DM
&
EDM
Radio
&
Paid
Search
Other
ATL
Display
Media
Straight
to
Site
Organic
Search
Partners
9/10/09
©
Datalicious
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Ltd
12
- 14. [
Email
iden:fica:on
points
]
@
Vodafone.com.au
Phone
Online
Receipt
Research
Credit
Check
Confirma5on
Conversion
Fulfilment
@
Adver5sing
Vodafone.com.au
Retail
Online
Receipt
Research
Credit
Check
Confirma5on
Campaign
Conversion
Fulfilment
@
Vodafone.com.au
Online
Online
Order
Online
Receipt
Research
Confirma5on
Credit
Check
Confirma5on
Conversion
Fulfilment
Cookie
ID
9/10/09
©
Datalicious
Pty
Ltd
14
- 15. [
Campaign
data
summary
]
Define
key
performance
indicators
for
each
phase
of
the
campaign
funnel
but
web
analy5cs
can
only
cover
part
of
the
funnel
Import
and
analyze
data
in
single
plaaorm,
i.e.
single
source
of
truth,
to
enable
comparison
of
channel
performance
and
avoid
duplica5on
of
orders
across
channels
Define
success
aQribu5on
model
including
cookie
expira5on
policies
for
your
company
and
test
different
budget
alloca5ons
and
channel
combina5ons
to
find
the
op5mal
mix
Use
search
calls
to
ac5on
on
above
the
line
broadcast
ads
such
as
TV
and
print
for
higher
recollec5on
and
response
rates
plus
an
improved
ability
to
track
offline
media
Consider
sending
email
receipts
for
phone
and
retail
sales
to
aQempt
to
more
accurately
track
impact
of
online
on
offline
sales
and
vice
versa
9/10/09
©
Datalicious
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15
- 18. [
Targe:ng
framework
]
Reten5on
Customer
Profile
Prospect
Customer
-‐12
-‐11
-‐10
-‐9
-‐8
-‐7
-‐6
-‐5
-‐4
-‐3
-‐2
-‐1
0
1
2
3
4
5
6
7
8
9
10
11
12
Considera5on
Visitor
Behaviour
Weeks
9/10/09
©
Datalicious
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18
- 20. [
Affinity
targe:ng
in
ac:on
]
Different
types
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe5ng,
response
rates
are
liged
significantly
across
products.
Click-‐Through
Rate
By
Category
Affinity
Message
Postpay
Prepay
Broadb.
Business
Blackberry
Bold
- - - +
5GB
Mobile
Broadband
- - + -
Blackberry
Storm
+ - + +
12
Month
Caps
- + - +
9/10/09
©
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20
- 21. [
Matching
segments
are
key
]
On-‐site
Off-‐site
segments
segments
On
and
off-‐site
targe5ng
plaaorms
should
use
iden5cal
triggers
to
sort
visitors
into
segments
9/10/09
©
Datalicious
Pty
Ltd
21
- 22. 9/10/09
©
Datalicious
Pty
Ltd
22
- 23. [
Combining
data
sets
]
Site
Behaviour
CRM
Profile
tracking
of
purchase
funnel
stage
one-‐off
collec5on
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
predic5ve
models
based
on
data
mining
search
terms,
referrers,
etc
propensity
to
buy,
churn,
etc
tracking
of
internal
promo5on
responses
historical
data
from
previous
transac5ons
emails,
internal
search,
etc
average
order
value,
points,
etc
UPDATED
CONTINUOUSLY
UPDATED
OCCASIONALLY
9/10/09
©
Datalicious
Pty
Ltd
23
- 25. [
Where
is
the
money
]
$
$
$
$
$
$
$
$
$
$
9/10/09
©
Datalicious
Pty
Ltd
25
- 26. [
Customer
data
summary
]
Customer
data
collected
online
can
and
should
influence
offline
decisions,
e.g.
retail
store
loca5ons,
outdoor
adver5sing,
print
placements,
etc
Website
data
cannot
answer
all
ques5ons
but
rather
only
iden5fies
poten5al
problem
points,
online
surveys
are
powerful
tools
to
find
out
more
about
what’s
actually
the
issue
Your
products
and
services
solve
different
problems
for
different
people
and
you
need
to
cater
for
that
and
adjust
your
adver5sing,
change
you
message
according
to
segments
and
purchase
funnel
stage
to
make
your
offering
more
relevant
CRM
databases
are
powerful
tools
to
target
but
their
informa5on
is
rela5vely
sta5c
and
needs
to
be
updated
manually
through
your
customers,
extend
your
customer
profiles
to
include
website
behavior
to
make
them
more
accurate
and
keep
them
up
to
date
In
the
end
this
is
about
iden5fying
the
20%
of
customers
you
make
80%
of
your
revenue
with
and
speak
and
treat
them
in
a
different
way
9/10/09
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Datalicious
Pty
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26
- 30. 9/10/09
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Datalicious
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30
- 31. 9/10/09
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Datalicious
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31
- 32. 9/10/09
©
Datalicious
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32
- 33. [
Australia
vs.
New
Zealand
]
australia.com
vs.
newzealand.com
9/10/09
©
Datalicious
Pty
Ltd
33
[
september
2007
]
[
datalicious.com.au
]
- 34. [
Compe:tor
data
summary
]
There
is
plenty
of
accessible
and
free
compe5tor
data
out
there
and
you
need
to
start
reviewing
it
on
a
regular
basis
Search
data
in
par5cular
is
a
very
powerful
tool
to
iden5fy
market
trends,
threats
and
opportuni5es
quickly
and
cost
effec5vely
as
it
provides
a
direct
insight
into
consumers
minds
Given
that
roughly
90%
of
searches
are
conducted
through
Google
in
Australia
the
overall
search
volume
of
brand
search
terms
on
Google
should
be
roughly
equivalent
with
the
brand
strength
or
at
least
awareness
But
interpret
all
data
sources
with
a
grain
of
salt
and
don’t
take
anything
for
a
fact
un5l
you
have
validated
it,
you
know
your
market
and
customers
the
best
9/10/09
©
Datalicious
Pty
Ltd
34
- 37. 9/10/09
©
Datalicious
Pty
Ltd
37
- 38. 9/10/09
©
Datalicious
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38
- 41. [
Consumer
data
summary
]
Search
is
not
only
useful
to
analyze
brand
trends
but
can
also
be
used
to
find
out
more
about
more
granular
trends
such
as
product
categories
or
single
products
Again,
online
data
can
and
should
be
used
to
drive
offline
marke5ng
decisions,
e.g.
products
matching
most
popular
search
terms
should
not
only
be
promoted
most
on
the
homepage
but
also
in
retail,
print,
etc
Google
Ad
Planner
data
can
also
be
used
to
iden5fy
what
websites
a
segment
that
is
searching
for
a
certain
terms
visits
regularly
which
in
turn
should
influence
partnership,
sponsorship
and
media
planning
If
your
company
has
customer
address
data
a
match
with
MOSAIC
might
be
a
very
powerful
tool
to
find
out
more
about
the
media
habits
of
a
specific
customer
segment
including
websites
visits
if
combined
with
Hitwise
data
9/10/09
©
Datalicious
Pty
Ltd
41
- 42. [
Main
data
categories
]
Campaign
data
Consumer
Customer
data
data
Compe5tor
data
9/10/09
©
Datalicious
Pty
Ltd
42
- 43. 9/10/09
©
Datalicious
Pty
Ltd
43
- 45. [
Useful
data
tools
]
hQps://adwords.google.com/select/KeywordToolExternal
hQp://www.google.com/sktool
hQp://www.google.com/insights/search
hQp://www.google.com/trends
hQp://labs.google.com/sets
hQps://www.google.com/adplanner
hQps://www.google.com/adplanner
hQp://www.socialmen5on.com
hQp://www.trendis5c.com
hQp://www.yureekah.com
hQp://www.wordle.net
9/10/09
©
Datalicious
Pty
Ltd
45
- 48. [
Best
prac:ce
approach
]
Con:nuous
op:miza:on
Research
SEM
SEO
Tes5ng
Targe5ng
Analy:cs
framework
9/10/09
©
Datalicious
Pty
Ltd
48
- 49. [
Wide
range
of
services
]
Data
Insights
Ac:on
Web
Analy:cs
Solu:ons
Keyword
Research
Search
Lead
Media
Marke:ng
System
Integra:on
Campaign
Repor:ng
Campaign
Op:misa:on
Cross
Channel
Media
Tracking
Segmenta:on/Data
Mining
Internal
Search
Op:misa:on
Online
Surveys/Panels
Quan:ta:ve
Research
Targe:ng/Merchandizing
Omniture
Specialists
Market/Consumer
Trends
A/B,
Mul:variate
Tes:ng
Google
Analy:cs
Specialists
Compe:tor
Analysis
Staff
Training/Workshops
9/10/09
©
Datalicious
Pty
Ltd
49
- 51. Ques:ons
cbartens@datalicious.com
Updates
twiQer.com/datalicious
blog.datalicious.com
9/10/09
©
Datalicious
Pty
Ltd
51
![[
Why
data
is
sexy
]
No
really,
it
is!](https://image.slidesharecdn.com/200909-adma-digital-certificate-v1-090909223744-phpapp02-091016223754-phpapp02/85/ADMA-Digital-Certificate-Why-Data-Is-Sexy-1-320.jpg)
![[
Main
data
categories
]
Campaign
data
Consumer
Customer
data
data
Compe5tor
data
9/10/09
©
Datalicious
Pty
Ltd
2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)