The presentation discusses course training on advanced analytics.

1. [
GroupM
Analy.cs
]
Advanced
analy+cs
training

2. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Quick
recap
]
August
2010
©
Datalicious
Pty
Ltd
2

3. [
Day
1:
Basic
Analy.cs
]
§ Defining
a
metrics
framework
– What
to
report
on,
when
and
why?
– Matching
strategic
and
tac+cal
goals
to
metrics
– Covering
all
major
categories
of
business
goals
§ Finding
and
developing
the
right
data
– Data
sources
across
channels
and
goals
– Meaningful
trends
vs.
100%
accurate
data
– Human
and
technological
limita+ons
§ Plus
hands-­‐on
exercises
August
2010
©
Datalicious
Pty
Ltd
3

4. [
Day
1:
Basic
Analy.cs
]
§ Hands-­‐on
exercises
and
examples
– Funnel
breakdowns
– Conversions
metrics
– Metrics
framework
– Search
insights
– Duplica+on
impact
– Sta+s+cal
significance
August
2010
©
Datalicious
Pty
Ltd
4

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010010101001001010010100100101001111101010100101001001001010
[
Course
overview
]
August
2010
©
Datalicious
Pty
Ltd
5

6. [
Day
2:
Advanced
Analy.cs
]
§ Campaign
flow
and
media
aSribu+on
– Designing
a
campaign
flow
including
metrics
– Omniture
vs.
Google
Analy+cs
capabili+es
§ How
to
reduce
media
waste
– Tes+ng
and
targe+ng
in
a
media
world
– Media
vs.
content
and
usability
§ Plus
hands-­‐on
exercises
August
2010
©
Datalicious
Pty
Ltd
6

7. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Media
a?ribu.on
]
August
2010
©
Datalicious
Pty
Ltd
7

8. [
Campaign
flow
and
calls
to
ac.on
]
=
Paid
media
Organic
PR,
WOM,
search
events,
etc
=
Viral
elements
=
Coupons,
surveys
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
C1
C2
CRM
Facebook
program
Twi?er,
etc
C3
POS
kiosks,
Call
center,
loyalty
cards,
etc
retail
stores,
etc
August
2010
©
Datalicious
Pty
Ltd
8

9. Exercise:
Campaign
flow

10. [
Unique
calls
to
ac.on
]
§ Unique
click-­‐through
URLs
§ Unique
vanity
domains
or
URLs
§ Unique
phone
numbers
§ Unique
search
terms
§ Unique
email
addresses
§ Unique
personal
URLs
(PURLs)
§ Unique
SMS
numbers,
QR
codes
§ Unique
promo+onal
codes,
vouchers
§ Geographic
loca+on
(Facebook,
FourSquare)
§ Regression
analysis
of
cause
and
effect
August
2010
©
Datalicious
Pty
Ltd
10

11. [
Search
call
to
ac.on
for
offline
]
August
2010
©
Datalicious
Pty
Ltd
11

12. [
Reach
and
channel
overlap
]
TV
audience
Banner
Search
audience
audience
August
2010
©
Datalicious
Pty
Ltd
12

13. [
Indirect
display
impact
]
August
2010
©
Datalicious
Pty
Ltd
13

14. [
Indirect
display
impact
]
August
2010
©
Datalicious
Pty
Ltd
14

15. [
De-­‐duplica.on
across
channels
]
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Central
Analy.cs
Pla^orm
Email
Email
Blast
Pla^orm
$
Organic
Google
Search
Analy.cs
$
August
2010
©
Datalicious
Pty
Ltd
15

16. [
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
August
2010
©
Datalicious
Pty
Ltd
16

17. [
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
August
2010
©
Datalicious
Pty
Ltd
17

18. [
Paid
and
organic
stacking
]
August
2010
©
Datalicious
Pty
Ltd
18

19. [
Full
path
to
purchase
]
Introducer
Influencer
Influencer
Closer
$
SEM
Banner
Direct
SEO
Generic
Click
Visit
Branded
$
Banner
SEO
Affiliate
Social
View
Generic
Click
Media
$
TV
SEO
Direct
Email
Abandon
Ad
Branded
Visit
Update
August
2010
©
Datalicious
Pty
Ltd
19

20. August
2010
©
Datalicious
Pty
Ltd
20

21. [
Impact
of
cookie
expira.on
]
Introducer
Influencer
Influencer
Closer
$
SEM
Banner
Direct
SEO
Generic
Click
Visit
Branded
$
Banner
SEO
Affiliate
Social
View
Generic
Click
Media
$
TV
SEO
Direct
Email
Abandon
Ad
Branded
Visit
Update
August
2010
©
Datalicious
Pty
Ltd
21

22. [
Success
a?ribu.on
models
]
Introducer
Influencer
Influencer
Closer
$
Even
25%
25%
25%
25%
A?rib.
Exclusion
33%
33%
33%
0%
A?rib.
Pa?ern
30%
20%
20%
30%
A?rib.
August
2010
©
Datalicious
Pty
Ltd
22

23. [
Forrester
media
a?ribu.on
]
Forrester
adds
another
dimension
to
media
aSribu+on
by
sugges+ng
to
change
the
allocated
credit
for
each
campaign
touch
point
based
on
addi+onal
factors
such
as
site
interac+on.
August
2010
©
Datalicious
Pty
Ltd
23
Source:
Forrester,
2009

24. Exercise:
A?ribu.on
model

25. [
Exercise:
A?ribu.on
models
]
Introducer
Influencer
Influencer
Closer
$
Even
25%
25%
25%
25%
A?rib.
Exclusion
33%
33%
33%
0%
A?rib.
?
?
?
?
Custom
A?rib.
August
2010
©
Datalicious
Pty
Ltd
25

26. [
Exercise:
A?ribu.on
model
]
§ Allocate
more
conversion
credits
to
more
recent
touch
points
for
brands
with
a
strong
baseline
to
s+mulate
repeat
purchases
§ Allocate
more
conversion
credits
to
more
recent
touch
points
for
brands
with
a
direct
response
focus
§ Allocate
more
conversion
credits
to
ini+a+ng
touch
points
for
new
and
expensive
brands
and
products
to
insert
them
into
the
mindset
August
2010
©
Datalicious
Pty
Ltd
26

27. [
Understanding
channel
overlap
]
Direct,
Paid
Organic
Display
Affiliates,
Email
Channel
Branded
Search
Search
Ads
Partners
Updates
Direct,
n/a
Branded
Paid
n/a
Search
Organic
n/a
Search
Display
n/a
Ads
Affiliates
n/a
Partners
Email
Updates
display
>
sem
>
seo
>
affiliate
>
email
>
direct
>
$$$
n/a
August
2010
©
Datalicious
Pty
Ltd
27

28. [
Understanding
channel
overlap
]
DM
Call
Centre
eDMs
Radio
Paid
Search
Display
Ads
Organic
Direct
Search
Partners
August
2010
©
Datalicious
Pty
Ltd
28

29. [
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
aSributed
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.
August
2010
©
Datalicious
Pty
Ltd
29

30. [
Ad
server
exposure
test
]
1
User
qualifies
for
the
display
campaign
(if
the
user
has
already
been
tagged
go
to
step
3)
1st
impression
2
Audience
Segmenta.on
10%
of
users
in
control
group,
90%
in
exposed
group
User
tagged
with
segment
Measurement:
Conversions
per
1000
unique
Control
Exposed
visitors
(displayed
non-­‐branded
message)
(displayed
branded
message)
User
remains
in
segment
N
impressions
3
Control
Exposed
(displayed
non-­‐branded
message)
(displayed
branded
message)
August
2010
©
Datalicious
Pty
Ltd
30

31. [
Research
online,
shop
offline
]
August
2010
©
Datalicious
Pty
Ltd
31
Source:
2008
Digital
Future
Report,
Surveying
The
Digital
Future,
Year
Seven,
USC
Annenberg
School

32. [
Track
offline
sales
driven
by
online
]
Adver.sing
Phone
Credit
check,
campaign
order
fulfilment
Retail
Confirma.on
order
email
Website
Online
Online
order
Virtual
order
research
order
confirma.on
confirma.on
Cookie
August
2010
©
Datalicious
Pty
Ltd
32

33. Exercise:
Offline
conversions

34. [
Exercise:
Offline
conversions
]
§ Email
click-­‐through
aner
purchase
§ First
online
login
aner
purchase
§ Unique
website
phone
number
§ Unique
website
promo+on
code
§ Unique
printable
vouchers
§ Store
locator
searches
§ Make
an
appointment
online
August
2010
©
Datalicious
Pty
Ltd
34

35. [
Media
a?ribu.on
phases
]
§ Phase
1:
De-­‐duplica+on
– Conversion
de-­‐duplica+on
across
all
channels
– Requires
one
central
repor+ng
plaoorm
– Limited
to
first/last
click
aSribu+on
§ Phase
2:
Direct
response
pathing
– Response
pathing
across
paid
and
organic
channels
– Only
covers
clicks
and
not
mere
banner
views
– Can
be
enabled
in
Google
Analy+cs
and
Omniture
§ Phase
3:
Full
purchase
path
– Direct
response
tracking
including
banner
exposure
– Cannot
be
done
in
Google
Analy+cs
or
Omniture
– Easier
to
import
addi+onal
channels
into
ad
server
August
2010
©
Datalicious
Pty
Ltd
35

36. [
Recommended
resources
]
§ 200812
ComScore
How
Online
Adver+sing
Works
§ 200905
iProspect
Research
Study
Search
And
Display
§ 200902
Forrester
Mul+-­‐Campaign
ASribu+on
§ 200904
ClearSaleing
American
ASribu+on
Index
§ 201003
Datalicious
Tying
Offline
Sales
To
Online
Media
August
2010
©
Datalicious
Pty
Ltd
36

37. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Reducing
waste
]
August
2010
©
Datalicious
Pty
Ltd
37

38. [
Reducing
waste
along
the
funnel
]
Media
a?ribu.on
Op.mising
channel
mix
Targe.ng
Increasing
relevance
Tes.ng
Improving
usability
$$$
August
2010
©
Datalicious
Pty
Ltd
38

39. [
Increase
revenue
by
10-­‐20%
]
By
coordina.ng
the
consumer’s
end-­‐to-­‐end
experience,
companies
could
enjoy
revenue
increases
of
10-­‐20%.
Google:
“get
more
value
from
digital
marke.ng”
or
h?p://bit.ly/cAtSUN
August
2010
©
Datalicious
Pty
Ltd
39
Source:
McKinsey
Quarterly,
2010

40. [
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
August
2010
©
Datalicious
Pty
Ltd
40

41. [
Prospect
targe.ng
parameters
]
August
2010
©
Datalicious
Pty
Ltd
41

42. [
Coordina.on
across
channels
]
Genera.ng
Crea.ng
Maximising
awareness
engagement
revenue
TV,
radio,
print,
Retail
stores,
call
Outbound
calls,
direct
outdoor,
search
centers,
brochures,
mail,
emails,
SMS,
etc
marke+ng,
display
websites,
landing
ads,
performance
pages,
mobile
apps,
networks,
affiliates,
online
chat,
etc
social
media,
etc
Off-­‐site
On-­‐site
Profile
targe.ng
targe.ng
targe.ng
August
2010
©
Datalicious
Pty
Ltd
42

43. [
Combining
targe.ng
pla^orms
]
Off-­‐site
targe+ng
Profile
On-­‐site
targe+ng
targe+ng
August
2010
©
Datalicious
Pty
Ltd
43

44. [
Combining
technology
]
On-­‐site
Off-­‐site
segments
segments
August
2010
©
Datalicious
Pty
Ltd
44

45. August
2010
©
Datalicious
Pty
Ltd
45

46. August
2010
©
Datalicious
Pty
Ltd
46

47. [
Datalicious
SuperTag
]
§ Central
JavaScript
based
container
tag
§ One
tag
for
all
plaoorms
incl.
Omniture
§ Either
hosted
internally
or
externally
§ Faster
tag
implementa+on
and
updates
§ Consistent
network
wide
re-­‐targe+ng
§ Transfer
or
profiling
data
between
sites
§ Iden+fica+on
of
exis+ng
customers
§ Re-­‐targe+ng
by
brand
preferences
August
2010
©
Datalicious
Pty
Ltd
47

48. [
Combining
data
sets
]
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
August
2010
©
Datalicious
Pty
Ltd
48

49. [
Behaviours
plus
transac.ons
]
Site
Behaviour
CRM
Profile
tracking
of
purchase
funnel
stage
one-­‐off
collec+on
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
predic+ve
models
based
on
data
mining
search
terms,
referrers,
etc
propensity
to
buy,
churn,
etc
tracking
of
internal
promo+on
responses
historical
data
from
previous
transac+ons
emails,
internal
search,
etc
average
order
value,
points,
etc
UPDATED
CONTINUOUSLY
UPDATED
OCCASIONALLY
August
2010
©
Datalicious
Pty
Ltd
49

50. [
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
August
2010
©
Datalicious
Pty
Ltd
50

51. [
Sample
customer
level
data
]
August
2010
©
Datalicious
Pty
Ltd
51

52. [
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
August
2010
©
Datalicious
Pty
Ltd
52

53. Exercise:
Targe.ng
matrix

54. [
Exercise:
Targe.ng
matrix
]
Phase
Segment
A
Segment
B
Channels
Awareness
Considera.on
Purchase
Intent
Up/Cross-­‐Sell
August
2010
©
Datalicious
Pty
Ltd
54

55. [
Exercise:
Targe.ng
matrix
]
Phase
Segment
A
Segment
B
Channels
Social,
display,
Awareness
Seen
this?
search,
etc
Social,
search,
Considera.on
Great
feature!
website,
etc
Search,
site,
Purchase
Intent
Great
value!
emails,
etc
Direct
mail,
Up/Cross-­‐Sell
Add
this!
emails,
etc
August
2010
©
Datalicious
Pty
Ltd
55

56. [
Exercise:
Targe.ng
matrix
]
Phase
Segment
A
Segment
B
Data
Points
Awareness
Seen
this?
Default
Download,
Considera.on
Great
feature!
product
view
Cart
add,
Purchase
Intent
Great
value!
checkout,
etc
Email
response,
Up/Cross-­‐Sell
Add
this!
login,
etc
August
2010
©
Datalicious
Pty
Ltd
56

57. [
Poten.al
landing
page
layout
]
Passing
data
on
user
Branded
header
preferences
through
to
the
website
via
Email
or
campaign
message
match
parameters
in
email
click-­‐through
URLs
to
customise
content
delivery.
Targeted
offers
Call
to
ac.on
August
2010
©
Datalicious
Pty
Ltd
57

58. [
Poten.al
newsle?er
layout
]
Using
data
on
Rule
based
header
theme
website
behaviour
imported
into
the
Data
verifica.on
NPS
email
delivery
plaoorm
to
build
business
rules
to
Rule
based
offer
customise
content
Closest
stores,
delivery.
Profile
based
offer
offers
etc
August
2010
©
Datalicious
Pty
Ltd
58

59. [
Affinity
targe.ng
in
ac.on
]
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe+ng,
response
rates
are
lined
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
h?p://bit.ly/de70b7
12
Month
Caps
- + - +
August
2010
©
Datalicious
Pty
Ltd
59

60. [
Quality
content
is
key
]
Avinash
Kaushik:
“The
principle
of
garbage
in,
garbage
out
applies
here.
[…]
what
makes
a
behaviour
targe<ng
pla=orm
<ck,
and
produce
results,
is
not
its
intelligence,
it
is
your
ability
to
actually
feed
it
the
right
content
which
it
can
then
target
[…].
You
feed
your
BT
system
crap
and
it
will
quickly
and
efficiently
target
crap
to
your
customers.
Faster
then
you
could
ever
have
yourself.”
August
2010
©
Datalicious
Pty
Ltd
60

61. [
ClickTale
tes.ng
case
study
]
August
2010
©
Datalicious
Pty
Ltd
61

62. [
Bad
campaign
worse
than
none
]
August
2010
©
Datalicious
Pty
Ltd
62

63. [
Recommended
resources
]
§ 201003
McKinsey
Get
More
Value
From
Digital
Marke+ng
§ 200912
Unbounce
101
Landing
Page
Op+miza+on
Tips
§ 201008
eConsultancy
TV
Ad
Landing
Pages
§ 200910
eMarketer
Bad
Campaign
Worse
Than
None
§ 201003
WebCredible
10
Unexpected
User
Behaviours
§ 200910
Myth
Of
The
Page
Fold
§ 201008
Sample
Size
Currency
Of
Marke+ng
Tes+ng
§ 200409
Roy
Taguchi
Or
MV
Tes+ng
For
Marketers
§ 200702
Internet
Retailer
Naviga+ng
Depths
Of
MV
Tes+ng
August
2010
©
Datalicious
Pty
Ltd
63

64. Summary

65. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Prac.ce
session
]
August
2010
©
Datalicious
Pty
Ltd
65

66. Exercise:
Web
analy.cs

67. [
Web
analy.cs
pla^orm
prac.ce
]
§ Google
Analy+cs
and
Omniture
SiteCatalyst
– Plaoorm
basics
and
comparison
– Describing
website
visitors
– Iden+fying
traffic
sources
(reach)
§ Campaign
tracking
mechanics
– Analyzing
content
usage
(engagement)
– Analyzing
conversion
drop-­‐out
(conversion)
– Defining
custom
segments
(funnel
breakdowns)
August
2010
©
Datalicious
Pty
Ltd
67

68. [
Top
5
Omniture
usage
.ps]
§ Bookmark
interes+ng
reports
and
frequently
used
report
sevng
right
away
so
they’re
easy
to
find
again
later
§ Use
mul+ple
browser
windows
and
con+nue
browsing
in
a
new
window
once
you
find
an
interes+ng
report
to
facilitate
comparison
and
data
explora+on
§ Set
automa+c
email
alerts
for
all
key
metrics
you
come
across
right
away
so
you
are
always
the
first
to
know
about
anomalies
rather
than
the
client
telling
you
§ Use
short
URLs
next
to
all
graphs
used
in
client
presenta+ons
to
facilitate
naviga+on
to
the
underlying
report
and
to
save
+me
on
poten+al
change
requests
§ Read
the
‘200708
Omniture
SiteCatalyst
Report
Descrip+ons’
and
ask
for
the
clients’
Solu+on
Design
August
2010
©
Datalicious
Pty
Ltd
68

69. [
Describing
website
visitors
]
§ Average
connec+on
speed
§ Plug-­‐in
usage
(i.e.
Flash,
etc)
§ Mobile
vs.
normal
computers
§ Geographic
loca+on
of
visitors
§ Time
of
day,
day
of
week
§ Repeat
visita+on
§ What
else?
August
2010
©
Datalicious
Pty
Ltd
69

70. [
Iden.fying
traffic
sources
]
§ Genera+ng
de-­‐duplicated
reports
§ Campaign
tracking
mechanics
– Google
URL
Builder
and
Omniture
SAINT
§ Conversion
goals
and
success
events
§ Plus
adding
addi+onal
metrics
§ Paid
vs.
organic
traffic
sources
§ Branded
vs.
generic
search
§ Traffic
quan+ty
vs.
quality
August
2010
©
Datalicious
Pty
Ltd
70

71. [
Analysing
content
usage
]
§ Page
traffic
vs.
engagement
§ Entry
vs.
exit
pages
§ Popular
page
paths
§ Internal
search
terms
August
2010
©
Datalicious
Pty
Ltd
71

72. [
Analysing
conversion
drop-­‐out
]
§ Defining
conversion
funnels
§ Iden+fying
main
problem
pages
§ Pages
visited
aner
conversion
barriers
§ Conversion
drop-­‐out
by
segment
August
2010
©
Datalicious
Pty
Ltd
72

73. [
Defining
custom
segments
]
§ New
vs.
repeat
visitors
§ By
geographic
loca+on
§ By
connec+on
speed
§ By
products
purchased
§ New
vs.
exis+ng
customers
§ Branded
vs.
generic
search
§ By
demographics,
custom
segments
August
2010
©
Datalicious
Pty
Ltd
73

74. [
Useful
analy.cs
tools
]
§ hSp://labs.google.com/sets
§ hSp://www.google.com/trends
§ hSp://www.google.com/insights/search
§ hSp://www.google.com/sktool
§ hSp://bit.ly/googlekeywordtoolexternal
§ hSp://www.google.com/webmasters
§ hSp://www.google.com/adplanner
§ hSp://www.google.com/videotarge+ng
§ hSp://www.keywordspy.com
§ hSp://www.compete.com
June
2010
©
Datalicious
Pty
Ltd
74

75. [
Useful
analy.cs
tools
]
§ hSp://bit.ly/hitwisedatacenter
§ hSp://www.socialmen+on.com
§ hSp://twiSersen+ment.appspot.com
§ hSp://bit.ly/twiSerstreamgraphs
§ hSp://twitrratr.com
§ hSp://bit.ly/listonools1
§ hSp://bit.ly/listonools2
§ hSp://manyeyes.alphaworks.ibm.com
§ hSp://www.wordle.net
June
2010
©
Datalicious
Pty
Ltd
75

76. Contact
me
cbartens@datalicious.com
Follow
us
twiSer.com/datalicious
Learn
more
blog.datalicious.com