The presentation discusses training on data, measurement and ROI.

1. >
Analyse
to
op-mise
<
ADMA
short
course
on
data,
measurement
and
ROI

2. >
Company
history
§ Datalicious
was
founded
in
late
2007
§ Strong
Omniture
web
analy@cs
history
§ 1
of
4
Omniture
Service
Partners
globally
§ Now
360
data
agency
with
specialist
team
§ Combina@on
of
analysts
and
developers
§ Making
data
accessible
and
ac@onable
§ Evangelizing
smart
data
driven
marke@ng
§ Driving
industry
best
prac@ce
(ADMA)
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
2

3. >
Smart
data
driven
marke-ng
Media
A:ribu-on
Op-mise
channel
mix
Targe-ng
Increase
relevance
Tes-ng
Improve
usability
$$$
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
3

4. >
Wide
range
of
data
services
Data
Insights
Ac-on
PlaGorms
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
a:ribu-on
models
Aprimo,
Trac-on,
Inxmail,
etc
Tag-­‐less
online
data
capture
Market
and
compe-tor
trends
Targe-ng
and
merchandising
End-­‐to-­‐end
data
plaGorms
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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
4

5. >
Clients
across
all
industries
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
5

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>
Course
overview
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
6

7. >
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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
7

8. >
Day
2:
Advanced
Analy-cs
§ Campaign
flow
and
media
a^ribu@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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
8

9. >
Training
outcomes
§ Aber
successful
comple@on
of
the
training
course
par@cipants
will
be
able
to
– Define
a
metrics
framework
for
any
client
– Enable
benchmarking
across
campaigns
– Incorporate
analy@cs
into
the
planning
process
– Pull
and
interpret
key
reports
in
Google
Analy@cs
– Impress
with
insights
instead
of
spreadsheets
– Know
how
to
extend
op@misa@on
past
media
buy
– Show
the
true
value
of
digital
media
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
9

10. >
Get
the
most
out
of
the
course
Category
Data
Metrics
Insights
PlaGorm
Why?
What?
How?
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
10

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>
Metrics
framework
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
11

12. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac-on
Sa-sfac-on
Social
media
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
12

13. >
Importance
of
social
media
Search
Company
Promo-on
Consumer
WOM,
blogs,
reviews,
ra-ngs,
communi-es,
social
networks,
photo
sharing,
video
sharing
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
13

14. >
Social
as
the
new
search
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
14

15. >
Simplified
AIDAS
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac@on)
(Sa@sfac@on)
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
15

16. >
Marke-ng
is
about
people
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
16

17. >
Addi-onal
funnel
breakdowns
Brand
vs.
direct
response
campaign
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
New
prospects
vs.
exis@ng
customers
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
17

18. New
vs.
returning
visitors

19. AU/NZ
vs.
rest
of
world

20. Prospect
vs.
customer
High
vs.
low
value
Product
affinity
Post
code,
age,
sex,
etc

21. Exercise:
Funnel
breakdowns

22. >
Exercise:
Funnel
breakdowns
§ List
poten@ally
insighful
funnel
breakdowns
– Brand
vs.
direct
response
campaign
– New
prospects
vs.
exis@ng
customers
– Baseline
vs.
incremental
conversions
– Compe@@ve
ac@vity,
i.e.
none,
a
lot,
etc
– 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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
22

23. Exercise:
Conversion
metrics

24. >
Exercise:
Conversion
metrics
§ Key
conversion
metrics
differ
by
category
– Commerce
– Lead
genera@on
– Content
publishing
– Customer
service
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
24

25. >
Exercise:
Conversion
metrics
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
25
Source:
Omniture
Summit,
Ma^
Belkin,
2007

26. Custom
conversion
goals

27. >
Conversion
funnel
1.0
Campaign
responses
Conversion
funnel
Product
page,
add
to
shopping
cart,
view
shopping
cart,
cart
checkout,
payment
details,
shipping
informa@on,
order
confirma@on,
etc
Conversion
event
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
27

28. >
Conversion
funnel
2.0
Campaign
responses
(inbound
spokes)
Offline
campaigns,
banner
ads,
email
marke@ng,
referrals,
organic
search,
paid
search,
internal
promo@ons,
etc
Landing
page
(hub)
Success
events
(outbound
spokes)
Bounce
rate,
add
to
cart,
cart
checkout,
confirmed
order,
call
back
request,
registra@on,
product
comparison,
product
review,
forward
to
friend,
etc
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
28

29. >
Addi-onal
success
metrics
Click
Through
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Page
Page
Product
Through
Bounce
Views
Views
$
Click
Call
back
Store
Through
request
Search
?
$
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
29

30. Pages
per
visit
Time
on
site

31. >
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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
31

32. >
eMarketer
interac-ve
metrics
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
32

33. >
Measuring
social
media
Sen@ment
Influence
Reach
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
33

34. Exercise:
Metrics
framework

35. >
Exercise:
Metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1
People
Level
2
Strategic
Level
3
Tac-cal
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
35

36. >
Exercise:
Metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1
People
People
People
People
People
reached
engaged
converted
delighted
Search
Level
2
Strategic
impressions,
UBs,
etc
?
?
?
Click-­‐through
Level
3
Tac-cal
or
interac-on
rate,
etc
?
?
?
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
36

37. >
ROI,
ROMI,
BE,
etc
R−I R
Revenue
= ROI
I
Investment
I
ROI
Return
on
investment
IR − MI IR
Incremental
revenue
= ROMI
MI MI
Marke@ng
investment
ROMI
Return
on
IR − MI
marke@ng
investment
= ROMI + BE
BE
Brand
equity
MI
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
37

38. >
Success:
ROMI
+
BE
IR − MI
= ROMI + BE
MI
§ Establish
incremental
revenue
(IR)
– Requires
baseline
revenue
to
calculate
addi@onal
revenue
as
well
as
revenue
from
cost
savings
§ Establish
marke@ng
investment
(MI)
– Requires
all
costs
across
technology,
content,
data
and
resources
plus
promo@ons
and
discounts
§ Establish
brand
equity
contribu@on
(BE)
– Requires
addi@onal
sob
metrics
to
evaluate
subscriber
percep@ons,
experience,
altudes
and
word
of
mouth
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
38

39. >
Process
is
key
to
success
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
39
Source:
Omniture
Summit,
Ma^
Belkin,
2007

40. >
Recommended
resources
§ 200501
WAA
Key
Metrics
&
KPIs
§ 200708
WAA
Analy@cs
Defini@ons
Volume
1
§ 200612
Omniture
Effec@ve
Measurement
§ 200804
Omniture
Calculated
Metrics
White
Paper
§ 200702
Omniture
Effec@ve
Segmenta@on
Guide
§ 200810
Ronnestam
Online
Adver@sing
And
AIDAS
§ 201004
Al@meter
Social
Marke@ng
Analy@cs
§ 201008
CSR
Customer
Sa@sfac@on
Vs
Delight
§ Google
“Enquiro
Search
Engine
Results
2010
PDF”
§ Google
“Razorfish
Ac@onable
Analy@cs
Report
PDF”
§ Google
“Forrester
Interac@ve
Marke@ng
Metrics
PDF”
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
40

41. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Data
sources
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
41

42. >
Digital
data
is
plen-ful
and
cheap
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
42
Source:
Omniture
Summit,
Ma^
Belkin,
2007

43. >
Digital
data
categories
+Social
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
43
Source:
Accuracy
Whitepaper
for
web
analy@cs,
Brian
Clibon,
2008

44. >
Customer
data
journey
To
transac-onal
data
To
reten-on
messages
From
suspect
to
prospect
To
customer
Time
Time
From
behavioural
data
From
awareness
messages
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
44

45. >
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,
shib
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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
45

46. >
What
analy-cs
plaGorm
to
use
Stage
1:
Data
Stage
2:
Insights
Stage
3:
Ac-on
Data
is
fully
owned
Sophis@ca@on
in-­‐house,
advanced
Data
is
being
brought
predic@ve
modelling
in-­‐house,
shib
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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
46

47. >
Poten-al
data
sources
Media
and
search
data
Website,
call
center
and
retail
data
People
People
People
People
Reached
40%
Engaged
10%
Converted
1%
Delighted
Quan@ta@ve
and
qualita@ve
research
data
Social
media
data
Social
media
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
47

48. >
Atomic
Labs
tag-­‐less
data
capture
§ Keep
all
your
favourite
reports
but
§ Eliminate
tag
maintenance
and
ensure
§ New
pages/content
is
tracked
automa@cally
§ Across
normal
websites,
mobiles
and
apps
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
48

49. >
Atomic
labs
integra-on
model
§ Single
point
of
data
capture
and
processing
§ Real-­‐@me
queries
to
enrich
website
data
§ Mul@ple
data
export
op@ons
for
web
analy@cs
§ Enriching
single-­‐customer
view
website
behaviour
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
49

50. >
Google
data
in
Australia
Source:
h^p://www.hitwise.com/au/datacentre
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
50

51. >
Search
at
all
stages
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
51
Source:
Inside
the
Mind
of
the
Searcher,
Enquiro
2004

52. >
Search
and
brand
strength
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
52

53. >
Search
and
the
product
lifecycle
Nokia
N-­‐Series
Apple
iPhone
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
53

54. >
Search
and
media
planning
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
54

55. >
Search
and
media
planning
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
55

56. >
Search
driving
offline
crea-ve
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
56

57. Exercise:
Search
insights

58. >
Exercise:
Search
insights
§ Iden@fy
key
category
search
terms
– Data
from
Google
AdWords
Keyword
Tool
– Search
for
“google
keyword
tool”
– Wordle
and
IBM
Many
Eyes
for
visualiza@ons
– Search
for
“wordle
word
clouds”
and
“ibm
many
eyes”
§ Iden@fy
search
term
trends
and
compe@tors
– Google
Trends
and
Google
Search
Insights
– Search
for
“google
trends”
and
“google
search
insights”
§ Search
and
media
planning
– DoubleClick
Ad
Planner
by
Google
– Search
for
“google
ad
planner”
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
58

59. >
Cookie
based
tracking
process
What
if:
Someone
deletes
their
cookies?
Or
uses
a
device
that
does
not
support
JavaScript?
Or
uses
two
computers
(work
vs.
home)?
Or
two
people
use
the
same
computer?
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
59
Source:
Google
Analy@cs,
Jus@n
Cutroni,
2007

60. >
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.
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
60
Source:
White
Paper,
RedEye,
2007

61. Datalicious
SuperCookie
Persistent
Flash
cookie
that
cannot
be
deleted

62. >
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
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
62

63. >
De-­‐duplica-on
across
channels
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Central
Analy-cs
PlaGorm
Email
Email
Blast
PlaGorm
$
Organic
Google
Search
Analy-cs
$
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
63

64. De-­‐duplica-on
across
channels
October
2010
©
ADMA
&
Datalicious
Pty
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65. De-­‐duplica-on
across
channels
October
2010
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65

66. Addi-onal
funnel
breakdowns
October
2010
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66

67. Exercise:
Duplica-on
impact

68. >
Exercise:
Duplica-on
impact
§ Double-­‐coun@ng
of
conversions
across
channels
can
have
a
significant
impact
on
key
metrics,
especially
CPA
§ Example:
Display
ads
and
paid
search
– Total
media
budget
of
$10,000
of
which
50%
is
spend
on
paid
search
and
50%
on
display
ads
– Total
of
100
conversions
across
both
channels
with
a
channel
overlap
of
50%,
i.e.
both
channels
claim
100%
of
conversions
based
on
their
own
repor@ng
but
once
de-­‐duplicated
they
each
only
contributed
50%
of
conversions
– What
are
the
ini@al
CPA
values
and
what
is
the
true
CPA?
§ Solu@on:
$50
ini@al
CPA
and
$100
true
CPA
– $5,000
/
100
=
$50
ini@al
CPA
and
$5,000
/
50
=
$100
true
CPA
(which
represents
a
100%
increase)
October
2010
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68

69. >
Reach
and
channel
overlap
TV
audience
Banner
Search
audience
audience
October
2010
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69

70. >
Es-ma-ng
reach
and
overlap
§ Apply
average
unique
visitor
count
per
recorded
unique
user
names
to
all
unique
visitor
figures
in
Google
Analy@cs,
Omniture,
etc
§ Apply
ra@o
of
total
banner
impressions
to
unique
banner
impressions
from
ad
server
to
paid
and
organic
search
impressions
in
Google
AdWords
and
Google
Webmaster
Tools
§ Compare
Google
Keyword
Tool
impressions
for
a
specific
search
term
to
reach
for
the
same
term
in
Google
Ad
Planner
§ Custom
website
entry
survey
and
campaign
stacking
to
establish
channel
overlap
October
2010
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&
Datalicious
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Ltd
70

71. October
2010
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71

72. Sen-ment
analysis:
People
vs.
machine

73. >
Al-meter
social
analy-cs
Social
Marke@ng
Analy@cs
is
the
discipline
that
helps
companies
measure,
assess
and
explain
the
performance
of
social
media
ini@a@ves
in
the
context
of
specific
business
objec@ves.
October
2010
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ADMA
&
Datalicious
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Ltd
73

74. Data
from

75. >
Overall
volume
and
influence
Data
from
October
2010
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Ltd
75

76. >
Influence
and
media
value
US
Data
from
UK
AU/NZ
October
2010
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76

77. >
Facebook
insights
Using
Facebook
Like
bu^ons
is
a
free
and
powerful
way
to
gain
addi@onal
insights
into
consumer
preferences
and
enabling
social
sharing
of
content
as
well
as
possibly
influence
organic
search
rankings
in
the
near
future.
October
2010
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Datalicious
Pty
Ltd
77

78. >
Facebook
Connect
single
sign
on
Facebook
Connect
gives
your
company
the
following
data
and
more
with
just
one
click
Email
address,
first
name,
last
name,
gender,
birthday,
interests,
picture,
affilia@ons,
last
profile
update,
@me
zone,
religion,
poli@cal
interests,
a^racted
to
which
sex,
why
they
want
to
meet
someone,
home
town,
rela@onship
status,
current
loca@on,
ac@vi@es,
music
interests,
tv
show
interests,
educa@on
history,
work
history,
family,
etc
Need
anything
else?
October
2010
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Ltd
78

79. Appending
social
data
to
customer
profiles
Name,
age,
gender,
occupa-on,
loca-on,
social
profiles
and
influencer
ranking
based
on
email
(influencers
only)
(all
contacts)

80. Exercise:
Sta-s-cal
significance

81. 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
Google
“nss
sample
size
calculator”

82. 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
Google
“nss
sample
size
calculator”

83. >
Addi-onal
success
metrics
Click
Through
$
Click
Add
To
Cart
Through
Cart
Checkout
?
$
Click
Page
Page
Product
Through
Bounce
Views
Views
$
Click
Call
back
Store
Through
request
Search
?
$
October
2010
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&
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Ltd
83

84. >
Importance
of
calendar
events
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
October
2010
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&
Datalicious
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Ltd
84

85. Calendar
events

86. >
Recommended
resources
§ 200311
UK
RedEye
Cookie
Case
Study
§ 200807
Kaushik
Tracking
Offline
Conversion
§ 200904
Kaushik
Standard
Metrics
Revisited
§ 201002
Kaushik
8
Compe@@ve
Intelligence
Data
Sources
§ 201005
Google
Ad
Planner
Data
Wrong
By
Up
To
20%
§ 201005
MPI
How
Sta@s@cally
Valid
Is
Your
Survey
§ 201009
Google
Analy@cs
How
To
Tag
Links
§ 200903
Coremetrics
Conversion
Benchmarks
By
Industry
§ 200906
WOM
Online
The
People
Vs
Machines
Debate
§ 201007
WSJ
The
Web's
New
Gold
Mine
Your
Secrets
§ 201008
Adver@singAge
Are
Marketers
Really
Spying
On
You
October
2010
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86

87. Summary

88. >
Get
the
most
out
of
the
course
Category
Data
Metrics
Insights
PlaGorm
Why?
What?
How?
October
2010
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&
Datalicious
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Ltd
88

89. >
Summary
and
ac-on
items
§ Defining
a
metrics
framework
– Develop
standardised
metrics
framework
– Define
addi@onal
funnel
breakdowns
– Establish
baseline
and
incremental
– Define
addi@onal
success
metrics
§ Finding
and
developing
the
right
data
– Ensure
de-­‐duplica@on
via
central
analy@cs
– Check
reports
for
sta@s@cal
significance
– Check
data
sources
and
their
accuracy
– Start
popula@ng
a
calendar
of
events
October
2010
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Datalicious
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Ltd
89

90. Exercise:
Google
Analy-cs

91. >
Google
Analy-cs
prac-ce
§ Describing
website
visitors
§ Iden@fying
traffic
sources
(reach)
– Campaign
tracking
mechanics
§ Analyzing
content
usage
(engagement)
§ Analyzing
conversion
drop-­‐out
(conversion)
§ Defining
custom
segments
(breakdowns)
October
2010
©
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Ltd
91

92. >
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?
October
2010
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Ltd
92

93. >
Iden-fying
traffic
sources
§ Genera@ng
de-­‐duplicated
reports
§ Campaign
tracking
mechanics
§ Conversion
goals
and
success
events
§ Plus
adding
addi@onal
metrics
§ Paid
vs.
organic
traffic
sources
§ Branded
vs.
generic
search
§ Traffic
quan@ty
vs.
quality
October
2010
©
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&
Datalicious
Pty
Ltd
93

94. >
Analysing
content
usage
§ Page
traffic
vs.
engagement
§ Entry
vs.
exit
pages
§ Popular
page
paths
§ Internal
search
terms
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
94

95. >
Analysing
conversion
drop-­‐out
§ Defining
conversion
funnels
§ Iden@fying
main
problem
pages
§ Pages
visited
aber
conversion
barriers
§ Conversion
drop-­‐out
by
segment
October
2010
©
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&
Datalicious
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Ltd
95

96. >
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
October
2010
©
ADMA
&
Datalicious
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Ltd
96

97. >
Useful
analy-cs
tools
§ h^p://labs.google.com/sets
§ h^p://www.google.com/trends
§ h^p://www.google.com/insights/search
§ h^p://bit.ly/googlekeywordtoolexternal
§ h^p://www.google.com/webmasters
§ h^p://www.facebook.com/insights
§ h^p://www.google.com/adplanner
§ h^p://www.google.com/videotarge@ng
§ h^p://www.keywordspy.com
§ h^p://www.compete.com
October
2010
©
ADMA
&
Datalicious
Pty
Ltd
97

98. >
Useful
analy-cs
tools
§ h^p://bit.ly/hitwisedatacenter
§ h^p://www.socialmen@on.com
§ h^p://twi^ersen@ment.appspot.com
§ h^p://bit.ly/twi^erstreamgraphs
§ h^p://twitrratr.com
§ h^p://bit.ly/listobools1
§ h^p://bit.ly/listobools2
§ h^p://manyeyes.alphaworks.ibm.com
§ h^p://www.wordle.net
§ h^p://www.tagxedo.com
October
2010
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&
Datalicious
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Ltd
98

99. Contact
us
cbartens@datalicious.com
Follow
us
twi^er.com/datalicious
Learn
more
blog.datalicious.com