The presentation discusses course training on advanced analytics.

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

2. [
Company
history
]
§ Datalicious
was
founded
in
2007
§ Strong
Omniture
web
analy+cs
history
§ One-­‐stop
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)
August
2010
©
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
$$$
August
2010
©
Datalicious
Pty
Ltd
3

4. [
Main
business
units
and
services
]
Data
Insights
Ac.on
PlaForms
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
plaForms
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
August
2010
©
Datalicious
Pty
Ltd
4

5. [
Clients
across
all
industries
]
August
2010
©
Datalicious
Pty
Ltd
5

6. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Course
overview
]
August
2010
©
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
August
2010
©
Datalicious
Pty
Ltd
7

8. [
Day
2:
Advanced
Analy.cs
]
§ Campaign
flow
and
media
aZribu+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
8

9. [
Training
outcomes
]
§ A^er
successful
comple+on
of
the
training
course
par+cipants
will
be
able
to
– Define
a
metrics
framework
for
any
client
– Incorporate
analy+cs
into
the
planning
process
– Enable
benchmarking
across
campaigns
– Iden+fy
data
gaps
and
recommend
solu+ons
– Use
more
than
just
ad
server
data
for
analy+cs
– Impress
clients
with
insights
not
spreadsheets
– Know
how
to
extend
op+misa+on
past
media
buy
– Show
the
true
value
of
digital
media
August
2010
©
Datalicious
Pty
Ltd
9

10. Plenty
of
hands
on
exercises

11. [
Prac.ce
session
prepara.on
]
§ Organise
client
placorm
logins
– Ad
servers:
DoubleClick,
Atlas,
Eyeblaster,
etc
– Bid
management:
Google
AdWords,
etc
– Web
analy+cs:
Google
Analy+cs,
Omniture,
etc
– Social
media:
Radian6,
S2M,
etc
§ Plus
any
addi+onal
data
or
logins
– Google
webmaster
tools,
Facebook
fan
pages
– Phone
calls,
retail
sales,
etc
August
2010
©
Datalicious
Pty
Ltd
11

12. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Metrics
framework
]
August
2010
©
Datalicious
Pty
Ltd
12

13. [
AIDA
and
AIDAS
formulas
]
Old
media
New
media
Awareness
Interest
Desire
Ac.on
Sa.sfac.on
Social
media
August
2010
©
Datalicious
Pty
Ltd
13

14. [
Importance
of
social
media
]
Search
Company
Promo.on
Consumer
WOM,
blogs,
reviews,
ra.ngs,
communi.es,
social
networks,
photo
sharing,
video
sharing
August
2010
©
Datalicious
Pty
Ltd
14

15. [
Social
as
the
new
search
]
August
2010
©
Datalicious
Pty
Ltd
15

16. [
Simplified
AIDAS
funnel
]
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac+on)
(Sa+sfac+on)
August
2010
©
Datalicious
Pty
Ltd
16

17. [
Marke.ng
is
about
people
]
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
August
2010
©
Datalicious
Pty
Ltd
17

18. [
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
August
2010
©
Datalicious
Pty
Ltd
18

19. Exercise:
Funnel
breakdowns

20. [
Exercise:
Funnel
breakdowns
]
§ List
poten+ally
insighcul
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
August
2010
©
Datalicious
Pty
Ltd
20

21. Exercise:
Conversion
metrics

22. [
Exercise:
Conversion
metrics
]
§ Key
conversion
metrics
differ
by
category
– Commerce
– Lead
genera+on
– Content
publishing
– Customer
service
August
2010
©
Datalicious
Pty
Ltd
22

23. [Exercise:
Conversion
metrics
]
August
2010
©
Datalicious
Pty
Ltd
23
Source:
Omniture
Summit,
MaZ
Belkin,
2007

24. [
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
August
2010
©
Datalicious
Pty
Ltd
24

25. [
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
August
2010
©
Datalicious
Pty
Ltd
25

26. [
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
?
$
August
2010
©
Datalicious
Pty
Ltd
26

27. [
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
August
2010
©
Datalicious
Pty
Ltd
27

28. [
Pion
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
August
2010
©
Datalicious
Pty
Ltd
28

29. [
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
August
2010
©
Datalicious
Pty
Ltd
29

30. [
eMarketer
interac.ve
metrics
]
August
2010
©
Datalicious
Pty
Ltd
30

31. [
Forrester
interac.ve
metrics
]
Different
metrics
should
be
viewed
as
complementary
parts
of
the
measurement
jigsaw.
August
2010
©
Datalicious
Pty
Ltd
31
Source:
Omniture
Summit,
MaZ
Belkin,
2007

32. [
Measuring
social
media
]
Sen+ment
Influence
Reach
August
2010
©
Datalicious
Pty
Ltd
32

33. Exercise:
Metrics
framework

34. [
Exercise:
Metrics
framework
]
Level
Reach
Engagement
Conversion
+Buzz
Level
1
People
Level
2
Strategic
Level
3
Tac.cal
August
2010
©
Datalicious
Pty
Ltd
34

35. [
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
?
?
?
August
2010
©
Datalicious
Pty
Ltd
35

36. [
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
August
2010
©
Datalicious
Pty
Ltd
36

37. [
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
so^
metrics
to
evaluate
subscriber
percep+ons,
experience,
amtudes
and
word
of
mouth
August
2010
©
Datalicious
Pty
Ltd
37

38. [
Process
is
key
to
success
]
August
2010
©
Datalicious
Pty
Ltd
38
Source:
Omniture
Summit,
MaZ
Belkin,
2007

39. [
Recommended
resources
]
§ 200501
WAA
Key
Metrics
&
KPIs
§ 200708
WAA
Analy+cs
Defini+ons
Volume
1
§ 200805
Forrester
Interac+ve
Marke+ng
Metrics
Guide
§ 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
§ 200612
Razorfish
Ac+onable
Analy+cs
Report
§ 200708
Enquiro
Search
Engine
Results
2010
§ 201004
Al+meter
Social
Marke+ng
Analy+cs
§ 201008
CSR
Customer
Sa+sfac+on
Vs
Delight
August
2010
©
Datalicious
Pty
Ltd
39

40. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Data
sources
]
August
2010
©
Datalicious
Pty
Ltd
40

41. [
Digital
data
is
plen.ful
and
cheap
]
August
2010
©
Datalicious
Pty
Ltd
41
Source:
Omniture
Summit,
MaZ
Belkin,
2007

42. [
Digital
data
categories
]
+Social
August
2010
©
Datalicious
Pty
Ltd
42
Source:
Accuracy
Whitepaper
for
web
analy+cs,
Brian
Cli^on,
2008

43. [
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
August
2010
©
Datalicious
Pty
Ltd
43

44. [
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,
shi^
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
August
2010
©
Datalicious
Pty
Ltd
44

45. [
What
analy.cs
plaForm
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,
shi^
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
August
2010
©
Datalicious
Pty
Ltd
45

46. [
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
August
2010
©
Datalicious
Pty
Ltd
46

47. [
Google
data
in
Singapore]
Source:
hZp://www.hitwise.com/sg/datacentre
August
2010
©
Datalicious
Pty
Ltd
47

48. [
Search
at
all
stages
]
August
2010
©
Datalicious
Pty
Ltd
48
Source:
Inside
the
Mind
of
the
Searcher,
Enquiro
2004

49. [
Search
and
brand
strength
]
August
2010
©
Datalicious
Pty
Ltd
49

50. [
Search
and
the
product
lifecycle
]
Nokia
N-­‐Series
Apple
iPhone
August
2010
©
Datalicious
Pty
Ltd
50

51. [
Search
and
media
planning
]
August
2010
©
Datalicious
Pty
Ltd
51

52. [
Search
driving
offline
crea.ve
]
August
2010
©
Datalicious
Pty
Ltd
52

53. Exercise:
Search
insights

54. [
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”
August
2010
©
Datalicious
Pty
Ltd
54

55. [
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?
August
2010
©
Datalicious
Pty
Ltd
55
Source:
Google
Analy+cs,
Jus+n
Cutroni,
2007

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.
August
2010
©
Datalicious
Pty
Ltd
56
Source:
White
Paper,
RedEye,
2007

57. Datalicious
SuperCookie
Persistent
Flash
cookie
that
cannot
be
deleted

58. [
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
58

59. [
De-­‐duplica.on
across
channels
]
Paid
Bid
Search
Mgmt
$
Banner
Ad
Ads
Server
$
Central
Analy.cs
PlaForm
Email
Email
Blast
PlaForm
$
Organic
Google
Search
Analy.cs
$
August
2010
©
Datalicious
Pty
Ltd
59

60. Exercise:
Duplica.on
impact

61. [
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)
August
2010
©
Datalicious
Pty
Ltd
61

62. Exercise:
Web
analy.cs

63. [
Reach
and
channel
overlap
]
TV
audience
Banner
Search
audience
audience
August
2010
©
Datalicious
Pty
Ltd
63

64. [
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
August
2010
©
Datalicious
Pty
Ltd
64

65. August
2010
©
Datalicious
Pty
Ltd
65

66. Sen.ment
analysis:
People
vs.
machine

67. [
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.
August
2010
©
Datalicious
Pty
Ltd
67

68. [
Facebook
insights
]
Using
Facebook
Like
buZons
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.
August
2010
©
Datalicious
Pty
Ltd
68

69. [
Facebook
Connect
single
sign
on
]
Facebook
Connect
gives
your
company
the
following
data
and
more
with
just
one
click!
ID,
first
name,
last
name,
middle
name,
picture,
affilia+ons,
last
profile
update,
+me
zone,
religion,
poli+cal
interests,
interests,
sex,
birthday,
aZracted
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
and
email
Need
anything
else?
August
2010
©
Datalicious
Pty
Ltd
69

70. 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)

71. Exercise:
Sta.s.cal
significance

72. 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”

73. 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”

74. [
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
?
$
August
2010
©
Datalicious
Pty
Ltd
74

75. [
Importance
of
calendar
events
]
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
August
2010
©
Datalicious
Pty
Ltd
75

76. [
Recommended
resources
]
§ 200311
UK
RedEye
Cookie
Case
Study
§ 200807
Kaushik
Tracking
Offline
Conversion
§ 200906
WOM
Online
The
People
Vs
Machines
Debate
§ 201005
Google
Ad
Planner
Data
Wrong
By
Up
To
20%
§ 201005
MPI
How
Sta+s+cally
Valid
Is
Your
Survey
§ 201005
Wikipedia
Sta+s+cal
Significance
§ 201005
Wikipedia
Sta++cal
Validity
§ 201005
Omniture
Campaign
Management
§ 200910
Eyeblaster
Global
Benchmark
§ 200903
Coremetrics
Conversion
Benchmarks
By
Industry
§ 201007
WSJ
The
Web's
New
Gold
Mine
Your
Secrets
§ 201008
Adver+singAge
Are
Marketers
Really
Spying
On
You
August
2010
©
Datalicious
Pty
Ltd
76

77. Summary

78. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
[
Prac.ce
session
]
August
2010
©
Datalicious
Pty
Ltd
78

79. Exercise:
Web
analy.cs

80. [
Web
analy.cs
plaForm
prac.ce
]
§ Google
Analy+cs
and
Omniture
SiteCatalyst
– Placorm
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
80

81. [
Top
5
Omniture
usage
.ps]
§ Bookmark
interes+ng
reports
and
frequently
used
report
semng
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
81

82. [
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
82

83. [
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
83

84. [
Analysing
content
usage
]
§ Page
traffic
vs.
engagement
§ Entry
vs.
exit
pages
§ Popular
page
paths
§ Internal
search
terms
August
2010
©
Datalicious
Pty
Ltd
84

85. [
Analysing
conversion
drop-­‐out
]
§ Defining
conversion
funnels
§ Iden+fying
main
problem
pages
§ Pages
visited
a^er
conversion
barriers
§ Conversion
drop-­‐out
by
segment
August
2010
©
Datalicious
Pty
Ltd
85

86. [
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
86

87. [
Useful
analy.cs
tools
]
§ hZp://labs.google.com/sets
§ hZp://www.google.com/trends
§ hZp://www.google.com/insights/search
§ hZp://www.google.com/sktool
§ hZp://bit.ly/googlekeywordtoolexternal
§ hZp://www.google.com/webmasters
§ hZp://www.google.com/adplanner
§ hZp://www.google.com/videotarge+ng
§ hZp://www.keywordspy.com
§ hZp://www.compete.com
June
2010
©
Datalicious
Pty
Ltd
87

88. [
Useful
analy.cs
tools
]
§ hZp://bit.ly/hitwisedatacenter
§ hZp://www.socialmen+on.com
§ hZp://twiZersen+ment.appspot.com
§ hZp://bit.ly/twiZerstreamgraphs
§ hZp://twitrratr.com
§ hZp://bit.ly/listo^ools1
§ hZp://bit.ly/listo^ools2
§ hZp://manyeyes.alphaworks.ibm.com
§ hZp://www.wordle.net
June
2010
©
Datalicious
Pty
Ltd
88

89. Contact
me
cbartens@datalicious.com
Follow
us
twiZer.com/datalicious
Learn
more
blog.datalicious.com