The presentation discusses about campaign tracking and analytics.

1. [
Analyse
to
op-mise
]
Campaign
tracking
and
analy0cs
digital
cer0ficate
guest
lecture

2. [
Company
history
]
§ Datalicious
was
founded
in
late
2007
§ Strong
Omniture
web
analy0cs
history
§ 1
of
4
Omniture
Service
Partners
globally
§ Now
360
data
agency
with
specialist
team
§ Combina0on
of
analysts
and
developers
§ Making
data
accessible
and
ac0onable
§ Evangelizing
smart
data
driven
marke0ng
§ Driving
industry
best
prac0ce
(ADMA)
October
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
$$$
October
2010
©
Datalicious
Pty
Ltd
3

4. [
Clients
across
all
industries
]
October
2010
©
Datalicious
Pty
Ltd
4

5. [
AIDA
and
AIDAS
formulas
]
Old
media
New
media
Awareness
Interest
Desire
Ac-on
Sa-sfac-on
Social
media
October
2010
©
Datalicious
Pty
Ltd
5

6. [
Simplified
AIDAS
funnel
]
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac0on)
(Sa0sfac0on)
October
2010
©
Datalicious
Pty
Ltd
6

7. [
Marke-ng
is
about
people
]
Media
and
search
data
Website,
call
center
and
retail
data
People
People
People
People
Reached
40%
Engaged
10%
Converted
1%
Delighted
Quan0ta0ve
and
qualita0ve
research
data
Social
media
data
Social
media
October
2010
©
Datalicious
Pty
Ltd
7

8. [
Google
data
in
Australia
]
Source:
hXp://www.hitwise.com/au/datacentre
October
2010
©
Datalicious
Pty
Ltd
8

9. [
Search
and
brand
strength
]
October
2010
©
Datalicious
Pty
Ltd
9

10. [
Search
and
the
product
lifecycle
]
Nokia
N-­‐Series
Apple
iPhone
October
2010
©
Datalicious
Pty
Ltd
10

11. [
Search
and
media
planning
]
October
2010
©
Datalicious
Pty
Ltd
11

12. [
Search
driving
offline
crea-ve
]
October
2010
©
Datalicious
Pty
Ltd
12

13. [
Facebook
insights
]
Using
Facebook
Like
buXons
is
a
free
and
powerful
way
to
gain
addi0onal
insights
into
consumer
preferences
and
enabling
social
sharing
of
content
as
well
as
possibly
influence
organic
search
rankings
in
the
near
future.
October
2010
©
Datalicious
Pty
Ltd
13

14. [
Conversion
funnel
1.0
]
Campaign
responses
Conversion
funnel
Product
page,
add
to
shopping
cart,
view
shopping
cart,
cart
checkout,
payment
details,
shipping
informa0on,
order
confirma0on,
etc
Conversion
event
October
2010
©
Datalicious
Pty
Ltd
14

15. [
Conversion
funnel
2.0
]
Campaign
responses
(inbound
spokes)
Offline
campaigns,
banner
ads,
email
marke0ng,
referrals,
organic
search,
paid
search,
internal
promo0ons,
etc
Landing
page
(hub)
Success
events
(outbound
spokes)
Bounce
rate,
add
to
cart,
cart
checkout,
confirmed
order,
call
back
request,
registra0on,
product
comparison,
product
review,
forward
to
friend,
etc
October
2010
©
Datalicious
Pty
Ltd
15

16. [
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
©
Datalicious
Pty
Ltd
16

17. 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”

18. 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”

19. [
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
©
Datalicious
Pty
Ltd
19

20. [
Exercise:
Duplica-on
impact
]
§ Double-­‐coun0ng
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
repor0ng
but
once
de-­‐duplicated
they
each
only
contributed
50%
of
conversions
– What
are
the
ini0al
CPA
values
and
what
is
the
true
CPA?
§ Solu0on:
$50
ini0al
CPA
and
$100
true
CPA
– $5,000
/
100
=
$50
ini0al
CPA
and
$5,000
/
50
=
$100
true
CPA
(which
represents
a
100%
increase)
October
2010
©
Datalicious
Pty
Ltd
20

21. [
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
October
2010
©
Datalicious
Pty
Ltd
21

22. [
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
October
2010
©
Datalicious
Pty
Ltd
22

23. [
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
October
2010
©
Datalicious
Pty
Ltd
23

24. [
Understanding
channel
overlap
]
October
2010
©
Datalicious
Pty
Ltd
24

25. [
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
Adver0sing
9%
SEO
Generic
7%
Display
Adver0sing
14%
SEM
Generic
14%
Email
Marke0ng
7%
Display
Adver0sing
7%
Retail
Promo0ons
14%
Affiliate
Marke0ng
9%
Referrals
5%
Conversions
aXributed
to
search
terms
Email
Marke0ng
7%
that
contain
brand
keywords
and
direct
website
visits
are
most
likely
not
the
origina0ng
channel
that
generated
the
awareness
and
as
such
conversion
credits
should
be
re-­‐allocated.
October
2010
©
Datalicious
Pty
Ltd
25

26. [
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
overes0mated
visitors
by
up
to
7.6
0mes
whilst
a
cookie-­‐based
approach
overes-mated
visitors
by
up
to
2.3
-mes.
October
2010
©
Datalicious
Pty
Ltd
26
Source:
White
Paper,
RedEye,
2007

27. [
Importance
of
calendar
events
]
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
October
2010
©
Datalicious
Pty
Ltd
27

28. [
Useful
analy-cs
tools
]
§ hXp://labs.google.com/sets
§ hXp://www.google.com/trends
§ hXp://www.google.com/insights/search
§ hXp://www.google.com/sktool
§ hXp://bit.ly/googlekeywordtoolexternal
§ hXp://www.google.com/webmasters
§ hXp://www.google.com/adplanner
§ hXp://www.google.com/videotarge0ng
§ hXp://www.keywordspy.com
§ hXp://www.compete.com
October
2010
©
Datalicious
Pty
Ltd
28

29. [
Useful
analy-cs
tools
]
§ hXp://bit.ly/hitwisedatacenter
§ hXp://www.socialmen0on.com
§ hXp://twiXersen0ment.appspot.com
§ hXp://bit.ly/twiXerstreamgraphs
§ hXp://twitrratr.com
§ hXp://bit.ly/listooools1
§ hXp://bit.ly/listooools2
§ hXp://manyeyes.alphaworks.ibm.com
§ hXp://www.wordle.net
§ hXp://www.tagxedo.com
October
2010
©
Datalicious
Pty
Ltd
29

30. ADMA
short
course
“Analyse
to
op-mise”
In
Melbourne
&
Sydney
October/November
By
Datalicious

31. Contact
us
cbartens@datalicious.com
Follow
us
twiXer.com/datalicious
Learn
more
blog.datalicious.com