The presentation discusses the concepts, principles and significance of data driven marketing.

1. >
P&O
Analy+cs
Workshop
<
Smart
data
driven
marke-ng

2. >
Short
but
sharp
history
§ Datalicious
was
founded
late
2007
§ Strong
Omniture
web
analy-cs
history
§ Now
360
data
agency
with
specialist
team
§ Combina-on
of
analysts
and
developers
§ Carefully
selected
best
of
breed
partners
§ Driving
industry
best
prac-ce
(ADMA)
§ Turning
data
into
ac-onable
insights
§ Execu-ng
smart
data
driven
campaigns
June
2011
©
Datalicious
Pty
Ltd
2

3. >
Smart
data
driven
marke+ng
Media
A@ribu+on
&
Modeling
Op+mise
channel
mix,
predict
sales
Targeted
Direct
Marke+ng
Increase
relevance,
reduce
churn
Tes+ng
&
Op+misa+on
Remove
barriers,
drive
sales
Boost
ROMI
June
2011
©
Datalicious
Pty
Ltd
3

4. >
Wide
range
of
data
services
Data
Insights
Ac+on
PlaIorms
Analy+cs
Campaigns
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
Tableau,
SpoIire,
SPSS,
etc
Alterian,
SiteCore,
Inxmail,
etc
Tag-­‐less
online
data
capture
Media
a@ribu+on
models
Targe+ng
and
merchandising
End-­‐to-­‐end
data
plaIorms
Market
and
compe+tor
trends
Internal
search
op+misa+on
IVR
and
call
center
repor+ng
Social
media
monitoring
CRM
strategy
and
execu+on
Single
customer
view
Customer
profiling
Tes+ng
programs
June
2011
©
Datalicious
Pty
Ltd
4

5. >
Clients
across
all
industries
June
2011
©
Datalicious
Pty
Ltd
5

6. >
Today
§ Data
Roadmap
Prerequisites:
1. How
do
you
want
to
differen-ate
your
promo-on
ac-vity
to
different
segments
of
consumers/web
users/customers?
(What
would
these
segments
be?)
OUTPUT:
Dra[
Targe-ng
Matrix
2. What
metrics
are
available
at
different
points
in
the
consumer
path
to
purchase?
OUTPUT:
Dra[
Metrics
Framework
June
2011
©
Datalicious
Pty
Ltd
6

7. Clive
Humby:
Data
is
the
new
oil
June
2011
©
Datalicious
Pty
Ltd
7

8. >
Corporate
data
journey
Stage
1
Stage
2
Stage
3
Data
Insights
Ac+on
“Leaders”
Data
is
fully
owned
“Followers”
Sophis-ca-on
in-­‐house,
advanced
Data
is
being
brought
predic-ve
modelling
“Laggards”
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
June
2011
©
Datalicious
Pty
Ltd
8

9. Oil
and
data
come
at
a
price
June
2011
©
Datalicious
Pty
Ltd
9

10. >
Google
Ngram:
Privacy
June
2011
©
Datalicious
Pty
Ltd
10

11. Collec+ng
data
for
the
sake
of
it
or
to
add
value
to
customers?
June
2011
©
Datalicious
Pty
Ltd
11

12. >
Data
driven
marke+ng
to
…
§ Improve
media
planning
and
targe-ng
§ Op-mise
media
placements
across
channels
§ Increase
campaign/content
engagement
§ Increase
website/call
center
conversion
§ Iden-fy
profitable
product
bundles/prices
§ Improve
targe-ng
and
increase
up/cross-­‐sell
§ Improve
travel
agent
engagement/training
§ And
much
more
…
June
2011
©
Datalicious
Pty
Ltd
12

13. Product
Partners
Price
Marke+ng
Process
Mix
Place
People
Promo+on
Physical
Evidence

14. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Targe+ng
matrix
November
12
©
Datalicious
Pty
Ltd
14

15. Targe+ng
The
right
message
Via
the
right
channel
To
the
right
person
At
the
right
-me
June
2011
©
Datalicious
Pty
Ltd
15

16. >
Increase
revenue
by
10-­‐20%
Capture
internet
traffic
Capture
50-­‐100%
of
fair
market
share
of
traffic
Increase
consumer
engagement
Exceed
50%
of
best
compe-tor’s
engagement
rate
Capture
qualified
leads
and
sell
Convert
10-­‐15%
to
leads
and
of
that
20%
to
sales
Building
consumer
loyalty
Build
60%
loyalty
rate
and
40%
sales
conversion
Increase
online
revenue
Earn
10-­‐20%
incremental
revenue
online
June
2011
©
Datalicious
Pty
Ltd
16

17. >
New
consumer
decision
journey
The
consumer
decision
process
is
changing
from
linear
to
circular.
June
2011
©
Datalicious
Pty
Ltd
17

18. >
New
consumer
decision
journey
The
consumer
decision
process
is
changing
from
linear
to
circular.
Online
research
Change
increases
the
importance
of
experience
during
research
phase.
June
2011
©
Datalicious
Pty
Ltd
18

19. June
2011
©
Datalicious
Pty
Ltd
19

20. Exercise:
Customer
journey
June
2011
©
Datalicious
Pty
Ltd
20

21. >
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
June
2011
©
Datalicious
Pty
Ltd
21

22. >
Coordina+on
across
channels
Genera+ng
Crea+ng
Maximising
awareness
engagement
revenue
TV,
radio,
print,
Retail
stores,
in-­‐store
Outbound
calls,
direct
outdoor,
search
kiosks,
call
centers,
mail,
emails,
social
marke-ng,
display
brochures,
websites,
media,
SMS,
mobile
ads,
performance
mobile
apps,
online
apps,
etc
networks,
affiliates,
chat,
social
media,
etc
social
media,
etc
Off-­‐site
On-­‐site
Profile
targe+ng
targe+ng
targe+ng
June
2011
©
Datalicious
Pty
Ltd
22

23. >
Combining
targe+ng
plaIorms
Off-­‐site
targe-ng
Profile
On-­‐site
targe-ng
targe-ng
June
2011
©
Datalicious
Pty
Ltd
23

24. Take
a
closer
look
at
our
cash
flow
solu+ons
November
2010
©
Datalicious
Pty
Ltd
24

25. 0
June
2011
©
Datalicious
Pty
Ltd
25

26. 0
November
2010
©
Datalicious
Pty
Ltd
26

27. >
Affinity
re-­‐targe+ng
in
ac+on
Different
type
of
visitors
respond
to
different
ads.
By
using
category
affinity
targe-ng,
response
rates
are
li[ed
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
- + - +
June
2011
©
Datalicious
Pty
Ltd
27

28. >
Ad-­‐sequencing
in
ac+on
Marke-ng
is
about
telling
stories
and
stories
are
not
sta-c
but
evolve
over
-me
Ad-­‐sequencing
can
help
to
evolve
stories
over
-me
the
more
users
engage
with
ads
June
2011
©
Datalicious
Pty
Ltd
28

29. >
Prospect
targe+ng
parameters
June
2011
©
Datalicious
Pty
Ltd
29

30. >
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
June
2011
©
Datalicious
Pty
Ltd
30

31. >
Search
call
to
ac+on
for
offline
June
2011
©
Datalicious
Pty
Ltd
31

32. Include
in
press
June
2011
©
Datalicious
Pty
Ltd
32

33. >
PURLs
boos+ng
DM
response
rates
Text
June
2011
©
Datalicious
Pty
Ltd
33

34. >
Unique
phone
numbers
§ 1
unique
phone
number
– Phone
number
is
considered
part
of
the
brand
– Media
origin
of
calls
cannot
be
established
– Added
value
of
website
interac-on
unknown
§ 2-­‐10
unique
phone
numbers
– Different
numbers
for
different
media
channels
– Exclusive
number(s)
reserved
for
website
use
– Call
origin
data
more
granular
but
not
perfect
– Difficult
to
rotate
and
pause
numbers
June
2011
©
Datalicious
Pty
Ltd
34

35. >
Unique
phone
numbers
§ 10+
unique
phone
numbers
– Different
numbers
for
different
media
channels
– Different
numbers
for
different
product
categories
– Different
numbers
for
different
conversion
steps
– Call
origin
becoming
useful
to
shape
call
script
– Feasible
to
pause
numbers
to
improve
integrity
§ 100+
unique
phone
numbers
– Different
numbers
for
different
website
visitors
– Call
origin
and
-me
stamp
enable
individual
match
– Call
conversions
matched
back
to
search
terms
June
2011
©
Datalicious
Pty
Ltd
35

36. >
Jet
Interac+ve
phone
call
data
June
2011
©
Datalicious
Pty
Ltd
36

37. >
Poten+al
calls
to
ac+on
§ Unique
click-­‐through
URLs
Calls
to
ac+on
§ Unique
vanity
domains
or
URLs
can
help
shape
§ Unique
phone
numbers
the
customer
§ Unique
search
terms
experience
not
just
evaluate
§ Unique
email
addresses
responses
§ Unique
personal
URLs
(PURLs)
§ Unique
SMS
numbers,
QR
codes
§ Unique
promo-onal
codes,
vouchers
§ Geographic
loca-on
(Facebook,
FourSquare)
§ Plus
regression
analysis
of
cause
and
effect
June
2011
©
Datalicious
Pty
Ltd
37

38. >
Combining
data
sources
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
June
2011
©
Datalicious
Pty
Ltd
38

39. >
Transac+ons
plus
behaviours
CRM
Profile
Site
Behaviour
one-­‐off
collec-on
of
demographical
data
tracking
of
purchase
funnel
stage
+
age,
gender,
address,
etc
browsing,
checkout,
etc
customer
lifecycle
metrics
and
key
dates
tracking
of
content
preferences
profitability,
expira+on,
etc
products,
brands,
features,
etc
predic-ve
models
based
on
data
mining
tracking
of
external
campaign
responses
propensity
to
buy,
churn,
etc
search
terms,
referrers,
etc
historical
data
from
previous
transac-ons
tracking
of
internal
promo-on
responses
average
order
value,
points,
etc
emails,
internal
search,
etc
Updated
Occasionally
Updated
Con+nuously
June
2011
©
Datalicious
Pty
Ltd
39

40. >
Customer
profiling
in
ac+on
Using
website
and
email
responses
to
learn
a
limle
bite
more
about
subscribers
at
every
touch
point
to
keep
refining
profiles
and
messages.
June
2011
©
Datalicious
Pty
Ltd
40

41. >
Online
form
best
prac+ce
Maximise
data
integrity
Age
vs.
year
of
birth
Free
text
vs.
op-ons
Use
auto-­‐complete
wherever
possible
June
2011
©
Datalicious
Pty
Ltd
41

42. >
Enhancing
data
sources
Customer
profile
data
Geo-­‐demographic
data
+
The
whole
is
greater
than
the
sum
of
its
parts
3rd
party
data
June
2011
©
Datalicious
Pty
Ltd
42

43. >
Geo-­‐demographic
segments
June
2011
©
Datalicious
Pty
Ltd
43

44. >
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.”
June
2011
©
Datalicious
Pty
Ltd
44

45. Exercise:
Targe+ng
matrix
June
2011
©
Datalicious
Pty
Ltd
45

46. >
Exercise:
Targe+ng
matrix
Segments:
Colour,
price,
Purchase
product
affinity,
etc
Media
Data
Cycle
Channels
Points
X
Y
Default,
awareness
Research,
considera+on
Purchase
intent
Reten+on,
up/cross-­‐sell
June
2011
©
Datalicious
Pty
Ltd
46

47. >
Exercise:
Targe+ng
matrix
Segments:
Colour,
price,
Purchase
product
affinity,
etc
Media
Data
Cycle
Channels
Points
X
Y
Default,
Have
you
Have
you
Display,
Default
awareness
seen
A?
seen
B?
search,
etc
Research,
A
has
great
B
has
great
Search,
Ad
clicks,
considera+on
features!
features!
website,
etc
prod
views
Purchase
A
delivers
B
delivers
Website,
Cart
adds,
intent
great
value!
great
value!
emails,
etc
checkouts
Reten+on,
Why
not
Why
not
Direct
mails,
Email
clicks,
up/cross-­‐sell
buy
B?
buy
A?
emails,
etc
logins,
etc
June
2011
©
Datalicious
Pty
Ltd
47

48. 101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010
>
Metrics
framework
November
12
©
Datalicious
Pty
Ltd
48

49. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac+on
Sa+sfac+on
Social
media
June
2011
©
Datalicious
Pty
Ltd
49

50. >
Simplified
AIDAS
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac-on)
(Sa-sfac-on)
June
2011
©
Datalicious
Pty
Ltd
50

51. >
Marke+ng
is
about
people
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
June
2011
©
Datalicious
Pty
Ltd
51

52. >
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
June
2011
©
Datalicious
Pty
Ltd
52

53. New
vs.
returning
visitors
June
2011
©
Datalicious
Pty
Ltd
53

54. AU/NZ
vs.
rest
of
world
June
2011
©
Datalicious
Pty
Ltd
54

55. >
Poten+al
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
§ Devices,
i.e.
home,
office,
mobile,
tablet,
etc
June
2011
©
Datalicious
Pty
Ltd
55

56. Exercise:
Metrics
framework
June
2011
©
Datalicious
Pty
Ltd
56

57. >
Exercise:
Metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1,
people
Level
2,
strategic
Level
3,
tac+cal
Funnel
breakdowns
June
2011
©
Datalicious
Pty
Ltd
57

58. >
Exercise:
Metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1
People
People
People
People
People
reached
engaged
converted
delighted
Level
2
Display
Strategic
impressions
?
?
?
Level
3
Interac+on
Tac+cal
rate,
etc
?
?
?
Funnel
Exis+ng
customers
vs.
new
prospects,
products,
etc
Breakdowns
June
2011
©
Datalicious
Pty
Ltd
58

59. >
Establishing
a
baseline
Switch
all
adver-sing
off
for
a
period
of
-me
(unlikely)
or
establish
a
smaller
control
group
that
is
representa-ve
of
the
en-re
popula-on
(i.e.
search
term,
geography,
etc)
and
switch
off
selected
channels
one
at
a
-me
to
minimise
impact
on
overall
conversions.
June
2011
©
Datalicious
Pty
Ltd
59

60. >
Importance
of
calendar
events
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
June
2011
©
Datalicious
Pty
Ltd
60

61. Don’t
wait
for
be@er
data,
get
started
now.
June
2011
©
Datalicious
Pty
Ltd
61

62. Contact
me
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
me
twi@er.com/datalicious
June
2011
©
Datalicious
Pty
Ltd
62

63. Data
>
Insights
>
Ac+on