Contenu connexe Similaire à ADMA Marketing Data Strategy (20) ADMA Marketing Data Strategy2. >
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
May
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
ROAS
May
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
May
2011
©
Datalicious
Pty
Ltd
4
6. >
Data
driven
marke(ng
§ What
is
data
driven
marke-ng?
§ Self
assessment:
Your
capabili-es
§ Strategies
for
effec-ve
data
collec-on
§ Campaign
development
and
data
integrity
§ Effec-ve
mul--‐channel
campaign
execu-on
§ Analysis
and
performance
measurement
§ In-‐sourcing
or
outsourcing
May
2011
©
Datalicious
Pty
Ltd
6
8. >
Major
data
categories
Campaign
data
TV,
print,
call
center,
search,
web
analy-cs,
ad
serving,
etc
Campaigns
Customers
Customer
data
Direct
mail,
call
center,
web
analy-cs,
emails,
surveys,
etc
Consumer
data
Geo-‐demographics,
search,
Compe(tors
Consumers
social,
3rd
party
research,
etc
Compe(tor
data
Search,
social,
ad
spend,
3rd
party
research,
news,
etc
May
2011
©
Datalicious
Pty
Ltd
8
9. >
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
May
2011
©
Datalicious
Pty
Ltd
9
11. Oil
and
data
come
at
a
price
May
2011
©
Datalicious
Pty
Ltd
11
13. Collec(ng
data
for
the
sake
of
it
or
to
add
value
to
customers?
May
2011
©
Datalicious
Pty
Ltd
13
14. >
Privacy
vs.
data
benefits
policy
§ Do
not
hide
behind
small
print
§ Use
plain
English
in
your
privacy
policy
§ Explain
exactly
what
data
you
are
recording
§ Explain
why
you
are
recording
the
data
§ Explain
the
benefits
for
the
consumer
§ Provide
opt-‐out
and
feedback
op-ons
§ Make
opt-‐outs
a
KPI
not
just
opt-‐ins
=
Data
benefits
and
privacy
policy
May
2011
©
Datalicious
Pty
Ltd
14
16. Product
Partners
Price
Marke(ng
Process
Mix
Place
People
Promo(on
Physical
Evidence
17. Targe(ng
The
right
message
Via
the
right
channel
To
the
right
person
At
the
right
-me
May
2011
©
Datalicious
Pty
Ltd
17
18. >
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
May
2011
©
Datalicious
Pty
Ltd
18
19. >
New
consumer
decision
journey
The
consumer
decision
process
is
changing
from
linear
to
circular.
May
2011
©
Datalicious
Pty
Ltd
19
20. >
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.
May
2011
©
Datalicious
Pty
Ltd
20
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
May
2011
©
Datalicious
Pty
Ltd
22
23. >
Combining
targe(ng
plaIorms
Off-‐site
targe-ng
Profile
On-‐site
targe-ng
targe-ng
May
2011
©
Datalicious
Pty
Ltd
23
25. Take
a
closer
look
at
our
cash
flow
solu(ons
November
2010
©
Datalicious
Pty
Ltd
25
26. >
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
- + - +
May
2011
©
Datalicious
Pty
Ltd
26
27. >
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
May
2011
©
Datalicious
Pty
Ltd
27
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
May
2011
©
Datalicious
Pty
Ltd
30
31. >
Search
call
to
ac(on
for
offline
May
2011
©
Datalicious
Pty
Ltd
31
33. >
PURLs
boos(ng
DM
response
rates
Text
May
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
May
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
May
2011
©
Datalicious
Pty
Ltd
35
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
May
2011
©
Datalicious
Pty
Ltd
37
38. >
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
May
2011
©
Datalicious
Pty
Ltd
38
39. >
Combining
data
sources
Website
behavioural
data
Campaign
response
data
+
The
whole
is
greater
than
the
sum
of
its
parts
Customer
profile
data
May
2011
©
Datalicious
Pty
Ltd
39
40. >
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
May
2011
©
Datalicious
Pty
Ltd
40
41. >
Customer
profiling
in
ac(on
Using
website
and
email
responses
to
learn
a
lille
bite
more
about
subscribers
at
every
touch
point
to
keep
refining
profiles
and
messages.
May
2011
©
Datalicious
Pty
Ltd
41
42. >
Online
form
best
prac(ce
Maximise
data
integrity
Age
vs.
year
of
birth
Free
text
vs.
op-ons
Use
auto-‐complete
wherever
possible
May
2011
©
Datalicious
Pty
Ltd
42
46. >
Exercise:
Customer
IDs
To
transac(onal
data
To
reten(on
messages
From
suspect
to
prospect
To
customer
Time
Time
From
behavioural
data
From
awareness
messages
May
2011
©
Datalicious
Pty
Ltd
46
47. >
Enhancing
data
sources
Customer
profile
data
Geo-‐demographic
data
+
The
whole
is
greater
than
the
sum
of
its
parts
3rd
party
data
May
2011
©
Datalicious
Pty
Ltd
47
53. transcape
Buyer
File
1
Buyer
File
Buyer
File
2
7
Buyer
File
Buyer
File
3
6
Buyer
File
Buyer
File
5
4
"IMP
have
been
working
with
Alliance
Data
ever
since
they
launched
and
have
using
their
Australian
&
NZ
data
with
great
success
across
a
range
of
products"
Victoria Coleman
Media Manager
International Masters Publishers
54. transcape
Selectable
by:
Recency
Frequency
Money
Recency Count Frequency Count Spend Count
0
to
6
mo. 371,012 1
x 734,436 Less
Than
$25 138,346
$25
-‐
$50 131,671
6
to
12
mo. 269,457 2
x 206,257
$50
-‐
$100 324,512
12
to
18
mo. 295,601 3x 110,751 $100
-‐
$250 329,338
18
to
24
mo. 397,162 4+ 281,788 $250+ 409,365
Total 1,333,232 Total 1,333,232 Total 1,333,232
55. transcape
Selectable
by:
Income
Age
Gender
Female
Male
56. RFM
Segmenta(on
(house
file)
0-‐6
mo.
7-‐12
mo.
13-‐24
mo.
25-‐36
mo.
37mo.+
<$10
1.20%
0.70%
0.50%
0.30%
0.10%
$10-‐$24
1.50%
0.90%
0.70%
0.40%
0.20%
$25-‐$49
1.80%
1.20%
1.00%
0.50%
0.30%
$50-‐$99
2.00%
1.70%
1.20%
0.80%
0.40%
$100-‐$249
2.50%
2.10%
1.50%
1.10%
0.50%
$250+
3.00%+
2.20%
2.00%
1.40%
0.70%
450,000
Buyers
50,000
57. Last
bought
from
YOU
25-‐36
mo.,
$25-‐$49
Response
Rate
=
0.50%
transcape
35,000
50,000
Buyers
matches
1
.4
million
names
58. Last
bought
from
you
Response
Rate
=
0.50%
0.90%
25-‐36
mo.,
$25-‐$49
Universe
=
50,000
35,000
20,000
Have
also
bought
elsewhere
Frequency
=
1x
1+
3x
2x
Recency
Value
0-‐12
mo.
12-‐24
mo.
25+
mo.
<$25
0.50%
0.30%
0.10%
$25-‐49
0.70%
0.50%
0.30%
$50-‐$99
0.90%
0.70%
0.50%
$100+
1.10%
0.90%
0.70%
Further
op(mise
your
house
file
segments
60. GeoSmart Segments
Geodemographic Profile Standard Normalised
# Description transcape % Client %
Index Index
1 Prestige 1.41 5.45 387.36 6.46
2 High Status Urban 1.11 0.63 56.99 -1.00
GeoSmart Groups 3 Desirable Suburban 2.36 7.35 310.86 8.82
4 Affluent Family 1.95 3.68 188.64 3.57
5 High Density Urban 1.07 0.44 41.39 -1.19
Standard Normalised 6 Urban Bohemian 1.25 0.32 25.28 -1.45
# Description transcape % Client %
Index Index 7 Affluent Multicultural 1.23 3.11 252.98 3.52
1 High Status Stronger Family 15.11 34.28 226.88 44.39 8 High Status Suburban 2.39 7.48 313.22 8.99
2 High Status Weaker family 5.18 5.51 106.38 0.76 9 Coastal Emplty Nest & Retirement 1.92 1.77 92.26 -0.34
10 Desirable Urban 1.75 4.12 235.94 4.59
3 Mid Status Stronger Family 24.90 25.54 102.55 1.64 11 High Status Family 2.80 0.63 22.65 -3.22
4 Mid Status Weaker family 4.83 6.97 144.43 4.79 12 Mature Affluent Suburban 1.06 4.82 456.31 5.57
5 Low Status Stronger Family 25.02 10.46 41.79 -31.28 13 Aspiring Family 2.89 3.11 107.37 0.48
6 Low Status Weaker family 9.51 11.47 120.66 4.61 14 Mid Status Family Starter 1.69 1.65 97.77 -0.08
15 Affluent Seachange 1.64 1.20 73.50 -0.96
7 Disadvantaged 13.70 4.69 34.24 -16.88 16 Established Multicultural Suburban 2.70 1.90 70.43 -1.75
8 Unclassified 1.75 1.08 61.50 -1.44 17 Urban Lifestyle 0.68 0.70 102.68 0.04
18 Mid Status Suburban 3.01 5.32 176.69 4.90
19 Provincial Fringe 2.11 0.82 39.08 -2.39
20 Metro Fringe 0.87 1.90 218.14 2.02
21 Mid Status Urban 1.66 4.75 285.75 5.58
22 Mixed Multicultural Suburban 0.87 0.13 14.49 -0.89
23 Mining 1.41 1.52 107.92 0.25
24 Mid Status Young Family 3.21 1.39 43.40 -3.53
25 Mature Mid Status Family 4.50 6.59 146.45 4.65
26 Multicultural Urban Lifestyle 0.57 0.00 0.00
27 University Enclaves 0.36 0.51 139.57 0.31
28 Holiday Lifestyle 0.45 0.25 56.21 -0.41
29 Multicultural Mixed Urban 1.10 0.76 69.17 -0.74
30 Establishing Multicultural Family 1.98 0.82 41.66 -2.19
31 Elderly Enclaves 1.15 0.51 44.19 -1.24
32 Establishing Provincial family 2.91 1.20 41.44 -3.24
33 New Age Lifestyle 0.91 0.95 105.02 0.10
34 Mature Provincial Suburban 4.30 1.01 23.60 -5.01
35 Mixed Suburban 0.96 0.19 19.82 -1.07
36 Inland Rural Fringe 1.43 3.36 234.37 3.72
37 Established Multicultural family 1.31 0.13 9.69 -1.16
38 Provincial Mixed Urban 2.17 0.82 37.94 -2.48
39 Low Status Rural Fringe 1.93 0.51 26.25 -2.25
40 Family Achiever 1.91 0.51 26.59 -2.23
41 Old European Blue Collar 1.03 0.95 91.95 -0.19
42 Established Blue Collar Suburban 3.04 6.59 217.10 7.15
43 Blue Collar Family 3.10 2.60 83.72 -1.14
44 Provincial Blue Collar Suburban 5.65 1.77 31.43 -6.73
45 Middle Eastern Multicultural 0.77 0.00 0.00
46 Poor Mixed Urban 1.14 0.82 72.07 -0.70
47 Low Status Mixed Multicultural 1.39 0.70 50.00 -1.40
48 Small Town Blue Collar Suburban 4.39 1.58 36.10 -5.12
49 Established Asian 0.60 0.00 0.00
50 Mobile Holiday Accommodation 0.21 0.13 60.52 -0.17
51 Elderly Provincial Urban 2.04 0.70 34.12 -2.38
52 Provincial Battler 2.93 0.76 25.98 -3.43
53 High Density Welfare 0.16 0.00 0.00
54 Suburban Welfare 0.83 0.00 0.00
55 Indigenous & Remote 1.62 1.08 66.49 -1.17
56 Unclassified 0.13 0.00
63. >
Exercise:
Targe(ng
matrix
Purchase
Segments:
Colour,
price,
Media
Data
Cycle
product
affinity,
etc
Channels
Points
Default,
awareness
Research,
considera(on
Purchase
intent
Reten(on,
up/cross-‐sell
May
2011
©
Datalicious
Pty
Ltd
63
64. >
Exercise:
Targe(ng
matrix
Purchase
Segments:
Colour,
price,
Media
Data
Cycle
product
affinity,
etc
Channels
Points
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
May
2011
©
Datalicious
Pty
Ltd
64
71. >
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.”
May
2011
©
Datalicious
Pty
Ltd
71
73. >
Develop
a
tes(ng
matrix
Test
Segment
Content
KPIs
Poten(al
Results
May
2011
©
Datalicious
Pty
Ltd
73
74. >
Develop
a
tes(ng
matrix
Test
Segment
Content
KPIs
Poten(al
Results
New
Conversion
Next
step,
Test
#1A
prospects
form
A
order,
etc
?
?
New
Conversion
Next
step,
Test
#1B
prospects
form
B
order,
etc
?
?
New
Conversion
Next
step,
Test
#1N
prospects
form
N
order,
etc
?
?
?
?
?
?
?
?
May
2011
©
Datalicious
Pty
Ltd
74
75. >
AIDA
and
AIDAS
formulas
Old
media
New
media
Awareness
Interest
Desire
Ac(on
Sa(sfac(on
Social
media
May
2011
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Datalicious
Pty
Ltd
75
76. >
Simplified
AIDAS
funnel
Reach
Engagement
Conversion
+Buzz
(Awareness)
(Interest
&
Desire)
(Ac-on)
(Sa-sfac-on)
May
2011
©
Datalicious
Pty
Ltd
76
77. >
Marke(ng
is
about
people
People
People
People
People
reached
40%
engaged
10%
converted
1%
delighted
May
2011
©
Datalicious
Pty
Ltd
77
78. >
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
May
2011
©
Datalicious
Pty
Ltd
78
81. >
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
May
2011
©
Datalicious
Pty
Ltd
81
82. >
Developing
a
metrics
framework
Level
Reach
Engagement
Conversion
+Buzz
Level
1,
people
Level
2,
strategic
Level
3,
tac(cal
Funnel
breakdowns
May
2011
©
Datalicious
Pty
Ltd
82
83. >
Developing
a
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
May
2011
©
Datalicious
Pty
Ltd
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84. >
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.
May
2011
©
Datalicious
Pty
Ltd
84
85. >
Importance
of
calendar
events
Traffic
spikes
or
other
data
anomalies
without
context
are
very
hard
to
interpret
and
can
render
data
useless
May
2011
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Datalicious
Pty
Ltd
85
86. >
Out-‐sourcing
or
in-‐sourcing?
Year
1
Year
2
Year
3
PlaIorms
Training
Support
Degree
of
in-‐house
control
and
sophis-ca-on
Reduce
vendor
reliance
to
absolute
minimum
Start
taking
control
of
but
consider
the
value
technology
and
data,
of
support
agreements
shi]
vendor
focus
to
for
both
maintenance
Engage
third
par-es
enhancements
and
the
as
well
as
updates
on
with
more
experience
provision
of
training
market
innova-ons
and
to
get
started
and
to
implement
technology
for
internal
resources
new
features.
Time,
Control
May
2011
©
Datalicious
Pty
Ltd
86
87. Contact
me
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
me
twi;er.com/datalicious
May
2011
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Datalicious
Pty
Ltd
87