Media mix modelling and media attribution to boost media ROI.

1. >
Media
Planning
<
Media
mix
modelling
and
media
a-ribu1on
to
boost
media
ROI

2. >
Smart
data
driven
marke3ng
Media
A5ribu3on
&
Modeling
Op3mise
channel
mix,
predict
sales
Tes3ng
&
Op3misa3on
Remove
barriers,
drive
sales
Boos3ng
ROMI
Targe3ng
&
Merchandising
Increase
relevance,
reduce
churn
“Using
data
to
widen
the
funnel”
May
2013
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Datalicious
Pty
Ltd
2

3. >
Wide
range
of
data
services
Data
PlaHorms
Data
collec3on
and
processing
Adobe,
Google
Analy3cs,
etc
Web
and
mobile
analy3cs
Tag-­‐less
online
data
capture
Retail
and
call
center
analy3cs
Big
data
&
data
warehousing
Single
customer
view
Insights
Analy3cs
Data
mining
and
modelling
Tableau,
Splunk,
SPSS,
R,
etc
Customised
dashboards
Media
a5ribu3on
analysis
Marke3ng
mix
modelling
Social
media
monitoring
Customer
segmenta3on
Ac3on
Campaigns
Data
usage
and
applica3on
SiteCore,
ExactTarget,
etc
Targe3ng
and
merchandising
Marke3ng
automa3on
CRM
strategy
and
execu3on
Data
driven
websites
Tes3ng
programs
May
2013
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4. >
Best
of
breed
technologies
May
2013
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Datalicious
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4

5. >
Clients
across
all
industries
May
2013
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6. >
Data
driven
media
planning
§ Media
mix
modelling
– Predic1ng
future
media
performance
– Macro
insights
to
inform
channel
strategy
§ Media
a-ribu1on
– Analysis
of
historic
media
performance
– Micro
insights
to
inform
channel
op1misa1on
May
2013
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Datalicious
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7. >
Media
mix
modelling
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May
2013
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Datalicious
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8. >
Media
mix
modelling
§ Predict
the
future
based
on
the
past,
i.e.
predict
sales
based
on
media
investment
using
a
model
that
is
based
on
historic
data
– Old
school
approach:
Regression-­‐based
§ Looks
at
correla1on
between
summary
data,
i.e.
media
investment
and
total
sales
per
week
– Recommended
approach:
Agent-­‐based
§ Simulates
market
condi1ons
through
a
set
of
consumers
(agents)
and
then
exposes
those
agents
to
different
media
scenarios
to
test
them
(predict
sales)
before
they
are
even
implemented
May
2013
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Datalicious
Pty
Ltd
8

9. >
ThinkVine
agent-­‐based
modelling
May
2013
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Datalicious
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Ltd
9
ThinkVine
uses
agent-­‐based
modeling
(ABM)
to
simulate
the
ac1ons
of
consumers
in
the
market
based
on
a
combina1on
of
internal
client
data
as
well
as
external
market
data.
This
enables
adver1sers
to
predict
and
test
the
impact
of
different
budget
mix
configura1ons
before
they
have
been
implemented
thus
improving
overall
media
effec1veness.

10. >
Data
requirements
and
process
May
2013
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Datalicious
Pty
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10
1. Training
the
system:
Datalicious
analysts
develop
and
calibrate
a
custom
model
based
on
agents
that
recreates
past
sales.
2. Proving
the
system:
Once
the
system
is
trained,
we
validate
it
by
comparing
predicted
sales
to
actual
sales.
3. Using
the
system:
Once
the
model
has
been
calibrated
&
validated,
it
can
be
used
to
predict
sales
for
different
media
scenarios
thus
tes1ng
them
before
they
are
executed

11. >
Modelling
data
requirements
§ Sales
data
– As
granular
as
possible,
i.e.
by
week
by
store
§ Media
data
– Data
on
the
media
ac1vity
that
lead
to
the
above
sales
§ Customer
profile
data
– Any
quan1ta1ve
and
qualita1ve
insights
on
exis1ng
customers
that
help
configure
the
ThinkVine
agents
(i.e.
simulated
consumers)
§ Consumer
profile
data
– Any
quan1ta1ve
and
qualita1ve
insights
on
Australian
consumers
in
general
that
help
configure
the
agents
(i.e.
simulated
consumers)
May
2013
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Datalicious
Pty
Ltd
11

12. >
Media
mix
modelling
outputs
May
2013
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Datalicious
Pty
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12
The
so^ware
enables
fast,
objec1ve
comparisons
of
marke1ng
plan
alterna1ves,
i.e.
you
can
vary
spending
levels,
1ming,
mix
of
tac1cs
and
consumer
groups
targeted.
The
so^ware
enables
your
team
to
easily
run
“what
if?”
scenarios,
i.e.
you
can
find
the
best
investment
levels
to
meet
various
strategic
and
tac1cal
objec1ves
by
predic1ng
poten1al
sales
for
various
different
media
budget
scenarios.

13. >
ThinkVine
food
services
May
2013
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Datalicious
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13
In
this
case
study,
the
company
achieved
greater
media
efficiencies
and
ROI
and
also
quan1fied
the
halo
effect
of
cross
brand
marke1ng.
More
specifically,
more
than
$10
million
of
media
spend
was
over-­‐saturated
due
to
a
mismatch
between
the
target
consumers’
media
consump1on
habits
and
the
deployed
marke1ng
tac1cs.

14. >
Media
a5ribu3on
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May
2013
©
Datalicious
Pty
Ltd
14

15. >
The
ideal
media
dashboard
Channel
Investment
ROMI
Return
Brand
equity
Baseline
($100)
n/a
$40
Offline
TV,
print,
outdoor,
etc
$7
330%
$30
Direct
Direct
mail,
email,
etc
$1
400%
$5
Online
Search,
display,
social,
etc
$2
1150%
$25
May
2013
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Datalicious
Pty
Ltd
15

16. >
ROMI
as
compe33ve
advantage
May
2013
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Datalicious
Pty
Ltd
16
74%
of
marketers
do
not
engage
in
any
form
of
media
a-ribu1on
aside
from
the
last
click
leaving
26%
of
marketers
with
a
serious
compe11ve
advantage
as
their
media
investment
is
likely
to
generate
a
much
higher
ROMI.

17. Closer
Paid
search
Display
ad
views
TV/print
responses
>
Full
purchase
path
tracking
May
2013
©
Datalicious
Pty
Ltd
17
Influencer
Influencer
$
Display
ad
clicks
Online
sales
Affiliate
clicks
Social
referrals
Offline
sales
Organic
search
Social
buzz
Retail
visits
Life3me
profit
Organic
search
Emails,
direct
mail
Direct
site
visits
Introducer

18. >
Custom
models
most
effec3ve
May
2013
©
Datalicious
Pty
Ltd
18
56%
of
marketers
consider
a
unique
or
custom
(weighted)
media
a-ribu1on
approach
that
does
not
use
a
standard
out-­‐of-­‐the-­‐box
methodology
as
most
effec1ve.

19. Touch
point
1
>
Analy3cs
to
pick
the
best
model
May
2013
©
Datalicious
Pty
Ltd
19
Touch
point
2
Touch
point
3
Touch
point
N
Closer
Influencer
Influencer
$
Introducer
Touch
point
1
Touch
point
2
Touch
point
3
Touch
point
N
Touch
point
1
Touch
point
2
Touch
point
3
Touch
point
N
✖
✔
✖

20. >
A5ribu3on
models
compared
May
2013
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Datalicious
Pty
Ltd
20
COST
PER
CONVERSION
Last
click
a-ribu1on
Custom
(weighted)
a-ribu1on

21. >
Media
a5ribu3on
May
2013
©
Datalicious
Pty
Ltd
21
Aussie
purchase
path
tracking
and
media
a-ribu1on
modelling
in
close
coopera1on
with
Amnesia
designed
to
op1mise
the
overall
Aussie
budget
mix
across
paid
and
earned
media
resul1ng
in
an
overall
project
ROI
of
910%.

22. >
Media
a5ribu3on
May
2013
©
Datalicious
Pty
Ltd
22
Suncorp
purchase
path
tracking
and
media
a-ribu1on
modelling
in
order
to
op1mise
the
overall
Suncorp
insurance
budget
mix
across
paid
and
earned
media
resul1ng
in
an
overall
project
ROI
of
2,078%.

23. May
2013
©
Datalicious
Pty
Ltd
23
Contact
me
cbartens@datalicious.com
Learn
more
blog.datalicious.com
Follow
us
twi5er.com/datalicious

24. Smart
data
driven
marke3ng
May
2013
©
Datalicious
Pty
Ltd
24