In this presentation you will learn: - Methods and models that support the effectiveness of predictive analytics applications - Demonstration of today's predictive analytics tools and solutions - How to translate respective data sets into predictive models and apply to business needs

1. Predictive Analytics:
Modeling Concepts
April 15th, 2014
Presented by:
Andrew Pulvermacher
Director | Predictive Analytics
in/drewpulvermacher

2. 2
“We’re
forge+ng
how
to
fly.”
-­‐Rory
Kay,
United
captain
and
co-­‐chairman
of
a
Federal
AviaAon
AdministraAon
commiCee
“among
the
accidents
and
certain
categories
of
incidents
that
were
examined,
roughly
two-­‐thirds
of
the
pilots
either
had
difficulty
manually
flying
planes
or
made
mistakes
using
flight
computers.”
This
reliance
on
computer-­‐heavy
flight
decks
and
the
“problems
that
result
when
crews
fail
to
properly
keep
up
with
changes
in
levels
of
automaAon”
now
pose
“the
biggest
threats”
to
airliner
safety
in
the
world.

3. 3

4. 4
Predic@ve
Analy@cs

5. 5
Forward-­‐Looking
Decision
Making
ObjecAve
|
Variables
|
Constraints
TODAY:
Modeling
Concepts
Predictive Analytics Series
1. ExecuAve
IntroducAon
2. Data
Modeling
3. SimulaAon
4. OpAmizaAon
5. Data-­‐Driven
Leadership

6. in/drewpulvermacher

7. in/drewpulvermacher
Predictive Analytics Modeling Concepts
WHAT is flying the plane…

8. 8
OBJECTIVE:
We
need
to
RETAIN
our
top
employees
and
RECRUIT
More
EffecAvely
Starts
with
Data
Easy
3
Step
Process
1) Data
2) What
Column
3) Select
The
TECHNOLOGY
does
it
for
me
Recommenda@on:
Give
everyone
a
PosiAve
Performance
Review
(?)

9. in/drewpulvermacher
Predictive Analytics Modeling Concepts
1. ClassificaAon
2. A/B
TesAng
3. Decision
Trees
4. Regression

10. 10
10
Classifica@on
1) Hidden
RelaAonships
2) Data
Familiarity
3) SegmentaAon

11. 11
11
A/B
Tes@ng
In
markeAng,
A/B
tes@ng
is
a
simple
randomized
experiment
with
two
variants,
A
and
B,
which
are
the
control
and
treatment
in
the
controlled
experiment.
It
is
a
form
of
sta@s@cal
hypothesis
tes@ng.

12. 12
12
A/B
Tes@ng
Customer
Response
Rate
Failure
Rate
Pricing
Loan
Default

13. 13
Summary Statistics
Probability
of
Breast
Cancer:
0.8%
• Woman
>
40
MAMMOGRAM
Unknown:
Breast
Cancer
YES
NO
Test
Posi@ve
90%
7%
Flesh
&
Blood
Example

14. 14
BAYES
THEOREM
1
70

15. 15
Decision
Trees
Reason
for
Being:
Contingent Probabilities
Noun:
the
probability
that
an
event
will
occur
given
that
one
or
more
other
events
have
occurred

16. 16
Decision
Trees:
Scenario
PRODUCT
A
Prob($)
=
60%
PRODUCT
B
Prob($)
=
20%
Prob($)
=
40%

17. 17
Let’s
Plant
a
(Decision)
Tree
3
Probabili@es
of
Success
for
Product
B
Results
are
NOT
Independent.
p(B)
=
p(B|A)
x
p
(A)
+
p(B
|
notA)
x
p(not
A)

18. 18
One
More?
Regression

19. 19
“As
our
machines
get
faster
and
ingest
more
data,
we
allow
ourselves
to
be
dumber.
Instead
of
wrestling
with
our
problems
in
earnest,
we
can
just
plug
in
billions
of
examples
of
them.
Which
is
a
bit
like
using
a
graphing
calculator
to
do
your
high-­‐
school
calculus
homework
–
it
works
great
un@l
you
need
to
actually
understand
calculus.”
-­‐
James
Somers,
“The
Man
Who
Would
Teach
Machines
to
Think,”
The
AtlanAc,
November
2013

20. 20
Predic@ve
Analy@cs

21. in/drewpulvermacher
Predictive Analytics Modeling Concepts
1. ClassificaAon
2. A/B
TesAng
3. Decision
Trees
4. Regression

22. 22
Drew@PerformanceG2.com
Q&A

23. Thank you for attending our webinar
23
Drew@PerformanceG2.com
" Call us: 877.742.4276
"
Email us: info@performanceg2.com or drew@performanceg2.com
"
Visit our web site: performanceg2.com
"
Read our Analytics blog: performanceg2.com/blog
"
Follow us:
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24. Predictive Analytics Series
1. ExecuAve
IntroducAon
2. Data
Modeling
3. SimulaAon
–
May
15,
2014
4. OpAmizaAon
5. Data-­‐Driven
Leadership
Visit
hCp://www.performanceg2.com/
events
to
register
for
the
upcoming
series.