In this presentation you will learn: - What is Predictive Analytics? - How can Predictive Analytics help you and your organization? - Averages are evil - Uncertainty is the source value in your business - How to interpret results and what questions to ask to uncover the truth - Predictive Analytics is only Predictive Analytics when a decision is made

1. Predictive Analytics:
An Executive’s Guide for Informed Decision Making
March 11th, 2014
Presented by:
Andrew Pulvermacher
Director | Predictive Analytics
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Autographed
by
the
author:
Sam
Savage
of
Stanford
Univ.

2. in/drewpulvermacher
Predictive Analytics Series
1. Execu5ve
Introduc5on
2. Data
Modeling
3. Simula5on
4. Op5miza5on
5. Data-­‐Driven
Leadership

3. 3
AGENDA
1. Foundation Building
2. Descriptive Analytics
3. Predictive Analytics
Informed
Decision Making
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4. 4
FOUNDATION
BUILDING
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5. TERMINOLOGY
5
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1. Predic:ve
Analy:cs
|
Risk-­‐Based
Decision
Making
2. Probability
|
Likelihood
of
an
event
happening
3. Standard
Devia:on
|
Risk
/
Varia5on
4. Correla:on
|
Rela5onship
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6. 6
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Reason for Being
Fundamental
Lack
of
Understanding
Forward-­‐Looking
Decision
Making
8
8
Average
Average
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7. Drew & Dane
Avg
4’
deep
Avg
2’
deep
7
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8. 8
Why is this
important?
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9. True Story
9
$80bln
Corpora5on
“AXer
spending
$40mln
on
the
last
campaign,
customer
order
frequency
increased
to
4.5
from
4.4;
an
incremental
liX
of
0.1”
“ROI
of
…..”
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#
of
Purchases
%
of
Customers

10. 4.5
10
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11. 11
AGENDA
1. Foundation Building
2. Descriptive Analytics
3. Predictive Analytics
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12. Analytics
12
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“Flaw
of
Averages”.
Used
with
Permission.

13. 13
What Does Tell Us
About Tomorrow?
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14. 14
AGENDA
1. Foundation Building
2. Descriptive Analytics
3. Predictive Analytics
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15. 15
Predictive
Analytics
1. Where to Start
2. Informed Action
3. Reinventing Decision Making
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16. Where to Start | Decision Making Blueprint
16
Ask
Yourself:
• What
is
my
OBJECTIVE?
• What
are
my
VARIABLES?
• What
are
my
CONSTRAINTS?
• Control
• Manage
• Influence
The
Hand
You’re
Dealt
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17. Blackjack
Average
Winning
Hand:
18.5
Chance
of
Winning
w/
Avg
Hand:
0%
17
Objec:ve:
Get
as
close
to
21,
without
going
over.
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Variables:
-­‐Hit
or
Stay
Constraints:
-­‐Hand
You’re
Dealt
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18. 18
Reinventing
Decision Making
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19. Building a Blueprint for Success
19
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C
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i
Objec:ve
Manage
Constraint
Influence
Control
• Iden5fy
key
Objec:ve
• List
relevant
Variables
• Find
Constraints
• Replace
Point
Es:mates
with
Uncertainty
Remove
BoZlenecks
Efficient
Data
Discovery
requires
instant
accessibility

20. Perhaps the Most Significant Benefit…
20
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Maximize
Decision
Throughput
and
Transparency

21. Example #1: Purchase Decision
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Objec:ve:
Match
Supply
with
Demand
to
Maximize
Profit
Variables:
-­‐
Order
Qty
-­‐Customer
Demand
Constraints:
-­‐Open-­‐to-­‐Buy
Purchase
Qty: 400
Selling
Price: 15.75$
Product
Cost: 10.50$
3rd
Party 25
|
100
Demand
Average: 400
Standard
Deviation: 50
What
is
the
Probability
Profit
will
be
less
than
$2,100?

22. Example #1: Purchase Decision
22
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Profit
Price
Cost
Demand
Order
Qty
Customers
#
$

23. Example #2: Employee Retention
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Situa:on:
Employee
Turnover
is
High
(~20%
per
Quarter).
Solu:on:
Increase
pay,
Time
Off,
Benefits,
etc..
20%
10%
40%
20%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Q1
Q2
Q3
Q4
Objec:ve:
Retain
Quality
Employees
Variables:
Pay
Benefits
Working
Condi5ons
Leadership
|
Rela5onship
Constraint:
Employee
Profile
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24. Example #2: Employee Retention
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Year
1
Pay
Increase
Department
Manager
Job
Role
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25. Example #2: Employee Retention Design
25
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R
D
i
S
C
Responsibility
Involvement
Feedback
&
Praise
Detailed
Objec5ves
Profile
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26. Example #3: Commodity Pricing
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Objec:ve:
Minimize
monthly
forecast
error.
Variables:
-­‐Commodity
Prices
-­‐Weather
Constraints:
-­‐Budget

27. Example #3: Commodity Pricing
27
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Leading
Indicator
X

28. Example #4: Health Care Optimization
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Service
Rates
Pa5ent
Arrivals
Rooms
Staff
Reason
Indicators
Objec:ve:
High
Quality
Care
and
Pa5ent
Throughput
Variables:
Staff
Levels
Constraints:
Rooms

29. 29
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Decision
Sciences

30. 30
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Drew@PerformanceG2.com
Q&A

31. Thank you for attending our webinar
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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:
" (Twitter) @performanceg2
" (Facebook) /performanceg2
" (YouTube) /performanceg2
" (LinkedIn) /performanceg2-inc

32. Predictive Analytics Series
1. Execu5ve
Introduc5on
2. Data
Modeling
3. Simula5on
4. Op5miza5on
5. Data-­‐Driven
Leadership
Special
Thanks
To:
Sam
Savage,
Stanford
University
University
of
Wisconsin’s
Opera5ons
&
Technology
Program