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Predictive Analytics:
An Executive’s Guide for Informed Decision Making

March 11th, 2014
Presented by:
Andrew Pulvermache...
in/drewpulvermacher	
  
Predictive Analytics Series
1.  Execu5ve	
  Introduc5on	
  
2.  Data	
  Modeling	
  
3.  Simula5on...
3	
  
AGENDA
1.  Foundation Building
2.  Descriptive Analytics
3.  Predictive Analytics
Informed
Decision Making
/	
  	
  ...
4	
  
FOUNDATION
BUILDING
/	
  	
  32	
  in/drewpulvermacher	
  
TERMINOLOGY
5	
  /	
  	
  32	
  
1.  Predic:ve	
  Analy:cs	
  |	
  Risk-­‐Based	
  Decision	
  Making	
  
2.  Probability	...
6	
  /	
  	
  32	
  
Reason for Being
Fundamental	
  Lack	
  of	
  Understanding	
  Forward-­‐Looking	
  Decision	
  Makin...
Drew & Dane
Avg	
  4’	
  
deep	
  
Avg	
  2’	
  
deep	
  
7	
  /	
  	
  32	
  in/drewpulvermacher	
  
8	
  
Why is this
important?
/	
  	
  32	
  in/drewpulvermacher	
  
True Story
9	
  
$80bln	
  Corpora5on	
  
“AXer	
  spending	
  $40mln	
  on	
  the	
  last	
  campaign,	
  customer	
  ord...
4.5
10	
  /	
  	
  32	
  in/drewpulvermacher	
  
11	
  
AGENDA
1.  Foundation Building
2.  Descriptive Analytics
3.  Predictive Analytics
/	
  	
  32	
  in/drewpulvermache...
Analytics
12	
  /	
  	
  32	
  in/drewpulvermacher	
  
“Flaw	
  of	
  Averages”.	
  	
  Used	
  with	
  Permission.	
  
13	
  
What Does Tell Us
About Tomorrow?
/	
  	
  32	
  in/drewpulvermacher	
  
14	
  
AGENDA
1.  Foundation Building
2.  Descriptive Analytics
3.  Predictive Analytics
/	
  	
  32	
  in/drewpulvermache...
15	
  
Predictive
Analytics
1.  Where to Start
2.  Informed Action
3.  Reinventing Decision Making

/	
  	
  32	
  in/drew...
Where to Start | Decision Making Blueprint
16	
  
Ask	
  Yourself:	
  
•  What	
  is	
  my	
  
OBJECTIVE?	
  
•  What	
  a...
Blackjack
Average	
  Winning	
  Hand:	
  
18.5	
  
Chance	
  of	
  Winning	
  w/	
  
Avg	
  Hand:	
  
0%	
  
17	
  
Objec:...
18	
  
Reinventing
Decision Making
/	
  	
  32	
  in/drewpulvermacher	
  
Building a Blueprint for Success
19	
  /	
  	
  32	
  
C	
  
in/drewpulvermacher	
  
i	
  
Objec:ve	
  
Manage	
  
Constra...
Perhaps the Most Significant Benefit…
20	
  /	
  	
  32	
  in/drewpulvermacher	
  
Maximize	
  Decision	
  Throughput	
  
an...
Example #1: Purchase Decision
21	
  /	
  	
  32	
  in/drewpulvermacher	
  
Objec:ve:	
  Match	
  Supply	
  with	
  Demand	...
Example #1: Purchase Decision
22	
  /	
  	
  32	
  in/drewpulvermacher	
  
Profit	
  
Price	
  
Cost	
  
Demand	
  
Order	
...
Example #2: Employee Retention
23	
  /	
  	
  32	
  
Situa:on:	
  	
  Employee	
  Turnover	
  is	
  High	
  	
  
	
   	
  ...
Example #2: Employee Retention
24	
  /	
  	
  32	
  
Year	
  1	
  Pay	
  Increase	
  
Department	
  
Manager	
  
Job	
  Ro...
Example #2: Employee Retention Design
25	
  /	
  	
  32	
  
R	
  
D	
  
i	
  
S	
  
C	
  
Responsibility	
  
Involvement	
...
Example #3: Commodity Pricing
26	
  /	
  	
  32	
  in/drewpulvermacher	
  
Objec:ve:	
  	
  
Minimize	
  monthly	
  foreca...
Example #3: Commodity Pricing
27	
  /	
  	
  32	
  in/drewpulvermacher	
  
Leading	
  Indicator	
  X	
  
Example #4: Health Care Optimization
28	
  /	
  	
  32	
  in/drewpulvermacher	
  
Service	
  Rates	
  
Pa5ent	
  
Arrivals...
29	
  /	
  	
  32	
  in/drewpulvermacher	
  
Decision
Sciences
30	
  /	
  	
  32	
  Drew@PerformanceG2.com
Q&A
Thank you for attending our webinar
31	
  /	
  	
  32	
  Drew@PerformanceG2.com
"  Call us: 877.742.4276
"  	
  Email us: ...
Predictive Analytics Series
1.  Execu5ve	
  Introduc5on	
  
2.  Data	
  Modeling	
  
3.  Simula5on	
  
4.  Op5miza5on	
  
...
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An Introduction to Predictive Analytics- An Executive's Guide for Informed Decision Making

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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

Published in: Business
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An Introduction to Predictive Analytics- An Executive's Guide for Informed Decision Making

  1. 1. Predictive Analytics: An Executive’s Guide for Informed Decision Making March 11th, 2014 Presented by: Andrew Pulvermacher Director | Predictive Analytics in/drewpulvermacher   Autographed  by  the  author:     Sam  Savage  of  Stanford  Univ.  
  2. 2. in/drewpulvermacher   Predictive Analytics Series 1.  Execu5ve  Introduc5on   2.  Data  Modeling   3.  Simula5on   4.  Op5miza5on   5.  Data-­‐Driven  Leadership  
  3. 3. 3   AGENDA 1.  Foundation Building 2.  Descriptive Analytics 3.  Predictive Analytics Informed Decision Making /    32  in/drewpulvermacher  
  4. 4. 4   FOUNDATION BUILDING /    32  in/drewpulvermacher  
  5. 5. TERMINOLOGY 5  /    32   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     in/drewpulvermacher  
  6. 6. 6  /    32   Reason for Being Fundamental  Lack  of  Understanding  Forward-­‐Looking  Decision  Making   8   8   Average   Average   in/drewpulvermacher  
  7. 7. Drew & Dane Avg  4’   deep   Avg  2’   deep   7  /    32  in/drewpulvermacher  
  8. 8. 8   Why is this important? /    32  in/drewpulvermacher  
  9. 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  …..”   /    32  in/drewpulvermacher   #  of  Purchases   %  of  Customers  
  10. 10. 4.5 10  /    32  in/drewpulvermacher  
  11. 11. 11   AGENDA 1.  Foundation Building 2.  Descriptive Analytics 3.  Predictive Analytics /    32  in/drewpulvermacher  
  12. 12. Analytics 12  /    32  in/drewpulvermacher   “Flaw  of  Averages”.    Used  with  Permission.  
  13. 13. 13   What Does Tell Us About Tomorrow? /    32  in/drewpulvermacher  
  14. 14. 14   AGENDA 1.  Foundation Building 2.  Descriptive Analytics 3.  Predictive Analytics /    32  in/drewpulvermacher  
  15. 15. 15   Predictive Analytics 1.  Where to Start 2.  Informed Action 3.  Reinventing Decision Making /    32  in/drewpulvermacher  
  16. 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   /    32  in/drewpulvermacher  
  17. 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.   /    32   Variables:      -­‐Hit  or  Stay   Constraints:    -­‐Hand  You’re  Dealt   in/drewpulvermacher  
  18. 18. 18   Reinventing Decision Making /    32  in/drewpulvermacher  
  19. 19. Building a Blueprint for Success 19  /    32   C   in/drewpulvermacher   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. 20. Perhaps the Most Significant Benefit… 20  /    32  in/drewpulvermacher   Maximize  Decision  Throughput   and  Transparency  
  21. 21. Example #1: Purchase Decision 21  /    32  in/drewpulvermacher   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. 22. Example #1: Purchase Decision 22  /    32  in/drewpulvermacher   Profit   Price   Cost   Demand   Order  Qty   Customers   #   $  
  23. 23. Example #2: Employee Retention 23  /    32   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   in/drewpulvermacher  
  24. 24. Example #2: Employee Retention 24  /    32   Year  1  Pay  Increase   Department   Manager   Job  Role   in/drewpulvermacher  
  25. 25. Example #2: Employee Retention Design 25  /    32   R   D   i   S   C   Responsibility   Involvement   Feedback  &  Praise   Detailed  Objec5ves   Profile   in/drewpulvermacher  
  26. 26. Example #3: Commodity Pricing 26  /    32  in/drewpulvermacher   Objec:ve:     Minimize  monthly  forecast  error.         Variables:   -­‐Commodity  Prices   -­‐Weather       Constraints:   -­‐Budget        
  27. 27. Example #3: Commodity Pricing 27  /    32  in/drewpulvermacher   Leading  Indicator  X  
  28. 28. Example #4: Health Care Optimization 28  /    32  in/drewpulvermacher   Service  Rates   Pa5ent   Arrivals   Rooms   Staff   Reason   Indicators   Objec:ve:     High  Quality  Care  and  Pa5ent  Throughput       Variables:     Staff  Levels       Constraints:     Rooms          
  29. 29. 29  /    32  in/drewpulvermacher   Decision Sciences
  30. 30. 30  /    32  Drew@PerformanceG2.com Q&A
  31. 31. Thank you for attending our webinar 31  /    32  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. 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      

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