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An Introduction to Predictive Analytics- An Executive's Guide for Informed Decision Making

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Information about An Introduction to Predictive Analytics- An Executive's Guide for...
Business & Mgmt

Published on March 12, 2014

Author: PerformanceG2

Source: slideshare.net

Description

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
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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.  

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

3   AGENDA 1.  Foundation Building 2.  Descriptive Analytics 3.  Predictive Analytics Informed Decision Making /    32  in/drewpulvermacher  

4   FOUNDATION BUILDING /    32  in/drewpulvermacher  

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  /    32   Reason for Being Fundamental  Lack  of  Understanding  Forward-­‐Looking  Decision  Making   8   8   Average   Average   in/drewpulvermacher  

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  order  frequency   increased  to  4.5  from  4.4;  an  incremental  liX  of  0.1”     “ROI  of  …..”   /    32  in/drewpulvermacher   #  of  Purchases   %  of  Customers  

4.5 10  /    32  in/drewpulvermacher  

11   AGENDA 1.  Foundation Building 2.  Descriptive Analytics 3.  Predictive Analytics /    32  in/drewpulvermacher  

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/drewpulvermacher  

15   Predictive Analytics 1.  Where to Start 2.  Informed Action 3.  Reinventing Decision Making /    32  in/drewpulvermacher  

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  

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   Reinventing Decision Making /    32  in/drewpulvermacher  

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  

Perhaps the Most Significant Benefit… 20  /    32  in/drewpulvermacher   Maximize  Decision  Throughput   and  Transparency  

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?

Example #1: Purchase Decision 22  /    32  in/drewpulvermacher   Profit   Price   Cost   Demand   Order  Qty   Customers   #   $  

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  

Example #2: Employee Retention 24  /    32   Year  1  Pay  Increase   Department   Manager   Job  Role   in/drewpulvermacher  

Example #2: Employee Retention Design 25  /    32   R   D   i   S   C   Responsibility   Involvement   Feedback  &  Praise   Detailed  Objec5ves   Profile   in/drewpulvermacher  

Example #3: Commodity Pricing 26  /    32  in/drewpulvermacher   Objec:ve:     Minimize  monthly  forecast  error.         Variables:   -­‐Commodity  Prices   -­‐Weather       Constraints:   -­‐Budget        

Example #3: Commodity Pricing 27  /    32  in/drewpulvermacher   Leading  Indicator  X  

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  /    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: 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

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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