two service science applications of business analytics professor vijay mehrotra school of management...
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Two Service Science Applications of Business
Analytics
Professor Vijay MehrotraSchool of Management
University of San Francisco
Presentation Roadmap
“Who is This Guy?” Case Study #1
Non-Profit Cash Flow Optimization Case Study #2
Alumni Donor Scoring Model Lessons Learned and Discussion
About Vijay “Scientist, Businessman, Teacher, Actor, Writer”
1986: BA, St. Olaf College Mathematics, Economics, English Lit “Very Warm People, Very Cold Weather”
1992: MS, PhD, Stanford University Operations Research (Statistics) Performance Analysis Methods for Closed Queueing Networks with
Applications to Semiconductor Manufacturing “A Long Way from Duluth”
1993 – 1994: Decision Focus Inc. [OR Consulting Firm] Mathematical Models and Systems for Revenue Management “Hey if these guys can do start a company it can’t be that hard”
About Vijay 1994-2002: Co-Founder and CEO of Onward Inc.
Operations Management Consulting Firm Call Centers, Software Pricing, Product Configuration, Internet Advertising
Optimization, and Software Application Development “Venture Consulting”
2002-2004: Vice President of Solutions, Blue Pumpkin Software Company Specializing in Forecasting & Scheduling for Call
Centers Led Services Staff of 60
Fall 2003 - Present: Radical Portfolio Adjustment Return to Academic World
Assistant Professor, SFSU, 2003 – 2009 Associate Professor, USF, 2009
Still in “Real World” Active OR/OM Consulting Practice Important to Bring New Stories to the Classroom
Presentation Roadmap
“Who is This Guy?” Case Study #1
Non-Profit Cash Flow Optimization Case Study #2
Alumni Donor Scoring Model Lessons Learned and Discussion
The Challenge$ of Non-Profit$
Every single non-profit organization Spends money on human and
material resources Budgets for program expenses Overhead expenses (“Indirect Costs”) Worries about its (near- and long-term)
financial future
The Challenge$ of Non-Profit$
Most Non-Profit Funding Comes From External Sources Government, Private, Foundation,
Partners Funds Are Defined By
Different Durations Different Sizes Different Restrictions
The Challenge$ of Non-Profit$:
The Revenue $ide
Jan Feb March April May June July Aug Sept
Unrestricted Annual Grant
Summer Program Grant
Restricted Grant for Two Specialists to Advise
“Use it or Lose It” Corporate Gift
Funds Remaining from Previous Year
Trick Question
Available Funds: $1,000,000 Total Budget: $1,000,000 Do You Have Enough Money?
The Challenge$ of Non-Profit$:Illustration of the Math Problem
EmployeeSalary+Ben
Individuals PaidBy One or MoreFunding Sources
AllocatedLabor Costs
Indirect Costs (Allocated Proportionally)
Printing, Postage, Rentals,Computers, etc
AllocatedDirect Non-LaborCosts
Solving This Problem: Important
Manual Process Does Not Work Well High Complexity Time Consuming Error-Filled Major Losses
Dynamic Operating Environment New Funding Sources Changes to Employee Base Changes to Cost Base (eg Emp Insurance)
Executive Director, Controller: “HELP!” Controller was MBA Student OM Class Caused Her to Say “Hmmm?”
Model Background Goal: Assign Costs to Funding Sources While
Satisfying a Variety of Constraints Linear Program Formulation to Get Optimal Solution
Planning Horizon One to Twelve Months (User’s Choice)
Key Inputs $ Available Per Funding Source Over Planning Period Employee Costs (Salary, Insurance) Total Indirect Costs Direct Program Costs Per Period Over Planning Horizon
Problem Formulation (Implemented in Excel)Minimize Unspent Funds
Subject to:Funds Available Per Source >= Costs Assigned
to Source (Direct Labor, Direct Non-Labor, and Indirect)
Indirect Costs Assigned to Each Source =Proportion Allocated to Employees Assigned
Costs Assigned to Each Source Are “Allowable”
All “Agency Imposed” Constraints Are Satisfied
Business Impact Before:
Re-Assignment was 20-40 hour activity Changes to funds or staff went unaddressed, with agency
carrying risks Several major errors due to lack of visibility into the
nature of available funds
Now: Solving LP in Excel Worksheet is <1 hour start to finish
(includes data pre-processing) Much stronger executive control of finances
Ability to easily explore different alternatives Deliver More Services With Same Funds Less Misspent or Unspent Funds
Huge Value in Current Fed and State Budget Climate
One Weird Insight: Connection to Finance and Call Centers “Flexibility of Funding Sources”
Analogous to Diversification of Portfolio Analogous to Cross-Skilling of Call Agents
INSIGHT: Requirement is “Sufficient” Flexibility Rather than Full Flexibility
Value of Model: Model Defines “Sufficient Flexibility” Model Specifies How to Utilize Flexible Funds
Intelligently Enables Agency to Pursue/Accept More Restricted Funds
Presentation Roadmap
“Who is This Guy?” Case Study #1
Non-Profit Cash Flow Optimization Case Study #2
Alumni Donor Scoring Model Lessons Learned and Discussion
Some Background on Fundraising at SFSU HISTORICAL CONTEXT
“We are a State Funded Institution…” “Alumni Database? What for?”
“SOME HAVE GREATNESS THRUST UPON THEM” [Shakespeare, 12th Night] Significant Expansion in Development Staff New Pressure to Raise Money to Help with Annual
Operating Costs, Build Endowment Hired Alumni Relations Officer with Direct Marketing
Background “Where’s Your Alumni Database???”
Some Background on Fundraising at SFSU
Enter Professor Vijay “It Helps to Really Know Your San
Francisco Giants!” Made Contact with New Alumni
Relations Officer with Direct Marketing Background
Identified Opportunity for Faculty-Supported Student Research Project
Initial Project Team
John Sammis and Peter Wylie Expert development consultants with specialization
in data analysis, worked with SFSU in 2006 Invaluable Domain Knowledge
Vijay Mehrotra Project Manager and Interpreter Commitment to Ongoing Support of SFSU Dev
Office Supported Annually by SFSU Undergraduate
Background on SFSU Alumni Fundraising and Development
KEY BUSINESS QUESTIONS How Should We Allocate Our
(Limited) Resources for Calling Alumni?
Which Alumni Are Most Likely to Give Money to SFSU If Called?
Statistical Methodology Pairwise Correlations Between
Demographic Variables and Propensity to Donate Money Candidate Predictors
Multiple Regression Model Regression Result Used to “Score” Each
Alum and to Create Ordered Groups Based on Likelihood of Giving
Phone Call Resources Invested in Top Groups (Most Likely to Contribute to SFSU)
Predictive Scoring Model
.358*’HomePhonePresent' + .118*BusPhonePresent‘- .021*‘HomeAddressAvailable' + .013*’MaritalStatusMissingorUnknown’ - .056*‘MaritalStatusisM‘ +.049*’PrefYearBefore1991' - .001*Prefix=Ms (neg)' + .024*‘Prefix=Mrs' +.595*‘SpouseNamePresent' - .006*’State=CA' +.095*‘ZipLength = 10' + .316*BusAddPresent' + .295*’BusStateNotPresent' + .047*BusZipLen = 10 + .047*CountofDeg' + .032*‘MA_Degrees' + .011*BA_Degrees' + .024*MBA_Degrees' - .057*‘CEL_SchoolCode' + .052*’S&E_SchoolCode=Y' + .365*’ActivityParticipation’ + .943*’ContactReport=Y’ + 18
The Predictive Scoring Model is a function of variables and weights for each variable. Those terms marked in BOLD have relatively large positive or negative weights.
This model is used to create Scores for each alumni record between 1 (least likely to give) and 10 (most likely to give).
Re$ult$
Without Scoring (Alums Called at Random):Participation: 14.45%Average Gift: $51Contact Rate: 36%Total Dollars Raised: $76,222
With Model (Used to Determine Who to Call):Participation: 16.73% +16%Average Gift: $63 +23.5%Contact Rate: 42% +17%
Presentation Roadmap
“Who is This Guy?” Case Study #1
Non-Profit Cash Flow Optimization Case Study #2
Alumni Donor Scoring Model Lessons Learned and Discussion
Lesson 1: Effective Business Analytics Are Often Not “Rocket Science”
Both of These Projects Had a Major Impact on the Organizations Involved
Both Projects Used Analytic Methods Taught in Introductory Courses
Implementation Challenges Typically Not Covered in Textbooks Data Pre-Processing Trial and Error Communication and Iteration
Lesson 2: Partner With Domain Experts to Develop Solutions Asking the Right Questions
Knowledge of Environment is Critical to Understanding What is Truly Needed
Workflow, Workflow, Workflow
Running the Right Tests Access to Data is Far, Far Simpler Validation of Results By Business Expert Invaluable
Deliver a Real Solution Achieve Something That Neither Could Do Alone Short Step to Implementation
Mission Leads Analytics Provide Support
Lesson 3: Analytic Models Shine When Embedded in Processes
LinearProgramming
Model
Tracking ofAvailable
Funds
MediumTerm
Planning
Staffing Data(Rosters,
Costs)
BillingProcess
TimekeepingSystem
TacticalPlanning
Lesson 3: Analytic Models Shine When Embedded in Processes
RegressionModel
AlumniPhone Calls
Alumni Mailings
Database Updates
AlumniDatabase
