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Moving Beyond Spreadsheets to Support Strategic Resource Alignment
NACUBO 2016 Annual Meeting
Higher Education Cost/Revenue Analytics
July 17, 2016
Course Description
Higher Education Cost/Revenue Analytics – Moving Beyond
Spreadsheets to Support Strategic Resource Alignment
Achieving transparency and financial stability in the higher education
business model is a complex and difficult task. This session will explore how a
well designed and constructed higher education cost and revenue model can
provide institutions with cost and margin analytic capability far beyond what
can be achieved through the use of spreadsheets. Further, the session will
explore how a costing system can support an institution’s budgeting process,
identify opportunities to focus attention for gaining efficiencies, explore the
relationship between resource investments and mission-related outputs, as well
as how organizations have utilized cost/revenue modeling to reduce, expand,
and reallocate their resources to support their mission and strategic plan.
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Learning Objectives
• Recognize the value that cost/revenue
analytics can bring to institutions
• Identify the questions that cost/revenue
analytics can help higher education leadership
answer
• Understand critical success factors when
establishing a cost/revenue analytics program
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Agenda
• Introductions
• Industry trends
• Higher education cost
and revenue analytics
overview
• Panel discussion
• Summary and questions
4
Introductions
Matt UntermanPrincipal, Higher Education Practice
Grant Thornton LLP
Anthony PemberSenior Manager, Cost and Performance Analytics
Grant Thornton LLP
Ken CodyVice President for Administration & Finance and
Chief Financial Officer
Bentley University
Maria AnguianoVice Chancellor for Planning & Budget
University of California - Riverside
Barbara LarsonExecutive Vice President of Administrative Services
Johnson County Community College
Susan RiderDirector of Accounting Services & Grants
Johnson County Community College
Lea PattersonCEO, Pilbara Group
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Industry TrendsUnprecedented Financial Pressures Optimization
Higher Education Performance Improvement
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Higher Ed Performance ImprovementCost/Revenue Analytics Is a Critical Component
Higher education institutions are transforming their organizations
They are using a number of tools to do so, including cost/revenue analytics
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HR &
PayrollStudent
Records
Schedule
GL Data Asset
DataRevenue
• Combining relevant data sets
to provide meaningful analysis
and allow insight into
institutional cost, revenue, and
performance
• Enterprise-wide, captures
academic and non-academic
outputs
• Captured at activity, course,
and program levelCost Revenue Performance
What is Cost and Revenue Analytics? Definition
8
• Establish a “baseline”
• Identify cost reduction, revenue enhancement,
and transformative initiatives
• Develop dynamic multi-year financial models of
potential strategic/operational initiatives
• Inform budget process using bottom-up
cost/revenue data
What is Cost and Revenue Analytics? Value
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• Start a dialog regarding how we serve our
mission
• Optimize organization’s investment in its mission
through strategic resource alignment
• Drive conversation across university to leverage
additional tools (e.g., shared services, IT
rationalization, etc.)
What is Cost and Revenue Analytics? Value: Performance Improvement
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• Report on the economics of the institution’s
current situation
• Departmental planning and resource allocation
• Degree program analysis
• Student segmentation
• School, campus, and system resource allocation
As defined by William F. Massy in Reengineering the University, How to Be Mission
Centered, Market Smart, and Margin Conscious (Johns Hopkins University Press,
2016)
What is Cost and Revenue Analytics? Value: Areas of Application
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Traditional Approach New Approach
• Disaggregated • Integrated
• Ad-hoc and inconsistent • Organized and consistent
• Measures not aligned • Aligns multiple measures
• Limited audience • Wide reaching
• Untraceable • Transparent
• Spreadsheet based • Centralized database
• Partial and incomplete data
sets
• Cohesive and complete data
What is Cost and Revenue Analytics?Analytics Approaches (Traditional vs. New)
Time spent gathering data Time spent performing analysis
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• What is the cost of an English major vs. a Physics major?
• Why are we interested in this question?- Consider pricing opportunities for stand-alone programs or differential pricing for
expensive majors such as business or engineering or music
- Consider whether the mix of teaching, research, and service efforts of faculty are
appropriate given the mission and whether there is fairness among and between
various faculty and departments
- Identify under-utilized capacity of space and faculty resources
- Identify changing enrollment trends and their effect on allocation of resources
- Determine need (or budget limits) for adjunct faculty to supplement tenured faculty
in some programs
- Determine cost of allowing highest paid faculty to teach high-level courses with
few students
- Establish minimums for class size by program
- Inform decision-making framework for newly proposed majors
Higher Ed Cost & Revenue AnalyticsAn example
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Department Level
Physics Major
English
Major
Course A
Course B
Course C
Course D
Teaching
Activities
Research
Auxiliaries
TA
Adjunct
Professor
Tutor
Non-Academic
Research
Activities
Administrative
Activities
Administrative
Activities
Student Serv.
Facilities
IT
Finance / HR
Auxiliaries
School Level
Non-Labor
University Level
RESOURCES ACTIVITIES PRODUCTS
FTE
FTE FTE
SQ. FEET
# STUDENTS
FULLY LOADED
COST & REVENUE
BY MAJOR
Rev
Rev
Exp
Exp
Higher Ed Cost & Revenue Analytics An example
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Cost and Revenue
Analytics Input
Higher Ed Cost & Revenue Analytics Historical Model Inputs
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Courses
• Courses
• Non-credit teaching
• By campus
• By semester
Programs
• Degrees
• Majors
• By campus
Auxiliary, Self-
Supporting and Other
• Athletics
• Dining
• Residential
• Research (Sponsored)
• Executive education
Cost/revenue analytics models can be used to
answer questions related to both academic and
other areas of the enterprise
Higher Ed Cost & Revenue Analytics Historical and Predictive Model Outputs
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Panel Discussion
Panel DiscussionBentley University’s Objectives
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Objective 1:Create a greater understanding of how Bentley is currently spending its resources for each
major program and activity enterprise-wide
Objective 2:Create a full understanding of the purpose, relationship to mission, metrics, history, future
prospects, etc. for major programs and activities
Objective 3:Generate actionable ideas related to how Bentley can expand its available resources and
realign its spending so that the Cabinet can make well-informed recommendations to the
President and Board as part of its budget/planning process on an ongoing basis
Objective 4:Create a more market-driven, forward-looking budgeting and planning process involving
more relevant data and an ongoing process of strategic program review and resource
planning/allocation
Milestone Dates
Determine need for SRA initiative Jun & Jul 15
Select GT & perform Scoping Study Aug - Nov 15
Engage GT to develop ABC model Dec 15
Collect final "fundamental" data sets for ABC model Dec 15 – Feb 16
Build preliminary ABC model & refine model Feb – Apr 16
Deliver model for internal validation, design reports, train May 16
Finance staff & Cabinet validation and discussions on key decision
points and ongoing ABC data analysisJun – Sep 16
Analyze programs using common framework (mission, metrics,
margin, quality, etc.)Oct – Dec16
Create actionable ideas & decide what to expand, reduce or
eliminateJan – Feb 17
Create sustainable SRA: Change processes & culture Mar 17 – Oct 17
Panel DiscussionBentley University’s Timeline
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Panel DiscussionUses of Model in Australia
• Course margin analysis
• Demand vs. margin analysis
• Annual course review
• Faculty/School/Campus performance
• Trend analysis
• Inform the budget process:- Increases and decreases in budgets modeled- Impact of changes in permanent and casual staff, academic
workload changes, changes in class sizes
• Identify spare capacity, charge for room usage
• Managing a known reduction in student numbers
• Possible fee deregulation
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Panel DiscussionThe Roadmap
Historical
Benchmarks
Predictive
Analysis (models of
models)
External Data (Big Data Analysis)
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Panel DiscussionProgram Margin Reporting Before and After
Revenue$37,816
Expense$135,922
Loss($98,106 )
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
Previous Version of Program Margin
Revenue$520,839 Expense
$465,152
Margin$55,686
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
Program Margin Using Model
BIOTECHNOLOGY
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Panel DiscussionHow are we using it? (course delivery method)
Revenue$1,120,951
Expense$745,104
Margin$375,847
33.53%
$0
$200,000
$400,000
$600,000
$800,000
$1,000,000
$1,200,000
Accounting IFace-To-Face
Revenue$557,524
Expense$296,105
Margin$261,420 46.89%
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
Accounting IOnline
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Panel DiscussionLessons Learned
Things to prepare for and consider before you jump in
• Consider the goals of institution
• Academic leadership is key
• Data was in silos; helped standardize data
dictionary
• Address the fear
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Have Questions?
We've got nswers!
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In Summary
• Given challenges within the industry,
cost/revenue analytics is a useful tool to:- Generate understanding of the institution’s business model
- Create effective dialog across the campus
- Identify opportunities for strategic resource allocation
• Cost/revenue analytics can be part of an overall
performance improvement initiative- Enhance financial performance
- Optimize delivery on mission
- Change budget policies, empower management decision-making,
and positively influence institution culture
Summary
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Contact Information
Maria AnguianoUniversity of California – Riverside
Ken CodyBentley University
Barbara LarsonJohnson County Community College
Susan RiderJohnson County Community College
Lea Patterson Pilbara Group
Anthony PemberGrant Thornton LLP
Matt Unterman Grant Thornton LLP
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Additional Information
Reengineering the University, How to Be Mission Centered, Market Smart,
and Margin ConsciousWilliam F. Massy (Johns Hopkins University Press, 2016)
Cost Structure of Post‐Secondary Education, Guide to Making
Activity‐Based Costing Meaningful and PracticalMaria Anguiano, Senior Advisor (Bill and Melinda Gates Foundation: Post‐Secondary
Education, 2013)
Evolving Higher Education Business Models, Leading with Data to Deliver
ResultsLouis Soares, Patricia Steele, and Lindsay Wayt (American Council on Education, 2016)
Prioritizing Academic Programs and ServicesRobert C. Dickeson (John Wiley & Sons, 2010)
Responsibility Center Management2nd edition, Curry, Laws and Strauss (NACUBO, 2013)
Further Reading
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1.Assess data availability and define objectives
2.Build a draft current state model
3.Refine assumptions in coordination with key
constituents
4.Collaborate to analyze current state economics
5.Build a predictive model and create forward
looking scenarios
Get the full story
grantthornton.com/higheredcostmanagement
Higher Ed Cost & Revenue Analytics 5 Steps to an Efficient Cost and Revenue System
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Higher Ed Cost & Revenue Analytics Keys to Success
1.Capture – establish data warehouse and analytical tools
to capture data and create correlations
2.Measure – establish performance metrics beyond
financial results and agree on mission-driven
measurements
3.Change – acknowledge the human element and the
importance of effective change management
4.Collaborate – build collaboration into the process and
view the institution as a whole rather than a collection of
departments and interests.
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Higher Ed Cost & Revenue Analytics Keys to Success (cont'd)
5.Develop – develop or acquire personnel to bring business
analytics and business intelligence skills into your
institution.
6.Commit – act on the trends and insights discovered
through business analytics.
7.Govern – create a cross-functional steering committee
that can set aside other biases in order to act on the
analysis.
8.Dedicate – ensure longevity to an institution-wide focus
for a data analytics program, as opposed to conducting a
one-time exercise.
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Higher Ed Cost & Revenue Analytics Historical versus Predictive Models
• Use historical data to provide backward looking view of institution
• Provides “snapshot” of institution at course/program level
• Identifies focus areas
• Multi-year trend analysis
• Use historical data to determine relationships between inputs and outputs
• Provides forward looking view of institution at course/program level
• Allows unlimited scenario analysis
• Basis is historical model
Historical Models Predictive Models
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University Level
Support
School Level
Support
Research
Courses
Course Delivery
Time
Activities(Teaching Pools)Department
Level Support
Academic FTE
Student demand determines resource requirements
Programs
`
Resources allocated to assess scenario economic viability & mission achievement
Leverages data and business
rules from historical model
“Closed-loop” methodology
predicts resource needs in order
to support “demand”
Higher Ed Cost & Revenue Analytics Predictive Model Methodology
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