Program portfolio analytics: Changing decision-making conversations
My goal here is to describe how program portfolio analytics can change the decision-making conversations among provosts, deans, financial officers, and faculty. I will, frankly, push the envelope in the sense that no institution is doing the analysis to be described. However, these are common-sense extensions of work actually being performed by Gray DI and its clients. The goal is to share insights about the complex and interconnected kinds of evidence that have recently become available. The analysis is designed to expand decision makers’ use of evidence, especially quantitative evidence, but without losing the essential elements of judgment upon which good academic tradeoffs depend.
My first blog described how a program portfolio conversation not adequately supported by analytics can degenerate into a cacophony of conflicting proposals. The issue being addressed was how to adjust target program enrollments to produce additional margin while maintaining mission contribution and market performance to the greatest extent possible.
“The discussion ranged widely and sometimes acrimoniously. Some deans and staff members suggested eliminating the smallest programs, on the grounds that they probably were money-losers. Others insisted on protecting the programs judged to be most important academically – which, unsurprisingly, often were resident in the speaker’s own school. Still others advocated programs with strong and growing presence in the marketplace. … Lacking a coherent set of principles and the data needed to exercise them, the meeting threatened to degenerate into an organizational power struggle.”
My second and third blog introduced two key analytical concepts needed to provide the requisite decision-making framework: course-based economic models that estimate program revenues, costs, and margins; and a scheme for balancing mission attainment, with all its complication and subjectivity, with the numbers-driven economic results. Other Gray DI blogs describe the development and use of market and competitive data in program portfolio analysis. The focus now is on how these results can be brought together to sharpen decision making.
Table 1 gives the data used to construct the illustrative program portfolio display presented in my first and third blogs. We’ll see these data can be used to evaluate SCH changes like the ones proposed in the aforementioned conversation. But first, let’s take a closer look at the definitions of mission and market used in the table (margin requires no special explanation).
Table 1. Data Used to Construct the
Illustrative Program Portfolio Display in logs 1 and 3
· Mission Contribution: relative effect of changes in SCH on the school’s academic objectives. The program portfolio’s overall mission contribution is the weighted average of the individual programs’ contributions (shown at the bottom of the table). Blog 3 described why schools should make this quantity as large as possible given the constraints under which they operate.
· Market Rating: composite score for the available market data. I have found it useful to define Market Risk as the negative of Market Rating, because lower ratings make it harder to achieve one’s enrollment objectives and conversely. Schools should try to avoid portfolio changes that sharply increase average market risk, and decrease this risk if possible.
These data allow us to estimate the effect of program resizing on mission contribution, market risk, and margin. For example, growing the Writing program by 10% would produce 158 more credit hours and additional $18,157 of margin, while, at the same time, boosting mission contribution by 0.05 and reducing risk by 0.81 (about 2.5 percent of the variables’ respective bases).
You can download the Excel-based tool for performing the calculations and displaying the results, along with a test data file like the one described herein, from Gray DI. The tool’s control panel appears in Table 2. Users start by inputting a target change in portfolio total margin in the upper right-hand cell. The evaluations are performed iteratively, by selecting programs from a pulldown list and specifying desired percentage changes in SCH. Proposals can be added or removed at any time, and a given program can be selected more than once if the user wants to evaluate multiple size increments.
Table 2. Illustrative Program Portfolio Adjustments
The tool also provides visual outputs (illustrated in the Figure) that track, for the list of portfolio changes, cumulative contribution mission, market risk, and the margin gap that remains to be closed. This allows proposals to be tested “on the fly,” so that shifts in the three variables can be evaluated, simultaneously, without interrupting the decision-making conversation. New proposals can be tabled until consensus emerges or the group leader (e.g., the provost or dean) has seen and heard enough to make a decision.
In this case, we see that our hypothetical planning group began by specifying growth in Writing, Mathematical Finance. and English Literature. The estimated consequences appear as the second, third, and fourth points in the charts. (The first point shows “base” or “no change” values—including the whole margin gap of $50,000.) Encouraged by the prospect of achieving the margin target while, simultaneously, improving mission contribution and reducing risk, the group went on to rationalize other elements of the portfolio: by shrinking Sports Management (to boost mission contribution) and Theater Arts (to boost margin) and eliminating Sociology (also to boost margin).
Visual Display of Cumulative Portfolio Adjustments
Testing alternatives this way pulls the available data together into a coherent set of displays that remind decision-makers about the multifaceted consequences of their choices. The estimates may represent rough approximations but, even so, the reminders can disrupt pre-conceived notions and open the way to much richer discussions about the possibilities for action and their probable consequences.