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8/17/2019 MELJUN CORTES RESEARCH PAPERS Decision Support for University Enrollement Management Implementation Ex…
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Decision support for university enrollment management:
Implementation and experience
Elliot N. Maltz a , Kenneth E. Murphy b,⁎, Michael L. Hand c
a Professor of Marketing, Atkinson Graduate School of Management, Willamette University, 900 State Street, Salem, Oregon 97301, United States
b Associate Professor of Information Systems, Atkinson Graduate School of Management, Willamette University,
900 State Street, Salem, Oregon 97301, United Statesc Professor of Applied Statistics and Information Systems, Atkinson Graduate School of Management, Willamette University,
900 State Street, Salem, Oregon 97301, United States
Received 2 April 2006; received in revised form 5 March 2007; accepted 18 March 2007
Available online 30 March 2007
Abstract
Enrollment management is a process critical to many universities that rely on tuition for a significant portion of their operating
budgets. This study describes how the development and implementation of a system to support decisions in the enrollment process
allowed for increased responsiveness and real-time management as well as substantially increased institutional knowledge of
the process itself. This, in turn, led to dramatic improvements in both operational performance and in the attainment of strategic
admission objectives.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Decision support system; Enrollment management; Data mining; Organizational learning
1. Introduction
Most private colleges, unless they have developed a
very large endowment, base their revenue primarily on
tuition income. Consider, as an example, a moderate-
sized undergraduate liberal arts program with a budget of $50 million. The college would require an endowment of
$500 million to cover half of their budget under standard
5% annual growth assumptions. Since few private liberal
arts colleges have an endowment of that magnitude, a
systematic approach to enrollment management is
critical to ensuring stability in fiscal planning. Schools
approach the technical challenges associated with en-
rollment management in a variety of ways, often relying
on offices of institutional research to perform this func-
tion or by staffing the admission office with statistical
specialists [5]. However, many smaller schools lack the
resources or the technical expertise to address these
problems internally. In these cases, outside consultantsare often hired to assist in determining which students to
admit and how much financial aid to offer in order to
recruit a desirable incoming class. This approach can
result in suboptimal performance, additional costs and
may curtail the opportunity for institutional learning with
respect to managing the admissions process.
This manuscript presents the design and implemen-
tation of a successful decision support system (DSS) for
enrollment management at a small liberal arts college.
The DSS, an integral component of the admissions
Decision Support Systems 44 (2007) 106–123
www.elsevier.com/locate/dss
⁎ Corresponding author.
E-mail address: [email protected] (K.E. Murphy).
0167-9236/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.dss.2007.03.008
mailto:[email protected]://dx.doi.org/10.1016/j.dss.2007.03.008mailto:[email protected]://dx.doi.org/10.1016/j.dss.2007.03.008
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process consists of two components—a predictive
model and a user-friendly interface which allows the
school to dispense with the services of outside con-
sultants while at the same time making significant op-
erational gains. The interaction between the DSS and the
admissions process can be thought of as the enrollment work system [2]. The two-year, two-phase implementa-
tion project improved the enrollment work system by
enhancing understanding of the admissions process
overall through the conversion of tacit process knowl-
edge to explicit and by its impact on financial measures
of performance.
A variety of data mining techniques and associated
methodologies were used to assist in developing the
predictive model. As will be demonstrated, the meth-
odologies employed to develop the DSS as well as the
DSS itself contributed to both the operational successof the system and the organizational learning achieved
during the design and implementation phases. The
enrollment management tool was implemented in an
environment that was based on principles that have been
recognized as important by the decision support system
literature [3,11,29,32]. As such the insights provided
in this paper contribute to both the enrollment man-
agement and decision support system implementation
literatures.
The balance of the manuscript is organized as fol-
lows. The following section provides a brief literature
review of decision support and expert systems in theadmissions setting as well as relevant observations on
system implementation from the DSS literature. The
legacy admission process and its associated challenges at
the institution where the decision support system was
implemented are then described, followed by a descrip-
tion of the data mining methodology used for constructing
the system. The manuscript then reviews the operation-
al and learning outcomes of the implementation. This
section provides guidance for the development of DSS
systems for enrollment management. The paper con-
cludes with a broader discussion of the insights for suc-cessful implementation of DSS systems.
2. Decision support systems in the admissions process
Applications of management science techniques in
academic administration go back forty or more years. In
early implementations, the issues addressed were
planning, budgeting or resource allocation problems
including the forecasting of enrollment levels as well as
facilities requirement planning, course scheduling and
staffing to support estimated enrollments (e.g., see [28]
and [30]). A survey of 146 articles identified 104 that
employed management science (optimization) techni-
ques while only 6 of 146 articles featured DSSs to tackle
academic management problems. In this survey, there
were no examples of DSS deployed directly for
enrollment management [34].
In general, research on enrollment management hascentered on two areas: developing forecasting models
for predicting overall enrollment levels and on tools for
identifying which individual applicants to admit. Many
of the institutional level forecasting studies build models
to identify the traits of students that choose the focal
institution over others (see, e.g., [13] and [27]). Multiple
linear, logistic and probit regression models were
observed as the most commonly employed techniques
for forecasting at this level [26]. Logistic regression has
been compared to neural networks for classifying which
students will and will not enroll in a university, based ona variety of applicant attributes, and neural networks
were found to outperform logistic regression for the
correct classification of admitted applicants who
ultimately will and will not enroll [33]. With respect
to the current problem, these results provide insight, but
unfortunately, none of these studies incorporate the
amount of financial aid awarded to applicants, a sig-
nificant factor in the enrollment decision [6].
Beginning in the late 1980s several researchers
discussed the use of expert systems for determining
which students to admit into a variety of academic
programs in Great Britain and elsewhere [10–12,19,23,24]. While several of these papers implicitly
consider the probability of enrollment in the analysis,
this body of work does not explicitly consider the
financial impact of enrollment decisions on the in-
stitution, a fundamental concern for many institutions.
As such, from an operational perspective, our work
builds on previous studies by considering both the
probability of enrolling and the amount of financial aid
awarded.
The quality of the predictive model incorporated into
the DSS is an essential element in improving the performance of its associated work system [2]. Howev-
er, DSS systems should also provide for the systematic
acquisition and sharing of tacit and explicit knowledge
to improve effectiveness and control [1] as well getting
the right information to the right people at the right time
[25]. In our context, the DSS system should incorporate
knowledge acquired from experience and historical data
on enrollment probability and provide a mechanism to
share this information explicitly with the admissions
decision makers. With this in mind, this paper describes
how the DSS' interface was crucial to make this
information available effectively and expediently.
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Hartono et al., [17] provide a valuable review of
factors that lead to DSS success, and in particular the
research demonstrates that the relative importance of
various implementation factors depends on how success
is defined. In this setting success for the DSS is defined
as “organizational impacts” [8], that is improving op-erational performance and increasing explicit knowl-
edge and knowledge sharing. Antecedents of success
on organizational impacts include management support,
organizational support and attitude, user participation
and system characteristics [3,15,17,29,32,35]. In partic-
ular Teo [32] suggests four categories of critical success
factors in a knowledge management DSS implementa-
tion: people and culture, implementation method,
content management and technology. Experience with
respect to these factors over the two year cycle of the
DSS design and implementation is described towardsthe end of the paper. However, as one might expect, the
specific aspects of these factors that are important differ
in each setting. As such, previous work is useful in
providing a basis towards a better understanding of how
particular critical success factors transferred to this
setting. The next section proceeds with a discussion of
the enrollment management process prior to the imple-
mentation of the DSS.
3. The traditional enrollment management process
The Willamette University College of Liberal Arts
(CLA) in Salem, Oregon is typical of small schools that
have traditionally relied upon outside consultants for
technical guidance. Each year, from a pool of more than
3000 applications, admission is offered to approximately
1800 applicants, to achieve a target entering class of
approximately 500. In fact, the actual percentage of
admitted students who will enroll is not known in
advance, and hence a critical task for any admissions
office is to accurately predict this percentage. The
percentage of admitted applicants who ultimately enrollis referred to as the enrollment yield. If the yield is
overestimated, fewer students than expected will enroll
and revenue to the university will be reduced. If the yield
is underestimated, a higher than expected number of
students will enroll, possibly exceeding the fixed capacity
Fig. 1. Traditional enrollment management process.
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of the school and resulting in significant incremental
costs for additional housing, faculty, and other resources.
In the worst case, over-enrollment could compromise
the quality of instruction, as classrooms become over-
crowded and student to faculty ratios exceed levels
conducive to optimal learning. Therefore an accurateestimate of the enrollment yield is essential to effective
fiscal planning.
A second critical decision for the CLA admission staff
is the allocation of financial aid to admitted students. All
universities offer financial aid to a large proportion of
their incoming students, both as a means of meeting
students' financial need and as a recruiting tool.
Financial aid allocations provide a powerful lever for
admissions, but these decisions have major fiscal
implications as well. Once the admission office has
identified a set of students to admit, the admissions staff conducts an assessment of financial need and merit to
determine how much aid to offer each admitted student.
Prior to sending out the final admission letters and
financial aid packages the admission office must
estimate the discount rate, defined as the percentage of
the total tuition which is offered to the enrolled class in
the form of financial aid. From a fiscal point of view,
operational performance of the admission office is
assessed based on the accuracy of both the predicted
enrollment yield for the admitted class and the discount
rate associated with the admitted applicants who actually
enroll.
3.1. The traditional process for estimating yield and
discount rate
The enrollment management process traditionally
begins by establishing targets for enrollment and the
discount rate for the incoming class (see Fig. 1).
Consultations begin in the summer prior to the year
when the admit decisions must be made to establish
enrollment and discount rate targets. For instance targets
are established in summer of 2005, for admissiondecisions to be made in the spring of 2006 that result in
an entering class in the fall of 2006. These discussions
between the Dean's office, the admission office, the
President and the VP of Finance attempt to balance the
long-term strategic goals of the college (e.g., academic
quality, geographic and ethnic diversity of the student
body) with the fiscal implications of attempting to
achieve those goals.
Once enrollment and discount rate targets are set,
the information is sent to an outside consultant who
returns a suggested financial aid allocation strategy for
achieving these goals. This strategy is embodied in a
grid corresponding to seven levels of financial need
and five levels of academic quality. The grid in Table 1
provides an example of a recommended financial aidfigure for each level of need and academic quality,
which is typically received from the consultant no later
than the middle of October.1
By February 1, all applications have been received.The
more than 3000 applicants are reviewed to make decisions
on the obvious candidates for admission or denial.
Preliminary decisions are based primarily on academic
credentials (e.g., GPA, classes taken, test scores). At this
initial stage of selection, other factors, including student
background, interests and activities play a lesser role.
Approximately 20% of the applicants who have good but
not outstanding credentials are then subject to asubsequent review. In this subsequent stage of selection,
the admissions staff confers to make final decisions as to
which students will be admitted, denied admittance or
entered onto a wait-list.
Once the final admit list is determined, admitted
applicants are classified by need and academic rank, into
the squares of the Need-Academic Quality grid and
counts are determined. The grid, along with the data on
admitted students and the total financial aid budget is
then sent to the consultant who uses their proprietary
models to estimate the enrollment yield and discount rate for the admit pool. The results of the consultant's
analysis are returned to the admissions office and, if the
estimates of yield and discount rate do not meet pre-
selected targets, the consultants offer advice on how the
dollar values in the grid might be altered to improve the
results. Based on this advice, the CLA admission office
makes final financial aid allocations for each cell of
the grid. Admitted applicants are then sent offers of
Table 1
Example of financial need-academic quality grid a
Academic rank
1 2 3 4 5
Need rank 1 $0 $0 $0 $1000 $2000
2 $0 $0 $1000 $2000 $50003 $0 $1000 $2000 $5000 $8000
4 $ 1000 $2000 $5000 $8000 $10,000
5 $2000 $5000 $8000 $10,000 $12,000
6 $5000 $8000 $10,000 $12,000 $18,000
7 $10,000 $12,000 $15,000 $18,000 $22,000
a Data in this table are for illustrative purposes only. Actual allocations
vary from year to year and are proprietary.
1 In November, the first of the applications are received. These
applications, identified as early-decision applications are for pro-spective students who have identified Willamette as their first choice.
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admission, including financial aid awards determined by
their position in the grid. Admission office personnel
wait for students to either accept or decline admission
which is indicated through personal contact or receipt of
a deposit from the student.
As acceptances and declines arrive at the admissionoffice, progress is evaluated based on historical trends to
determine whether developing yields appear to be on
target. If deposits arrive at a rate lower than anticipated,
the admission staff may turn to the wait-list to admit
additional students. If acceptances are higher than
anticipated, the university faces the prospect of being
substantially above targets for enrollment, discount rate,
and total financial aid budget.
3.2. Drawbacks of the traditional admissions process
Several shortcomings are apparent in the traditional
CLA enrollment management process. First because the
consultant's work utilizes proprietary models, admis-
sions staff gains limited explicit knowledge into what
factors are most important in influencing enrollment.
Fig. 2. The CRISP Methodology [5].
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Second, the consultant's model is based upon assump-
tions that are used to build models for a variety of
clients. Thus, forecasts may not adequately account for
idiosyncratic differences of the CLA. The model used is
built based simply on the previous year's model and
modified to incorporate, to a limited degree, any shift instrategic goals. Finally and perhaps most significantly,
because the analysis is performed by an outside agency,
admission personnel are limited in their decision making
by the timing and scope of the information provided by
the consultant.
The admission process has become much more fluid
and unpredictable due to the sophistication of applicants
who are likely to research and apply to multiple
institutions over varying time periods [14,18]. This
behavior leads to significant shifts in the make-up of the
applications pool even within a specific year. As newapplications are received the admission office must
make intermediate decisions with respect to the admitted
pool, and changes in the admit pool requiring on-
demand model adjustments. While the consultant would
typically provide updates on request, the timeliness of
these updates is dependent on the consultant's workload
at the time of the request. Since the consultant has
multiple clients all with similar admission decision
calendars, the CLA admission office often did not get
the information when it was needed to make timely
decisions.
The combination of the limited incorporation of theCLA specific factors and lack of timely updates led to a
loss of control of the enrollment management process at
CLA resulting in poor operational performance. This, in
turn, led to the need for and development of an in-house
DSS. As will be described in the sections that follow, the
resulting system resulted in dramatically improved
operational performance and increased institutional
knowledge.
4. Building the predictive enrollment model for CLA
Data mining is the process of discovering trends and
usable patterns in data. The objective of this process is to
sort through large quantities of data to extract new
information [16]. Data mining models are built guided
by key outcomes desired by the users of the model (e.g.,
accurate yield prediction,) and what the data suggest are
the key factors relating to the outcome. A model de-
ployed via data mining may often rely upon a mixture of
traditional statistical techniques (e.g., logistic regres-
sion,) in combination with standard data mining tech-
niques discussed below.The system developers, in this case professor and
graduate students, followed the CRISP paradigm for
data mining projects [7]. The paradigm suggests six
steps to developing successful data mining models. The
developers involved closely followed the steps from
Institutional understanding, Data Understanding, Data
Preparation, Modeling to Evaluation and Deployment.
Fig. 2 presents a broader description of these steps and
Fig. 3 shows the two-year development process actually
used at the CLA.
4.1. System development —
2002 –
2003
Work began on the system in 2002 as part of a
graduate data mining course being developed at the
university. The developers agreed to, over a three-year
period, build a DSS consisting of: (1) a predictive model
Fig. 3. The CRISP Process Applied to the Admissions Management DSS Project.
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to project, for individual applicants, the likelihood of
enrollment and, in aggregate, the enrollment yield and
discount rate; and (2) a managerial tool with a user-
friendly interface that could be used to provide timely
and effective guidance for policies and decisions sur-
rounding admissions and financial aid.In the fall of 2002, the following business goals were
identified: annually enroll approximately 500 students with
the highest academic quality possible; achieve diversity
goals; maintain a discount rate at or below the target level.
Following the CRISP methodology model, development
began with interviews with enrollment managers to gain a
better understanding of the institutional setting (i.e., dev-
elop business and data understanding). This also provided
opportunities for institutional knowledge creation as
admissions personnel became more familiar with data
that would be used to drive the models.Once data preparation was complete and the initial
analysis database was constructed, the model building
stage commenced. The initial analysis data set consisted
of applicant records for thousands of applicants from
three preceding admissions cycles, 2000–2002, and
comprised over 60 variables, 40 of which were
suggested by enrollment managers. The remaining
variables were chosen from among those available but
not initially considered important by these same
managers. The data set was split equally into training
and validation sets. All model development was
conducted using training data.As is typical of data mining initiatives, meta-level
modeling was employed using a multiplicity of
approaches – neural networks, decision trees, and
logistic regression models – to arrive at the ultimate
predictive model and tool. Because neural networks
have the ability to accurately predict outcomes in
complex problems ([9] p. 64), and because neural
network models were found in a previous study to
outperform other techniques in correctly classifying
admitted applicant who will ultimately enroll or not
enroll [33] modeling began with neural networks. Allavailable predictors were included in a neural network
model to: lend insight as to the most influential variables
and to set initial benchmarks for the predictive accuracy
that might reasonably be attainable from the available
data. In this way, the goal of correctly predicting the
ultimate enroll/decline decision for admitted students
71% of the time was established for any predictive
model. Using the relative importance of inputs data
reported the original collection of more than 60
candidate predictor variables included in the neural
network model was narrowed to 30 as input for the
logistic regression model building process. Because it is
difficult to determine the exact relationships being
modeled in neural network approaches and because this
limited model transparency for the managers, the
decision tree and logistic regression approaches were
employed to determine if similar predictive accuracy
could be achieved.In contrast to regression-based approaches, decision
trees offer potentially attractive modeling alternatives as
they do not rely upon assumptions about the linearity
relationship between the response and selected predictor
variables, nor does their interpretation suffer from
correlation among the predictor variables. Thus, in theory,
decision trees potentially promise greater predictive
accuracy and simpler interpretation. In the course of the
modeling process, decision tree models based on C5.0 and
C and R Tree algorithms2 were developed, both as
additional benchmarks of predictive accuracy and to lendadditional modeling insights to further modeling develop-
ment. The best of the tree-based models exhibited an
overall predictive accuracy of 70.3%, comparable to that
attained via neural networks. However, from the stand-
point of the enrollment management application, decision
tree models have a significant drawback, producing only
discrete breakpoints to describe the influence of financial
aid on applicant propensity to enroll. CLA managers,
instead, required a tool that would allow them to assess the
impact of relatively small adjustments to financial aid
policies and individual award packages. Moreover, the
decision tree models could not be easily deployed in asoftware package available to the managers. With these
factors in mind a logistic regression was considered to
determine if a model with similar predictive accuracy
could be developed. Logistic regression offered the
potential to provide insights offered into the relative
strength and effect of individual predictors and in particular
the ability to smoothly assess the impact of financial aid
allocations. It was also easily deployed in the form of a
Microsoft Access or Excel-based decision support tool.
These factors together maximized the ease of understand-
ing and implementation by enrollment managers.The modeling process began by exploring main effects
that would ultimately provide a parsimonious description
with a reasonable degree of fit. A model consisting of
eight variables – including applicant characteristics and
financial aid allocations – was ultimately selected to
predict the likelihood of enrollment. It accurately
predicted enroll/decline decisions for a little over 70%
of the applicants in the training set. To test the predictive
2 See David Hand, Heikki Mannila and Padhraic Smyth, Principles
of Data Mining, MIT Press: Cambridge 2001, pages 327–
367 for a broader discussion of these models.
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accuracy, the model was then scored using the validation
data set, and accurately predicted enroll/decline decisions
68% of the time, only marginally lower than training set
results. The model was further evaluated over the summer
of 2003 based on the actual enrollee data from 2003, and
again, the model performed well, accurately predicting
70% of the time. Consultation between managers and
analysts revealed one principal concern. While the overall
predictive accuracy of the model was acceptable themodel did a much better job of predicting who would not
enroll than those who would enroll (see Table 2). This
finding is not surprising because the model is designed to
maximize the probability of correctly classifying any
individual and approximately 70% of those admitted
decline to enroll. However, for CLA, it is more important
to predict which applicants will enroll than those who
won't. Thus, it was agreed that future models would place
a greater emphasis on predicting those who would
actually attend.
One of the key pitfalls of data mining models is that
they often may be too complex for managers to interpret and use. To address this issue, during summer of 2003, a
user-friendly deployment tool was developed to allow the
admission personnel to make use of the predictive model
with minimal understanding of the underlying model. This
interface was developed in the Microsoft Access database
environment with the focus on maximizing ease of use for
the managers. Functionally, the interface allowed man-
agers to predict total yield and discount rate at the
aggregate level. It also allowed managers to assess the
likelihood that any individual admitted applicant would
attend. This enhanced the transparency of the process for CLA managers and afforded them the opportunity to react
in real time to shifts in strategy and/or the enrollment
environment. However, because of only limited under-
standing of the Microsoft Access database environment
among enrollment managers, this interface did not allow
the managers to easily view components of the underlying
models, limiting transparency to some degree.
In the fall of 2003, the CLA admission office began
using the initial DSS to guide enrollment management.
Because of the instant access to model results, the
admission office was able to generate timely initial
estimates of yield and discount rate based on the inputs
to the earlier mentioned financial aid matrix. Combined
with the ability to predict enrollment on an individual
basis, this provided the opportunity to better manage the
discount rate by allowing the admission office to fine
tune financial aid allocations to the individual level and
optimize yield while still maintaining desired academicand diversity profiles. Thus, as will be discussed in the
next section, operational results improved significantly.
Further, the initial DSS development process yielded
significant enhancements to institutional knowledge. The
inclusion of certain variables in the yield model suggested
others that should be considered for inclusion in subse-
quent analysis. In addition, improved data understanding
suggested that a more comprehensive predictive model
incorporating interactions between variables might be
useful in increasing accuracy. In terms of the interface,
while predicting at an individual level was thought to beuseful for managing enrollment yield and discount rates, it
was found to be prohibitively time consuming. To address
this, managers suggested that the interface be modified
to provide a vehicle to break out projected enrollments
by geographic region to support strategic CLA geograph-
ic diversity initiatives. Finally, the manager of institu-
tional research suggested that the interface be provided
in a format that would allow easy modifications to the
underlying model in order to support additional analyses
as will be described below.
Table 2
Enrollee classification matrix (Overall 70%)
Predicted 2003
Actual 2003 Enroll Decline
Enroll 25% (Goal is to maximize) 12% (Goal is to minimize)
Decline 75% (Goal is to minimize) 88% (Goal is to maximize)
Fig. 4. Decision trees for interaction detection. A: interaction bet-
ween geography and aid award. B: interaction between geography andcampus visit.
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4.2. System refinement 2003 – 2004
The second year of DSS development began by
incorporating a number of new variables into the
database and reformatting others to make the model
development process more efficient. The total timedevoted to understanding and setting up the data for
the second stage of modeling was reduced from eight
months to four. The modeling phase for the second year
began with the investigation of interactions between
predictors suggested by the 2002–03 model develop-
ment process. Four years of data (2000–2003 admits)
were used to identify highly correlated variables (via
the use of plots and correlation matrices) and highly
correlated cases (through the use of cluster analysis). In
terms of the highly correlated cases, the team searched
for large groups of applicants with similar characteristicsthat appeared to have an unusually high or unusually
low propensity to attend. When these groups were iden-
tified, indicator variables were created to capture group
membership information. Following data exploration,
the full data set was again split randomly into two equal
parts and the training set was used to develop a revised
logistic regression model for deployment.
Potential extensions of the 2002–03 model were
investigated including using variable interaction terms
determined via additional meta-modeling. The refine-
ment work began by executing several decision tree
modeling routines on the training data set to identify potential interaction terms for inclusion.
The process of interaction detection was as follows:
▸ First, a decision tree model with the outcome variableenroll/decline was created. This model included:
○ All the variables included in the 2002–03 model.
○ Any variables suggested by managers in working
with the model in 2002–03.
○ Flag variables to identify the effects of being a
member of 2 groups suggested in the exploration
phase described above.▸ The output of the decision tree was examined to
determine if any of the most important variables
suggested by the tree differed at lower levels based
on additional variable included in the model. For
example:3
○ In Fig. 4A admitted students from Oregon were
somewhat more likely to enroll (55% likelihood
of enrollment). However, if they were promised
high levels of financial aid (in this case more than
$10,000) they were much more likely to enroll
(75% likelihood of enrollment). However, out-
of-state applicants did not show a similar increase
in propensity to enroll at similar levels of
financial aid. This might suggest an interaction between Oregon residence and aid provided.
○ In Fig. 4B, admitted students from outside
Oregon were not very likely to enroll (22%
chance of enrollment). However, out of state
applicants who also visited campus were much
more likely to enroll (55% chance of enrollment).
For Oregon applicants, the likelihood of enroll-
ment was relatively high whether they visited
campus or not (55%). This suggests an interac-
tion between non-residents and campus visits.
The main effects (variables from year 2002–03 model,
new variables suggested by managers based on their
learning from the first year, new flag variables suggested
in the exploration stage) and interaction effects (suggested
by the decision trees) were included in a preliminary
logistic regression model. The new model initially
consisted of 18 variables (see Table 3 for the set of
predictors used in the model.) The predictors of
enrollment probability included entrance scores, high
school grade point average, geographic origin, the
3 Examples include actual significant interactions. However, the
magnitude and influence of these interactions, as measured by theactual estimated regression coefficients, is proprietary.
Table 3
Main effect predictors in the model
Predictor Description
Hsgpa High School GPA
Othersch Number of other schools to which the applicant applied
based on FAFSA information applied
Need grant Amount of aid award based on need
Merit grant Additional award based on merit
Workstudy Promised amount of work dollars
SATSOFT A measure of SAT and/or imputed SAT based on ACT
score
Sex Gender of the applicant
Apptype Early admit or Regular admit Alum Were the applicant's parents alumni?
Appfacstaff Were the applicant's parents faculty or staff?
Visit Had the applicant visited campus?
High school
type
Public or private
Comp1 Had the applicant applied to an identified competitor
based on FAFSA information?
Comp2 Had the applicant applied to an identified competitor
based on FAFSA information?
Comp3 Had the applicant applied to an identified competitor
based on FAFSA information?
Territory What part of the country was the applicant from?
Need rank Need rank on the grid
Merit rank Merit rank on the grid
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financial need of applicants as well as grant, scholarship
and loan amounts in the financial aid package.4 Prior to
commencing the final model selection process, the
training data set was balanced to reduce the model's
tendency to over-predict the decliners. Many successive
iterations of the logistic regression model were investi-
gated before arriving at a final model with a prediction
accuracy of 69.2% detailed in the Table 4 below.Examining the table reveals that by including selected
interactions and balancing the data, the ability to suc-
cessfully predict those admitted applicants who would
actually enroll, as per management requirements, in-
creased substantially from 25% to 65.7%.
The 2003–04 predictive model was submitted to
test –retest validation, and while the results of this test
were somewhat lower than those observed in the test set
(see Table 5 below), they were deemed consistent with
respect to the training data set. This conclusion is further
supported by the observation that the same variables
were statistically significant in both data sets and that the predictive validity was similar.
The DSS interface was also altered substantially from
the first year to enhance functionality and ease of use.
Specific changes included:
▸ The DSS was re-deployed in Microsoft Excel. Thisapproach permitted the institutional research man-
ager to easily develop auxiliary tools to better
inform the enrollment management process.
▸ The revised DSS incorporated a new screen where the
managers could modify financial aid allocations for each cell in the grid, to assess the effect on yield within
particular cells and overall. For example, in Fig. 5A,
the amount of financial aid given a student deemed a
“3” on the academic quality scale and a “4” on the
financial need scale is $3000, yielding 25%. Fig. 5B
shows changing the amount of financial aid in the cell
to $5000 increases the projected enrollment yield to
32.7%.
▸ The revised DSS includes a screen which breaks out the expected yield by in-state and out-of-state
admits. This feature was introduced to support
CLA's strategic goal of geographic diversity.
▸ In addition to providing cell based results, the screenalso provides aggregate level predictions of total
financial aid outlays and the discount rate.
The DSS continued to include a screen that allowed
managers to assess individual applicant level probabil-
ities of enrollment. Users simply enter actual data values
for the required variables and the interface displays the
predicted enrollment probability for an individual appli-
cant. If desired, the manager can then experiment with
alternative financial aid awards to increase the probabil-
ity of an applicant enrolling (See Fig. 6A and B).
5. Organizational impacts on the admissions work
system realized with the DSS
The implementation of the DSS resulted in superior
operational performance, and perhaps even more impor-
tantly, the modeling and system development activity
provided a number of learning opportunities for CLA
admissions office and the internal developers. The most
significant indicator of the impact of the new DSS and its
associated implementation activities was that it substan-
tially and beneficially altered the process by which
admissions activities are carried out. Using the knowledge
acquired through the project and the resulting system, the
admission staff altered the sequence, effectiveness andexpediency of enrollment decision making (See Fig. 7.)
5.1. An improved enrollment management process
Through direct participation in the model develop-
ment, the CLA admissions staff gained new insight into
their own process, insight that would never have
emerged under the traditional approaches employed by
outside consultants. For example, admissions staff was
now able to create an initial financial aid allocation grid,
demonstrating the admissions decisions makers' explicit understanding of what may formerly have been only
tacit knowledge. Managers now understand how
Table 4
Enrollee classification matrix-training data set (Overall 69.2%)
Predicted 2000–2003
Actual 2000–2003 Enroll Decline
Enroll 65.7%
(Goal is to maximize)
27.3%
(Goal is to minimize)Decline 34.3%
(Goal is to minimize)
72.7%
(Goal is to maximize)
4 The actual model is proprietary.
Table 5
Enrollee classification matrix-validation set (Overall 66.8%)
Predicted 2000–2003
Actual 2000–2003 Enroll Decline
Enroll 61.0%
(Goal is to maximize)
30.4%
(Goal is to minimize)
Decline 39.0%
(Goal is to minimize)
69.6%
(Goal is to maximize)
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financial aid choices affect discount rate and can cap-
italize on previous experience as well as an improved
understanding of process goals, resulting in more precise
estimates of enrollment yield and discount rate.
The DSS reduces the time spent by admissions staff on
the grid construction and assessment, increasing the timeavailable to manage individual cases. Under the old
process, almost all evaluation of financial aid alloca-
tions was conducted at the grid level and individual
applicant level modifications were limited. Under the new
process, up to 50% of the individuals admitted receive
applicant-specific adjustments to their financial aid
package, and the estimated impact of these adjustments
is immediately assessed. The number of adjustments to
the financial aid grid and the level of attention to directed
towards individual financial aid packages would not have
been possible without the analytical support of the DSStools or the in-depth knowledge gained through the
implementation process.
In addition more precise estimates of admission yield,
enrollment predictions at the individual level support
better estimates of incoming class characteristics –
expected revenue and class profile with respect to
academic quality, geographic and ethnic diversity. For
individual applicants with given academic qualityindicators, geographic origin and financial need, sensi-
tivity curves can developed for varying levels of financial
aid. As grants and scholarships increase while, corre-
spondingly, loans decrease as a proportion of the total aid
package, the probability of enrollment increases. Analyz-
ing groups of admitted applicants that are homogeneous
with respect to academic quality and financial need,
financial aid sensitivity curves can also be obtained,
allowing for the identification of aid thresholds above
which the probability of enrollment moves a group into
the “
likely to enroll”
range. This information is used toadvise CLA admission policy and to inform the dis-
tribution of financial aid.
Fig. 5. A: financial need-academic quality grid. B: revised financial aid example in financial need-academic quality grid.
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5.2. Real-time management of enrollment during the
acceptance period
Once offers of admission are sent to applicants, these
potential students have approximately one month to
accept or decline. Admissions staff can use the system to
rank students based on the model's estimate of their
probability of enrolling. Once enrollment accept/de-clines begin coming in, the managers can use the
system's interface to track how well the model predicted
actual enrollment decisions. If a trend is detected where
enrollment acceptance rates are coming in below pro-
jections, admissions managers can move quickly to start
making offers from the wait-list. In addition, they can
observe acceptance trends in financial aid, and gain
insight into how much additional financial aid incen-
tives might be available to influence admitted students
who have not yet responded. In this way, the model and
system interface support real-time enrollment manage-
ment as the early results unfold.
5.3. Shifts in responsibilities of admissions managers
The introduction of the interface resulted in several new
responsibilities for the managers that were formerly
performed by the outside consultant. Shifts in responsibil-
ities are summarized in Figs. 1 and 7, depicting the tradi-
tional and revised enrollment processes. These include:
➢ The initial financial aid grid, which serves as the
foundation for the overall strategy, was previously
developed by the outside consultant, largely on
the basis of a single year of data. The revised
process relies upon CLA top management using
knowledge that they have accumulated through
the in-house development process.
➢ The new interface allows managers to systematical-
ly analyze individual applicants, resulting in more
individual adjustments to aid awards. Thus, the DSS
and supporting interface is driving a migration from
grid level analysis to individual level analysis.
Fig. 5 (continued ).
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➢ The lead time required when working with the
outside consultant was significantly reduced. Pre-
viously, it was difficult to determine in a timely
fashion, if enrollment projections for enrollment
developing according to plan. The in-house DSS
interface allows for more rapid adjustment to
unexpected market conditions, permitting enroll-ment decision makers to manage their wait list more
strategically.
5.4. Operational results
The operational results attributable to the revised
data-driven process for enrollment management (see
Table 6) are quite impressive, especially relative to those
realized in the two years preceding implementation. The
first row of Table 6 illustrates the percentage variance
between the targeted and actual enrollment. In the
preceding years, the actual enrollment was 17–21%
above or below desired enrollment.5 In 2004–05, using
the new system, the variance was less than 5%. Variance
from the target discount rate was reduced from 2–3.5%
under the consultant to less than 1%.6 Moreover, a 1%
reduction in the discount rate can yield hundreds of
thousands of dollars in additional revenue to the
5 Note that while, on the surface, it may seem beneficial to enroll more
students than planned because it will result in more revenue. However,
over-enrollment is problematic on two dimensions. First, when a school
enrolls significantly more students than anticipated, the costs of housing
and other ancillary costs increase disproportionately as the school is
forced to look outside its fixed set of assets, resulting in significantly
higher cost per student. Second, because the preceding year's enrollment
had been so far below expectations, the admission office increased the
financial aid awards substantially in 2003. The higher discount rate
resulted in substantially lower revenue per student. These two factors
combined to produce significantadverse impact on university cash flows.6 Actual targets for enrollment and discount rate are proprietary.7
Note that variable names are concealed here for proprietary reasons, but are transparent in the actual interface.
Fig. 6. A: individual prediction 7of probability of enrolling. B: individual prediction7 of probability of enrolling with increase in aid.7
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university. From 2003 to 2005, using the new DSS, the
CLA was able to reduce the discount rate by over 10%.
These results were achieved without any decline in
academic quality (as indicated by SAT scores in Table 6.)
There was a precipitous drop in ethnic diversity in 2004
and hence, ethnic diversity was identified as a point of
emphasis in developing the underlying enrollment forecasting for the subsequent year. In year four, 2005,
ethnic diversity rebounded along with significant gains
in geographic diversity, with 4.6% more students from
outside Oregon and 5.8% more students from outside the
northwest region, while median SAT declined.
5.5. Costs of DSS implementation
The CLA paid the University's school of management a
fixed yearly fee to support the purchase of specialized
software, and a faculty member's time for project oversight.
During the development period, total monetary outlays
were less than $50,000. As noted above, a 1% reduction in
the discount rate yields hundreds of thousands of dollars in
additional revenue to the university and the discount rate
decreased 10% over the three-year implementation period.
Thus, from a purely monetary cost-benefit perspective the
implementation was hugely successful.
Other costs that are more difficult to quantify relate tothe time that CLA management spent in: 1) educating
project participants (graduate students) in enrollment
business procedures; 2) providing input on interface
development to achieve both maximum functionality
and ease of use; 3) learning how to assimilate DSS tools
into business processes. Initial time costs of education
and development were substantial in the first year of the
project but declined dramatically in years two and three.
However development costs also yielded intan-
gible benefits equally difficult to quantify. The activities
associated with DSS development and the use of the DSS
itself resulted in immeasurable gains in process knowledge
Fig. 6 (continued ).
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and significant process improvements. The DSS imple-
mentation project assists enrollment managers in making
more precise financial projections and allows for
significant gains in real-time response to shifts in the
enrollment environment. All of these factors, led to
improved enrollment management as well as major strides
in class quality, diversity, and financial performance.
6. Insights on the implementation of DSS systems
Project outcomes detailed in the preceding section
provide guidance for managers developing and imple-
menting DSS systems in enrollment work systems. This
section considers the general contributions of the project
to the DSS implementation literature.
In terms of the frameworks provided in [17] and [32],
implementation insights from the enrollment manage-ment DSS support the necessity of top management and
organizational support, in particular a culture accepting of
knowledge sharing. Both the Vice President for Enroll-
ment and the University President, spurred by the
lackluster performance of the traditional process, were
strong advocates for process change. This permitted
internal developers initial access to data and personnel
that would not otherwise have been available. Over time,
the internal developers were able to gain the deep
enterprise and data understanding that are crucial to any
successful DSS development project. It was also noted in
[3,11,17,29] that top management support was a key to
Fig. 7. The revised CLA admission process.
Table 6Enrollment outcomes for new freshmen
Outcome 2002 2003 2004 2005
Enrollment variance (actual-
target)/target
−16.9% +21.1% +2.5% −4.4%
Tuition discount variance
(actual-target)/target
−3.5% +2.0% −0.5% −0.4%
SAT median 1230 1250 1260 1230
Ethnic minority representation 20.8% 19.2% 15.4% 18.0%
Non-Oregon representation 59.8% 59.7% 61.4% 66.0%
Non-northwest representation
(students from outside,
Washington, Montana, Idaho,
or Wyoming)
32.5% 34.2% 34.4% 40.2%
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success in the development of an admissions DSS with
respect to “opening doors”.
The internal developers spent a great deal of time
educating the admissions staff to the potential of various
solutions alternatives. The decision to use a logistic
regression model as the fundamental predictive tool wasone result of this interaction. Over the duration of the
project, the internal developers and the admissions staff
built up trust, resulting in the reciprocal knowledge
sharing culture that has been suggested as a key success
factor [3,15,17,32]. Further, the transparency of the DSS
interface further supported the knowledge sharing culture,
especially during the second year when it was redeployed
in Microsoft Excel. By providing a mechanism whereby
non-technical users and technical users could collaborate
and share ideas, the interface promoted a free flow of
knowledge that led to significant enhancements.Project leadership and organization (during imple-
mentation) are other “ people” factors generally believed
to be critical to the success of a DSS implementation
project [3,15,17,32]. Given that the number of project
stakeholders was relatively small, fewer than ten, project
leadership and team composition were perhaps less
important than observed in larger project settings
[11,32]. Moreover, given the environmental factors
(poor historical performance) and the clearly articulated
goals, individuals on both sides (admissions and internal
developers) were highly motivated to succeed.
As is typical of many DSS and data mining projects,the admissions DSS was implemented in two phases and
continues to be enhanced annually. Similar to the ex-
amples in [11] and [32] components were added over
time, with more sophisticated functionality added in the
later phases of the project. The key observation here is that
learning by DSS implementers must continue to occur
even after initial implementation. For example, user
feedback from the 2002–03 year helped drive interface
movement from MS Access to MS Excel, improving
system functionality and transparency. Overall, the
gradual implementation of new complex functionality is preferred to all at once rollout [11].
System characteristics, and in particular content man-
agement, was central to the successful implementation of
a knowledge management DSS for Singapore's housing
department [32]. In the enrollment management project,
content is not as directly relevant to success as is the
management of the predictive model's quality. The model
is currently reevaluated and updated annually by the
internal developers with ongoing performance evaluation.
In this sense, the project requires continual attention from
both the internal developers and admissions staff. As
observed elsewhere, incentives are a requirement for
ongoing participation [15,17,32]. In this case admissions
managers provide their insight in order to improve
performance gains with respect to their activities. The
internal DSS developers continue to view the system as a
learning opportunity and real-life experience, both for the
professors and participating graduate students.At the heart of many DSSs is the technological plat-
form upon which the system is built. The enrollment
management model was developed using Clementine, the
SPSS data mining product, but the end-user sees only the
final Microsoft Excel deployment. Deployment migration
from Microsoft Access to Microsoft Excel was based on
the admissions staff desire for instantaneous feedback on
the impact of grid adjustments that was not so
immediately achievable in the initial database application.
This also supports the contention of many [11,29,32] that
choosing technology with minimum training and/or providing effective training for the technology chosen
can enhance information flows in the organization.
7. Conclusion and further research
DSS design and implementation is, at its core, an
iterative activity. Data mining procedures lend them-
selves well to data intensive DSS implementations as
additional data and new modeling approaches can be
readily incorporated. In any case, properly built DSSs
require regular user interaction and trust, both from end-
user and designer perspectives. Effective implementa-tion processes promote knowledge sharing through
business and data understanding phases and favors
deployment mechanisms that provide for the capture
and free flow of tacit knowledge between managerial
and technical project personnel.
Enrollment management essentially requires deci-
sions on which students to admit and what price to
charge for each available slot in the university; in order
to maximize student quality; with constraints on
capacity, discount rate, and target demographic compo-
sition of the admitted class. Optimization solutions for seemingly related yield or revenue management pro-
blems in the airline and hospitality industry have been
broadly investigated [4,20–22,31]. However, rather than
optimizing on a financial objective, enrollment man-
agers seek to maximize quality, long-term customer
value, subject to financial constraints, capacity and
discount rate. Continuing research will seek to more
systematically address optimization objectives and
incorporate optimization tools into a further improved
DSS.
This paper presents an example of successful DSS
design and implementation to improve the enrollment
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work system at a small liberal arts college. Comprised of
two interrelated components, a predictive model and
a user friendly deployment tool, the DSS and the asso-
ciated implementation have significantly improved
financial performance in enrollment. More importantly,
the two-year implementation yielded dramaticallyenhanced understanding of the enrollment work sys-
tem and serves as a vehicle for the conversion of tacit
process knowledge into readily deployable explicit
understanding.
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Elliot Maltz received his Ph.D. in Marketing
from the University of Texas at Austin. Dr.
Maltz's current research focuses on effec-
tively transmitting and combining market
information to facilitate new product devel-
opment and respond to changes in market
conditions. He has published in the Harvard
Business Review, Journal of Marketing,
Journal of Marketing Research, The Journal
of the Academy of Marketing Science, andSloan Management Review.
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Kenneth Murphy holds a Ph.D. in Opera-
tions Research from Carnegie Mellon Uni-
versity. Dr. Murphy's work on integrated
systems has followed several threads includ-
ing the financial justification of large-scale
systems using both tangible and intangible
factors, and investigating the tools and
methods for successful system implementa-
tion. He has published in Operations Re-
search, Communications of the ACM, and the
Information Systems Journal among others.
Michael L. Hand is a Professor of Applied
Statistics and Information Systems. Dr. Hand
has been widely recognized for his distin-
guished teaching and is a two-time recipient
of Willamette University's highest teaching
award. Professor Hand is the coauthor of a
leading college statistics textbook and is the
author or coauthor of a number of scholarly
articles in statistics and statistical computing,
both basic and applied. He is an experienced
management consultant, with clients includ-
ing — Hewlett Packard, Safeco Corporation, the State of Arizona, and
most major agencies of the State of Oregon.
123 E.N. Maltz et al. / Decision Support Systems 44 (2007) 106 – 123