Database Marketing: Marketing Mavericks
Whether it’s to dramatically improve enrollment numbers and quality levels, or
boost advancement dollars, the latest analytic theories and database marketing
strategies are making a huge difference on college and university campuses nationwide.
And although Business Intelligence (BI) software is the newest find for many US
schools, many of these insightful approaches go beyond basic BI tools, which are
fundamentally designed to help technologists and administrators turn masses of
data into sensible reports. Instead, these newer approaches provide more targeted
methods of, say, attracting prospective students or communicating with alumni
who donate most frequently. These newer tactics organize business intelligence
in a way that benefits users across the institution.
Nobody said attracting the most qualified students was easy. The first hurdle
is the cost of getting the “right” students interested— a process that can run
into six figures before a single marketing pitch is made. According to a recent
report by Marquette University (WI), for instance, the school recently
shelled out nearly $200,000, or $3 per brochure, just to get its annual average
of 65,000 brochures out the door. Compounding the problem of “finding” students
is the rise of online applications and the preponderance of low application
fees: More students are applying to schools than ever before, making prospect
pools virtually unmanageable. To cope with these issues, many schools routinely
resort to a funnel approach, buying tens of thousands of prospect names and
mailing expensive direct mail pieces to attract interest. Yet, the challenge
of sifting through endless data continues to daunt campus marketing professionals.
The newer enrollment modeling approach to database marketing identifies the
key characteristics of enrolled students, allowing a school to determine the
likelihood of prospects and inquirees to enroll. It’s actually predictive enrollment
modeling that uses student characteristics to build a statistical model that
assigns a score to each prospect or inquiry. With this approach, Lawrence Henze,
managing director of
Analytics, says schools can target their marketing efforts to those prospects
who are most likely to respond, saving costs and ensuring admission departments
meet their enrollment goals (see box). “The reliance on large numbers is reduced
when there is a strategic enrollment system in place to guide prospects through
inquiry, application, admission, and enrollment,” says Henze, whose company
is a new division of the software firm Blackbaud.
“Many schools may find they can cut their initial search purchases when they
target prospects using enrollment modeling.”
How d'es such modeling work? Vendors such as Blackbaud, Datatel
SPSS, and SAS
each have different products that boast proprietary algorithms, but in essence,
all of the approaches revolve around a 1,000-point scoring system that grades
prospects on the likelihood they will enroll. The scoring matrix awards points
based upon characteristics such as geography, grade point average, and average
household income (these characteristics vary by institution). Once the grades
are assigned, prospects with high scores (generally in the range of 700 to 1,000)
receive diligent communications, while prospects with lower scores become secondary
priorities, and prospects with the lowest scores are dropped from the radar
" Value increases as you get more specific about your data and your marketing groups "
On Amir, Rady School of Management, UCSD
According to Henze, higher education institutions using predictive enrollment
modeling enjoy a number of advantages and cost savings. First, by targeting
applicant pools, schools can identify the best enrollment prospects by geography,
gender, ethnicity, field of study, academic standing, or other characteristics,
targeting recruitment effortsto those students who are most likely to enroll.
Second, he says, schools can save money on postage and printing by distributing
communications more effectively, and save time and effort by making sure that
admissions staffs are focusing only on those students who are likely to come
to the school and matriculate.
Abilene Christian University (TX) has been using predictive enrollment
modeling to assist in market segmentation to build and enroll better pools of
students since 2000. According to a 2003 report published by the Journal
of Marketing for Higher Education (vol. 13, issues 1 / 2) which compared
Abilene’s first year of predictive enrollment with the previous two years, ACU
received 25 percent more applications and increased enrollment by 10 percent,
with between 22 and 40 percent more applications coming from the core segments
the university wanted to grow. Bart Herridge, the school’s director of Enrollment
Planning and Research, told the Journal he was delighted with the results.
“We saw predictive modeling as an opportunity to create an inquiry pool shaped
like we wanted,” he was quoted as saying. “[It] allowed us to leverage our costs
while dramatically increasing the quantity and frequency of direct mail and
telecounseling efforts to those students in our inquiry pool who were most likely to enroll.”
Leading By Example
THE LOCATION OF SKIDMORE COLLEGE in Saratoga Springs, NY, a city of about 26,000, imposes challenges on marketing some of the 35 summer programs that the school offers every year.Without a significant local base to draw from, the school has to be more intentional and strategic in its marketing, making sure it targets its approach to get the greatest enrollment for the money it spends. With a new strategy for managing its database, Skidmore may do just that.
The new strategy, unveiled in part this past summer, is a combination of predictive enrollment modeling and permission-based marketing. The system seeks to include information about anyone who’s had any kind of relationship with the college—from youngsters who come for sports camp, to alumni who’ve been donating for years. Every year, the school weights certain criteria associated with this information and pulls the highest scores, to determine who will receive direct marketing efforts. An example: the Science Institute for Girls. In the spring, Jim Chansky, director of Summer Sessions and Summer Special Programs, gets a list of alumni who have daughters between the ages of 12 and 15. If a girl—or her parents, for that matter— ever publicly expressed interest in science of any kind, the child is added to the “high priority” list and receives marketing material immediately. If the school d'esn’t know much about her, she’ll receive nothing, or will receive information further down the line.
“The idea is that these are people who have an existing relationship with the college and might be interested in knowing what kinds of other things they can do with us,” says Chansky. He notes that by expressing interest in the college, prospective students are giving their permission to be part of a marketing effort down the road. “In many cases, we start compiling information on a prospective student when he or she is very young.”
So far, on just the limited rollout, Chansky says results of the new approach have been phenomenal. For starters, he says that the more targeted approach has enabled him to slash marketing costs for the program from $25,000 each summer, down to $5,000. Beyond that, he says with the amount of money the school is making from increased enrollments, the school should earn back the $100,000 it spent on the new system by the end of this year. Now that’s ROI.
Marketing to alumni and prospective donors presents whole new sets of challenges
to the campus marketer. In fact, entire technology segments have sprouted in
past years to help colleges and universities tackle the chore of cleaning data
so that they have the most current information for alumni. This segment, the
“address reclamation” niche, is now well established, and vendors such as Market
Data Retrieval, Cleanlist,
Business Credit Information,
and Melissa Data have
been pivotal in helping schools manage their data. What is new, however, is
the theoretical notion of applying standard Customer Relationship Management
(CRM) standards to a group of alumni, treating these prospective donors more
as long-time family members than as occasional friends.
Results of research in this area are surprising. At Columbia University
(NY), for instance, Assistant Professor of Marketing Oded Netzer has developed
a method to assess how an entire sequence of a school’s marketing actions, and
other contacts with alumni influence alumni donating behavior. Using a sample
of 2,000 alums from
Stanford University (CA) over a period of 26 years,
Netzer’s model determines the evolution in the relationship between the alumni
and the university, the impact of development activities such as reunions, and
the donating behavior of alumni in general. By establishing patterns of donation
behavior from the donation database, Netzer has developed a new way to estimate
donations, a schematic marketers can use accordingly.
" The most successful business intelligence is a 'business first, technology second' endeavor "
David Wells, director of Education, TDWI
“Previous methods were limited in their ability to bring together all customer-
brand touchpoints, and therefore are less accurate in predicting long-term customer
behavior,” says Netzer. “Applying this new method, [schools] may find that some
of [their] marketing actions have a more enduring impact on [their] customers,
which in turn implies a different marketing strategy.”
As Netzer explains, his method takes CRM to a higher level. Using the wealth
of data from these programs, the Netzer approach lets schools a) segment their
alumni according to what kind of relationship they have had in the past, b)
determine how the relationship changed over time and where is it heading, and
c) calculate which marketing actions are most likely to move alumni to a higher
level of relationship and donation. The approach also provides a marketing strategy
decision-support system that predicts the effects of different actions on different
types of customers, over time.
Embracing the notion of taking a broader approach to database marketing is
On Amir, a professor of Marketing in the brand-new Rady School of Management
at the University of California-San Diego. Like Netzer, Amir emphasizes
the need to optimize the types of data a school is keeping on its constituents.
In a candid interview, he says that for the greatest return on their investment
(ROI), schools engaged in database marketing always should ask themselves the
following questions: How old are constituents? What are they interested in?
Do they prefer personalized messaging? How many different types of groups exist?
Amir notes that with more advanced databases, schools could even attempt to
assess emotional and personal attributes such as what time of year an alumnus
is most likely to donate, or what kind of attachment a prospective student feels
for the institution, from the beginning of the campus/ student relationship.
“The trick in marketing is that you want to create the most value you could
have, with long-term ROI,” he says. “That value increases as you get more specific
about your data and your marketing groups.”
Looking forward, database marketing experts see challenges ahead for colleges
and universities that plan to leverage data to get more of a return on their
First, schools using predictive enrollment modeling or more adventurous approaches
like Netzer’s are few and far between. Second, while a majority of colleges
and universities rely on BI software to report historical data, the online analytical
processing (OLAP) component of this software is frequently illequipped to guide
But another big issue with BI is where to put the software programs to make
them most valuable. David Wells is director of Education at The
Data Warehousing Institute, which provides education, training, certification,
and market research for executives and business intelligence professionals worldwide
(TDWI is a sister organization of this publication). Wells says academic technologists
can’t seem to figure out whether business intelligence programs should report
to IT, university administrators, or the academic side. The most successful
BI engages the business, and is a “business first, technology second” endeavor,
he explains, adding that the optimal structure removes the organizational separation
of business and IT for those engaged in business intelligence work, creating
an organization where business knowledge and technical knowledge are contained
within the same business unit and frequently within the same individuals.
This approach d'esn’t have to involve major restructuring; on the contrary.
Wells suggests that schools create the BI organization as a federation of skills
and knowledge, with everyone working toward the same goals, the same purpose,
and with shared accountability for effectiveness and value.
“In typical universities, information technology skills are found both centrally
and dispersed throughout academic and administrative departments,” he says.
“It’s best to comprise a federation of people, knowledge, and skills that encompass
academics, research, university administration, and IT.”
Another challenge for higher ed institutions pursuing database marketing is
applying the concepts of Enterprise Data Warehousing from the start, recognizing
that “one size fits all” analytics for the entire institution might not be the
most efficient way to approach a database. Here, Wells notes that most large
universities are not single enterprises, but conglomerates of many. Technologists
at the University of Washington understand this reality perfectly. In
addition to the undergraduate and postgraduate programs, the school runs two
hospitals, and is the second largest recipient in the nation of Department of
Defense research grant funds. With this in mind, the school actually operates
as three distinct enterprises— education, research, and health care. Each “enterprise”
has its own data warehousing effort, which means that each also takes responsibility
for its own database marketing.
“When it comes to database marketing, particularly in higher education, half
the battle is making sure that you’ve got enough control of your resources to
make it worthwhile,” Wells observes. “What you do from there is up to you.”
Targeted Marketing Gone Too Far?
COLLEGES AND UNIVERSITIES AREN’T the only ones dabbling in database marketing. This June, the Pentagon quietly announced plans to create a database of the names of every college student and many high school students as well. According to the government, the purpose of the database is to help market the US military and aid in recruitment efforts at a time when soldiers are in short supply.
The data will be managed by BeNow (www.benow.com), one of many marketing firms that use computers to analyze large amounts of data to target potential customers based on their personal profiles and habits. According to the official notice of the program, the purpose of the database is to “provide a single central facility within the Department of Defense to compile, process, and distribute files of individuals who meet age and minimum school requirements for military service.”
The release indicated that the database would include those students who have taken the Armed Services Vocational Aptitude Battery (ASVAB) test; those who have responded to various advertising campaigns since July 1992 seeking enlistment information, and current military personnel who are on active duty or in the Reserves. The document also noted that the database will include individuals who are in the process of enlisting, as well as those individuals who specifically have asked to be removed from any future recruitment lists (for related information, go to www.defenselink.mil/privacy/notices/osd/DHRA04.html).
Privacy groups were quick to label the database a violation of the Pentagon’s own privacy rules and are saying that it would needlessly risk students’ rights. Almost immediately, the Electronic Privacy Information Center (www.epic.org) filed a lengthy complaint about the database, calling it an “unprecedented foray of the government into direct marketing techniques previously only performed by the private sector.”
In general, timing for the announcement couldn’t have been worse. The proposal comes amid considerable controversy over a proposed Department of Education database on students that would allow government officials and educators to track the progress of students throughout the educational system.