Why you need to track source code performance in your college student pool

I had a recent discussion with several colleagues regarding the need in higher education to better understand source code performance and its impact on enrollment success, as well as how it informs and affects future recruitment strategies and effectively projects enrollment. This conversation triggered a realization that seems so simple yet eludes so many of us: A major issue in enrollment management today begins at…the beginning.

What I mean by this is that a growing number of institutions lack a fundamental understanding of which initial source codes result in the most inquiries, applications, admits, and enrolled students. Not only do many enrollment managers have little understanding of source code performance, others either do not have the time, resources, or full understanding of how to track the effectiveness of those events that bolster results at the top of the funnel as well as the rest of the funnel.

In addition, there is a need for enrollment professionals to become reacquainted with which sources to deliberately track. Although many schools have been quite effective in knowing the who, what, when, and where of tracking data to inform strategy, others have continued to follow the path of “this is what we have always done” instead of using data to guide decision making as much as they should. By no means am I insinuating that this comes from poor leadership in most cases; instead, I see that this is a product of antiquated technologies, few professional development opportunities, a lack of data analysts on campus who also understand enrollment management, and the cumulative effect of poorly collected data over many years.

So what is wrong and how do we begin to fix the issue? I believe that the first steps toward getting on the right track can be followed by answering three basic questions:

• What sources currently produce any inquiries in our pool?
• Are we effectively tracking these sources, and if so, how are they converting through the entire funnel?
• How are we using the data to help inform and affect recruitment strategies?

Here’s how to answer each one of those questions in order to make your college student source code analysis more strategic and illuminating.

Identifying which sources produce college student inquiries in your pool

We first have to understand which initial sources produce inquiries, applicants, admits, and, hopefully, a strong percentage of enrolled students. Many schools know some of their sources, but through my consulting work, I have come to realize that a pretty good percentage have lost track of their sources. The list of sources has grown at such a rate that enrollment managers can no longer effectively keep up with what they have invested in to attract inquiries and applicants. In many cases, campuses have outsourced not just the search process, but also the data analysis and therefore the understanding of the sources used to develop the prospect pool.

While it often is strategically sound to enlist third parties to help manage your college student search, it is also crucial for the campus enrollment managers to maintain the understanding, review, and control of each step of that same process. Many times when I have met with my campus partners, the enrollment managers are surprised to see particular sources on reports I provide as part of my consulting service, and all too often do not even recognize others.  In order to build a robust and wide top-of-funnel strategy, we need to know which sources we currently have, what we are therefore investing in, and what type of return on investment we are getting from each source. Pertaining to the question of which sources we should inevitably track, the following are the key ones every enrollment manager should know intimately:

• Prospect/Purchased names – and by vendor
  • Include search provider information if outsourcing
• Travel-initiated sources
  • High school visits
  • College fairs
  • Interviews
• Campus visits
  • Individual
  • Group
  • Open houses
• Referrals
• Student self-initiated
  • Call
  • Email
  • Web
• Application
  • Web
  • Hard copy
  • Type of application (campus app, Common App, etc.)

Depending on an institution’s specific needs, others can be added to this list, but these are the foundation for strategic source code analysis. You can find additional ideas in our white paper, 7 Categories of Admissions Data to Guide Decision Making.

And what do I mean when I say enrollment managers should know these sources intimately? Every enrollment manager must know how each of these sources performs through every stage of the funnel on an annual basis. They also should know how these sources compare against historical data, what was spent for each source group, and in what way the data help provide some predictive insights for future enrollment. This intimate knowledge lays the foundation for a more informed strategic recruitment planning process. All of this therefore requires — once we know what to analyze — an understanding of how to effectively track performance.

Tracking sources effectively and seeing how they convert throughout the entire college enrollment funnel

Once we know what we should be keeping an eye on, the next concern is whether any tracking of each of these sources is occurring, and to what level. From my experience, I have seen everything from extensive tracking of each initial source, to limited tracking of just response rates or applicant conversion rates, to no real tracking at the source code level at all (and analyzing each funnel stage solely at the aggregate level instead).

Enrollment managers must track their sources as one would track the overall freshman enrollment funnel. Each source should be broken down by funnel stage progression (n-count and conversion/yield rates). Simply stated, treat each source as its own micro-funnel. This deeper understanding and ability to gauge effectiveness at the source code level will provide the clarity that inevitably will inform more robust strategic enrollment decisions.

Using the data to inform and affect college recruitment strategies

The results of this level of reporting provide admissions professionals with data that determine the effectiveness of particular  initiatives geared toward increasing student numbers at the top of the funnel, while also gauging their effectiveness in bringing in students who had a higher probability of persisting through that same funnel. Among the campuses I work with, those that do the best job of tracking their sources not only have a strong sense of performance by each source, but understand that students who initially came into the funnel via a particular source may (or may not) have a higher likelihood of persisting through to enrollment. With such information on hand, a campus can inform strategic decisions on:

• Which source(s) of names will resources be expended on in the future.
• How much time and money will be dedicated to travel – specific to source (i.e. high school visits, college fairs, etc.).
• What should be done to drive students to visit.
• How to improve the online experience to further increase the web visitor’s likelihood to inquire or apply.

An institution can also begin to understand how each source provides an accurate glimpse as to which students the admissions office may want to concentrate more or less time on based on their initial source code.  How a student initially enters the funnel is a behavior that does correlate to likelihood of enrollment. Campus partners who I work with that use our predictive modeling service consistently see how initial source code is almost always one of the major variables in their model. The ability, through effective data collection and analysis, to determine which source(s) performs at or above average therefore not only informs future resource allocation, but also shows how to stratify communication strategies as students move through the funnel.  Some campuses have used their knowledge of source performance to do just this. Schools using predictive modeling are doing the exact same, but including additional variables (major of interest, geography, etc.) to further clarify their understanding of the probability of enrollment for each and every student in their pool. The benefit for campuses using predictive modeling – apart from their obvious ability to have each student qualified and the statistical work done by Noel-Levitz – is they also receive diagnostic reports for each variable in the funnel, including funnel breakdowns and performance by source code.

How do you begin to get a better understanding of your admissions data so you can build stronger recruitment strategies?

There is no denying that schools must have a more granular understanding of their data, and the first step must be to ensure a clear view from the top. It’s also something my colleagues and I have a wealth of experience doing. We are happy to answer any questions you have about funnel strategies, admissions data analysis, predictive modeling, and related topics. We can also set up a free phone consultation to discuss strategies with you and your admissions stakeholders as well. Simply email me with your questions or requests.