For most of us in the post-COVID landscape, the seasonality of enrollment and financial aid has largely disappeared and this work has become a year-round effort to respond to the ebb and flow of highly volatile market factors and unpredictable changes in applicant funnels. This leaves schools with few opportunities throughout the year to reflect upon and analyze all of their data from the past 12 months. One of these few remaining opportunities happens during the month of September. That’s why I’d like to resubmit that we, as a collective community of enrollment and financial aid professionals, officially dub September as “Data Month.”

To help kick off Financial Aid Data Month, I’d like to share with you my top 5 September data to-dos:

1. Reconcile net tuition revenue (NTR) with last year’s financial aid budget
   If you’re tracking the tuition revenue generated by enrolled students, it’s important to reflect on those metrics. Consider tracking the average NTR per student on a whole-school as well as per-grade level. These are important markers to understand the relationship between discounting and revenue. For example, if you see that the average revenue is low and discount rate is high for a younger grade (like 2nd), this would suggest that you need to be more circumspect in the amount of financial aid or other discounting for that grade this year. The opposite would be true for a grade level with a low discount rate and high average NTR; in that case, you could leverage the headroom that you have in order to maximize enrollment within the framework of a healthy relationship between your revenue and discounting. For more detailed information on how to implement a net tuition revenue strategy, check out my blog post.
   
   
2. Begin making initial forecasts for the coming year
   Forecasting what the year might look like can be one of the most impactful ways to leverage the data you’ve built not just over the past year, but in prior years as well. In fact, the more years you spend tracking the same data points, the better your modeling will be. There are many tried and true ways to set up forecasting models using graphs of funnel size over time, yield rate, attrition, and demographics. However, don’t forget to consider external factors as well when considering data modeling: This could include factors such as feeder program enrollment, birth rates, population of school-aged children in target areas, income trends, housing construction, and local companies hiring or downsizing in your market. For more on this regarding financial aid planning, check out Jen Bash’s blog post on using last year’s data to inform the coming season.‍
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3. ‍Create reports for key metrics
   For example: Do a zip code analysis on your full- and high-pay families as well as high-need families, look at financial aid vs full pay yield %, track average NTR across discount categories (like aid vs merit), examine summer financial aid data, analyze your attrition data and longitudinal funnel KPIs (What did this year look like on a monthly basis vs prior years, including summer?). For more examples of tools and reports, take a look at the Mapping Magic blog from Linda Haitani and Joe Corbett’s blog about leveraging census data.
   

4. Learn from your industry-level reporting
   Fall is the time for industry reporting to organizations such as DASL and INDEX, and although completing those reports can represent a big lift, it also represents a huge opportunity for schools to understand how their practices lead to the data that they generate over the course of the year. If you’re already taking a deep dive into your data, it makes sense to spend a bit more time learning from your data rather than simply collecting and immediately reporting. A great example of this can be found in Jackson Marvel’s blog.
   
   
5. Prepare your data collection and KPI tracking for the season
   When reflecting on last year’s data, it’s important to not just continue tracking the same data year after year, but to also look for opportunities to start tracking new data. If you notice that there are some questions that your data isn’t answering (in part or in full), such as who is applying for financial aid (as covered in Ron Beckman’s blog), then I would recommend taking that as an indication to start tracking some new data points in order to form a more complete picture. The key thing to remember is to take it slow; don’t try to go from tracking very little data to tracking every single metric you can possibly imagine. Look at 1-3 new data points each season, and keep those additions focused on the most important questions that you feel need to be addressed.
   

Data Month can feel overwhelming, and it is difficult to find the time even in the “slower” months to truly take a beat and sit with your numbers. That said, it is a critically important practice, as the hours you spend diving into your metrics and learning what stories the data are telling will pay huge dividends in your effectiveness and success throughout the season. As a bonus, the weather is usually pretty nice all around in September, so have a data party outside with your colleagues! Make this a fun and engaging tradition in the office to keep you and your team ready and willing to dive back in each year.

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