...

Research Focus

Funnel Progression Rates

• Submit Rate, RFI App Start Rate

• Admit Rate, Reject Rate, Commit Rate

• Fraud Rate, Domestic Rate

Matriculant Yield Rates

• Admit Yield (Traditional Selectivity)

• Total Apps Yield, Submit Yield

• Commit/Uncommit Yield (Domestic/International)

Potential Next Research Areas

...

• Projections/Actions Adjusted for Student Status
  • Applying a different submit rate based on the student status may give a better indication of the pool of potential submissions in the funnel.

RFI App Start Rate (NEW)

The percentage of the total RFIs that resulted in a started application.

Observations

• The current data only supports consistent RFI counting from 2020 forward.
• While there is some consistency, this is only enough data to make broad generalizations of how RFI rates will impact overall performance.
• There is a clear difference in the rates between domestic and international, but with only 2020 and 2021 (and a portion of 2022), and the known impact of COVID on the 2020 for international, it is too early to look for patterns across this population.
• We should revisit this when we have complete data from 2022.
• The number of fraudulent applications that started as an RFI is pretty small.
  • This means that we don’t have an RFI to fraud problem, and that will allow us to rely more on the accuracy of the app start rates from RFIs.
• The rates for the two years are in the ~20-30% range, and hold fairly steady within the programs.

...

• RFI Rate View
• Add an indicator to the Actions page of the report to show how the program is tracking against historic RFIs, and if they are within the expected app start rate.
• Show the program how many app starts have come from RFIs, and give them an indication of how generating more RFIs will impact the overall applicant pool.

Admit Rate (Selectivity)

The percentage of applications that were submitted that ended up being admitted to the program.

Observations

• Admit rates are noticeably higher for domestic applicants across the board, but particularly in the programs that have high concentrations of international matriculants.

Commit Rate

The percentage of applications that were admitted that ended up committing to the program.

Observations

• For all years except 2020, the commit rate is regularly within 5-10% of other years, sometimes much closer, and the rate tends toward the 50% mark in the aggregate.

• In 2020, the commit rate is still consistent across the programs, but is closer to 30-35%, particularly for the largest programs.

• Executive programs have a consistently high commit rate (80%+).

• This is a likely indicator of how serious the applicants are about the program when looking at executive offerings.

Opportunities

• Executive Programs
• Packaging offerings in a condensed “executive” format could be a way to attract a more serious audience around any flagging programs, particularly in the non-degree segment where changes can be implemented more rapidly, with fewer hurdles.
  • While considerably more complicated to execute, offering more masters degrees in an executive format could be an opportunity to grow this clearly motivated audience.

Reject Rate (NEW)

The percentage of the total submitted applications that were rejected.

...

Other Indicators to Investigate

• Abandoned Applications
• Submitted applications that are never completed.
• Abandon Rate: Percentage of all apps that are never completed
• How does this track over time? Any interesting patterns?
• Waived Deposits
• How does waiving the deposit impact the matriculation rates?
• Do we see higher or lower matriculation rates for applicants that are not required to pay a deposit before attending?
• Waived Application Fees
• How does waiving the application fee affect starts and submits?
• Is there a correlation to fraud cases when there is no actual fee associated with submission?
• Do we see higher or lower matriculation rates, and is it possible to factor in fee waivers when analyzing the strength of the pipeline within the projection formula?
• Decline Rate
• Determine if there is any pattern to the rate of declines across programs, and if so, include it in the projection calculations.
• Student Status
• As noted, the part-time designation can be an indicator of a lower interest level from the applicant. Viewing key indicators through the student status lens could yield interesting insights.
• One notable exception to the part-time designation appears to be within online-specific programs. Taking a closer look at how indicators shift, including the student status, based on the modality of the program is another untapped area for analysis.
• Deferral Matriculation
• It is apparent that most of the applicants that deferred over the last two years are not behaving as historic deferrals.
• A deeper analysis of the deferrals starting in 2019 to date showing how many have become matriculants would be useful on two fronts:
• Understanding how many potential deferrals are still out there, and the likelihood of closing the deal with them.
• Using the deferral matriculation rates to adjust the numbers for the years that were impacted by the pandemic. This could give us a better sense of whether  behavior has truly changed, or if the behavior was the same, though postponed.
• International/Domestic Targets
• While our enrollment projection model does not break down by region, we could use history to determine how much of the target should come from domestic vs. international applicants.
• By exposing this in the ATM, it would make it more clear when the admission numbers are or are not coming from the populations expected by the program.
• RFI Matriculant Yield
• This is a complicated change to the data model for the current research, but it would be interesting to know how many RFIs result in a matriculant, and to see if it is consistent over time.
  • Watching this rate as RFI strategy changes could show if the new approach is working.
  • This will allow us to measure the effectiveness of the RFI approach, and perhaps to see if there are cases where the RFI is more effective than others and alter the information that is being shared to improve the yield rate.
• Watching this rate as RFI strategy changes could show if the new approach is working.

Other Thoughts

• New Prediction Basis Scenarios
Some programs were dramatically impacted by the pandemic, while others remained stable.
• • This was most evident in those programs that are heavily dependent on international matriculants
.It would appear that there are three potential models for prediction that could be applied to the admissions funnel
• • .
  • Traditional: Previous year indicators. 
  • How is the funnel tracking against PY metrics?
  • What are the expected final numbers based on the current pipeline when compared to the PY.
  • This could also be used to track against any given year, instead of the PY.
  • Pre-COVID: Use only 2019 calendar year indicators.
  • How is the funnel tracking against Spring-Fall 2019 metrics?
  • What are the expected final numbers based on the current pipeline when compared to 2019.
  • New Normal: 2021 or later.
    • Are the rates and yields returning to history, or moving in different directions?
    • Which programs have remained stable? Which have seen unexpected growth, which are losing ground?
  • How is the funnel tracking against Fall 2021+ metrics?
  • What are the expected final numbers based on the current pipeline when compared to Fall 2021 (or later).
  • Looking more closely at Fall 2021 could be an opportunity to better understand the long-term impact, if any, on programs based on how the world has changed over the past two years.
• Are the rates and yields returning to history, or moving in different directions?
Which programs have remained stable? Which have seen unexpected growth, which are losing ground?
• Some programs were dramatically impacted by the pandemic, while others remained stable.
• It would appear that there are three potential models for prediction that could be applied to the admissions funnel.
• Is there a correlation between RFIs and Started apps?
  • Similarity alone is not enough, even if it plays out. Look at how RFIs are related to outcomes, particularly matriculation.
• Keeping in mind that correlation is not causation, a quick review shows a lot of similarity between these two indicators, there could be something there.
Similarity alone is not enough, even if it plays out. Look at how RFIs are related to outcomes, particularly matriculation
• .
• Now that there is a more robust dataset for events, it would be interesting to create a blended view of the admissions funnel data and the recruitment data.
• With the full funnel plotted out across the timeline, how do events impact those numbers on or near the delivery date?
• Just curious: How much money do we make from fees for the fraud cases?
• Is there any link to waived fees, or are we actually getting a decent sum by allowing these cases to continue instead of trying to crack down?