Student Number Planning
Accurately Projecting Student Numbers and FP&A to Improve Decision-making
Download Case StudyStudent Number Planning
Accurately Projecting Student Numbers to Improve Decision-making
Download PDF VersionHome - Case Studies - York St John – Student Number Planning
Key Facts
Client: York St. John University
Department: Strategy and Planning
Tech Solution: Alteryx
Challenges Faced:
- Manual data manipulation across many Excel and Access files
- Different future scenario planning required too much effort
- Modelling of outlier cases made planning process very difficult
Alteryx in Higher Education
Challenge
With nearly 7,000 students, York St. John University’s original student number planning solution involved a mainly manual, Excel-based approach that was dependent on a handful of Finance team individuals. The process was not sufficiently documented or visible to a wider teaching audience. This sort of ‘black box’ process led to a distrust amongst management in regards to the outputs and made longer-term forecasting of numbers difficult and time consuming.
Solution
To streamline the complex process of creating one accurate projection, the team has decided to leverage Alteryx, cutting down calculation time from over a week to just a couple of seconds, all the while improving their predictive capabilities. Aside from the business logic itself, the workflow also contains various data validation rules, testing key data joins and automatically highlighting and identifying data quality issues should they arise. This makes it very easy for everyone to understand, use, edit or customise.
Student Number Planning in Action
(This video is a general higher education solution video, and is not specific to York St. John University)

Benefits
The undergraduate home student’s model was fully automated, catering for 85% of university’s tuition fee income. The team now focuses on building a separate model covering non undergraduate home students, further improving projections’ accuracy and eliminating human-induced errors. The more transparent model helps them make informed, focused decisions.
- Key process automation was completed within 3 business days
- Future analysis planning/what if analyses are run within seconds
- The transparent and accurate model improves decision-making