Every admissions office wants to predict the future—how many students will apply, which marketing channels will convert, and what final enrollment numbers will look like. But most predictive models in admissions are deeply flawed—not because of bad math, but because they rely on outdated assumptions, static data, and a false sense of certainty.

In this episode, we break down:
— Why traditional enrollment forecasts fail to predict real student behavior
— How rigid forecasting locks schools into bad assumptions instead of adapting
— What it takes to build an adaptive model that actually improves over time

If your enrollment predictions are still based on last year’s trends, you’re already behind the curve. The future of admissions forecasting isn’t about being right—it’s about building models that learn, adjust, and guide real-time strategy.

🎧 Listen now to learn why predictive modeling in admissions needs a major upgrade.

🔥 Huntress on Analytics is created, written, and produced by Katherine R. Lieber, founder of Huntress Analytics and TitaniumBlue Insights.