Emily Chase Coleman

In this conversation, we interview Emily Chase Coleman from HAI Analytics about the importance of analytics and statistical analysis in higher education. Emily's company is dedicated to helping colleges and universities optimize their enrollment and financial aid strategies through predictive modeling, ensuring that these institutions meet their goals while maintaining financial sustainability.

Emily shares her background in social psychology and statistics and how she got into the field of enrollment management and predictive modeling. Sher and Emily discuss the types of data that institutions collect, including application data, financial data, and student behavior data. Emily explains how predictive modeling can help institutions make data-driven decisions and optimize their financial aid strategies. They also touch on the challenges of using standardized testing in predictive modeling and the importance of collaboration and understanding the student life cycle in implementing data-driven strategies.

Key Takeaways:

Analytics and statistical analysis are crucial in making data-driven decisions in higher education.

Predictive modeling can help institutions optimize their financial aid strategies and improve student retention.

The types of data collected in higher education include application data, financial data, and student behavior data.

Standardized testing is not always the best predictor of student success and should be used in conjunction with other factors.

Collaboration and understanding the student life cycle are key in implementing data-driven strategies.

Learn more and connect with Emily here:

https://www.linkedin.com/in/emily-chase-coleman-95062779/

https://haianalytics.com/

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