Case Study:

Recruitment Optimization

Online University Programs

The Challenge

Two online university programs came to Lityx seeking help with their recruitment efforts. The universities weren’t meeting recruitment goals and found themselves overpromising and underdelivering.

One of the primary problems was the issue of segmentation. Both universities received a substantial number of leads from a variety of sources. But they weren’t able to clearly differentiate the leads—everyone in their pipeline was treated the same.

They needed to improve the efficiency of their recruiting efforts to get higher quality leads through the pipeline faster and more effectively.

“LityxIQ provides segmentation models to understand the drivers of key recruitment metrics.”
– Paul Maiste, President & CEO, Lityx

The Solution

Initial insights were developed to understand key recruitment metrics such as lead quality, recruitment success, and potential student retention. In addition, recruitment success models were built to predict the likelihood of enrollment for new prospective students at various points in the recruiting pipeline. These models also provided the ability to identify the characteristics of the leads who were most likely to respond, enroll, and graduate from the program. As a result of the analysis, recruitment efforts were identified to focus resources on those leads with the highest likelihood to enroll.

As a recruit moves further through the pipeline, more information becomes available about the individual. The models built were dynamic in nature, using progressively more complex data as it became available. For example, the number and types of interactions with advisors along the way, as well as third-party demographic and geographic data, become increasingly useful for creating accurate predictions as the lead moves further through the pipeline.

Dynamic scoring allows recruiters to adjust and optimize their resource allocation and focus their efforts over time, from the lead first hitting the database through to closing.”

The Results

  • 2.5x incremental lift in identifying potential enrollees
  • Potential for 33% increase in enrollment
  • Identified improvements of efficiency of marketing programs and outreach

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