QS Enrolment Solutions: University of Stirling

Black and white image of Lisa Wilkisky-Dick, Director of Marketing at the University of Stirling

“QS machine learning enables us to identify and focus on the offers with the highest propensity to convert.”

Lisa Wilkisky-Dick, Director of Marketing, Deputy Director Communications, Marketing & Recruitment

How QS machine learning impacted the University of Stirling’s student recruitment:

  • High propensity group converted 2.9 x better than when no machine learning was applied.
  • Delivered on their student diversification objectives.

“A large proportion of our application growth is coming from emerging international markets that traditionally have lower offer to enrolment conversion rates. We need to be able to manage expectations and assist our internal stakeholders to forecast and plan. We also need to be able to prioritise our conversion efforts and ensure that we focus on areas that provide us with the best return on our investment.

The key benefit of using QS machine learning technology is that it enables Stirling to identify and focus on the offers with the highest propensity to convert and to prioritise our efforts to increase conversion. Increasing that conversion rate by even the smallest margin, like 1 to 2 per cent, can make a significant difference to the outcome. Machine learning assists with increasing our enrolment numbers and helps us to improve the diversification of enrolments from international markets and provide an excellent student experience.”

Black and white image of Lisa Wilkisky-Dick, Director of Marketing at the University of Stirling

More Testimonials

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