Creating an online media strategy to attract students for Teesside University


Over the past five years, we’ve worked with Teesside University on a number of projects. We’ve helped them with a variety of services, including their online media planning, and buying services. But they were facing a new challenge – to improve ROI from their advertising spend in an increasingly competitive market. To do this, we advised that they needed to understand their audiences and the different journeys they take in more detail.

Our project roadmap that would enable us to create this robust online media strategy included…

Propensity modelling

Analysing proprietary and sector data to identify which audiences they recruit well from and where there’s potential to grow market share. This has been used to inform geographic targeting by specific campaign / school brief.

It enabled us to optimise our PPC bid strategy by postcode area. In those areas with high potential for growth, i.e. a high percentage of Teesside’s target audience but currently more likely to convert to a competitor institution. In these areas we uplifted our bids by 30%, and for those postcodes we needed to protect, already high converting, we uplifted bids by 20%. This strategy delivered positive outcomes across all key metrics.

To enrich our Propensity Modelling, we used YWare – our proprietary tool integrating key data sources (HESA, UCAS, Census and demographic data).

The tool enables us to compare Teesside with their competitor base, and to examine course trends, the demographic and ethnic profile of students, enrolment volumes and market share for all courses at all levels by feeder school / college.

Ultimately, we identified audience segments and propensity rates and by then assessing the market size of each segment we were able to identify the market potential for Teesside.


Surveying the full range of their student cohort to identify core segments / personas, and using our understanding of these (in terms of motivations, challenges, touchpoints, media habits) to inform more targeted, personalised communications by enquirer, to optimise conversion.

In terms of paid search, we have an indepth understanding of which keywords, messaging, bid strategies and geographies deliver the optimum results, and which landing pages are most effective for their various audiences. And performance metrics segmented by device and location allow us to up or down-weight bids towards particular users depending on their location and how they are searching.

We also use cookie data to inform interests outside education, allowing us to adjust bids to those with particular browsing characteristics. We also apply this to social and display targeting and content creation.

And we use SimilarWeb to analyse competitor activity in terms of audience, keywords, share of traffic and creative approach, allowing us to compete more directly and aggressively.

We have tested various strategies with regard to RLSAs (re-marketing lists for search ads) – creating Smart Lists from Teesside’s Google Analytics account has allowed us to bid higher for the most engaged users.

The results were in…

  • 79% increase in UG conversions
  • 95% increase in PG conversions
  • 30% increase in CTR due to greater relevance
  • 3% increase in overall UG conversion rate
  • 13% increase in overall PG conversion rate
  • 34% reduction in UG CPA
  • 39% reduction in PG CPA

Users are more engaged: – 3% increase in pages viewed per session – 20% increase in average session duration – 18% reduction in bounce rate.