How student intent changed during lockdown
Updated: Jun 11
Over the past two years we’ve been developing our app, cues.ai, which guides prospective students to take key actions in their journey to application via personalised directive messaging. The app measures the user's intent as they progress through to making an application. We then feed this information into agile marketing for our clients.
Our proprietary technology gives our team exclusive visibility into overall student engagement on a per course basis, across the sector. This means we can make ultra-targeted and strategic marketing and user experience recommendations to maximise a university’s brand message to the right prospects at the right time.
With this detailed visibility across the sector, we were able to quickly see how usage behaviour shifted after the UK lockdown was fully instigated on the 23rd March.
Our key findings were the following:
Content engagement dropped amidst the uncertainty
Alongside the downward turn in search activity in Google, the levels of overall web content interaction engagement suddenly dropped by up to 50%. Browsing behaviour shifted and felt less engaged. It wasn’t until the last week of April where engagement increased again to pre-lockdown levels.
What does this tell us? Priorities changed and predictable engagement became unpredictable.
How did we help? Our advanced segmentation of users by overall level of intent enabled us to reassure the cohort with the highest likeliness of making an application, but tailor on and off-site messaging to those who were perhaps more unsure.
Those who didn’t want to apply on lockdown week, came back quicker.
We were particularly interested in what happened to users who indicated a negative intent towards making an application on the week of the lockdown, and how this differed to overall trends.
The number of people who react negatively but then return to explore more course content generally tend to hit 36% at 9 weeks after negative reaction. However, the cohort of users who reacted negatively on the week of the 23rd March came back much quicker - 43% of these users returned after just 3 weeks, and by week 9 an average of 64% had returned.
The line chart below shows the percentage of users returning to university websites after they have indicated via the cues.ai app that they did not want to study.
The graph compares the overall average across our entire sector dataset for 2020 intake to that of the cohort who responded negatively on the week commencing 23rd March. Week 0 is when users first indicated they weren’t interested in studying.
What does this tell us? It demonstrates how volatile the decision making process has been for applicants. With the prospect of impacted employment opportunities due to economic damage, prospects may have been reevaluating their options.
How did we help? We spotted this uplift in interest from users with previous levels of more negative sentiment as it happened, and was able to action recommendations for our clients on how best to nurture this rekindled interest in making an application..
The most engaged users moved towards their desktop computers
It is a known fact now that mobile devices amount to 52% of global internet traffic, and this is often reflected in external users of a university website. However, we noticed a shift in device usage with the cohort of users that cues.ai detected as having a high interest in courses.
For 5 weeks after the 23rd March, mobile usage dropped by up to 11% and was replaced predominantly by desktop usage. Mobile usage only rose to around pre lockdown levels from the first week of May.
Interactions on course page elements which related to module content started trending higher on desktops than mobiles post-23rd March. This appeared similar to how it does two to three weeks prior to a key date in the recruitment cycle such as the January UCAS deadline. Cues.ai let us filter out staff and students to get a good view on this trend.
What does this tell us? We often find that the usage of desktop computers is linked to longer session lengths, more on-page interactions and more pages viewed per session when compared to mobile. With the correlation to increased desktop usage prior to key dates, it may be that prospects were taking a closer look at what they could study.
How did we help? We look at web usage trends in this level of detail to spot movement that we react to via on-site messaging or ad account optimisation? If we see that a specific segment of users are showing higher levels of intent, such as what we’ve seen on desktop, we can rapidly optimise ad accounts to maximise on this.
This has obviously been a period of great change and we can see this in our engagement data. The student expectation of the university experience has needed to rapidly evolve in light of the coronavirus crisis, and decisions are clearly being made to secure futures. In response to this, marketing and recruitment strategies have had to rapidly evolve to provide reassurance to applicants and help cater for the “new normal”.
The main approach we’ve taken with our clients for marketing strategy has been focussed on being extremely agile and highly responsive throughout these uncertain times. We used our data to react to the shifts in prospect expectation and demand, delivering targeted personalise messaging both on and offsite to segments of high intent users through the cues.ai platform.
The cues.ai platform is a managed service we offer to all universities and is your key to our offering of key strategic marketing insight. It was made specifically for the higher education sector and can sit alongside existing digital marketing activities.
If you’d like to learn more, either give our Brighton-based team a call on 01273 977885 or use the form at the bottom of this page to sign up for a free demo.