Shark Squad originally launched when the owners found that the current swimming lessons on offer were inadequate. There was a lot of “box ticking” and not enough focus on each child’s individual progress.
The business has grown in recent years primarily by “word of mouth” referrals, with the current website being launched and maintained by a team with a non-technical background.
Reviews from clients attribute their high ratings to the friendly and unique teaching style that they have not encountered at other swimming schools.
With the swimming schools popularity growing, Shark Squad required a complete refresh of how they presented & positioned themselves online.
Their existing website was not accurately presenting their unique take on teaching and their friendly learning environment centred around children’s individual abilities.
The website content required more structure & informative relevancy to the target audience, particularly when being discovered by users with no verbal referral from someone already using the services.
A new UI was designed to replace the dated old one with a focus on accessibility, informative content and mobile optimisation.
Clear CTAs were identified and implemented to reduce cognitive load and increase booking conversions.
A new digital booking system was designed to streamline the experience and reduce the back and forth that previous booking methods required, with the focus of reducing drop-off rates caused by the over complicated, vague and long winded process that was in place before.
I conducted both qualitative user interviews, user testing and quantitative data analysis to find the following insights:
of the existing userbase access the website from their mobile device.
of participants were unable to complete the task of booking a place in unmoderated User Tests.
of interviewed users were not satisfied with the information available to be confident booking.
1. The website is currently being developed and is scheduled for launch on the 10th November.
2. Further iterations will be made based on additional user feedback after the official launch using a larger data set.
3. Identify areas for A/B testing. Optimising any underperforming areas.