Case study by Amar Puni
Wembley Stadium and the NFL
We were asked to create a mobile phone app which would help guests navigate points of interest i.e toilets, concession stands, around the stadium and also allow them to order food & drinks from the comfort of their seats, for either collection or delivery
TOOLS
Paper, sharpie’s, scissors, tape, post-it-notes, InVision prototyping, Sketch, Omnigraffle.
SKILLS
Business analysis; competitive analysis; research
User research; user interviews; user flows; sketching; paper prototyping;
BRIEF
To leverage the current Wembley app to give attendees better discovery of the stadium facilities and to simplify the process of purchasing concessions and merchandise from the counter and their seats.
PROBLEM
Because of the size of the stadium people find it hard to navigate their way around. The current app offers very limited information in terms of navigation and assistance with delivery or orders. Queuing is also a major problem which we need to try and rectify while considering re-designing the app. Because the current app is more of an information tool, we have been able to easily identify some key feature that will help enhance the in-stadium experience.
HYPOTHESIS
● Enhance the existing app with more proactive features to assist users.
● Provide information to the user on demand
● Help users navigate the stadium to find and use all stadium facilities
● Proactively offer ways of avoiding long queues
● Incorporate in-seat ordering to prevent users from missing any game action
We believe that adding an in-seat order function with collection or delivery will alleviate the stress of queuing and missing the game.
We’ll know this is true when we see more people using the app and food/merchandise sales increase.
OUR ROLE
UX Researcher UX Designer
RESEARCH
We undertook initial research on Wembley Stadium, the existing Wembley App and the NFL. We needed to understand who our “client” was as well as knowing how the existing product works, and understanding what environment our users would be using the app.
Further research was then done on NFL Stadiums in the USA and existing NFL Apps. These provided great insight into what can be done and how well the NFL are already doing this.
We also did competitive analysis on other UK Stadiums and other Apps which offer similar services to what we might be able to incorporate, such as food delivery.
As a team we created a user survey on google docs which allowed us to narrow down to our target market.
The responses that we gained from this helped us to compile questionnaires that were more directed to our target audience — Stadium goers and people that have informed us of good services that they have received not solely from stadiums but places where they deemed to have received good service for example cinemas that bring you food & drinks and festivals.
The User questionnaire allowed us to understand what experiences our interviewee’s liked and also helped us identify the pain points when they are at these events. As Wembley Stadium hosts many events, other than just NFL we wanted to include all events where they have had an enriched experience, this is because the App will potentially be used for more than just NFL. So broadening the research and analysis to incorporate festivals, music events, large venues etc helped to understand other issues users may have.
Research on trends in technology and digital experiences for sporting events was also done to understanding what the future of Stadiums may look like.
AFFINITY MAPS, TASK ANALYSIS & USERFLOW’s
After we received feedback from our questionnaire we added some of the main points we collated from our questionnaires and mapped this out onto a affinity map. A affinity map
was the perfect tool for us to use in this exercise as we were dealing with a lot of data from multiple sources in this case a user survey & questionnaire.
This allowed us to group some of the answers we gathered and map them onto specific relationships we had in relation to our questionnaire.
A task analysis helped us understand the pain points associated with arriving at the stadium, finding a food stall, ordering and buying food and then navigating to our seat.
Once we understood our key issues and our key features we were able to sketch out our user flows. We divided this up between the team and produced a user flow each. There was a crossover between Food and Merchandise with the checkout process, as well as the delivery/collection options.
I conducted the flow for purchasing merchandise & collecting your items from a pick-up point.
This was then transferred into a digital format by using omnigraffle -
The user flows showed us that the food/merchandise processes were going to be the most difficult areas to introduce so as a team we did a design studio for this process.
SKETCHES
Initial drawn sketch screens were tested with users to get feedback. This also gave us ideas for other features as well as improving the flow as it exists.
This was the ordering food and choosing your collection point low fidelity wireframe -
We then went on to conduct some user testing to gain some feedback into our approach, which allowed us to make changes where we needed to i.e integrating a traffic light system into the queuing time to allow users to visualise the the length they would need to wait the time highlighted in green will be the quickest to go to, red will be the longest wait and amber would be in-between the two.
PROTOTYPE
Moving to digital screens meant we could stitch them together in InVision and create a clickable prototype for further user testing.
The initial version was low fidelity with no images, but did show layout, buttons and labelling and the feature functionality.
We had great feedback from two very different users: a group of guys who are not technologically advanced and didn’t really use Apps.
The other user feedback came from James who is a big sports fan and attends games regularly. He has an iPhone and an Apple Watch so matches our key persona very well. His feedback was great and we amended the checkout process with additional payment options based on his suggestions.
With each digital iteration we further tested and gained further insight into user preference, navigation and understanding of the App. We added on-boarding screens and made the whole process much more proactive rather than reactive.
We redesigned the App menu to be a bottom tab menu rather than hamburger menu to make it more user friendly.
We also moved Transport out of the main menu and into ‘More’ option as this was not highly used, most users said they would use Google Maps or CityMapper to get to and fro the stadium. This gave us the opportunity to add Merchandise to the main menu and pull it out of the food ordering section. This highlights both the Orders (food and drink) and the Merchandise and will help highlight the sales aspect of the App.
OUTCOME
We now have a fully functional clickable prototype which we can further test with users.
The features include:
● Order food and drink from seat for collection or delivery
● Order merchandise from seat for collection
● Find a point of interest in the Stadium, such as food and merchandise stands,program stands, toilets, areas of other entertainment during the game
● Find an available nearby toilet based on your seat or current location
LOOKING FORWARD
From the user interviews and user testing we received a lot of feedback and ideas of what features could potentially be added to the App. Keeping in mind our key user types and their needs we prioritised this long list of features.
During this process we have focussed on the in-stadium experience for our users. Next version we will help improve the out-of-stadium experience.
● Exit queue information — so users can exit the stadium quickly and easily without large queues
API to Google Maps or CityMapper — to assist with getting to and from the stadium easier, as well as in-stadium navigation
Parking information and live updates — to making finding a parking spot easier and to avoid traffic queues when exiting the stadium area
PROTOTYPE LINK
Our Wembley Stadium app prototype can be found here:
