Nike Training Club Case Study
Role
UI/UX Designer, Graphic Designer, User Researcher
Timeline
Semester-long project in Spring 2021
I conducted a case study of the Nike Training Club (NTC) app to better understand how users feel about fitness and fitness apps. I found areas for improvement in NTC, and prototyped suggested solutions.
Overview
I have always enjoyed fitness, playing sports, and have considered myself an athletic person. Yet, despite enjoying exercise, there were times I found it incredibly difficult to gather motivation to plan and execute workouts. I suspected other people had the same struggles in building and maintaining a workout routine, so I set out to conduct user research on how people interacted with fitness and technology. Specifically, I used one of my favorite fitness apps to better understand how people feel about exercise. In Nike Training Club, users can browse for workouts, follow programs, and view thematic collections of workouts. While these avenues for workout selection are effective, they burden the user with decisions about their fitness. This can make exercising feel intimidating or too big of a task.
I went on to design a set of features to address the shortcomings in workout apps. View my design in Figma.
The Problem
When people exercise, they want to be happy and satisfied with their fitness, but they can’t achieve this because it’s difficult to schedule and maintain a workout routine and hard to get up and start a workout.
Idea
While there are a plethora of workout apps that my interview participants and many others use, they often leave all of the training plan creation and workout selection in the hands of the users. The apps— like Strava, Nike Run, FitBit, Shred, and the Nike Training app— benefit by giving their audience complete autonomy over their routines. This might be helpful for regimented athletes, but for many others, they can feel lost and unmotivated by struggling to find the perfect workout for the day or to help them reach their goals.
The problem with these apps— and why I think my proposed feature is so valuable— is that these features are typically hidden behind a high paywall. While some features of these apps are free, personalized content typically requires a monthly subscription fee which excludes a lot of users. Even though the paid-for features might be worth the money and sometimes include one-on-one advice from personal trainers, I think there is a way to algorithmically offer personalized workout plans and suggestions that would put NTC a step above competitors.
Challenges
The primary consideration I needed to keep in mind was balancing autonomy and automation. It was important to my target audience that they had control over their workout plans, but also that they could be given recommendations and plans with minimal time spent curating those plans. Further, this feature needed to seamlessly fit into NTC’s existing design, and offer something that makes the best usage of existing features.
Feature 1: Goal-Based Planning
From my user interviews, it was clear that people chose workouts based on general goals and preferences. These overarching goals stayed relatively consistent, like “increasing my cardio stamina and losing weight” or “increasing my upper-body strength.” Users also had general preferences for how much time they dedicated towards workouts and where they were exercising, which I also considered when building my features. The entry point to my new set of features was inputting such preferences.
The process of adding goals.
Users enter workout frequency, preferred duration, focuses, goals, target muscle groups, where they will be working out, and if they want reminders.
From my user research, it was clear that people don’t want to spend a lot of unnecessary time planning workouts, so the input needed to be simple, clear, and fast. Categories were based on what users told me they considered when selecting a workout, as well as their motivations and long-term goals. The goals were located on a new “My Goals” tab, which fit into the existing organizational structure of NTC, and users were clearly prompted to enter goals upon their first visit to this tab.
Feature 2: Weekly Scheduling
One thing that interviewees mentioned was that it can be hard to pick a workout even when they blocked out the time for it. Because of this, the suggested workouts are based on the number of times the user wants to workout in a week, and the user can view a timetable of suggestions for that week. Workouts focus on a specific goal that the user inputted to increase feelings of satisfaction and provide more tangible progression.
How to view weekly workouts
Users can view a weekly routine on the goals page, by selecting the “This Week” link next to the day’s workout. This shows workouts by the day, along with the focus of that day.
Having scheduled workouts removes some of the burden of planning workouts from the user, while also not locking them into anything— users are still free to choose different workouts from those suggested. It also offers workouts that the user may not have been able to find on their own, ro may not have considered on their own.
Feature 3: Mood-Based Suggestions
Perhaps the most challenging aspect of keeping a workout routine was not the planning, but gaining motivation short-term to get up and begin a workout. For this, I implemented a feature on the top of the goals tab to select how the user is feeling in the moment and provide more specific suggestions than the weekly options. This would encourage users to keep their routines even when they aren’t feeling their best.
The interaction of selecting mood-based workouts.
There is a horizontal-scrolling carousel at the top of the goals page with a list of mood options. The user can select several, and tap “Next” to view suggestions.
This step allows a sort of fine-tuning of workouts; not only would the app consider the mood inputs to suggest options, but could also offer choices that fit into the user’s long term goals. This is also perhaps the most unique feature I suggest, as it considers not only the long-term, goal-based preferences, but also short-term feelings and emotional state. It fits nicely with the proposed features and existing functionalities of NTC to offer more wholistic suggestions that can encourage fitness even on bad days.
The full designs.
This short clip shows end-to-end how the features fit together in NTC.
The Takeaway
This was my first time doing a case study on a preexisting tool, and my first time working on a design by myself. I think adding features to existing designs presents a challenge distinctly different than creating completely new concepts. Namely, my main struggle was fitting the entry point of my feature into NTC because it was more difficult to understand the importance of each aspect, and how much value my feature had.
The goal-centered workouts feature I prototyped for NTC addresses the lack of personalization and desire for motivation that users want. The feature offers the right amount of autonomy while giving a guideline to follow when users are feeling lost. Fitness is an important part of many people’s lives to improve both physical and mental well being, and this feature has the potential to reduce barriers to entry. Helping users stay motivated to exercise and reach their goals will help them keep their fitness habits — even on the bad days.