Fundamental Problem to Solve
1. Whole mobile fitness industry
Daily activity levels have significantly reduced due to restrictions on various activities caused by the COVID-19 pandemic. As a result, obesity and adult diseases arose as an even bigger problem than before as mentioned in many media outlets.
Pandemic-induced restrictions on offline activities have led to increased demand for more online fitness platforms, which resulted in rapid market growth.
The number of global downloads of online fitness apps increased by 46% based on the half-year of 2020 compared to before the pandemic (2019) (Source: Visual Capitalist, US.2020), and many companies have jumped into this market. Since then, many have predicted the success of these companies.
However, quantitative growth did not lead to qualitative growth.
As an example, we analyzed the most symbolic cases below.
2%
: Average Daily Active User (DAU) ratio of online fitness apps (Appboy, 2021)
The business model for most online fitness apps is to produce exercise content and charge for it. Many platforms entered the market with additional features such as a well-organized exercise curriculum, a physical diagnostic algorithm, an online personal trainer, and influencers as content models, nevertheless the result was a very low DAU of 2%, as mentioned above.
We analyzed the factors of the very low DAU of fitness apps in two main ways through research.
First, YouTube’s Barrier 79% of YouTube viewers said they watch related Sports & Fitness content on YouTube. (Google/Ipsos Connect, Sports Viewer study, US. aged 18-54)
Even though, there was the inconvenience of watching advertisements and no functions such as personalized exercise curriculum or physical diagnosis algorithm that, many users still chose YouTube. The existence of YouTube, where all content is free, has been a huge barrier to the business model of fitness apps, where the cash cow is paid content. However, it was not possible to know how many YouTube fitness content viewers actually exercised.
Second, failure to target effective demand According to the Bureau of Labor Statistics (US, 2022), about 30 percent of adults regularly participate in sports and fitness activities in the United States. It is possible to infer that if the United States, where exercise is more common and has better infrastructure than other countries, has an activity rate of 30 percent, the global average will be lower. Even if this statistic is applied with a high sample error rate, it is considered that more than 60% of the population rarely exercises. This population clearly has problems with exercising, which is difficult to solve by relying on fitness apps.
Whether it's a fitness brand app that we're familiar with or an app made by a startup, the categories are divided by mostly based on the exercise level/purpose/body part/type.
The issue is not about “category” but about the “program content”.For example, some fitness apps have 40 minutes of exercise content for even the beginner level. And other categories are similarly structured.
Do you think these programs fit a population that rarely exercises?
No matter how well categorized, it is not easy for the “rare exerciser” to use the app in this content circumstance. That’s why fitness apps have been so low in usage. In fact, 30% of the regular exercise population cannot be defined as the effective demand for fitness apps. They can exercise in any environment with their own will and organized routine. They have a high understanding of their bodies and know what kind of exercise they should do. In other words, more than 60% of the population of non-regular exercisers, which is the effective demand for fitness apps, should be treated differently from the regular exerciser group.
Non-regular exercisers are the people who don't have any exercise habits yet. Therefore, the exercise approach for them should be different from the very beginning.
The goal of our project is to bring as many people as possible out of the majority of the “irregular exercise population” into 30% of the “regular exercise population”.
2. Base theory of solution
Motivation and reward are key factors to achieve our goals. Our motivation & reward model was designed based on the following theory.
Base Theory A: Expectancy theory
– a combination of expectations, rewards, and preferences
(Source : Victor Vroom, Psychologist, Canada.)
"Here is a bride-to-be who has to lose 5kg before marriage. Her father promised to buy her a new car if she lost 5 kg by exercising before the wedding ceremony. But she had never worked out before, and she was not confident in it, so she just decided to starve.".
Vroom saw that motivation is determined by:
Expectancy, a belief that effort will produce results,
Instrumentality, a belief that performance will lead to rewards,
Preference, a degree to which individuals prefer rewards.
In other words, the theory is that motivation is improved when the reward for what you are confident in is certain, and the reward is what you prefer.
If you compare Vroom's expectation theory to the above case,
The bride-to-be has no physical experience of losing 5kg by exercise, so there is no "expectation" for performance.
The father usually didn't keep his promise. There was no "belief" that he would buy her a new car even if she lost 5kg by exercising.
In fact, she already has a car and doesn't like driving, so she doesn't need a new car.
In the above case, she was not sure that exercise would produce results even if she worked out hard, had no faith that she would be rewarded for it, and even if she was rewarded, she decided to just starve because it wasn't what she wanted in particular.
Motivation is maximized when you believe that performance will be achieved, and there is a reward for it, and the reward is what you prefer.
Base Theory B: Goal Setting Theory - Conditions of Good Goals
(Source: Edwin A. Locke, Psychologist, US)
Locke's "goal-setting theory" emphasized the importance of appropriate goals. Locke said that individuals compare their current situation with their value, and they find out dissatisfaction with the current situation. Finally, that dissatisfaction leads to setting goals and action. According to the theory, in this process, goals not only determine the direction of action, but also become the criteria for evaluating behavior. If so, first of all, we need to define what a "good goal" is.
According to goal-setting theory;
A good goal should have a specific and appropriate difficulty level.
Simple and formal work rather than complicated has a high motivation effect from goal.
To achieve the goal, individuals need “Goal immersion”. And, to increase their “Goal immersion” level, their goal should be shared with others or should be executed together.
3. Suggested solution
Based on the solution-based theory, the solutions we present can be briefly described in the following graph.
Solution A
“Belief of Performance” based on Expectancy Theory
& “appropriate level of difficulty” based on Goal Setting Theory.
We have defined two main variables that can affect "belief in performance" based on expectation theory and "appropriate difficulty" based on goal setting theory, and the solutions we have presented are as follows.
1) EXERCISE EXPERIENCE:
The Less the exercise experience, the less the “belief of performance". Therefore, “Exercise experience with appropriate difficulty level" is required for them.
Our main demand group is people with less exercise experience. If they want to achieve their goals, they need to experience activities they believe they can achieve. When a goal is achieved step by step, from easy to difficult activities, the experience is learned, and the belief in achievement goes up together according to the steps. As shown in the graph above, we solve this through “Step-by-step Reward.“ If we raise the users to the highest level through "step-by-step compensation,“ they can be incorporated into 30% regular exercise group through “Continuous Reward”.
Our easiest Quest level starts with activities that don’t require any exercise. You could achieve results just by tracking food intake or body measurements. And even if it is easiest level, you can also get rewarded.
2) Duration of goal achievement:
The longer the duration to achieve goals, the less the “belief of performance”.
Our main demand group is people who lack willpower when it comes to exercise, and on top of that, if it takes a long time to achieve the goal, it becomes even harder to get there. That is why the goals should be set as short term goals.
Our quests are organized so that even if the goal is short, users can achieve results. And when results come out, “Belief of performance" goes up.
We solve this through "short-term compensation."
Solution B
Preference of reward based on Expectancy Theory.
As explained earlier, the preferences for reward may vary by individual, but we cannot provide users with a reward for the mental or unmeasurable achievements.
Of course, we have “Donation” quest or "Taking a Walk with Someone You Love“ quest which are not really material, but our reward is always “Material reward” If the users achieve the goal faster through material compensation, the satisfaction from achieving a goal itself can be considered as a bonus reward.
As shown in the graph, the reward level depends on the activity level. If you want a bigger reward, just move more.
As shown in the graph, the reward level depends on the activity level. If you want a bigger reward, just move more. We reward you with "points" for completing a quest, which you can exchange in the form you want as below:
Reuse of service in the platform
Exchange to Crypto Currency
Purchasing Alliance’s Products
Using Alliance’s Facilities
Solution C
“Act together” based on Goal Setting Theory.
Goal-setting theory emphasized the importance of sharing and act together to achieve goals.
Among our functions, Social Mode brings users together offline, increasing the rate of goal achievement based on homogeneity and competition. In addition, verification reliability ensures transparency in achieving goals between each other.
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