Amanda Dong's Profile
Really interesting new business model! I have two concerns over their business model though.
1. Traditional big beauty brands claim their brands and chemical formula as the two most important assets. But when Volition outsource ideas and innovation, what is the competitive advantage and core assets left to Volition? For example, there are many skin care and beauty OEMs in China and Korea with fantastic manufacturing capability and some product innovation capability. Will Volition became more like the OEMs then?
2. A follow-up concern is that the profit margin for transparent products is very low compared with traditional brands. For example, ‘The Ordinary’ sells their products at very low prices because they only sell the functionality not the emotional attachments with consumers.
Great post! I am also concerned about how much differentiation barriers they have against giant brands. With more capital, R&D experience, and user bases, it might be really easy for giant brands to just copy the 3D printing shoes and consumer value proposition.
Great paper to learn about the real life-saving applications of 3D printing! My concerns are more on the commercialization of the technology or equipment. Does it require very high technical design knowledge or capability of the physicians? If yes, then the whole process of technology education for physicians might take a very long time?
I agree with the quality or compliance issue too. I remembered that Tik Tok was once banned in some Southeast Asia countries. As the business growing so quickly, there is a huge compliance or culture risk of harmful contents.
Another opportunity for Tik Tok’s commercialization might be to create a social e-commerce model. For example, many Alibaba sellers already started to sell products on Tik Tok.
As a personal RTR user, I see there are three fashion paradoxes that leave open opportunities or might not be solved by the RTR model.
1. Although everyone wants to wear luxury designer clothes at a low price, no one wants others to know that they wear used clothes. Therefore, the social embarrassment of admitting wearing used clothes is the natural obstacle for users to recommend RTR to their friends.
2. The purchase pattern for fashion is that you may want to try something new, for example, a new style for a date, new dresses for an important party, etc. This fashion purchase behavior brings the divergence in consumer preference even for the same individual. However, machine learning is about improving convergence by predicting the preference of individual user with more historical data. There is a natural tension then.
3. Individual users want to get access to unlimited selections when they sign up the service. However, RTR has to increase the inventory turnover rate as much as possible. Therefore, a very common user experience is that when you finally selected your clothes, it’s already run out and was sent to someone else. The increase in inventory turnover rate is actually harming the user experience. This is because the fundamental network effect is not that obvious for the rental of a limited number of physical objects (clothes). In traditional tech companies, the network effect means that the marginal cost for serving one more customer is really low, because the digital content or infrastructure can be easily shared and used by many users at the same time. However, when it comes to physical stuff, one more user means decreasing probability of resource availability to other users.
Among the 3 issues I listed here, I think #3 is the most critical one. It challenges the fundamental hypothesis of the RTR shared economy model.