This is an interesting read. It exciting that 3D printing is polarizing a number of industries, including eye wear. However, the 3D printing industry is relatively young, and there are significant technological hurdles that it must pass before it moves into industrial scale. Although, I believe in the potential of 3D printing to live up to its ambitious promises, my main concern is that while high-end industrial printing systems works well with plastics, certain metals, and ceramics, the range of material types that cannot yet be printed is extensive. How this potentially affects Safilo and its ability to scale needs to be a key consideration.
This is an interesting read. I agree with your point that machine learning can further perpetuate sub-conscious dating biases that we may have, especially if the inputs into the algorithms are information from us versus more implicit observations from a third party. I agree that Hinge has come up with ways to track these observations by presenting an opportunity for its customers to like a number of attributes on a profile rather than just swiping. Although, I agree that this technique is superior to alternatives in data gathering, I don’t believe it has scratched the surface of what makes an individual attractive to other individuals. I think that an individual’s online history may be more revealing and provide more implicit observations, however, privacy concerns maybe a hindrance, among others. Despite all this, I think predicting human interactions is a very complex process because are as humans are very complex beings. Today my preference my be X, depending on certain events and tomorrow, it may be Y. I guess what needs to be pondered is whether our preferences are fixed and the ability of a machine to make predictions on a variable that is dynamic, where the underlying causes of the dynamism is unattainable.
I enjoyed reading your article. Having worked in a number of frontier markets, I have witness how limitations in credit scoring infrastructure has starved SMEs from much needed capital. I agree that machine learning can be leveraged to compute credit scores that can form the basis for lending to this segment. Among many reasons, the barriers to loan products for SMEs stems from how expensive it is to serve this market. Given the limited amount of consolidated data on companies in this segment, data gathering and analyses is very time consuming and expensive. Also, there is no consensus on the basis upon which credit score should be calculated and school of thoughts on credit scores are very dynamic.
Specific to Mines.io, I agree that regulations that require a banking license can make this product expensive to execute. However, I am weary about the product diffusion in the market as my predication is that adaption would be low. A key concern for banks, its potential customer, is the credibility of this model. Machine learning becomes intelligent overtime and one of the ways it learns is from how past credit scores have been useful in predicting credible individuals to given loans. Who bears this learning cost and who provides the data for the machine to learn. I think this two points would pay a key role in the decision process of banks. Expecting a bank to adopt an unproven model is a herculean task and expecting a bank to pay for a value that they are instrumental in creating may be an uphill battle.
Thank you for a great read!
Very interesting article! My view is that this can become a commercial product. I can particularly see how this can be useful in my home country, Nigeria, to track the Boko Haram, the terrorist group. This group resides in northern Nigeria, which is rural and has a landmass that is covered with vegetation, which an effective hiding place for this group. Imagine if our military has this software, which can be used to track movement of this group. I think this can play a critical role in identifying their hide-outs and levelling the playing field, in terms of knowledge of the terrain.
Very interesting article! Glossier product development strategy is definitely unique and aligns with changing consumer behavior. Increasingly, we have witnessed the transition from a passive consumer to a more active consumer. Today, individuals are more interested in what goes into the beauty products they use and how it was made. Individuals are also more likely to understand the needs of their skin and are more informed about beauty products in general. However, my query with crowd-sourcing is that it may be too broad in identifying products that should be developed because it is trying to be many things to many people. Another risk, as you alluded to, is that it seems easy to replicate, so I wonder if this strategy is sustainable. I think the beauty industry is becoming commoditized and a company who is able to differentiate themselves and connect with customers by addressing their specific pain points would have a better chance of growing a sustainable business. My view is that customization of beauty products would be a winning strategy going forward, so a key consideration for Glossier is how they can leverage their platform, relative to competitors, to address this demand.
Very interesting article! My view is that the fashion industry has the responsibility to improve the environment and the social elements within its control. While I believe that Chanel is taking steps in the right direction, I don’t not think that it can disrupt the industry on it own. I agree with the BCG article that states that industry as a whole must develop partnerships and ecosystems that can commercialize and scale the most promising innovations on the horizon. 3D printing is one of them. Transformational change is not easily achieved and requires collaboration from other players in the industry to boost the speed of change. Nevertheless, the question remains whether consumers wants to take on the burden of printing clothes at home. Is this a value proposition that customers want? What is the value add to customers? I think a convenience element is removed when fashion brands such as Chanel become design companies offering design specifications that will be printed by customers at home. Also, the human expert touch is lost, which I think is a key components that drives the premium customer pay for these type of brands.
This is very interesting, especially given the role of Sephora in the value chain. As a retailer and not a manufacturer of majority the make up products that are inputs into its algorithms, I wonder whether there are further concerns that needs to be highlighted. How does the frequency of listing (launching) a new product and un-listing a product affect the learning capabilities of a machine. Also, given that the definition of beauty has evolved over time and seems to be evolving what is the merit of leveraging machine learning that uses largely historical data and how can a machine learn beauty trends that seem to be socio-cultural and dynamic.