This was such an interesting read, thanks for sharing! I think it’s great how PepsiCo is using customer inputs to create its products. It’s a win-win situation: customers get what they want and PepsiCo does well as a result! And kills two birds with one stone: (1) product development and (2) marketing. It’s quite genius, really!
This was an excellent read, thanks for sharing! I think the opportunity for high customization in the shoe industry represents a massive gap in the market and a huge area for growth for UA. Shoes and their comfort play a critical role in our lives. As Harvard professor Tal Ben-Shahar shares in his positive psychology class, one of his researched 14 key points explaining how to be happy in your life includes wearing comfortable shoes and clothes. If UA can find a way to customize shoes in a cost efficient manner and be first to market, I think they’ll be the winners in the space.
Great read – thanks for sharing. It’s fascinating to see how additive manufacturing is shaping the beauty industry and how Chanel is leveraging this technology to design its mascara brushes. However, I question what the barriers to entry may be. What’s stopping others or will stop others, such as Dior, from doing the same? Additionally, given the relatively low price of this product for Chanel versus the rest of its product portfolio, a high volume of sales is required to make it an attractive product for the company to offer. Are there significant benefits to using 3D printing versus traditional manufacturing to manufacture this product? To your point about customization, however, it would be amazing for customers to be able to customize makeup products to their specifications.
This was such a great read, thank you for sharing! It’s crazy to see how machine learning is permeating every aspect of our lives from what we watch to who we date! It was very interesting to learn how machine learning is being used to push customized date “options” similar to what Netflix is doing with content. My fear, however, is that by doing so, a users options would be limited in terms of “playing the field” or “exploring options”, a common desire for those turning to dating apps, who often want to diversify their current option pool. With respect to matching you to people who are most like you through sharing your iCal etc., I also question whether this is the right move. Often times, people are most compatible with people who are different than them – as they say, “opposites attract”, no? Again, I fear that this may limit a user’s options.
Thanks for sharing this piece, Tina! I am fascinated by machine learning in the fast fashion space and am very excited to see how it continues to inform the industry’s future. While machine learning may help H&M better understand its target customer (e.g., women vs. men) I believe that their use of machine learning will not be meaningful on its own in turning around the company’s performance. As you mentioned, one of largest problems H&M currently faces is its inability to respond to trends quickly enough. In order to succeed in competing against others in the space like Zara, H&M must first improve its current manufacturing / supply chain. Only then will it be able to take the learnings from its machine learning technology to inform the products they are creating for consumers.