Great article Bilge! I really enjoyed reading about how Burberry could use machine-learning to grow its business. This topic is very relevant to my topic of how H&M is using machine-learning to turn its company around. Since H&M is a fast-fashion retailer, its metric for success is successfully predicting a trend. On the other hand, I would argue that Burberry, a renowned fashion-house, is responsible for CREATING the trend (not predicting one). Consequently, I would recommend that Burberry not use machine learning to inform its designs because it is not only a risk to their brand but also they can not keep up with the quick pace of manufacturing that trend prediction and execution requires. Lastly, I’m not a big fan of Burberry using chat bots. As you noted, I think Burberry is a luxury brand and needs to maintain an elevated experience. In the current state, I find chat bots to be quite “mechanical” – which is the opposite of a warm, tailored experience a luxury customer would require.
Issei – awesome piece! Really enjoyed reading about ZOZO – an innovator in the e-commerce space that I have not heard of! I couldn’t agree more with your recommendation to explore brick and mortar. As a Warby Parker veteran, I’ve learned that there’s nothing more valuable than live customer feedback as the customer interacts with the product. There is so much information that is communicated “off the cuff”/informally to retail associates that companies can use to improve their product or customer experience. I’m really curious how ZOZO collects “accurate” size information – I’ve learned that consumers are generally misinformed about their sizing / how to measure themselves. I worry that inaccuracies with this data will be a detriment to their machine learning algorithm.
Thanks so much for the interesting read and perspective! Tesla’s “Open Innovation Policy” reminded me a lot of how many technology companies have a policy/culture of open source to encourage innovation and collaboration within the industry with the goal of bettering society. I question whether open innovation hurts Tesla – given that only 1% of vehicles sold use alternative energy, it’s my sense that the only way for Tesla to drive down costs (and become more competitive in the industry) is for there to be a larger mass of alternative-energy cars being made by other manufacturers. I’m curious how other manufacturers have responded to this policy – do they feel any pressure to also adopt this policy from Tesla/their customers?
Thanks so much for sharing this fun and interesting use of open innovation! To echo some of the comments above, it’s unclear whether Pepsi uses open innovation for product development versus for marketing purposes. I’d be really interested in learning a bit more about the process Pepsi uses whittle down the list of submissions to the 3 finalists on Facebook. For example, do they use the full list of submissions and analyze trends/frequent ideas? To your point, the ideas they’re receiving from customers are “flavor” names versus actual recipes, so I wonder how they distinguish between “trendy” flavors verse flavors they can execute well. I found it interesting that they collect votes on Facebook – this is very smart since they can ultimately learn so much about what their target segment may want outside of just this contest (for future product flavor profiles).
Really fascinating read and topic! Thanks so much for sharing your perspective. I was really excited to hear that Adidas is taking an aggressive approach on its 3D printing initiative with the primary goal of improving customer experience. In an industry where shoe fit is likely one of the biggest determinants of conversion, if Adidas can nail this technology, it will have a huge advantage on its competitors. I also think its a very smart move for Adidas because it will allow the company to collect a ton of data on customer’s feet, which will provide it better insight into how to design better fitting shoes at scale. To address your cost question, I don’t think it makes sense for Adidas to jump into 3D printing all-in yet, but I do think they need to be aggressive in order to acquire learnings quickly and eventually use those learning to drive down costs.
Awesome read – thanks so much for sharing your perspective. You shared some interesting use-cases for 3D printing – specifically, GE’s turboprop engine and Porsche’s rare spare parts. The framework that you presented (highly engineered & low volume+high cost) was really helpful in understanding how to most effectively apply 3D printing. Given the framework you presented, I question whether it makes sense to add 3D printers in dealerships in order to speed up the lead time on spare parts. I think it would greatly depend on the demand of these rare spare parts to justify the cost of installing these 3D printers and train the team on how to use it. I would assume that the demand for spare parts is infrequent, and as a result would recommend adding 3D printers to BMWs distribution centers where they could ship the small parts on an as-needed basis instead (and produce just in time). It would be interesting to see a cost analysis as a follow up to this research to better understand the most effective way to implement this 3D printing within their business.