I’m not from your section but I used to work on the Einstein product team prior to business school, it made me so happy to read your insights/write-up!
Great article! It was interesting that you highlighted the resistance to adoption from physicians (especially respected ones) despite the benefits of this 3D printing application. I’m interested to see if such applications are already included in medical education (both in medical school and continuing education programs)/explore when the best time is for practitioners to be exposed to this technology. Since such applications are still in nascency, I also wonder if risks around lawsuits also act as a barrier to adoption.
Great article and interesting business model (sort of an incubator for cosmetic ideas) which is quite different from other players in the industry! However, I have several reservations around the sustainability of this model given the low barrier to entry/no clear moat, and completely agree with you that it is key for the company to implement incentives to retain innovators on their platform. This will ultimately determine their success. If they are able to do this effectively, I can see them scaling/replicating this model to cover many other adjacent categories (haircare, etc).
This is a really interesting application of ML in a unique space, thank you for sharing! I do think that it will be difficult to apply this solution to green fields given that the availability and accuracy of data forms the cornerstone of an ML solution’s effectiveness. I also wonder if companies would be even willing to fund/work on such “green field” solutions given it is extremely risky with high costs of research and development, and also high real costs and opportunity costs of “failure” if the solution is implemented and does not work.
We often think of large companies as being very “traditional/bureaucratic” and slow to embrace innovation/unwilling to cede control over their processes, so I really enjoyed your article as it showed a positive example of how Nestle is going against this stereotype. With regard to your open question, I do think that the company could pursue a dual strategy where it continues its actions around releasing competitions/requests to collaborate, but also works with relevant partners such as accelerators and incubators to connect with startups who would be most well-equipped to build solutions for these questions.
This is a really interesting application of AM which is quite different from what we’ve seen in other commercial applications. I like your recommendation of encouraging Field Ready to develop training programs to empower locals to use 3D printers. This also serves a mutual benefit to them since you’ve highlighted that AM’s biggest challenge lies in finding trained engineers to design solutions. I would love to understand who Field Ready’s investors are and the organization’s relationships with local NGOs/foundations since I think this would help to address your open question of how feasible and cost-effective this solution could be in the long term. I could also envision a world where Field Ready works closely with local governments to leverage existing infrastructure to reach disaster-hit areas cheaply and quickly.
Looking at Modiface’s augmented reality product offering, I’m finding difficulty understanding how the platform leverages machine learning specifically to generate its recommendations so would have loved to see more detail on that front (e.g. are they leveraging a customer’s previous purchasing behavior to refine their model? Or is it simply based on superficial features like face shape, etc). I agree with you that there might be some distrust from customers in any of these technological offerings since they may not fully understand the logic behind predictions (e.g. are the recommendation engines providing predictions based on higher margin SKUs or truly based on what is best for the customer) so it might be in L’Oréal’s interest to provide more transparency to the consumer and also give consumers a chance to provide direct feedback to the model.
I think you’ve made a very salient point that in our hyper competitive world of video streaming, it is extremely key for Netflix to leverage ML to create a differentiated recommendation engine. I’m interested to learn how the competitors you mentioned are tackling this problem and if this differs greatly from Netflix’s ML approach. I also found it very interesting that Netflix used to host the “Netflix Prize challenge” but has not done so in recent years. I agree that it is important for Netflix to keep innovating on this front not just in terms of improving their algorithms, but also in terms of including more training data/sources and incorporating more user feedback. To address your second open question on potential biases, I think it is important that Netflix increases the transparency to users around how their data is being used and how the recommendation engine uses that data (e.g. giving users the opportunity to understand some simple logic behind predictions which could also help to build trust between users and the engine). Additionally, Netflix should also give users some sort of option to give feedback to the engine (maybe with a simple button interface) about whether they found the predictions to useful.
I think Alibaba has executed extremely well in leveraging their immense repository of customer data to build in-house ML systems which add value to key business problems (e.g. customer service, recommendation engine). As you have outlined, the productivity and revenue gains for the company have been tremendous, along with added benefits to Alibaba’s merchants and customers. Since AliMe was launched in 2015, I would have loved to learn more about what Alibaba has done to incorporate employee feedback and improve on their ML systems. To address your first open question, I think Alibaba can also apply its ML solutions to help develop their own private label brands and services since they have an intimate understanding of customer behavior and preferences.