Very interesting read!
Some thoughts in response to your questions:
i) In terms of competitors, I am less worried about employers in-sourcing the talent development courses since I believe they do not have a competitive advantage in education/training and are thus best served by outsourcing (particularly in a rapidly changing environment where course content is quickly adapted). However, I do agree that the current lack of internal content production, makes it possible for competitors to replicate their model and create a similar platform. In this sense, I believe that the strength of their existing relationships with universities/employers will determine their vulnerability to competitors.
ii) Finally in terms of content creation vs. control, I think there are different stages at which the company can exercise control: what courses/content do you request from your collaborators?, ii) What content do you then select to put on your site etc
Great article! Moving forward, I think Align should focus on i) ensuring that their product continues to be the best in the market (in terms of convenience of software for dentists, ease of access, consumer experience wearing the aligners, etc.), ii) Thinking of ways to access the customer more directly. In this line, I do think that having smaller, localized production centers may be helpful, and also see the opportunity to a) offer dentists a product that enables them to produce the invisalign themselves, b) assess the possibility of going direct to consumer (don’t know exactly how they would get access to the consumer’s teeth). I believe that direct to consumer is where the future is going both in terms of reducing middle-man costs and in terms of building brand loyalty.
Very interesting article! Outsourcing idea generation and selection to a broad consumer base can increase the potential for innovation (by increasing the variability). Also from a supply chain perspective, it serves to directly connect the consumer with the supplier reducing time-to-market, mis-information in terms of what consumers want vs. what the company produces etc.
A couple additional thoughts:
i) Like you, I agree on the importance of incentivizing the community of innovators/participants and ensuring it has adequate size
ii) I believe it is important for the innovators/contributors to be a representative sample of the company’s target market (the risk of only having a niche sub-set of brand-obsessed consumers giving input is that the brand may remain niche/not be able to adapt)
iii) Which products are most popular need not be aligned with profitability (does Volition take into account profitability or is it going for an exclusive growth play?)
Great article! In line with many of the above comments, I am surprised that Nike is not taking 3D manufacturing to the general consumer given the expected benefits in terms of: i) reduced operational costs, ii) better fit for consumers, iii) consumers perceiving Nike as innovative.
That being said, their focus on working with top athletes and ensuring that they have access to leading innovation and that they have a great experience in terms of performance of product and speed of delivery is in line with their historic marketing strategy of acquiring top talent and letting that drive broad consumer demand. Moreover, perhaps Nike wants to perfect their use of 3D manufacturing, before they invest in a huge expansion to the general public.
Super interesting article, really enjoyed it! While I do think that machine learning can yield benefits in terms of estimating likelihood of injury, or developing a personalized sleeping schedule and nutrition plan, I am less bought into the upside of taking it a step further to, for instance, prepare the optimal starting line up for a top team. This is because I am wary of its ability to capture exogenous data inputs. For instance a player’s on the pitch performance can be strongly affected by his on-the-day psychological state, which in turn can be affected by his personal life, the crowd, a successful tackle/drible early-on in the game, etc. These influences are hard to predict/capture in a model and can lead to confounding/limit the model’s ability to predict performance. That being said, I think there is opportunity to mitigate this lack of data by collecting a player’s health during the match.
Finally, I also question the ability of machine learning to identify future talent given the different development patterns of different players (i.e. some players physically/mentally develop superior abilities later in life).
I clearly see the societal benefit that Facebook can bring by flagging terrorist/suicidal posts, and agree that Facebook should partner with governments to determine policy (given the importance of this issue and that they are not the sole company grappling with this dilemma). Similarly, I believe that Facebook needs to be more transparent with general consumers with regards to their rights and how Facebook is using their data. I believe that consumers would be more open to data mining, and the risk of backlash would decrease if consumers felt they were in ultimate control. In this line, Europe has recently applied GDPR giving individuals more control over their personal data and the right to request that a company delete all their data (research suggests that US consumers would want to see similar initiatives implemented https://www.janrain.com/resources/industry-research/consumer-attitudes-toward-data-privacy-survey-2018).