In formulating your recommendations for recruiting new members while re-engaging existing members, did you consider somehow establishing a partnership between power users and new members, instead of having one “disrupt” the other? Something like a mentorship program or team-based challenge? There would definitely be difficulties in implementation, considering the various locations where users live, but I wonder if there is value in asking community members from very different backgrounds, who don’t already know one another, to collaborate on a single solution.
This is really interesting. Though some people may push back against AM because it will eliminate human jobs, it can deliver a lot of social benefits as well. Specifically, AM in construction could help solve the affordable housing crisis in the US (https://www.thedailybeast.com/can-3d-printed-homes-solve-the-urban-housing-crisis). Government-backed projects and public works could help get AM construction off the ground and shift public perception around the technology.
I think there will be push-back to 3D printed plane parts from commercial airline customers. It seems like a less reliable manufacturing method to those who don’t know enough about it. I came across this article from 2017 about how the aerospace industry is advocating for FAA approval of AM (https://spacenews.com/faa-prepares-guidance-for-wave-of-3d-printed-aerospace-parts/). It would be interesting to see Boeing partner with military and space-related organizations to champion AM and increase confidence among customers.
I can see your point, that focusing on one existing product dissuades employees of a startup from dreaming up solutions to new problems, but for FDNA this focus makes sense. The information it stores is extremely sensitive. The inputs to this algorithm, patient images, contain classified medical information and are probably hard to anonymize. With such a high-risk product, I think specialization is key.
How does iFLY prioritize what feedback and ideas to engage with? It seems like the sample includes consumers from diverse age ranges and levels of experience and expertise–that must be tough to sort through. For this to be sustainable, I think there needs to be a clearer funnel for evaluating information that comes through these crowdsourcing platforms.
I’m really interested in your point about how Disney can leverage innovation and machine learning to develop content for a wider spectrum of consumers. Interpreting “wide spectrum” as “more diverse”, I wonder what data Disney already has access to that it could use as inputs for this model. Perhaps it could use data from sibling networks in the Disney family, like Freeform, that have been piloting multicultural programming for the last few years. I also wonder if there is an opportunity, through customer loyalty accounts that power “magic bands”, to connect their off-screen behavior in parks with on-screen behavior for their new programs & delivery channels.