Great article! I had no clue Lego had turned to open innovation for product development and product improvement.
I believe that Lego can further expand its reach and attract bright minds and great ideas but creating some form of award for the best innovative ideas. For example, for a person that comes up with an idea, Lego can use a crowdsourcing form of voting for the idea. If the ideas get enough traction, Lego can select the top ideas and provide some sort of monetary benefit to these inventors. In addition, it can implement these ideas and the one with the highest ROI in a year time period could receive some sort of certificate, or additional monetary rewards (if only one idea was implemented, if the ROI crosses some sort of threshold, we can reward the person).
Very interesting article!
Crowdsourcing has been implemented by multiple government across the world to actively engage their constituents, leverage their collective intellect and collect insights. I believe that governments can leverage it for multiple reasons: gather information (e.g. report problems), help with some tasks (e.g. classification of records), ask a problem and find solutions, test the popularity of an idea through voting, raise funds (e.g. crowdfunding for presidential campaigns), etc.
In my opinion, AI and machine learning can have a huge impact on crowdsourcing. For example, AI and machine learning can support users reporting a problem through the crowsourcing platform by directly providing them with a list of solutions/ contacts based on previous similar reports. Another example where I see AI and machine learning helping is by quickly filtering and narrowing down on important posts (e.g. filter comments of users who have always put bad ideas, identify key words in posts and elevate their importance). Another way could be using AI and machine learning to directly classify users into topic expertise and potentially automatically push relevant problems to topic experts and provide them with incentive to participate (e.g. monetary award, recognition).
This is a great article!
I believe that Adidas, along with many of its competitors and other players in the fashion industry, are rightfully investing and growing their 3D printing capabilities. Not only can Adidas allow customers to customize their own shoes, but it can also use 3D models to quickly test new ideas, models, features, shapes, etc.
I see some risk in losing customers to other 3D printing companies. The positive thing for Adidas is: the brand has already a loyal base and has very high visibility (leveraging some Marketing terms – thank you Roger’s 5) – people like being seen wearing the Adidas brand. However, I believe this could impact Adidas mainly in terms of costs. As other competitors become increasingly good at leveraging 3D printing and as the technology progresses, their costs will start falling drastically and if Adidas doesn’t catch up, then I don’t think the brand value is strong enough to explain such high prices.
In addition, I agree with Raleigh about the possibility to leverage machine learning to improve, mainly in terms of performance. In fact, the ability to quickly produce and test can allow Adidas to include some sensors in the shoes to monitor key attributes (e.g. pressure points, inertia). This data coupled with consumer input will enable Adidas to develop better shoes for its customers. (They could even potentially test it with 3D printing and manufacture it normally based on consumer preference/ shoe success).
This is a good article, very informative!
I believe the 3D printing still has not reach its full capabilities in order to be adopted by the mass.
I don’t believe I will be using 3D printing in the near future given that to my knowledge (which is limited on the topic), the ability to print different items depends on key factors: availability of raw material and pricing of these material, the know-how in order to manipulate the printer or the existence of templates that can be leveraged, etc. But I believe if we were to overcome these barriers, I think it is a very useful tool. Personally, I believe I would use it mainly for small objects because the bigger the object, the more sophisticated the printer has to be (thus more expensive), and the less likely that the template will be easy to get (e.g. expensive, patented). In addition, I do not feel that I would be comfortable using it for items that could potentially harm me or anyone around me. For example, I would not feel comfortable printing tires (given that the malfunction of these items could potentially lead to an accident), butI wouldn’t mind printing cup holders or in-car mirrors.
This is a very interesting article! In respond to question 3, I believe once a user has a interacted with the site enough times, Airbnb can not only recommend the best house based on previous needs but also recommend the next destination based on previous behavior. I believe Airbnb can leverage its data (e.g. destinations visited, types of houses, number of guests, who are these guests, reviews posted, number of messages with hosts) to develop predictions and engage customers further by offering house suggestions, destinations suggestions and much more.
In addition, I believe Airbnb can capture more loyal users by launching a loyalty program. The company was considering launching this loyalty program but still didn’t go through with the plan. I believe by launching the loyalty program, the company can potentially leverage more data (e.g. by making sure loyal members reserve using their accounts not other accounts). The company could provide points to the users based on the number of times they booked, how many different locations did they visit, the number of helpful reviews they posted, the properties they own that have been ranked highly/ reviewed positively, etc. This can be helpful to boost brand loyalty (beyond the typical loyalty programs of hotels); however, appropriate weights should be assigned to each actibity-to-point conversion and key controls should be put in place to avoid/ detect fraudulent behavior.
Great article, very interesting information!
I believe by continuing to monitor behavior and expanding its data set, Netflix can improve not only its recommendation engine, but also its product development engine (i.e. insights on what content to produce / acquire).
On monitoring behavior, Netflix can monitor multiple features in order to improve its predictions. These include, but are not limited to, the type of content watched, time (e.g. morning, evening, night), weekday vs. weekend, frequency, starring actors, duration of show/ movie, language of show/ content, etc. However, all these features can also be monitored by cinemas with good loyalty programs (I mean strictly in terms of features, not in terms of size of data set or number/ type of content available). However, Netflix could explore even more features (if not already explored) such as: how many times did you stop while watching? How long did it take to finish one show/ movie? Which parts did you repeat? Did you finish the show/ movie (which could potentially indicate if you like it or not)? Did you change the original language of the show? Did you add subtitles? etc.
In terms of data set, Neflix can grow this in multiple ways. Examples include attracting more customers, getting more content on the platform, etc. Another way could be trying to strike data exchange deals with its partners (e.g. with Comcast). This might have a lot of regulatory implications, but could be worth investigating.