Lemonade, a company offering home insurance policies, is a pioneer in the InsurTech world where its use of machine learning (ML) goes beyond satisfying customers and driving efficiencies to underwriting risks and managing claims Despite its exponential growth, Lemonade’s scalability [...]
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Great article! While monetary compensation could be an attractive revenue source for teachers who are not usually paid well, I am not sure how attractive this program is for teachers. Why would a good teacher share their own material which differentiates them from the rest of the teachers?
Also, how does TPT vet the information shared by teachers? Do they check the teachers’ degrees to ensure they are credible?
I completely agree with your concern on scalability of this program given how rigid schools tend to be about their programs and since schools prefer to unify the curriculum across classes that are taught by multiple teachers who do not necessarily use TPT. I also wonder if TPT will be able to expand geographically given the language differences between countries and the variance in their curriculums.
It would be interesting though if TPT opens it up to students who are interested in learning new subjects or to parents who homeschool their children. Of course the pricing model would need to change in that case.
Very interesting article M! I wonder how attractive this model is for startups though. I understand that Unilever provides guidance and potentially an actual budget if it approves the idea, but how does this model compete with VCs that actually invest in these projects. Also given that entrepreneurs are usually very attached to their ideas and prefer to be autonomous when running their company, I am not sure how interested they will be in participating in this program. Could Unilever possibly offer something else? Perhaps this program could lead to partnerships as opposed to actual integration of the startup’s idea with Unilever’s brands?
Very interesting content! Looking at the costs and benefits of 3D printing Adidas shoes, I wonder if mass producing 3D printed customized shoes is a good idea to begin with. For starters, I am not sure how big the market for customized shoes is and how much people are willing to pay for this (to your comment on the shoes being expensive). Also, it seems that the cost benefits from this technology are not that significant for Adidas else they would have replaced their factories with 3D printers.
My suggestion for Adidas in the short-medium term would be to make 3D printed shoes a niche offering for athletes or for people with exceptional shoe sizes (e.g. very big shoe size). This way, they will manage to charge the premium price while guaranteeing demand for their products. In the long run, if this technology improves making production costs a lot less, then it would make sense for Adidas to mass produce it.
I also suggest that Adidas uses this technology in the design process of their shoes before they manufacture them in their traditional factories as opposed to using it in the production process.
Very interesting article!
I agree with Sam that ASOS will probably lose its competitive advantage if e-commerce giants like Amazon replicate this technology which is highly probable given how technologically advanced they are. If this happened, Amazon would basically mask ASOS given the huge amount of data they have.
My recommendation for ASOS in that case would be to try to differentiate themselves from a fashion perspective. So, they can capitalize on the fact that they have very creative fashion designers create trending clothes as opposed to commodities which Amazon sells. However, ASOS will need to find how to converge the creative thinking of their designers with the insights they are getting from machine learning. so if stats show that customers are into black boots, designers should build on this knowledge and design new/original black boots that could not be found outside ASOS.
Great content! I found the topic you chose to be very interesting not only because Diabetes is a dangerous diseases, but also because it severely impacts the patient’s lifestyle. However, It is not clear to me how the ML models use data to improve. It seems to me like they are simply using data to gain knowledge about diabetic people’s habits as opposed to using it to predict actual patterns. For example, I would be more intrigued by their model if it predicted what the next sugar level of the patient would be and advise him on the Insulin intakes accordingly.
Also, i think your third question is spot on as this is a concern I had when reading your article. A lot of the data that these companies need is historical medical data which is usually confidential. How can they get access to it and when do we know if we are invading patients’ privacy? Will patients still feel safe using Abbott’s products knowing that their data is being used. What if this data was shared with insurance companies?
Great content! Your suggestion for Spotify to create their own music content based on machine learning insights is very intriguing. My main concern with their model is that their “understanding” of the customer’s music preference is solely based on the music provided by Spotify and thereby their ML models are being trained on a limited set of data which could potentially define a certain aspect of the customers’ music tastes but not the whole music profile of the customer. For example, Spotify’s international music database is still very limited compared to other music streaming platforms around the world which means Spotify’s customers could potentially be into different music genres and songs that Spotify’s ML models have no idea about. This could lead to an inaccurate grouping of the customer’s preference and new content created by Spotify may not be very successful. One solution could be that Spotify partners with international music platforms or expand geographically.