This was very interesting, Gigi! I also wrote about a machine learning agriculture startup focused on local farmers. It was fascinating to read about the go-to-market strategy and see how XAG decided to focus on what appear to be relatively small farms, as opposed to the large commercial farms that seem to be the target of most machine learning-based agriculture startups. I assume this is at least in part driven by the characteristics of the agricultural sector in China. It was interesting that XAG decided to go with the agri-equipment dealers rather than the seed/chemical dealers, given that cross-sell opportunities for pesticides/fertilizers the platform is recommending to farmers. But the need to provide an after-sales process for drones make sense.
Fascinating post! It was really interesting to see how the value of machine learning in this application was not only about the amplification of computing power, but changing the methodology of flood prediction itself–from bottom-up to top-down. I agree that one of the main challenges will be managing the sales process. It definitely seems like there would be a huge market for this product across the public and private sectors, but both governments and the large private sector organizations that are likely to be interested will have relatively long sale cycles.
Very interesting post, Yifei! It was interesting to read about the competitive landscape. I wonder if this is a segment that the music streaming platforms are well-positioned to enter given the amount of data they have on consumer preferences–and the various patterns they might be able to recognize about trends in musical styles. I agree with your take that AIVA should focus on the production agencies as the end customer. Listening to the sample from Pixelfield, it sounds like AIVA’s product is perfect for a businesses that would otherwise purchase “stock” background music with simple chord patterns.
Thanks for this interesting post, Paulina! Reading about Mercado Libre, it was hard not to see the parallels to Alibaba in terms of where the businesses started and how it enabled them to expand. In both cases, there was significant pressure to expand scope in order to scale. And likewise, there were synergies on both the supply and demand side. The data flywheel enabled data generated through one service (e.g., payments) to enable another (e.g., credit). And for customers, using one service (e.g., marketplace) made it easier to use another (e.g., payments). It will be interesting to see whether Mercado Libre will expand outside of Latin America, perhaps through partnerships, as Alibaba has done.
Super interesting, Aditya! While it seems like Dunzo has done phenomenally since it launched, your post makes me wonder about the business’s sustainability. To date, a lot of the success seems to be driven by the team’s operating excellence and great customer service. I would guess that customer acquisition costs are particular high given the competition in the on-demand delivery space and the need to attract a robust supply of drivers on the platform to deliver on the platform’s customer promise for quick service. My main concern would be competition from the ride hailing and food delivery services, given the tendency to multi-home (as your anecdote at the end illustrates). It seems like it would be relatively easy for these platforms to expand their service offerings to compete with Dunzo.
This was fascinating, Yifei! Your account of why mPharma eventually failed highlights the importance of understanding the ecosystem when you are trying to build a platform business. As you noted, it seems like mPharma spent time focusing on one side of the ecosystem (doctors), when it also needed to pay attention to others. The network that mPharma was trying to build seems exceptionally complicated because many of the successful platforms we have studied are simply two-sided. Here, mPharma had at least 4 separate parties whose buy-in was necessary for the platform to be valuable to the others. A related lesson is that platform businesses sometimes have to build the infrastructure that they need to succeed. The fragmented nature of the drug supply chain seems like a permanent bar to onboarding patients on the platform, so it makes sense that mPharma has pivoted to trying to solve this problem first.
This was fascinating, Nitya. Like others, I wondered how much difficult it will be for PRH to compete with Amazon. But your post also reminded me about an interesting article I read a few years ago about the CEO who turned around the Waterstone’s bookstore chain in the UK. Waterstone’s basically stopped taking money from the publishers, who had dictated which titles would be prioritized in the bookstores. Instead, Waterstone’s started delegating those decisions to local branches and sales took off. The big takeaway for me was that Amazon’s data analytics are “passive” and so while it can tell you what is selling well, “it doesn’t spotlight unknown books that deserve a wide audience. It can’t make a literary star, something Waterstones now does with regularity.” (https://www.nytimes.com/2019/08/08/books/watersones-barnes-and-noble-james-daunt.html)
Thanks for sharing this, Jiwon–very interesting! I saw a lot of parallels between Marriott and The Weather Company (which I wrote about). In particular, The Weather Company was bought by IBM, and as you noted, around the same time Marriott moved its services to IBM’s cloud. It seems like both companies benefitted in similar ways from consolidating their systems and moving them to the cloud–namely, it offloaded the need for both companies to manage their own IT infrastructure and it enabled rapid scaling as businesses needs changed. It was interesting to learn about the security incidents that you mentioned, however, as I noticed in my research that one of IBM’s main pitches to businesses about moving to their cloud architecture centered on better data security and monitoring.
This was super interesting, Isha! Following Netflix’s first quarter of subscriber loss earlier this year and the company’s ballooning original content budget, I wonder what challenges Netflix’s big data-centric strategy poses in the long run. As you noted, Netflix uses data to make choices about content such as casting or storylines. But this strategy has also generated concerns about shows written by algorithims. Confronting players like HBO and Disney, I wonder whether Netflix will be able to leverage the huge amounts of data it has gathered about customers in ways that help augment, rather than dictate, the creative instincts that seem so important in this industry.