I think you’ve hit on something really important here: the platform is what matters – not the device, or even the content. While I do think content is better than physical product (in this case), it is true that the platforms connecting users and powering the overall industry are likely to be the most successful and robust businesses as the sector accelerates.
Aside from its positioning as a platform, I am curious to hear what makes you think that MindMaze will win? What do competitor platforms look like, and how are they likely to advance in the coming months/years? Is anyone else going after this space, with a lead on MindMaze? If so, how can they differentiate?
This is VERY interesting.
A few thoughts:
1) The HBX program is so much more sophisticated than any other MOOC or online course I’ve tested in the last few years. It is much more engaging, enablesg o cross-student interaction, and encourages students to use a diverse set of interaction modes in order to learn and retain content. I imagine HBS would want to promote this platform, if only to be out on the leading edge of online education technology and content – regardless of whether they are able to fully replicate the MBA experience.
2) I think your suggestion of pre-populated online classrooms spurred by machine learning is an interesting one. But perhaps instead of using avatars for the most insightful comments, they should simply allow the professors to periodically pose thought-provoking questions to the class? I agree with Ilene that the greater the diversity of perspectives, the better the learning quality. Thoughts?
This is fascinating! I interned at Medtronic in their Surgical Innovations division last summer, and did plenty of research on the Da Vinci robotic system as part of my portfolio management work. I do think there is tremendous value created for patients when surgeries can be done using minimally invasive methods. And I do think that AR/VR will be critical in improving robotic surgery technology in the years to come; I especially liked your segment on telemedicine.
However, it is not clear that hospitals (and payers) are really seeing the value in robotics yet. Many hospitals don’t yet have all of their surgeons trained on the robots, and therefore cannot actually perform many robotic surgeries (or at least, are nowhere near 100% capacity utilization for robotic surgery systems such as Da Vinci). Indeed, it seems like for many provider networks/hospitals, the robot is just an attractive marketing gimmick: “We are the most advanced hospital, and have robotic surgery capabilities!” I wonder how long it will take before adoption on the provider side ramps up – as surgeons are trained – and payer acceptance increases – as lower total hospital visit costs make up for the increased up-front expenses of robots and higher surgery costs.
Andrew – this is super interesting, thank you for this post.
I imagine one major challenge to VIBE’s success is that slack users must be unaware of VIBE, or willing to be analyzed, in order for it to be sustainable. For example, if I knew the CEO was getting a report about disgruntled employees based on their Slack chatter – and I was a disgruntled employee – I might switch to texting or emailing my coworkers when I wanted to complain, to avoid the CEO’s/VIBE’s notice. Or I might simply encourage my circle of colleagues to switch to a different chat platform, such as GroupMe, if that were an option.
Does VIBE have plans to be ubiquitously integrated with all platforms? How can they maintain their edge when the data points can easily defect?
This is fascinating! I’m obviously biased in favor of data-driven initiatives across the board, and definitely am not shy about surrendering personal data if I think the benefit will outweigh the risk. But my in-laws, for example, would have been terrified of InBloom’s data play and the data security issues it presented.
It seems, however, that there are ways to make people more comfortable with data gathering. I wonder what strategies InBloom could have employed to ensure relevant groups that the data they gathered and utilized would be managed responsibly, securely, and then used inclusively. How could they have proven to core constituencies that the benefits of data outweighed the risks in this case?
Ellen, great post. I appreciated all of the comments above, as well.
One of my key takeaways here is that this is a clear example of how data can reduce the probability of an incorrect prediction, but not eradicate it altogether. As we discussed in class, statistical analysis of any data set reveals, quantitatively, how likely/unlikely an outcome is to occur. But just because an outcome is unlikely to occur, does not mean that it WILL not occur! So Nate Silver’s results may have been accurate – and that low-probability event occurred despite its data-driven unlikeliness.
The question that keeps lingering in my mind is: does the revelation of data analysis have any impact on human behavior? In this case – did all of the predictions that a Trump win would be highly unlikely actually cause people to go out and vote (or not go out and vote), and ultimately sway the results by driving a shift in behavior?
Ali, great post. I am intrigued by 23andMe’s strategy, especially as it pertains to the growing momentum behind precision medicine. Increasingly, we believe that genomic data will enable patients to be treated as individuals, with their diseases (especially cancer) perceived as unique and related more to genomics than initial emergence. I see 23andMe’s partnership with Pfizer as interesting, but only a small part of their potential to contribute to the advancement of science and medical treatment.
However, it does seem that there are some issues around data security – especially when dealing with something as personal as one’s genome/DNA. How does 23andMe get consent from customers to use their data for large-scale studies? And do you think there might be obstacles to growing this beyond their current market penetration, as people become more suspicious of how their data is being used to generate value for 23andMe (when they paid out of pocket to give that data to 23andMe, rather than the other way around)?
Ophelia, I think this is a super interesting post – thank you for sharing.
I’m wondering how the numbers work out here. If a patient pays $149/month for a package, which includes $200 to ‘give away’ to medical detectives as compensation, how does CrowdMed make any money? Wouldn’t that individual patient result in a $51 deficit? Perhaps I am missing something, but it seems like the math doesn’t quite work in their favor.
Also – I wonder a little bit about the choice to do a subscription model with monthly fees as high as $749 – this seems so high! I could see most potential users wanting this for specific incidents, but probably not wanting to pay a monthly membership fee for a service they might not use every single month. I wonder if a lower membership fee, plus a fee-per-case dependent on the case’s complexity, might have made more sense.
Adam, this is completely fascinating! This reminds me so much of Captcha and Duolingo, which both involve crowds doing one action – which actually serves a completely different purpose. I think it is fascinating to see cancer research gamified in this way. I can see many people (myself included) downloading all of these games as a way of having a small amount of social impact while relaxing.
How have they gone about attracting users to download these apps? It seems that users would be intrinsically motivated to play them (out of a desire to help cancer research while playing) – but that many people probably still don’t know that these exist.
Also – I wonder if these need to be monetized on the user side at all. Perhaps their development could be covered in part by cancer research budgets?
Megan – awesome post!
As we’ve discussed in the past, I obviously know nothing about dating apps, so take my comments with more than one grain of salt…
My impression is that Tinder’s original advantages – first mover, network effects, etc. – has been significantly eroded by the entrance of competitors that have successfully differentiated their products. I agree with Danielle’s point above that Tinder has a reputation for it’s hot-or-not swiping, ephemeral mentality – that suggests most users to be disinterested in finding true relationships, and more focused on entertaining themselves by evaluating other people’s attractiveness. Other apps that have entered the market that escape this mentality seem more likely to capture people who are actually interested in dating (and ending up in a relationship).
I wonder how well Tinder will be able to monetize its users in the long-term. If it’s monetization schemes target users who are most interested in going on dates and getting into relationships (i.e. through Boost, the subscription, etc.), but these users eventually migrate to other platforms because of their reputations and points of product differentiation – will Tinder see revenues grow or subside over time? Multi-homing further complicates this; how much will any individual user be willing to invest in their online dating platforms? Say it’s a max of $15/month – that doesn’t leave room for subscribing to more than one $10/month platform. Will Tinder be what users to choose to spend their money on?
Andrew – great post! I have been a big fan of Etsy, but also see some of its problems (and actually launched a startup based on one of those problems some years ago).
One of the interesting things about Etsy is that it seemed like a classic case of network effects – up to a point. Certainly, as with any marketplace, Etsy was more attractive to buyers as it increased its seller volume, and more attractive to sellers as it increased its buyer volume. However, there seemed to be a little bit of a ‘breaking point’ for the network effects. As time passed, and more and more sellers flocked to Etsy to peddle their wares, a problem emerged: there was a lot of CRAP (technical term) being sold and advertised on Etsy, and sorting through it was a time-consuming and frustrating process for the end-user. Sellers joined the platform who didn’t make decent-quality products, or who copied other sellers’ designs more cheaply, or who just made unappealing products. Buyers had to sift through the immense amount of junk on the website to find anything truly great, and even then, the quality was questionable. In a way, after a point, Etsy suffered from the gym problem: the more people who used it, the more other people didn’t want to use it…
Cassie – this is SO interesting!
I’ve heard of tons of startups in the agriculture space, who are all trying to help farmers optimize everything from their use of fertilizers, ability to identify crop diseases early, share/rent equipment. But it DOES seem like John Deere has a huge advantage because of its installed base of users already familiar with the brand, and already using its products. It particularly helps with both the network effects and multi-homing questions, by strongly-encouraging (forcing?) users to adopt the John Deere platform – and eventually driving more sales of their actual equipment. In a way, it’s the opposite of the simulation – where there was no money to be made on the device, and all economic opportunity came from the apps/digital components. Here – the digital component is the free platform, and the devices will drive revenues. Overall – that seems like a better model – since the platform component is scalable with limited variable costs!
Amazing pun. 🙂
Ravneet, this is fascinating! I love Sephora but honestly didn’t know about all of these different ways they were leveraging digital to maintain their presence. I do love the idea of ColorIQ, in particular, as I think it addresses a real consumer problem.
The one question I have is: do you think Sephora will need to divest some of their stores and reduce their brick-and-mortar footprint, eventually? Or is it essential that they maintain the store presence in order to persuade customers to buy and remain loyal? Physical stores are always going to be expensive, and unless they can take some of that PPE off the balance sheet, the value proposition is still tricky.
Gil, sharp analysis here. Sadly, I think you’re right. Jibo is really cute, but I don’t think that the actual technology behind it is superior to Alexa or Google’s offerings. It’s an interesting case where first-mover advantage wasn’t really a factor. Amazon’s existing brand awareness meant that they barely had to advertise Alexa (except on the Amazon homepage) in order to get the tech into households across the country; Jibo would have been quite another story, requiring the company to build brand awareness and trust before making a sale.
I also think pricing is a big factor here. I somehow recall the Jibo pricing being upwards of $500 – maybe even more. I remember wanting to snag one in their initial campaign, and then balking at the price – it was high enough that you wouldn’t buy Jibo on a whim, to test out something fun. Alexa is much more approachable, by contrast!
Megan, loved this post! So well thought-out – I really appreciated your analysis. I loved Quirky when it launched, and really wanted it to succeed, but see how the many issues you outlined above led to bankruptcy.
It is so true that online voters are not the same as consumers. I wonder if this is what makes platforms like Kickstarter and Indiegogo ultimately more viable in this arena. While the ideas aren’t being crowdsourced/voted on in the same way, individual ideas do get ‘voted on’ with dollar pledges – a much better way of actually assessing consumer demand. Your post also made me think about Bolt VC (here in Boston), focused on hardware. Since the technical issues were often an obstacle, I wonder if individual idea inventors/owners could have partnered with a hardware startup incubator, and benefited from their expertise. Alternatively, both Bold and Kickstarter/Indiegogo may be more sophisticated and market-robust manifestations of Quirky’s main thesis.