I agree that the Adaptiv app is compelling in concept, but has a lot of improvement to go. The main difference between Netflix and Adaptiv is that people like to watch TV alone, but like to workout with friends (which you touched on in your post). In fact, social pressure and engagement is a huge motivator when it comes to exercising – services like Nike fitness, Strava, Peloton, Class Pass have been able to tap into this and create the stickiness that Adaptiv seems to be struggling with. Of all your suggestions (which are great), I think this is probably the place where they can make the biggest impact with the smallest amount of investment. I also think they could explore pricing… people tend to engage more/value things more when they cost more, especially when it comes to exercise…. (ex. soul cycle is $34 for one class!). The low price point may prevent users from trying to “squeeze” the most out of this investment and consider it a “last resort” source of exercise rather than their go-to.
Thanks for the interesting post! I don’t follow the entertainment industry that closely so I wasn’t aware about Disney’s recent move into the D2C space and agree that the imperative to move to this model is stronger with each passing day. I assume that at some point all content creators will have to move in this direction in order to survive, but I also wonder what the competitive dynamics will look like at that point. Will consumers be expected to buy separate $9.99 subscriptions to all production studios they are interested in? Will this competition drive subscription prices down and content prices up, making the economics less attractive? Will the old cable companies create streaming packages to re-aggregate the production studios in an online format? It will be very interesting to see how this all plays out in the long term, once the initial migration to digital has been finalized.
I agree that the current credit system is ripe with issues and I think the concept behind what Lenddo is doing is very needed, but I do have some concerns about the data they are using. In building predictive models, it is very important that the input data is clean and not manipulated. However, as social media usage data becomes used more heavily and users become more aware of how their online behaviors are incorporated into their credit scores, they are incentivized to change their behaviors to influence their results. In this scenario, predictions would be warped. For this reason, Lenddo is incentivized to not disclose how they are measuring credit scores which puts us back in the paradigm we are in today which is a credit system that is very opaque and hard to understand.
Thanks for this post Anton – this is truly fascinating. As for what this means for google, I am not particularly concerned. As a search engine, google started collecting vast amounts of data before the word truly had an understanding of what this data could mean and the power it could wield. By the time it became clear how important data would be, Google was already materially ahead. I view this as the same situation… we don’t yet know what these new types of algorithms that don’t need data could mean, but Google is already ahead of everyone else with its acquisition of DeepMind and is well positioned to be a future leader even if its previous moat becomes obsolete. As for what this means for mankind… I am far more concerned, but I suppose that’s outside the confines of this discussion board.
Like MHS, I am also very skeptical of the real value that the matching algorithm for tinder adds. Because of the business model for the site, they are incentivized to have users swipe and swipe and swipe and are thus actually incentivized to show many bad matches in a row and intersperse the good matches very sparingly so people stay in the app longer and they can serve them more ads. Other matching services have limited the amount of “swiping” users can do and are thus more incentivized to collect (and verify) data in order to ONLY provide high quality matches. While tinder probably has the CAPABILITY to create a great matching algorithm, they do not have the INCENTIVES to do so.
Quantopian is a great idea in theory, but in practice I think it is incredibly lacking – for many of the ideas you mentioned as well as others. Creating a promising quant investment strategy requires both idea generation and portfolio construction/risk management. This platform uses crowds to try and supplement the idea generation component, but the way in which they do so severely hinders the ability for the fund to execute on portfolio construction (I’d also say their approach to idea generation with 14 years of data is also inherently limiting). In order to protect the contributor’s ownership of the algorithm, the platform never has full access to the code. Without full access to any individual piece, it is very challenging for them to combine ideas in a meaningful way while also ensuring they are not duplicating risks. Any value they create on the idea generation component is likely sacrificed in what they have to give up in portfolio construction. For this reason, I am also heavily skeptical that this platform will create any true value over time.
I love what this nonprofit is doing and am so glad you shared it with us. Like you, I agree that the NGO option is really the only viable option of the ones you laid out (I think there are some potential privacy issues when it comes to using the data elsewhere – especially for refugees), but the issue you pointed out to circumvent the fees is a real one. If the platform can think of ways to make the use of the platform relevant to the NGOs (i.e. refugee management, a way to deliver and submit the filled out forms, etc.) that could be one potential avenue that would make the NGOs willing to pay for the services rather than have the refugees look for translation directly. Another option of course is to continue applying for government grants or solicit donations, as this is a nonprofit venture.
Thanks for this post! Echoing others – the decision to make the 5% fee ‘optional’ seems like it could be the nail in the coffin for this business as I’m guessing most people would not voluntarily pay for this service. Psychology would indicate that they should have made it an option for users to opt-out of this payment rather than opt-in, so I wonder if the intentional decision to use the latter approach is indicative that they are also considering other business models/avenues for revenue.
Another issue I see with this platform is controlling for the authenticity of campaigns as it scales. As gofund me becomes an increasingly popular resource for genuine people dealing with real pain points, more nefarious actors may also try to make use of the platform. Controlling the crowd of posters, and this the reputation of the platform will be critical to continued success.
Thanks for this post Will! I’ve never heard of Fiverr before, but it obviously has been making a very successful run at making its presence known in the freelance market. From the sounds of it, it seems that the company has been able to navigate a pretty impressive pivot from a gimmicky site offering what amounts to $5 dares and silly activities with little quality assurance to a genuine platform for serious professionals. I’m guessing that the company had to dramatically change the nature and demographic of both sides of the platform in order to make this change happen, so I am curious how they thought about navigating that change to become the platform it is today.
This is a really interesting application of a lot of the topics we have covered in class. If I think about HomeJoy, I would classify it as a business model where there are modest network effects (so long as there are enough people on the platform on both sides for liquid matching the service works fine but there is no benefit to data and no direct benefits to me if my friends join). Multihoming would also be quite easy for both cleaners and homeowners. The combination of these two characteristics reminds me of the scenario in the simulation where it wasn’t necessarily a winner take all market and therefore the path to hockey stick growth was not absolutely critical to survival. It seems to me the right approach here should have been a moderate push for growth, focusing on creating a differentiated service that could overcome disintermediation rather than the path of reckless growth you describe here.
I love OpenTable as a consumer, but as a restaurateur, the value proposition is much less compelling these days than it used to be. Some of this is due to the rise of new entrants as many former commenters have mentioned, but I think another important piece is the rise of influencers like Eater, Infatuation, TimeOut. I’d venture to guess that most consumers use these sorts of resources for discovery and purely use OpenTable as a booking platform. Because discovery is an important component driving the WTP for the OpenTable platform, I’d argue that this degradation is as important as the competition for the booking platform. In order to survive, OpenTable is going to need to take a deep look at other ways and places it can add value in the restaurant industry.
It’s crazy to think that in many ways Starbucks is evolving into a payments company masquerading as a coffee shop instead of the other way around. I am a huge consumer of starbucks and I think there are many lessons to be gleaned from their digital transformation. However, like Iryna I worry about them striking the right balance between tech forward and invasive. For example, I’m not sure I want starbucks sending me push notifications to go buy coffee. That’s a nuisance and just adding to the noise of increasingly useless information that we all have pushed to our phone every day. I just hope that the company can push its tech agenda without losing its soul and it’s goal of being the world’s :third place.”
Great post, Brittany! I, like you, am huge fan of the concept and technology underlying the wave of roboadvisors. However, I worry about whether roboadvisors actually have the unit economics to survive as stand alone players. As you mentioned, there are very high acquisition costs for new customers in the robo advisor space – estimates range from $300-$1000. At a .25% fee on a $25,000 dollar account the robo can only recoup $100 per year, which means it could take 3-10 years to have any positive returns (and that’s assuming that there are no major market sell offs and that people will chose to stay with an automated approach as their wealth accumulates across their lifetime).
For this reason, many robo advisors have turned to partnerships and acquisitons as you referenced. In this way, they serve as funnels into some of the more expensive, traditional wealth management services rather than an end iteslf. While I really believe in this technology and that many people of our generation and beyond will want to use these services, I think the business models still need some work before they can really “win” on the scale we would like to see them at.
Netflix has certainly been a leader over the last few years both in terms of how it has changed consumption behavior and in the quality of content produced. I would argue that – at least for many Netflix subscribers – the biggest of the customer value propositions you laid out is (1) the cost and (2) the ability to consume whatever content, whenever the user wants it. With the recent repeal of net neutrality, both of these value propositions are at threat. If I end up paying more for access to Netflix at a speed that allows me to watch whatever, whenever, then I am right back in the position I was before “cord cutting”. In the future, the relative appeal of Netflix to other media solutions could dramatically shift. Of course, what the repeal of net neutrality means for company’s such as Netflix remains to be seen, but I wonder what changes will need to be made in order for this to continue being a winning model.