Great post, Micah. I took the dialect quiz again and confirmed that I am indeed from Texas. I’ve got about thirty questions I want to ask you but will try to limit myself to a few. Did you feel like the Times’ push into analytics yielded results during your time there? Is it inevitable that the barrier between the business and the journalistic staff will be totally disassembled? My very cursory, likely naive, and sadly pessimistic guess is “probably,” so the business can survive against free content and the tech giants. Is there any concern users could get caught in a content loop or a quasi-echo chamber with the recommendation engine?
Awesome post, Alex. I think there are some very valuable lessons in here. You mention two points of failure, but of those, I would argue only one was actually controllable — embedding analytics more fully into the corporate structure. Do you think the second failure you mentioned could have been avoided? In hindsight, it’s easy to see what you missed, but you can’t measure everything. Ultimately, you’ve got to identify variables you think matter and hope they will continue to matter into the future.
The title of this post is epic. I suspected Starbucks used data in some way (like putting stores on the right side of busy inbound city roads to facilitate the morning rush), but had no idea they used it so extensively. Every time I walk into a Starbucks, it amazes me how many people use features like remote ordering and payment via the app. I wonder how much data informs Starbucks’ broader strategy and whether they might be missing the forest for the trees re: trends in the coffee shop industry.
Awesome post, Micah. Hard to trust crowds when there’s no curating team and contributors have no reputational risk. I was going to post the same thing Meili did above (Boaty McBoatface, whose historic expedition began on Friday). On the subject of machine learning, you bring up an interesting point in that machines can inherit biases based on data input. Maybe the task then is to work first on some filtering mechanism, similar in nature to Facebook’s fake news filtering effort, but even then, the human touch could still introduce bias, without the engineers even realizing it. Still, it seems important that we land on a solution; the internet’s anonymity allure isn’t going anywhere.
Yun, not gonna lie here, I’ve always thought Slickdeals was some sort of scam. You have proven me very wrong. $4B in savings is monstrous. Is the platform substantially better than Google’s shopping functionality? I assume yes, given the involvement of an actual community (and curators) in addition to some sort of web crawler. I wonder if there’s some way the Slickdeals team can better incentivize future participation. Reliance on goodheartedness might not be the best long term strategy. Great post!
This is really interesting, and sounds like a much more effective model than Vetr (which I think you responded to). Feels like incentives are better aligned here. I can imagine the volume of incoming investment strategies is hard for Quantopian to manage effectively. Users are incentivized to find strategies that maximize returns based on a backtest, but if those users are constantly tweaking strategies to fit the data, they could show deceptively favorable results. For Quantopian, sifting through the different strategies, avoiding spurious correlation, and looking for some semblance of causation has got to be tough. I’m very curious as to how they manage that effort. I’m also interested to see performance results whenever they’re released!
Great post, Hao. I agree with your assessment that multi-homing is likely common among users, since it’s easy to navigate between apps on a phone, but I wonder if multi-homing is common on the restaurant side of the platform. My intuition is that it is common, since most restaurants are interested in reaching as many potential customers as possible, but for certain sections of the market, perhaps multi-homing erodes the “exclusivity” factor. If you’re a high-end local eatery, it might make sense to use one app (like Reserve, which touts a “curated list” of restaurant partners) to prevent the throngs of OpenTable users from harming your brand. I suppose the insight is that there may be room for multiple winners in such a massive and diverse market.
Yezi, great post. Bitcoin is one of the most exciting technologies evolving right now, but so many people remain oblivious as to what it is! I get the sense that this could be a winner-take-all market, if Ethereum can figure out how to reduce multi-homing or gain mass adoption on the user side. Does that intuition make sense to you? Also, do you think the recent news about the successful SHA1 hash collision change the game for bitcoin more broadly? If so, can Ethereum differentiate itself in any way when it comes to security versus other blockchain reliant technologies?
Thanks for the post, Cameron — this is great. The value proposition is very clear, and as you said, it’s probably worth quite a bit to big enterprises, particularly those with high turnover. I had no idea first-round interviews were sufficiently rules-based to be conducted by a piece of software (my assumption was that each interviewer tailored the interview depending on the interviewee’s experience and qualifications). Concerning the network effects, as the first commenter pointed out, a case could be made for a high level of multi-homing among both job seekers and employers. I wonder if HireVue could strengthen network effects and/or reduce multi-homing by partnering with LinkedIn.
It’s a good thought experiment to run. Markets are incredibly fickle, complex, and sometimes irrational. In that sense, it might be impossible for two funds to arrive at the same set of conclusions (i.e., signals), even if they started with the same objective and same sets of data. Minor differences in assumptions or initial conditions could yield different results. Still, I agree with you; it feels intuitive to me that as the number of funds grows, the likelihood of signals being arbitraged away also grows, causing returns to suffer.
I love the value prop, but I tend to agree with Meili. Having been in the military, I understand how painful it can be to convince a government organization to adopt new technology, particularly when a non-zero percentage of your workforce doesn’t even use email for professional purposes. True, police forces likely have less strenuous procurement processes, but I’m sure “if it ain’t broke, don’t fix it” or “this is how we’ve done it for 20 years!” are still tossed around when Mark43 tries to make a sale. I’d also be curious to hear about their experience with the Special Projects Team and how they calculated 230,000 hours of saved time. Thanks for the write up!
Google Maps consistently amazes me. I use it just about every single day, and it seems to be light-years ahead of the competition with regards to data, features, and users (something like 70% of iPhone users choose Google Maps over the native Apple Maps). The app creates huge amounts of value for users: they no longer have to purchase, and spend time deciphering, paper maps, nor do they need an expensive in-car GPS to drive in unfamiliar places. And users (generally) don’t have to worry about getting lost! That likely adds up to hundreds of dollars a year for the average user in both time and money saved. But the company gives the app away for free! It’s a great example of how digital business models are changing how we think about creating and capturing value.
Hi, Will. This is a great post. I recently wrote a piece on Comcast, and in an interesting twist, Comcast is considerably worried about mobile carriers snatching customers away and diminishing the utility of its fiber network as mobile technology and consumer behavior evolve. Think about the evolution of mobile networks thus far (2G through LTE) and how use cases have changed (simple voice calls to email to watching HD videos). AT&T and Verizon have kept up with those changes remarkably well and have healthy profits to show for it, though I’m not sure how technologically challenging it was then relative to what they face now. Which of the changes you list above are substantially different from the changes the company made as it transitioned from fixed voice all the way through LTE?