I have been wondering for quite some time how effective the recommendation engines for Netflix etc. actually are. My personal impression is that Netflix has not quite figured it out. I rarely enjoy the movies that are recommended to me.
Do you know what the value for Sky is if the company is able to provide even better recommendations? Is it beneficial because users will assign a higher perceived value to the movie selection and are willing to pay a higher price? I am wondering if Sky actually saves money for movie rights if a user watches an older movie after the movie was recommended to him or her. If this is the case, there would be a clear value in convincing consumers to watch non-blockbusters if these movies are equally enjoyable.
I wonder how JD thinks about the fact that the AI capability of the one tractor might reduce the need for an additional tractor due to increased efficiency. For example, a self-driving tractor which runs day and night without supervision could do as much work as two people with two tractors during a typical day shift. Thus, increasing the performance of their machines could reduce market volume. Since technology add-ons (e.g. camera systems, AI software, etc.) are typically cheaper than an additional vehicle, overall revenues could decline. Furthermore, I heard that after-sales revenues (due to the recurring nature always chased by investors) are much higher, with higher margins, for vehicles than for technology components due to the higher number of moving parts. Therefore, shifting from vehicles to technology might also reduce the overall profitability of the company.
You write that the company is able to predict airfare prices with 95% accuracy up to one year in advance. What really drives the use case for the customer is how much money can be saved by employing the technology, and, in this regard, I am a little skeptical. I wonder if there are that many price reductions over a reasonable time frame that a higher accuracy in forecasting prices really translates into relevant savings. For example, I always book as soon as I know that I need to travel and all dates are confirmed since prices only become more expensive when coming closer to the travel dates. However, the recommendation engine seem to be really interesting as it allows Hopper to optimize capacity utilization since customers can be motivated to travel on dates or to locations for which still spare capacity exists. This is also a very valuable value proposition for any hotel, cruise line, travel agency, etc.
I believe that both companies, BK and McDonald’s, are a perfect fit for the use of big data. Both companies have a very mature and established business model, supply chain, product selection, pricing, etc. That means that improvements are rather incremental, and creativity in business decisions or radical innovations are not needed. It is very unlikely that either of the companies will drastically change their menu in the near future or use other cooking processes. I expect that both companies will further engage in a war of resources, in terms of marketing expenses, but also big data analysis. In this market segment, customers are very price sensitive. Any cent that the companies could save by leveraging their large amounts of data could be used to lower the prices of their products and put further pressure on each other.
Many comments address the privacy issues potentially created by Disney’s big data initiatives. This is certainly a valid point. However, I am wondering if the company even risks loosing something fundamental to its business model – creativity and magic. The article does not explicitly discuss it, but Disney also uses data to decide which products or content to produce. I am sure, data helps in this regard, but I am always skeptical when creativity is replaced by mechanics. Think of the early times when GPS systems for cars (without dynamic route optimization) were introduced. I remember that suddenly everybody was using the same routes, and traffic jam was created on roads which used to be empty. If content companies such as Disney focus too much on data analysis, could they loose their competitive edge?
I think the Monsanto case shows some of the problems about data analysis in general – it takes the human aspect out of the equation. By only optimizing for the maximum output, Monsanto disregards that food is produced by humans for humans. Monsanto’s approach to data feels like the old idea that companys’ only purpose is to maximize shareholder value, which many agree is an outdated concept. By taking this approach, Monsanto does not only accept the risk that their business activities could overall be harmful for people, but it also creates the perception that the company does not really care about it. The Monsanto acquisition already makes it more difficult for Bayer to attract the best employees. Eventually, the negative perception could also affect customers’ purchase decisions.
This is one of the great examples of the shared economy. The company essentially creates value from nothing. Space that otherwise would not be used is rented out to campers. For AirBnB, I see the problem that increasingly hotels and professional landlords use the site. This seems to undermine AirBnB’s original ideal of connecting consumers with consumers and could potentially hurt the company in the long term. However, Tentrr is immune to this trend as it creates a market which would otherwise not exist. Obviously, creating a market is more difficult and takes more time than just taking away market share (you described the efforts to source space), but I don’t see a problem to scale as you suggested. In contrast, there does not seem to be a natural competitor.
This is one of the applications where I am skeptical that the introduction of a “platform” concept creates outsized value. The company essentially tries to remove the timing mismatch and information asymmetry between buyer and seller. However, a car sale typically involves a visual inspection, perhaps some testing, transportation, etc. – so many manual tasks. Additionally, there are some geographic restrictions in the sense that sellers only drive so far for delivering the car. I believe that this together leads to somewhat lower scale effects than one might expect. The financing of used cars requires a large working capital which needs to be financed. Finally, as you mentioned, this is not a “winner takes it all” market but there are many of these “platforms” which compete with each other in terms of the margin. Do you believe we see some consolidation among car dealer platforms in the near future?
You talk about the community aspect which incentivizes participants. I am always wondering if these kinds of platforms are not just smart outsourcing tools for organizations. Instead of paying in-house personnel high salaries and social security for solving complex problems, organizations use external platforms where highly talented people work, on average, for less than what they would be paid as full-time employees because these people value the community aspect as partial compensation. I guess this is where the job market is moving, and many platforms have the same tension, but a data science platform makes this problem even more obvious due to the need for highly-skilled labor. Therefore, I am not sure this platform really “democratizes AI for all” or rather “outsources AI problems to all”.
What are your thoughts on false positives, meaning if the algorithm indicates that a potential employee lied although he or she did not? While I am sure that the algorithm will make background checks much more efficient, I am concerned that some people will have a much harder time to secure a job for no reason.
A nice example is a certain company providing automated credit ratings. There is a large Turkish population in Germany which statistically is more likely to become insolvent. Many Turks have the letter “ü” in their last name. Thus, the machine learning algorithm recognized that those who have an “ü” in their last name are more likely to not pay back their credit―the algorithm assigns those people a lower credit rating. However, what does this mean for all other Germans who have an “ü” in their last name, such as your class mate?
You asked to what extent digital fitness solutions will be able to cannibalize the demand for traditional health clubs and fitness studios. I am wondering if the digital solutions, for which you give one example, and conventional gyms actually compete for the same customers? For example, the people who go to the gym to lift weights seem unlikely to entirely switch to digital devices. Those who like the social or wellness aspects of gyms and health clubs will also be hesitant to cancel their gym membership and switch to a home workout. Most of the remaining gym-goers will only complement their work-out with digital devices without canceling their membership. Therefore, I believe an increasing market penetration of digital devices will have two effects―those who did not have enough time or motivation to go to a gym will start doing workout at home, and those who have a gym membership will go less often to the gym and use their device instead. If gym-goers go less often to the gym without canceling their membership, operating profits of the gym will actually increase. Thus, gyms might even profit from an increasing use of digital tools. What do you think?
You point out that Carvana makes a net loss which seems to grow proportionally to revenues. I believe the company is a good example for a trend I have observed over recent years, especially in the US. While Carvana’s business model is essentially buying and selling cars—a very tangible exercise—it is presented and valued as a digital business model. In class we pointed out, that the benefit of a digital business model is its scaling at essentially zero cost. However, for Carvana’s business model, this is not true. As I understand it, the company provides services (financing, delivery, expert advice, etc.) which are characterized by a high share of variable costs which only marginally decrease with increasing company size. Thus, I regard the company as an offline business supported by digital tools, not a truly digital business. However, an offline business which does not scale at close-to-zero costs should be bankrupt by now, if it has not generated any net profits. A similar example is Uber which still has negative unit economics.