Adam Smith's Profile
Really interesting article and I learned a lot! To add to one of the previous comments, it’s a bit of a myth that Apple doesn’t do customer research. While the popular narrative is that Apple told customers what they should want, in reality Steve Jobs talked to customers relentlessly. He stood inside Apple stores, listening to customer reactions, questioning how people perceived the products, listening for unmet needs. Jobs and others at Apple did constant customer research to influence product design.
Xiaomi’s approach of buying a plethora of startups could somewhat be called a form of open innovation. By investing in a wide variety of disparate industries, Xiaomi is incorporating new ideas, new technology, and new people. I question the longer term business viability of this strategy — while incorporating new technologies and exposure to new ideas is good, it isn’t clear to me that casting a wide net across many product categories is the right approach. Does that lead to a lack of focus and a scrambled org? Should Xiaomi instead focus on the core smartphone business and continue to use open innovation to improve that vertical?
I see this a bit differently. To me, no doubt that ridesharing will become a larger share of car usage, and the cars used in this application will likely be standardized, meaning this portion of the category may not benefit from the personalization benefits afforded from 3D printing. Ridesharing and autonomous vehicles are still decades away from replacing all car usage, and individual car ownership will likely endure at least through the medium term, especially outside urban environments. For these customers, personalization may actually be a key differentiator and more in demand than before. The idea of personalized ergonomic seats or otherwise customer-specific parts is intriguing. While it may not make sense to install these products at the automotive factory, it may be possible to build these parts separately and install the parts at a dealer. Aftermarket, dealer-installed parts are already commonplace in the industry, and supplying more individual-specific pieces could be a real advantage for the brand with customers.
Colm brings up a good point about additive manufacturing being applied to tool construction — this is likely a relevant and useful application.
I agree with Riju that Singapore Airlines may not be focusing on the most significant problems here, and the public may not be well equipped to help answer operational questions. If anything, open innovation contests may be useful from a PR perspective in that it gives the impression that the company is listening to consumers and is open to improving. Post-flight customer survey emails are now a ubiquitous industry practice, so airlines clearly want feedback, or at least give their customers an opportunity to be heard, regardless of if their feedback results in any action. Airlines are the type of industry where there are many users and these users may have intense opinions about customer service, making it an ideal area to solicit innovative ideas from the public. Airlines are such a complex business with many interconnected parts largely unseen by the public, so public feedback may not be able to accurately weigh the tradeoffs involved in adopting ideas.
I think that collecting ideas for app optimization and offloading compensation are good areas for comment and exploration — these are areas that are acutely felt by the customer and critical to the overall experience as an airline passenger. While there may be a few dozen employees (at most) working on this at Singapore Airlines, there is large potential for radical and unexpected ideas when pooling the collective wisdom of thousands of customers.
I agree with MrMayhem’s insight that Lemonade’s best potential value may be in connecting tech-savvy millennials with existing legacy insurance companies, rather than successfully creating an entirely new model of the insurance business. There seems to be a fundamental problem with ensuring that users are honestly submitting accurate claims and not attempting to fleece the system, which could create a vicious cycle in driving up the cost of paying claims and therefore the price of premiums charged. Other tech driven, machine learning companies have some sort of feedback loop for enforcement that is scalable, such as ratings submissions by both parties on Airbnb and Uber. This identifies bad actors in a scaled way. Lemonade relies on a “pledge of honesty” and may be able to audit a sampling of claims, but the business model depends on machine learning accurately validating the veracity of claims.
Further, the legacy insurance business somewhat relies on long processing times for claims to improve their cash conversion cycle as well as reduce the amount of claims ultimately paid out. By delaying processing and putting up high roadblocks and standards to pay claims, it somewhat attempts to weed out frivolous or less-than-truthful claims. Lemonade does the opposite of this, with instant approval and quick processing. While this may appeal to customers and win new users, in the long run it may lead to a spiral of increasing costs and premiums due to adverse selection, meaning bad actors will be especially attracted to the platform to try to make dodgy insurance claims. It’s unclear if the ML technology is sophisticated enough yet to prevent this.
It’s interesting that Lemonade’s product front is a facade for humans running the system in the background — this is a technique common to startups wanting to quickly scale growth but it’s not certain that Lemonade has proven that it can actually transition to ML algorithms eventually instead of humans.
I agree with Kay Holmes’ concerns about users’ expectations of privacy from the Spotify algorithm and potentially negative impact from public perception of Spotify knowing “too much” about one of its users. Spotify’s ad campaign last year featured billboards with machine learning insights, revealing the most-listened to songs of the year. Additionally, Spotify created a personalized “musical year in review” for each user based on their listening habits. Revealing to consumers the extent of the data a company collects about them can be risky business. Users may see the data as “creepy” rather than insightful. Indeed, there was considerable backlash when Netflix released similar tweets about holiday movie watching — while users may subconsciously know that all of their behavior is being tracked and captured into internal data, they don’t necessarily like being reminded of this or have a feeling of being “judged” for what content they are consuming. Our content consumption (whether it be music or TV shows) is a window into our inner feelings, almost a diary of sorts. In this way, Spotify must be very careful about its personalization being useful but being cognizant that users don’t like the feeling of giving up privacy.
Streaming licensed non-exclusive music content is a very low margin business, similar to TV / movie rental on-demand on iTunes or Amazon. Therefore, we would expect that the Spotify’s sole source of profit and competitive advantage in this market would be from its personalization algorithm and convenient UI. This is shaky and unclear how much users are really willing to pay for this — customers may prefer a similar service with all the same music content with a less advanced UI, for a lower price. It’s unclear that Spotify’s current business model is sustainable. As others have pointed out, its profits are slim and there isn’t much of a theoretical case to be made for margin expansion. A better strategy would be for Spotify to create its own original content, perhaps by signing mega hit artists like Taylor Swift or Drake to have their music only available on Spotify. This was similar to Beyonce’s strategy with Tidal, though it’s unclear if she was better off for this or not, since the lack of radio play or other distribution likely dampened the potential media buzz and word of mouth.