Scott Briggs

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On November 15, 2018, Scott Briggs commented on Comcast: Don’t cut the cord :

I agree with your assessment that Comcast needs to first focus on using machine learning (and perhaps some added basic human decency) to improve their customer service before they can expect to successfully move into other product areas. Comcast has been able to be successful despite their infamously terrible customer service in large part because, throughout most areas of the country, there are only 1 or 2 cable and internet providers to choose from. Once they enter more competitive markets like home security, I would suspect that most customers would choose to turn to any company but Comcast. Personally, my biggest complaint with Comcast is their use of pricing and bundling structures that force customers to purchase products they don’t want or need to access services they do want. To this end, there may be an opportunity to use machine learning to design better region-specific or even demographic-specific cable bundles that customers view as being of better value, thus decreasing their desire to “cut the cord.”

On November 15, 2018, Scott Briggs commented on Open Innovation Startup Quirky Couldn’t Crowdsource Its Way to Success. :

Sounds like a very interesting business model! It’s interesting that crowdsourcing platforms like GoFundMe have produced so successful companies while this model, which relies on developing crowdsourced ideas internally, has faced so many challenges. I would suspect that part of the challenge is that no company, particularly a startup, can do everything well. It’s difficult to execute on a wide range of products bounded only by what users of a crowdsourcing platform deem desirable. There may also be a trade-off between relying exclusively on open innovation and learning. By using crowdsourced opinions as the ultimate barometer of consumer tastes, you are likely to miss important nuances that individuals with experience in product design and development for a particular product category may contribute.

On November 15, 2018, Scott Briggs commented on 3D Printing Straighter Smiles :

I think the most interesting challenge for Align moving forward is how to differentiate within a mature market that has an increasing number of competitors and waning patent protection. One option could be to improve the economics for orthodontists. Often, patients who might be candidates for clear aligners are instead encouraged to get traditional braces, which tend to be more profitable for orthodontists. If Align can simplify the manufacturing process even further (down to one step, without the use of a mold), it seems possible that they could decentralize the manufacturing process entirely and allow orthodontists to manufacture trays in the office. In theory, this would be a positive development for all parties involved: it would help Align reduce manufacturing costs (they would only be responsible for the tray designs and providing manufacturing equipment to orthodontists) and retain market share, increase revenues for orthodontists, and make the entire process more efficient for patients.

On November 15, 2018, Scott Briggs commented on The Future at Nike: 3D printing customized shoes at home :

Very interesting and timely article! I agree that Nike should move quickly into 3D printing for other sports equipment besides shoes. As we discussed during this week’s Nike Marketing case, Nike views itself as an innovator in the industry, so I think it is strategically important for Nike to stay ahead of the curve (or at the very least on par) with its competitors when it comes to additive manufacturing. I do question, though, whether the move towards additive manufacturing in this space, and the increasing product customization that it allows, actually diminishes Nike’s competitive advantage. Nike has built a valuable brand around quality and performance. When I go to a store to buy running shoes, I can try them on, but I can’t really get a perfect sense of how they will perform “in action,” so I might rely on what I know about the brand and, as a result, be willing to pay more for a pair of Nikes. But if I can design customized shoes from Nike, Adidas, or New Balance all to the same specifications, does the Nike brand become less relevant and the customer more price-sensitive?

On November 15, 2018, Scott Briggs commented on Open your open innovation to suppliers :

Very interesting article! I agree that most companies tend to think about open innovation from an exclusively customer-focused vantage point. Given how well-known Dell is for its management of suppliers, it would seem like the company is particularly well-suited to be a pioneer in engaging in open innovation with suppliers. Beyond ensuring that suppliers are incentivized to engage in innovation with us versus our competitors, I would also be concerned with ensuring that our suppliers are incentivized to promote the “right” kinds of innovation. Suppliers may be financially incentivized to propose process changes that reduce their own costs without materially benefiting Dell or its customers (or, in the worst-case scenario, actually harming Dell and its customers). With this in mind, I think Dell needs to think critically about what innovations they’re looking to their suppliers for and how to incentivize them appropriately. For example, innovations designed to specifically improve the customer experience should be tied to incentives related to end customer product sales.

Very well-researched and thought-provoking article! Every time I move apartments and am forced to go through the process of soliciting new property insurance quotes and calling insurance agents who ask endlessly redundant questions, I think about how ripe the industry is for disruption. It certainly seems like there are ample opportunities for machine learning to improve upon this process. However, when it comes to making decisions about offering coverage and determining premiums, I wonder if machine learning is better suited to some forms of insurance than others (i.e., property vs. life insurance). I’m reminded of our discussion in class about Amazon’s fiasco with its “accidentally sexist” hiring algorithm. Is there a risk that a machine learning-enabled platform tasked with making life insurance coverage determinations based on health and demographic information might arrive at conclusions that could be viewed as sexist, discriminatory, or otherwise undesirable?