Tommy PF

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On December 4, 2019, Tommy PF commented on IntelligentX: Changing the world, one beer at a time :

Going to add to the love for this article (congrats in advance for winning the in-class prize). This is a really fascinating business model – the company is able to capture value by actually creating the beer that it sells to consumers. This prevents disintermediation, because there is no alternative channel to get the beer. One of the concerns I have is that this is a service that requires a high-degree of scale: customers will likely need to order a surplus of beer in advance, because of the shipping fees (it’ll be hard to ever offer this beer at bars or at physical retailers because it is customized) and because it will take time to have the scale to produce a batch.

Very fascinating – thanks for sharing! The interview process is an imperfect screening tool that hasn’t been thoroughly tested for effectiveness in hiring decisions. Two questions popped into my mind while reading through the article: 1. If there is a limited set of games that are available, how can Pymetrics prevent users “gaming” the system by practicing those same games elsewhere? 2. The set of games that you described seem to have optimal solutions (e.g. there is probably a “right” or “better” way to play the prisoner’s dilemma). Why does the company need to gather data about high-performers within a company as a first-step? As an extreme example, an HBS MBA grad might perform very well on the games – wouldn’t they be a good fit for most companies? Or would they appear to be a poor fit for some companies that just happens to have lower-quality employees?

Great article – thanks for sharing! Like several cases we’ve seen in class, we have a company that is scaling an AI service (QA-testing) that other firms would purchase as a subscription. I agree that one of the major uphill battles the company faces is in getting customer buy-in. It will be challenging to build a scalable service when each application is unique (how can the company really test for whether a feature is not working as expected vs. a bad design decision) and companies are still skeptical about the value creation provided by the firm (which will likely require demoing to customers).

On November 13, 2019, Tommy PF commented on Kanga – Creating the Ultimate Destination for Gaming Fans :

I think this is a fascinating application of data analytics that is at the frontier of our technical capabilities. There is active work in building AI that can analyze and compete in real-time strategic software games (such as league of legends or DOTA). Leveraging this work and ongoing research on computer vision is a very clever way to process unstructured data such as videos or streams.

It’s interesting how kanga is using their algorithms for discovery. I think there will be ample opportunity in the future as the volume of data explodes. Curating the content that is produced and providing “highlights” seems to be a valuable service for customers who are facing an increasingly large choice set of how they spend their leisure time on the internet. For example, streamers will often do multi-hour/day events – having a service that identifies the key moments and creates short compilations would be of value for both content producers and consumers.

On November 13, 2019, Tommy PF commented on Big Data Behind Disney Magic :

Great read, thank you! It’s fascinating to read about how Disney has built a capability in digesting and analyzing data. It seems to be embedded in the culture of the company now, which in turn has led to more customer satisfaction and revenue. The examples you gave all provide different approaches and metrics that the company has used to optimize the customer experience. It really shows that building a data capability requires flexibility and the appropriate skilled workers (i.e. data scientists) to carefully evaluate and leverage the information.

On November 13, 2019, Tommy PF commented on Cargill: Revolutionizing Dairy Farming with Magic Big Data :

Great read, Leo. Very interesting to see how data analytics is being used in agriculture. It’ll be exciting to see how Cargill plans to capture value and scale their products. Given that they hire consultants to analyze data with the farmers, it’ll be crucial for them to automate this process as they expand to other markets. It’s also interesting they plan to monetize – is this a complementary service that Cargill will provide for customers that gives them tailored suggestions about what Cargill products to buy? Will the service be valuable enough to sell as a standalone service (perhaps as a subscription)? Will Cargill subsidize the hardware, or will it also charge for setting up the infrastructure in farms?

On October 17, 2019, Tommy PF commented on Old Clothes, New Strategies: ThredUP’s Evolution :

This is really cool to read about; thanks for sharing! I love Thredup’s strategy to overcome the chicken-or-egg problem by first targeting children’s clothing. That makes perfect sense.

Awesome write-up on how Thredup is providing value to buyers, sellers, and retailers. The pricing structure is really interesting to see – I can see how Thredup hopes to capture more value in return for doing most of the heavy-lifting for sellers, while incentivizing them to sell high-quality clothing through the platform still. How has that been working for them in practice? Do you see a lot of low-quality/outdated clothing on sale on the platform, or is there a good mix? Is there a certain perception of what kind of products are most often found/sold on Thredup?

Also, I would love to use Thredup’s ML algorithm the next time I go thrifting…

On October 17, 2019, Tommy PF commented on StockX – Trading Exchange for Sneakers :

Thanks for the great read!

It’s interesting how Stockx has been able to successfully target a niche market and differentiate itself from competitors like Grailed or theRealReal. While the UI seems to be quite novel, the concept itself sounds pretty similar to other auction sites like eBay. It’s interesting that StockX has been able to change certain elements that seem minor to create a new marketplace. Perhaps this also part of the culture, but I find it amazing to think how obsessed buyers and sellers are with the products. In the example you show, the delta in the bid/ask in the Retro Travis Scotts is $1k. I can definitely see the value creation provided by StockX given hard it must be to find a match, given the scarcity of any particular product.

On October 17, 2019, Tommy PF commented on Kaggle: How a Platform Democratizes AI :

Great read, thanks for sharing! Always cool to learn more about developer culture and the persistence of creating “fun” competitions to gain access to skilled programmers/creative solutions. There seems to be commons elements with hackathons and Topcoder in this setting. Interesting to think about how Google plans to grow the platform as the owner. It seems like a great way to introduce people to data science, specifically on the Google cloud platform.

Great read – thanks Leonardo! When I think about Walmart (and other brick-and-mortar retailers), I always think about what their strategy could be to keep up with Amazon. As you mentioned, many of the assets that made these retailers a Goliath have turned into technical and cultural debt in the era of digital transformation. Would be curious to think of exemplar retailers who have done a good job of managing the transformation of the retail space to digital, and why they have been able to do so.

On September 24, 2019, Tommy PF commented on Carvana:The new way to buy a car :

Great read; thanks Matthieu! Really curious about how this digital platform will fare. Given the product and market, Carvana is handling most of the operations. As a consumer, I would want to test out a vehicle on premise before making a commitment to buy. It’ll be interesting to see how Carvana can scale and how it will be able to manage the offline experience that most customers would want before making a major purchase.

On September 24, 2019, Tommy PF commented on MoNBAll :

This was a really fun read – super interesting to see this deep dive into how digital is transforming sports. It’s amazing how far NBA organizations are going to optimize player performance – if we had biometrics in the workplace, there would likely be resistance from most employees. It’s also funny to think about how lots of folks in the old school like Charles Barkley have their own biases against data analytics, and rely on their own heuristics. To be fair, Chuck might not have been the optimal athlete in today’s data-driven sports world.