Thank you for the great read! I had no idea that Starbucks used open innovation to source ideas through MyStarbucksIdea.com. This write-up left me with one question and one reaction to your question about investing in self-service machines.
Question: Given the lack of transparency on the mystarbucksidea.com, I would love to better understand how Starbucks actually sorts through the data and chooses which comments or suggestions are worth pursuing. Is there an entire team that goes through these comments/questions? It feels like this might be a good opportunity to use machine learning / natural language processing to sort through and gain insights from the thousands of comments.
Separately, I feel strongly that Starbucks should NOT invest in self-service machines. I was not sure if you meant self-service machines in the stores or at home, but I think both would be destructive to the Starbucks brand. Starbucks is highly focused on providing excellent service, and the self-service machines would completely go against this brand mission as these machines do not involve any human interaction.
This was an excellent read! I really liked your description on what it would be like to walk into a UA store and have the company 3D print a custom shoe for you on the spot. Upon further research, it looks like adidas has actually already released models that are utilizing 3D printing: https://techcrunch.com/2017/04/07/adidas-latest-3d-printed-shoe-puts-mass-production-within-sight/. Regarding your question on if the incumbent footwear players will be disrupted by a startup — I think it’s very possible. While clearly players like adidas are moving quickly with respect to 3D printing, I do think its possible for new entrants who focus on the customization aspect of 3D printing to gain traction. As someone who has worn custom orthotics in my running shoes for years, I think customized insoles could be a massive market for UA or other footwear players. Custom orthodics can cost hundreds of dollars, so I think there is a massive product opportunity for footwear players to incorporate this into standard footwear.
This was an excellent read. I really liked your write up on the “Do Us a Flavor” competition. While I agree that crowd sourcing ideas can be a creative, cost effective solution, this write up also got me thinking about what the limitations are. In addition to your point in the last paragraph about IP, I also worry about what the following article calls “Danger of Manipulation”: https://www.ispo.com/en/markets/id_79709436/crowdsourcing-pros-and-cons-and-how-you-can-profit-from-it.html. What if your competitors feed you “false feedback”? While it is more challenging to manipulate something like “do us a favor”, I think it could be very easy to manipulate something like a Facebook poll. This is something that Pepsi should weary of when it uses crowdsourcing methods.
I am a big Etsy fan and loved reading this essay – it was excellent! I agree with your idea about using NLP to gain further customer data regarding product reviews. There is an interesting article about how this process can actually be applied: https://towardsdatascience.com/how-to-use-natural-language-processing-to-analyze-product-reviews-17992742393c. That said, one thing to consider is how much data can really be captured. For instance, I have purchased from Etsy many times, but I have never felt compelled to leave a review. Given this, I would encourage you to consider what changes would have to be made in order for this ML option to work. For instance, maybe before the customer can make their next purchase on Etsy, they will have to complete a review. Or perhaps Etsy could simply send emails after the product has been delivered prompting the customer to leave a review. Without some tactic to get customers to write reviews, I worry there may not be enough data to draw valuable insights.
This was an excellent essay – I really enjoyed learning about how Wayfair has utilized machine learning with respect to improving its search and product offerings. Additionally, I thought you had a number of great ideas with respect to your recommendations. To address your final question – I don’t believe that Wayfair’s machine learning model will become “self-sustaining”. Firstly, while more and more data will become available as Wayfair gains more customers or more active users, it is still important to review the data with a critical eye. Is there bias in the way that we are collecting the data? What inputs are we putting into our ML model? There is still a critical human component in machine learning that requires ongoing effort. Secondly, as the company grows, there will be many other potential applications of machine learning that can benefit the company.