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Before this article I had never considered how 3D printing could drive innovation in the vending machine industry. As you noted in your final paragraph, there are actually so many use cases for vending machines in locations that don’t typically have them. 3-d printing as a prototype mechanism would be especially valuable here given the flexibility that would be required to know if a certain shape, size, or functionality of a machine were appropriate for a certain setting. In a broader context, it may also be useful for cities to adopt this type of technology as a way to offer free guides to the city, feminine products for the homeless, or even free water to kids when it’s hot. This is inspiring so many great ideas!

On November 15, 2018, sashafierce commented on Chanel has a magic wand for beautiful eyelashes, thanks to 3D printing :

I think it’s interesting and certainly innovative for Chanel to use additive manufacturing to prototype different mascaras, but it seems like the best way to compete with inidie make-up brands would be more on the ingredients of the their face products. Where additive manufacturing can be of some assistance is within the packaging space, as you’ve identified. The most challenge part of putting on makeup is keep all the pieces organized in an efficient manner. I would love to see a product that enabled me to use multiple elements of my makeup pack in one kit.

On November 14, 2018, sashafierce commented on Crowdsourcing snack food trends at PepsiCo :

The most interesting thing about this campaign is that regardless of the actual winning flavors, Pepsi still comes out on top. They’ve not only collected an immense amount of information about their core consumes ( i.e, people who would participate in this competition), but they’ve also collected information on their taste preferences. This can be critical data if (and when) another brand comes in to take over Lays’ snack dynasty. Pepsico will at minimum be equipped with this data set that might be predictive of the flavors and food categories that folks are interested in. Maybe this is all just a ploy to see if Pepsico should sell more than just snacks. Frozen meals perhaps?

On November 14, 2018, sashafierce commented on H&M Bets Big on Machine-Learning to Survive :

I wonder if they could be using machine learning to actually predict shopping trends more efficiently. As we discovered in our marketing case on GAP, a lot can be gleaned from google search metrics and social media, specifically on the items and style’s promoted via those channels. I think there’s also something to be said about their lack-luster eCommerce site coupled with the lower quality of their clothing. As a consumer, I’m less likely to purchase low quality clothes online than I am to purchase something that’s durable. The way they optimize their store is important, however and they should and possible still can take additional steps to address those issues.

On November 14, 2018, sashafierce commented on It’s all open at Wikimedia Foundation :

Wikimedia sounds like a very interesting concept. What I like most is that the system seems to feed itself and drive more knowledgeable users to stay on the platform. I do recognize that there can be a high likelihood of bias, but I think that can be solved by curating topics that attract diverse perspectives.

From a revenue generating perspective, I wonder if they’d consider allowing education organizations like the Discovery Channel to “host a conversation” on the site to collect feedback on pedagogical topics they may have been considering. These sorts of opportunities can be attractive to the more knowledgeable members of the community considering how high profile a show may be. Funds from this revenue could help them scale potentially.

I think it’d be interesting for Perception to also think about doing predictive analytics and providing recommendations or action plans that HR professionals could use as a jumping off point to either improve or maintain employee sentiments. Quite a bit of learning would be required, of course, but the potential outcome is very strong. Thinking back to my corporate job, I know one challenged we faced with surveys was that our action plans post-survey were always short-sided and never really substantial. It seems like a objective third-party recommendation would be helpful in steering us the right way as opposed to forcing the employees to come up with solutions that would improve their experience. The challenge we saw there was that it was hard for junior level employees to be total forth coming, so a database that can translate and synthesize natural language would be helpful to ensure everyone is heard.

On November 14, 2018, sashafierce commented on Airbnb: Utilizing Machine Learning to Optimize Travel :

Great essay! I think you’ve made an excellent observation about Airbnb’s mission to own the entire travel experience. As they continue to build out their offerings, I believe it would be beneficial for them to partner with airline companies or already established travel websites that alert consumers about deals. The normal progression of someone looking at their website is because they just looked at a travel method right before or intend to look at the travel method soon after. A partnership with a well established company can 1) provide insight to airlines or travel partners that a certain location has garnered a lot of interest and 2) that working with Airbnb to round out the travel experience my guarantee a final purchase (i.e., co-offering deals). Machine learning can help easily integrate those systems and track consumer search habits.

On November 14, 2018, sashafierce commented on Using machine learning to improve lending in the emerging markets :

This seems like a great product especially because its solving a salient need for SME’s in emerging markets. I’d be curious to see if this same type of predictive modeling could be leveraged by investors who want to assess the “riskiness” of a new venture. Although I’m sure this is apart of their secret sauce, I’m curious to know what information is fed into the model that is starkly different from the information used by creditors. As a consumer, I’d be careful to only offer access to things that tell the best credit story about me so I wonder how they’re planning to solve for that in their algorithm, assuming they can’t access information without someone’s permission.