The business world is captivated by direct-to-consumer companies because of its fascination with machine learning. However, what if you cannot capitalize on this data and are forced to turn to wholesale channels for scale, therein “diluting” this data that made [...]
GDC's Profile
GDC
Submitted
Activity Feed
If Adidas moves towards more 3D printing, I wonder about the eventual role of other groups within the company. If 3D printing comes front and center, then those with specialized roles in dealing with manufacturing and sourcing will have to pivot towards items used in making 3D printing. Someone whose role is to source the rubber soles of sneakers might not have a job or function anymore in the company because now you need someone who sources the “ink” that goes into the 3D printer. We should consider the impact on the organization from such a change and the transition time that this will require.
Large consumer product companies have had the reputation of not being able to innovate for a long time and it is very interesting to be able to hear about how General Mills has fought to combat this slowing growth. It would be interesting to hear more about how exactly General Mills differentiates itself from a venture capital or incubator standpoint especially given that a lot of other CPG companies have been starting their own funds. See below for some examples:
Campbell Soup – $125M fund – http://fortune.com/2016/02/17/campbell-soup-vc-fund/
Kraft Heinz – $100M fund – https://www.businesswire.com/news/home/20181005005048/en/Kraft-Heinz-Announces-Launch-100-Million-VentureI wonder if product expertise (Kraft Heinz with its condiments or Campbell with its canned soups) is something that motivates founders to want to sell to these companies, or if it’s purely based on price. I also wonder if it is the expertise of those who run these funds that drive these decisions. Regardless, General Mills should look to see how those motivators potentially leave gaps in their overall portfolio or if perhaps they’re losing to these other CPG companies due to human capital, reputation or other factors.
The one thing I always struggled with for machine learning for clothing is that clothing is fashion is supposed to be a way to express and present oneself. We spoke about this during the Gap case in marketing but how do you reconcile using fashion to express uniqueness when fashion and apparel companies start to use machine learning and start looking more and more similar to each other? Can you differentiate if you are all using the same trends to predict consumer demand? I completely agree with the point made above of how to work with the future merchant role to solve this problem.
My other main question would be around the future differentiation of luxury brands who may not use machine learning and continue to use designers to drive style and create a voice. In a world where conforming is less “cool” and having your own voice is more “in,” I wonder if luxury brands will pull more and more ahead in growth and even market share given their further and further differentiation in this area.
Brand loyalty is an interesting concept when it comes to Sephora because you want to have a connection to your customer as a retailer (and therefore drive loyalty for your retailer brand) but also want customers who have both brand loyalty to manufacturers and those who have the desire to switch brands to drive demand and growth. The 80% of loyal customers to Sephora is a very high statistic but I wonder what the split between customers who only order the same items (loyal to manufacturer brands) and those who love to try samples and switch products very often. This will shed light on how best to use machine learning to drive demand. If the majority is just customers who order the same brands, I wonder if investments in programs such as the Google Home hub to play makeup videos from Sephora’s YouTube makes sense as an advertising vehicle and if promotions or other discounts of items they consistently buy would make more sense.
I think the internal incubator of AB-InBev is a very interesting way to go about open innovation because of its proactive stance on getting ahead of trends. The one thing I question is whether the types of companies and ideas they’re chasing actually make sense for their industry. As described above, AB-InBev had created a beer in house called Black Crown that received high ratings in a blind test. However, this beer despite its high ratings, it was not a commercial success, which begs the question if beer rating applications are good companies to be purchasing to predict trends in the space. It might be worth investing more in areas to predict what drives these trends, such as companies that can provide or track POS data.