Hi Hermione, great piece! I haven’t thought about open innovation as the solution to increase repeat purchases and customer lifetime value before. I find what iFLY did quite brilliant.
Reading your post it seemed like all these content creators are currently non-incentivized and most of them are past or current customers. As an idea, to make this design process sustainable in future, the company could start an outbound marketing program targeted to the skydiving community through which creators are rewarded for VR videos with monetary and non-monetary prizes. And as you suggested when proposing to build an online platform, creating competition among creators can help making it even more engaging.
This strategy can be tested with very little capital. If it proves to be an efficient way of building content, drive brand awareness and have loyal customers, then multiple purposes are served at once.
If it doesn’t work, then iFLY probably needs to invest in in-house innovation as soon as the supply of content from the current customer base is not enough to meet demand.
This is a very interesting topic Hypatia! I see few major concerns related to your questions and the way Quantopian currently operates:
(1) The way the authors of the algorithms are incentivized might create perverse incentives in the short-term as these authors are getting a fee without risking their capital. This could be perhaps mitigated by requiring the authors to investing their own capital in the strategies they propose.
(2) I am very skeptical about authors sharing winning investment strategies openly, especially when the barriers to invest are becoming so low. If such as thing as a strategy that beats the market exists, why would an author not keep it for herself / himself?
(3) How fast do these strategies become obsolete?
(4) Is it really possible to properly evaluate the quality of an investment algorithm? Even using backtesting as a technique does not mitigate the fact that history does not repeat itself.
Overall, I am very intrigued by what Quantopian does but also not very optimistic on the sustainability of the company in the longer term.
Jane, I found this post really insightful. Savings six weeks of lead time over a normally forty week long process through additive manufacturing is encouraging data on the future and adoption of the technology as a whole.
To your question, when deciding whether to outsource to third parties versus building in-house, I would do a cost-benefit analysis but also think about how the following factors affect the decision in the long term:
(1) Is R&D a core capability of BIA? By browsing BIA’s website, it seems like the company already invests a lot in innovation.
(2) Connected to the above, will there be synergies with the current resources (both human capital and machines) when allocating efforts to additive manufacturing?
(3) When outsourcing, is there a risk that specific knowledge will be shared to competitors through suppliers?
Perhaps, as HeartyHarvard shared, there might be benefits of outsourcing in the short term while 3D printing machines quickly become obsolete as the technology progresses.
I really liked the article Tom, and just like Saggitariutt, I also find it really encouraging that a giant such as GE is heavily invested in the technology. Like several other ground breaking innovations in our history, the capital required to make the technology work at scale is out of reach for many players. However, the fact that the economics of the technology are not viable for mass production now, certainly does not mean they won’t be in future. GE could be one of the organizations that is best suited to take on the challenge in terms of resources required and for that, I believe it could be a smart investment. When thinking about industries to expand into, I would think about those use cases in which customization is important and reducing the number of players on the supply chain as well as increasing the speed of production can generate savings high enough to balance the implementation costs.
Hello Andres, I found your article very interesting. The problem of last mile delivery is a key problem for companies taking on the challenge of delivering same day through independent contractors. Solving the problem is particularly challenging because, as you also pointed out, there is a high number of factors increasing variability in delivery times: the size of the order, how many unique SKUs the customer is choosing, how busy is the supermarket at a particular time of the day, how much traffic there is on the streets, how far is the driver from the supermarket, the cumulative delay coming from the fulfillment of different orders in a row, whether the picking and delivery tasks are done by the same person and many more. The use of neural networks here is certainly beneficial but since you asked about what other techniques could be considered to make the process even more efficient, I would highly recommend Cornershop to keep a close eye on what other peers in the same industry around the world are doing to solve the issue. For example, check out this article wrote by the data science team at Instacart on the usage of quartile regressions to improve delivery times: https://tech.instacart.com/how-instacart-delivers-on-time-using-quantile-regression-2383e2e03edb. This is just one of the many articles Instacart posted on the topic and they are all available online.
Dear CrimsonCookies, this is a very interesting topic. While I agree with you that achieving a holistic understanding of the user journey across different distribution and marketing channels is fundamental, we should remind ourselves that machine learning and data are means to an end rather than the end goal itself. I don’t think a company like Blue Apron or any other digitally native company should avoid placement channels such as retail just because of the lack of data they would receive from the wholesalers. That would obviously preclude such companies from pursuing potentially great growth opportunities. And if companies required full transparency and data in order to invest in a growth channel, they would stop investing in television, print, influencer marketing (at some extent) etc. as those are also non fully trackable and measurable channels. I also believe waiting is not an option, especially when competition is as fierce as it is in the meal kit industry. Check for example this article, showing that HelloFresh, Blue Apron’s main competitor has also expanded into retail: http://fortune.com/2018/06/04/hellofresh-grocery-stores/. Assuming the relationships with these wholesalers are exclusive, then the first mover has a real advantage and leaving this money on the table doesn’t even seem to be an option even in the scenario in which it turns out that retail cannibalizes some of the online revenues (thinking of game theory principles).
Having said that, even if data is not complete, I think that there are still tremendous benefits for the company to invest in machine learning for product development and to better understand and predict at least the digital journey of customers. And some of those benefits can also be translated into competitive advantage in retail. For example, recipe development based on customer choices online can also help predicting the demand for those recipes in store.