Good point – I think this is why LEGO is targeting the middle ground between digital and traditional. Specifically, their digital offerings have been very strategically targeted in markets where they’ve previously had local success and strong partnerships, i.e the investment with Tencent and China. Additionally, in the Tencent example, LEGO is directly responding to Chinese parental desire for safer digital platforms for their children. In other offerings, LEGO has been quick to remove failed digital products from the market, keeping with the spirit of open innovation and the idea that “the customer knows best.”
Very interesting topic. I agree with a lot of the points made above and remain concerned that Gantri does not have a true competitive advantage. Gantri is connecting small scale artists to the technology they lack and consumers to the customized art they crave.Yet, Gantri’s unique offering depends on the continued willingness of small scale artists and businesses to partner with Gantri. As the company scales, I think the idea of ownership and branding will become a challenge. Is the lamp a piece from John Smith’s collection sponsored by Gantri, a Gantri lamp designed by John Smith or simply a Gantri lamp?
That’s an interesting point. I think the beauty of LEGO’s offering is that within their traditional brick toy sets, the end design is ultimately determined by the user. LEGO should keep in might that concern as they offer more specialized products, especially in the digital area, that do not offer this original flexibility to customize output.
Thanks for the comment. I agree that VR is likely too far from LEGO’s core competencies to successfully implement and appreciate you elaborating on the point. My concern was that the impending increase in VR offerings in the video game industry more broadly may significantly increase the competition LEGO faces, challenging the capacity and resilience of Lego’s open innovation strategy to foster growth in the future.
Thanks for sharing! I think the structure of the contest will highlight flaws in Amazon’s current warehouse structure and suggest different means of organization. However, my concern is that Amazon’s warehouse is a game of Tetris, carefully organized to manage the flow of inbound and outbound inventory. In a system this complex with robotic specialization, I question if robotic systems will be able to spot and fix problems created in storing, sorting and optimizing across thousands of products. Since issues in the system can create systematic problems, the lack of a human andon cord or a timely kaizen can prove problematic to the overall warehouse’s long-term success.
Separately, I agree that there is a limit to which customers are willing to pay for quicker shipping. I think Amazon can use open innovation and small scale roll outs to test the customers’ willingness to pay for quicker shipping across products.
Great article! Shopping for skincare products and make up is a very individual experience. With price variation, a consumer may distribute their beauty purchases over a number of brands. The decision comes down to prioritizing color options, skin sensitivity concerns, cost and experiential factors (i.e free makeovers after minimum spend). To combat this, Glossier should increase its feedback funnel with its customers, offering free samples in exchange for reviews and pop-up makeover stands that use minimum viable prototypes, allowing the customer to influence the final offering. These small-scale efforts will allow for rapid ideation with minimal waste and investment.
To capture a larger share of this market, longer term, Glossier should not only involve the customer more deeply in the product considerations, but also help guide the customer to a few targeted product offerings specific to their needs. Utilizing an extensive database generated from open innovation, Glossier can implement machine learning to highlight which products address the customer’s selected pain point or interest area. This data can then serve as another funnel to innovation with the customer, providing real-time purchase data (and missed sales) on products, highlighting what’s selling in each category and what products the customer is passing on. This allows Glossier to see if its value proposition to the customer aligns with how the customer perceives its respective products, measured by sales.
Thanks for sharing. I found it interesting that orders would be processed within two to three weeks.  I appreciate that customization creates lead time; however, I worry the purchasing cycle for razors, especially amongst price sensitive millennial customers is shorter. For example, razors are frequently purchased after an existing razor is broken or dulled. Assuming the customer shaves a few times a week, this only leaves a few days before the next purchase, without increased customer planning. Additionally, the price concerns me, ranging from $19-45.  The market for cheap alternatives is saturated and margins in razors typically come from associated shaving products, i.e razor heads and shaving cream. Is this the appropriate focus for this market?
1) Saunders, Sarah. “Customers Customizing Their Own 3D Printed Razor Handles with Gillette’s New Razor Maker Platform,” October 18, 2018, [https://3dprint.com/227672/customers-customizing-their-own-3d-printed-razor-handles-with-gillettes-new-razor-maker-platform/], Accessed November 14, 2018.
2) Jackson, Beau. “Formlabs Trails Mass Customization in 3D Printed Razor Handles for Gillette,” [https://3dprintingindustry.com/news/formlabs-trials-mass-customization-in-3d-printed-razor-handles-for-gillette-141665/], Accessed November 14, 2018.
Thanks for highlighting this interesting topic. From my time working at a bank, machine learning was an important tool we utilized to stay competitive.
For example in FX trading, as an increasing percentage of the FX market moved to electronic platforms, it was crucial for Citi to price and execute electronic trades correctly, as narrow bid-offer spreads left little room for error. In this manner, we used machine learning to give clients the tightest bid-offer possible given hedging costs and market conditions. As the machine learned over time, with new data, our electronic pricing offering improved in tandem. Specifically, we utilized machine learning algorithms to analyze current market liquidity relative to averages and to determine the appropriate bid-offer spread adjustments for market moving events (such as new data). Additionally, algos allowed us to hedge more efficiently, skewing our pricing based on real time inventory needs.
Overall, for Citi, this allowed us to compete in the commoditized space of spot e-FX, while putting the customer first. However, we did not see this as innovation, rather as a necessity. Nevertheless, humans were still needed to make targeted business decisions, i.e do we need to take a loss on these trades to win bigger business?
From my experiences partnering with the buy side, clients trusted and found success utilizing algo trading strategies, capturing approximately 60% of major market trends. However, the issue was these funds became victims of their own success. After massive growth in 2016-2017, their AUM grew in proportion and their trades began to outstrip market liquidity. This was manageable in normal market conditions; however, in times of crisis, especially emerging market events, issues arose. What do you do when the model says to sell a large position and there’s no liquidity? For these decisions, humans are still needed.
Thank you for challenging my understanding of the arts. I found this topic extremely interesting as it questions what it means to be creative.
I appreciate that Sotheby acquired Thread Genius to stay ahead of a disruptive trend, but I remain skeptical about the long-term viability of machine learning in the arts. At the heart of it, art pieces are often limited in number with valuations subjected to shifting preferences and economic cycles. Art is only worth what someone is willing to pay for it in the moment, as seen in the significant price variation that occurs between pieces in an artist’s collection.
With limited number of sales and thus data, I’m concerned that the data will not be relevant enough or representative enough for machine learning. The machine risks valuing pieces on outdated sales or drawing comparisons between dissimilar pieces of art that share similar features. There’s also a subjective element to the valuation, as part of the price stems from an emotional connection to the work. These subjective elements are hard to control.
Additionally, as someone who has choreographed some of their best dance pieces randomly walking on the sidewalk or dealing with a crisis, creativity can’t always be timed. It tells a story and provokes emotion. Without these human experiences, I question the machine’s ability to showcase the human condition, the key ingredient to timeless art.