I think a critical input into Netflix’s engine is it’s smart tagging system in the categorization of “micro genres”. You touched upon how Netflix collects viewership data in how users engaging with the browsing and watching stage — which is key — but the crux of their recommendation engine where titles are surfaced is around big data tagging. Here are a few super interesting articles on that topic, and why your personal categories may seem so strangely specific and strangely relevant based on your previous selection behavior…For example, “Romantic Indian Crime Dramas”, “Time Travel Movies starring William Hartnell”, “Visually-striking Foreign Nostalgic Dramas”, and “Cult Evil Kid Horror Movies” are just a few genres out of 76,897 that The Atlantic author was able to mine from Netflix’s database:
Most folks in the dialogue thus far have commented on the impact of additive manufacturing in construction on labor and construction jobs — however, I’m most curious about the long-term impact on city development. For example, given the large capital investment in generating the 3D printer, laser cutter, and underlying model design relative to the much lower variable cost of producing each incremental new 3D home, I imagine the effect on cities is mass uniformity in how people live in homes. In particular, there will likely be an increase in more standardized floor layouts, blueprints, and design. Another impact is that home-buying will become more of an “equalized” process, and more accessible across socioeconomic factors, as costs of home-building and buying are reduced due to automation and reduced waste. As a result, in the long run, rather than home-owning being a ‘milestone’ in individuals’ lives, it may become a more transactional, mundane part of daily human life.
Thanks for raising this topic! I’ve been following the Cambridge Analytica story for quite some time throughout the election season and our current President’s term in office. As I’ve discussed this topic with peers and friends, what is unnerving is how analogous this approach of using “psychographic digital ads” and “identifying personalities to influence behavior” is to how businesses approach marketing. For example, many e-commerce companies hire data scientists and ad tech experts to achieve the same drilldown in categorizing your demographics as a user, identifying your browsing behavior on other platforms, and surfacing ads to influence your next purchase — it’s quite intentional that the pair of shoes you searched for on Google is now being presented in a Facebook ad or web banner, a day later on your mobile phone. What businesses are doing today to optimize your conversion to click on ‘BUY’ is a similar vein of experimenting on human behavior. Influencing purchasing behavior vs. voting behavior…can we spot the difference? Quite unsettling.
I wonder what sort of impact 3D printing has on the retail industry at large as it is being increasingly used for mass production — in particular, how it impacts prevalence of counterfeit goods on luxury and other high-end brand names. In the long-term, it seems that 3D printing may cannibalize Nike’s strong brand equity, leading to retail sales loss with the ability of fraduluent manufacturers to produce replicas. It appears that once manufacturers are able to create the ‘template’ and acquire the capital, there is a very low barrier to produce identical versions. In the news today, journalists consistently report on how this is becoming harder to detect given the improvements in technology; on the other hand, technology for artificial intelligence is also battling this with anti-counterfeit detection (Sample Article –> https://www.racked.com/2018/7/17/17577266/artificial-intelligence-ai-counterfeit-luxury-goods-handbags-sneakers-goat-entrupy )
Thanks for sharing this super cool lens on open innovation in supply chain. Typically, we think of crowdsourcing only in the “ideation” phase of new product development, rather than other parts of the development process in reducing costs, iterating on consumer needs, and incorporating production innovations (e.g., reduced sugar content ingredients). I believe the biggest challenge here is not simply integration of ideas, but the **validity** of these crowdsourced ideas as applied to the business strategy. When we think about the distribution curve of who may contribute to Modelez’s OI ecosystem at this time, the ideas likely appeal more toward the tail-ends of the distribution — rather than what may “stick” with their target mainstream consumer. Without ensuring that the source of OI is applicable to key target market, the OI products may be more “interesting” than value-add to the business. For example, what @nicivey alluded to in the performance of Cherry Cola Oreo’s above.
LEGO’s approach to OI is an incredibly clever way to remain relevant in parent and child purchasing behavior — however, I wonder if this is an opportunity to expand beyond the traditional “building blocks” of physical playsets to venture into digitally-delivered products (e.g., gaming). As the LEGO target market of youth today continue to evolve in next generations to become more savvy iPad and mobile phone users, it is critical to build on top of the platforms that their target consumers are using — even if that means cannibalizing the core form of their traditional playsets as we know it today.