Nick Van Exel's Profile

This is very interesting – especially given its ability to keep users engaged while lowering the overall cost for the Company’s R&D efforts in a difficult toy environment (e.g. you note troubles at Toys R Us). I’d hypothesize there would be value in leveraging this model in two areas: (i) introduce educational aspect and (ii) electronic gaming element. Parents are increasingly focused on finding games for their children that can also deliver valuable education – LEGO can expand its open innovator base as well as its customer base with this approach. Secondly, with the increasing adoption and spend towards Ipads and other electronic devices, LEGO should expand into electronic simulations to increase their addressable market as well as improve the financial profile of a game/toy. Keeping the open innovation in an electronic environment will also keep the open innovation in an online setting and excite “gamers” and coders into the mix.

Using machine learning to predict churn is an interesting proposition across many industries beyond financial services. I’d agree that with vasts amount of data, you can find the typical overall predictors of churn with a high degree of accuracy (especially in a transnational type environment). A concern I have is that different customers or businesses (in a B2B context) churn for very different reasons and typically will be hard to capture in the data – it would require data inputs by AmEx or other vendors that are highly manual (because you don’t know if a customer churned because of budget issues, consolidation, etc., which likely won’t be included in the data). So while you may be able to predict churn overall, it is critical to understand the reason for churn to inform retention campaigns. This is where i believe machine learning may fall short given inadequate data capture on the front end (you need those churned customers to provide additional information).

Car OEMs are consistently focused on cutting costs given the thin margin profile and increasing global competition. 3D printing is an interesting vehicle to continue to optimize the cost structure while providing value to the customer via increased customization. Quicker turnaround times and less reliance on suppliers to perform manufacturing (and testing) will create a lot of value for the OEMs. The risks I see are in (i) potential negative implications on quality given the streamlined nature (less batch shop and specialization) and (ii) diversion of focus away from some other important strategic initiatives such as self-driving cars.

Acquiring startups and using those companies as a form of open innovation is an interesting proposition. What becomes challenging here, as you noted, is the integration issues – this more than just an idea generation engine; these companies and individuals are actually coming onboard forming many cultural and operational challenges. How do these companies strategies influence actual process? Without much influence there, General Mills won’t improve the value of the brand and process and those startups would feel incentivized to stay independent or join another platform that is more in line with their independent visions.

I agree customization will continue to be key in sustaining differentiation in an increasingly competitive market with new entrants. 3D printing is an interesting vehicle to deliver this in an efficient manner. Interestingly, another member of the community touched on Adidas’s focus on this. They have the resources and scale to at least experiment with this appropriately, but also each can’t take on the risk of falling behind the other in such an important piece of the value chain.
Is now the time however to further invest in the “in store” experience given the shift to e-commerce? Per your point above, is this simply just a marketing scheme? Does 3D printing have the same appeal in a factory/warehouse setting in terms of overall value proposition to the customer relative to cost to the company?

Very interesting piece. Agree that markets function based on relationships between many different variables and market constituents will move markets based on pattern recognition. Based on this, I believe machine learning will continue to be highly valuable in public market investing. Would be interested to see how this relates to other areas of investing where human to human relationships matter more, like building relationships with managers in private equity. Separately, in public market investing, i believe quant based trading and a machine learning based approach have become more prevalent given the shift to passive investing (via increase of mutual funds and ETFs) – what would happen to accuracy of machine learning if the pendulum shifts back to active investing (i.e. individual stock picking based on fundamentals vs. technicals)?

Great job laying out the underlying spend issue, the key use cases and the inherent risks. Wanted to hone in on the competitive issue here – I would be concerned that the low barriers to entry in an open innovation environment would allow Pfizer’s competitors to have access to the same research they rely on which has historically been a differentiator. Pfizer and its peers are focused on bringing to market valuable medicines efficiently, but are also heavily focused on creating sustainable moats and shareholder value. The coders and US residents applications seem to be attractive from a low cost resource perspective and not very intrusive from a competitive standpoint. For the research / academia, would a better path be to develop machine learning to take vasts amounts of research/data to make suggestions on potential paths forward for internal research at Pfizer?

Very interesting/high potential product in a very attractive area. As you noted, technological adoption in senior care is considerably low in both the “front-office” and the “back office”. The back office has made significant progress in recent years with the adoption of Electronic Health Record and Practice Management software from companies like Kinnser Software, but the provision of care or the “front office” continues to lag and i wonder if this is due to the high sensitivity towards removing the human from the decision-making. These are highly fragile situations and the cost of a mistake (as you note above as a risk) can be massive. Would hypothesize that this product works for lower acuity patients that can be tracked with less intervention. What would be the right go to market strategy here – would it be through the senior living care communities or the health plans (particularly Medicare Advantage)? Would imagine the latter is more sensitive to these issues and has more capital to spend. Additionally, from a machine learning perspective you can pair this data with other internal data from the insurance plans to enrich the algorithms and the product further.

I agree customization will continue to be key in sustaining differentiation in an increasingly competitive market with new entrants. 3D printing is an interesting vehicle to deliver this in an efficient manner. Moreover, players such as Nike and Adidas have the scale and footprint to be first movers in this space.
Is now the time however to further invest in the “in store” experience given the shift to e-commerce? Per your point above, is this simply just a marketing scheme? Does 3D printing have the same appeal in a factory/warehouse setting in terms of overall value proposition to the customer relative to cost to the company?