I agree with the suggestion to create a permanent group focusing on natural disaster preparedness and contingency planning. I think this is really the only short-term solution to the natural disaster question…route adjustments (e.g., avoiding hurricane-prone areas) don’t seem possible before populations shift away from these same spots, and the airlines likely have long-term gate leasing arrangements which would preclude simply moving operations elsewhere.
One option on the air temperature front may be to start offering “flex” tickets at a discount to customers with flexible travel plans. This may work in these hotter US southwest climates given the higher mix of retirees in the area (e.g., Phoenix). Passengers could buy tickets within a certain window (e.g., one or two calendar days) and be informed of the exact flight time a week or three days ahead of time (once weather patterns are known). This could allow the airline to shift some passengers onto night flights if temperatures are indeed too hot during the day. This could allow the airline to avoid costly “bumps” where they have to compensate passengers.
I thought it was interesting to follow the IPO and days since — their IPO was considered “busted” as they closed the day up just 1% after pricing below the previously-communicated range . This seemed due mainly to all the concerns voiced above (i.e., undifferentiated business model that could be attacked by Amazon). Since then, however, their stock has traded up (including +17% so far today) and is at trading at $23.5, up 57% from the IPO with no material business developments between 11/16 and today besides a strong Black Friday industry-wide. It seems like WalMart’s positive earnings news gave some folks confidence that there may indeed be multiple winners in any given industry, and that Amazon may not be conquering everything in such an absolute way as previously thought.
That said, I do think StichFix needs to do a lot more than invest in machine learning / automation. Amazon has far more customer data than they do, and a more robust tech / data science platform. I think StichFix will need to compete via more qualitative methods (fashion sensibility, brand selection, exclusivity of private-label merchandise) as these can’t be as easily copied by Amazon. I have a hard time seeing how Amazon won’t win the “data-driven clothes in a box” war, especially given their ability to fund that operation at a loss for a long time.
The two questions posed at the end of the piece here are important to think through when addressing the WalMart / Amazon question — I think people have been a little quick to hand the trophy over to Amazon. I think a successful retailer in the age of digitization will leverage their installed store base as an asset for both brick-and-mortar and online sales. Whether it’s fulfilling ecommerce orders from store inventory, accepting online returns in-store, or delivering items to a customer’s door that he/she picked out in person, there are myriad ways in which WalMart’s 5,412 US stores can be used to its advantage vs. Amazon . Similarly, there are things (perishable groceries, pharmaceuticals, last-minute items) for which online sales penetration is very low (and may remain so for some time). WalMart can up its game in these categories. Lastly, they need to stop taking cash in store and provide a card-based solution for unbanked consumers. The card processing fees are minor compared to the lost insight on customer buying behavior that you get with credit card data. They need to know their customers inside and out in order to serve them best in whichever channel they transact in.
As an aside, it has always slightly perplexed me why ecommerce adoption was slower in rural areas than urban ones, given the value proposition (bringing the world to your doorstep) seems higher when your doorstep is potentially miles away from the closest store (vs. mere blocks). What can ecommerce companies do to make the value proposition to rural shoppers more compelling?
This piece reminded me of a case study in Freakonomics about real estate agents in Chicago. Levitt and Dubner looked at 100,000 Chicago-area home sales, specifically looking at homes sold by real estate agents on behalf of clients, vs. real estate agents selling their own homes. On average, agents keeps his/her own home “on the market an average of ten days longer and sells it for an extra 3-plus percent, or $10,000 on a $300,000 house. When she sells her own house, an agent holds out for the best offer; when she sells yours, she encourages you to take the first decent offer that comes along” . There is a principal-agent problem where the relatively small additional commission the real estate agent would receive doesn’t incentivize him/her to do their purported job (sell your home for the best price). As information spreads and more people understand the split between what’s best for agents vs. homeowners, I agree with the author that ReMax will need to change their value proposition and service offering. Things like home staging, though, seem like tough businesses (high cost base includes cost not only of designing and moving / installing furniture in homes, but also of the initial furniture procurement and warehousing costs of everything not in homes). Redfin’s valuation comes from the fact that it’s a tech platform with low marginal costs — ReMax should also consider how it can leverage technology to reduce the people costs associated with its business.
1. Freakonomics, Steven D. Levitt & Stephen J. Dubner, p. 8. Excerpt available online here: https://www.nachi.org/forum/f11/exerpt-freakonomics-book-regarding-real-estate-agents-57929/
This commentary was insightful and timely, as an article about CFOs moving their teams away from Excel came out just this past week in The Wall St. Journal and makes for good reading . I agree with Kieron that fashion may prove a useful application for Prophet or other smarter forecasting tools, but encountered lots of potential issues when looking at Anaplan, Adaptive Insights, and some other Excel replacements when at my last job. The crux of the issue is that once your core model is built, you still need to adjust for lots of different variables. These adjustments can take just as much time in these other programs as with Excel, and just as Michael discusses with technology, in retail you have product / store launches, promotional periods, holidays, weather changes (weekend snowstorms in Q1’15 impacted our bookings about ~5%), etc. Any model is only as good as the inputs, and my concern with Prophet and other products like it is that people assume the “machine learning” going on captures everything and therefore don’t pay enough attention to the inputs. Eventually we will certainly reach a place where a holistic algorithmic model understands your business and the linkages to endogenous / exogenous events better than any human-built model. But I don’t think we are there yet, and the hurdle to learn a new program vs. keep plugging away in Excel hasn’t been crossed (for most applications).
I would agree with Alexandre — while the company is facing headwinds from a margin and competition standpoint, the net result for end consumers (and humanity in general) is positive as cost per megawatt-hour of clean wind power comes down. The real question to me is the extent to which government subsidies and/or a flood of venture money into the space historically skewed economics in the space. Government incentives subsidized the clean energy industry when there wasn’t much competition and while costs to end consumers were high. Similarly, venture funding allowed companies to operate with potentially less efficient business models than would have otherwise been required. Cleantech VC investing has dropped from a high of over $7B in 2011 to $5B in 2016 . As venture funding / incentives drop, these companies will have to reckon with marketplace adjustments. That’s not necessarily a bad thing and in the end, we will benefit from more resilient companies / business models supplying the world with clean energy.