Bounding Problems

In countless games, the parameters, once set, never vary. There are only a given number of spaces on the chessboard. American football always uses an elliptical spheroid built to strict specifications. All NBA basketball rims are the same diameter, ten feet in the air.

Markets seem different. They don’t have instruction manuals. At first glance, it would seem you could do anything you want in them.

Still, they have rules. Not understanding them can sink a project.

In addition to DeLorean not understanding the value of horsepower (see the last post), they also failed to appreciate the Demand Frontier they faced in 1981. As A shows us, DeLorean thought they could exceed that limit by nearly two standard deviations, despite no one else beating it by half that much.

In this and many other markets, Product Market Demand Curves form. Always flatter than the overall Demand Frontier they build, they describe the product price limits as quantities sold increase. These curves set boundaries producers must consider before they enter any market. Ignoring them can have disastrous consequences.

How do Product Market Demand Curves compare to their Learning Curves? Look to the next post for answers.

#markets #demand #pricing #boundary #innovation #business

DeLorean CSI

In October 1982, the US government charged John DeLorean with cocaine trafficking in a deal he thought would stave off bankruptcy for his self-named company.

How did it all go wrong?

His DeLorean Motor Company sports car (A) came with several sales features. It had a rear-mounted engine, brushed stainless steel body panels, and its iconic gull-wing doors. Its original designation was the DMC-12, the “12,” reflecting its price, in thousands. But, when it came time to start taking orders, DeLorean dropped the name and raised the price.

The renamed DeLorean entered the market with 130 horsepower, priced at $25,000. As we see in B, no car with that amount of power came close to its price. The 1981 Audi 5000 Turbo, with the same horsepower, sold for $7,000 less.
Statistics reveal the sustainable prices for 1981 cars were a function of their horsepower and units sold (both P-values < 0.01).

As shown in C, the DeLorean’s predicted sustainable price was $15,500; its posted price was nearly three standard deviations too high.

To sell all 7,500 units it produced for $25K, D shows us its installed horsepower should have doubled to 262. As 1981 ended, it only sold 3,000.

Moral of the story: do market math.

#innovation #marketanalysis #valueanalysis #pricing #cars

Demand By Proxy

You can’t always get what you want…

Finding data can be hard. Say you needed to model demand for Japanese bullet train travel, or the London to Paris run. Ideally, both firms would post these figures, and you could reduce that to insight. While you can locate that information for NYC cab data (see one of my previous posts), you won’t for The Shinkansen or the Chunnel. What to do?

Let’s suppose train operators match the number of seats offered by class to their demand. Why wouldn’t they? If they had too many high-priced seats open, they’d either drop the price or change the seating arrangement. Eventually, they would come to a configuration that works most of the time.

Below, we see the number of seats by price for Japanese (A) and European (B) high-speed rail services. By themselves, neither has enough data to form a viable study. Together, in (C), they reveal collective thinking from different sides of the planet – and it’s much the same. Yes, that’s only two routes. More would be better. But this shows us we can gain an understanding of a market using the files we have instead of the ones we want.

…If you try sometimes, you just might find…you get what you need (Mick Jagger/Keith Richards).

#innovation #demand #markets #marketanalysis #strategy

Fast Trains

“I knew I was going to take the wrong train, so I left early.”
Yogi Berra

So, what’s the right train?

Specifically, what are the most appropriate specifications for new trains we need to move people about here in the United States? Happily, the world already has some blueprints for success.

With over 50 years of experience and a perfect safety record, Japan has been using high-speed rail for some time. They’ve blazed a trail followed by Europe and later by China. Any new railroad project should consider the Shinkansen. In 2011, I had a look.

I found the Value (sustainable price) of a Japanese train ticket as a function of its trip length, MPH, and seat area (all P-values < 0.001) with a 91.3% adjusted R2. Tripling the distance traveled and speed pushed prices up by 88% and 27%, respectively. The added fare for distance made sense, but the speed change seems low. I used net speed, however, which likely understated the Value of the train going fast when it could.

Interestingly, as seat square area increased 47% from unreserved (A) past reserved (B) and green (C) to gran (D), ticket prices went up 88%.

What is the demand for this service compared to its Value? See the next post for some insights.

#innovation #technology #travel #strategy #tips

 

Electrifying

Helicopters are highly versatile machines able to take off and land in tiny areas. They’re perfect for landing on buildings in dense urban areas. But since they beat the air to death with thunderous rotors turned by noisy jet fuel turboshaft engines, they’ve been banned from many a city. What to do?

Let’s go electric.

Nine years since the first manned all-electric helicopter flew, https://lnkd.in/eXi3bV8, there’s a race to build versions that can take passengers. With battery energy densities tripling since 2010 and prices falling simultaneously, there’s a vast potential market. Several firms are in it.

Perhaps the one closest to putting an electric rotorcraft up for sale is newcomer Volocopter, with its 2X (in A). It uses 16 motors, one to a rotor, and carries a pilot and one passenger.

Longtime manufacturer Bell offers its Nexus 4EX (B). More capable, it transports four passengers and a pilot. An entirely different configuration, it has four ducted, rotating propellers mounted atop wings.

If flights in these new machines are substitutes for cab rides, given Yellow Cab data for NYC in January 2017 (C & D), which part of the market would you address first?

#innovation #technology #entrepreneurship #travel #business

Economic Entanglement

Inventors need manufacturers who require operators. The fate of each depends largely on the others. If just one player fails, an entire enterprise could sink; this is economic entanglement.

Such arrangements are highly intermeshed. Revenue to the inventor, 2, is a cost to the manufacturer, who pays license, technical development, and royalty fees to gain access to technology, 1. After a manufacturer finishes development and begins production, it wins sales, and their revenue from that, 3, is a cost to operators who buy it.

An operator may agree to pay for part of the manufacturer’s development costs to be among the first to gain access to the latest tech. Once it begins to receive and use the new products, it earns revenue from its users, 4.

Key to the success of such operations is satisfactory financial metrics, 5 and 6, as determined by each participant’s performance and Return On Investment (ROI) parameters to which the parties agree, 1. In this case, under the Manufacturer Costs & Loan, we see their build costs have ballooned to 200% of their original prediction, which might sink the program, if not for exercising a significant portion (87.3%) of an available USG grant.

#innovation #entrepreneurship #economicentanglement #partnerships

Visualizing Value

In Multidimensional Economics, Value is a sustainable product price based on its features.  Producers set Prices.  Customers determine Value.  When they don’t match, problems arise.  Buyers pay no mind to cost when considering Value.  If you paid $1000 for a laptop, you don’t care if its cost was $1900, $900, or $90.  You just know it satisfied your Value proposition.  How do markets establish Value?

Value is whatever the market says it is.  For business jets, Fig. A shows us there is a positive correlation between speed and price.  The faster the planes go, the more buyers who can are willing to pay.  Note, though; there is high variation in A near 560 MPH, reflected in the Mean Absolute Percentage Error in D.  Fliers like to be able to take people along with them; thus, it makes sense in B that buyers pay for added capacity.  No one wants to be cramped, either, so observe in C that taller cabins fetch more money than shorter ones.  As we add features B & C, we lower errors in D.

Aircraft speed, capacity, and comfort value terms are analogous to those for computers. Laptop buyers want processor speed, short- and long-term memory, and easy to read screens.

Analysts should consider all features markets find useful.

#business #value #marketanalysis #price #innovation

Solve Profit First

Suppliers make products and see what markets will bear for them.  That’s precisely backward.

Instead, we can solve for profit potential first and discover product specifications second.

Suppose a market has products for which there are particular quantities, and prices demanded, as shown by the red dots.  We want to avoid competition, so we choose a Target Price, 1, that exploits a price gap.  Given a Demand Frontier, this sets a quantity limit, 2.

With some work (not shown), we find the market supports Features A & B with a green Value Surface (supportable prices based on those features), and that there’s an area of interest with no competition.  Linked to that region are the costs for 1 and 200 units of our new product.  If we constrain the problem (orange planes), we form an enclosure.

We then run Financial Catscans through this region.  Much like brain scans, they are virtual market section cuts.  At the optimum, we solve for the specs of Features A (3) and B (4), and the per-unit profit (5).  Per unit profit (5) times the demand limit quantity (2) yields max potential profit.

In the process, we’ve solved a 4D problem (Feature A, Feature B, Price, Quantity) from a 1D goal (profit).

#innovation #price #value #markets #profit #sales #manangement

COVID-19 Over Time

Weeks ago, we considered the world’s countries populations on the horizontal axis and the number of their COVID-19 cases on the vertical as the points in blue, below. Several countries formed Upper COVID Infection Limit (those with white triangles inside blue circles). The regressed blue line through these points described 98.5% of their variation.

By May 22, things changed, indicated by the green dots. While the US still led the world in infections, and all states on the Limit in April remained there, some other countries reached this unenviable level (i.e., the line through the green dots with white squares, correlated to 98.4%). The case count in Bahrain more than doubled; Kuwait’s infection rate went up nearly 5.5x. Along with Qatar, with over triple the cases of a few weeks ago, these Gulf States were hard hit. This phenomenon is baffling as most countries in Africa to the west and Asia to the east are doing better.

Peru and Chile did especially poorly, dispelling the idea COVID might be hemispherically-based. Vitamin D levels are of particular interest. Low levels of it correlate to high European mortality rates (https://lnkd.in/gh2tbej)

#covid #inthistogether #innovation #health #covid19analytics