Excellent endocrinologists

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In Average is Over Tyler Cowen predicts that future jobs will reward people who work well with machines and humans. There will be good careers for people who understand people and data.

For instance, a doctor spends years of her life in medical school and residency and attends continuing medical education courses to ‘know a thing’. But she also must convince her patients. The way we do each of these will change with time but these are the two parts any job. Put another way, it doesn’t matter how good the model is if people don’t follow it.

“The (diabetes treatment) model we created beats 95% of primary care physicians, not because they aren’t smart, but they don’t go through the six million (treatment) combinations in their head. For endocrinologists there’s a top quintile who get results as good as the best output of our algorithm. They don’t do it by choosing the best algorithm, they use their humanity to talk to their patients about adhering to the drug regime. They are getting results a different way.” – Len Testa, Causal Inference, October 2021

Good convincing outperforms better medicine! This is why financial education does not work. Action does not follow information like a tail follows a dog.

Testa doesn’t elaborate how the doctors describe the diabetes deterrents, but it’s probably in the listener’s language. “Excise the statistical jargon,” said David Spiegelhalter and communicate better.

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Sounds like a JTBD post no?

JTBD is iteration

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The 2012 job-to-be-done at Calm was meditation. But when engineers looked at the usage data they noticed something interesting, there was a lot of Calm usage at night. “When they started productizing around sleep,” explained Vinny Pujji, “that’s when it opened up from being just a meditation focus thing to what they are today, which is mental fitness.”

We’ve looked at a few JTBD ideas: does the bundle of good explain the transaction, as it does with free breakfast? Is there zombie revenue? Even Jazzercise was job-ercised.

With hindsight ‘jobs’ sound easy, but they are iterated solutions. David Packles of Peloton shared (October 2020) two instances where Peloton had to iterate on their first JTBD solution.

First, Packles and his team looked at the largest Peloton Facebook groups. Rather than build for the power users, a no-no, they looked for wider use cases, and thought peole wanted to see when their friends were working out.

“People hated it,” said Packles. While the camaraderie between instructors and peers was important to users, the now-ness was not. So they tinkered. There was almost always at least two people in the same workout at the same time. ‘Friends working out now’ became ‘here now’. This worked, forty percent of daily active users now use this feature.

A second instance was the location field. Rather than where, people used it for what. Packles himself is a ‘Peloton dad’. So Peloton added tags which per Packles, “exploded in popularity” and “became a means of expressing yourself rather than connecting with club.” Half of DAUs have some kind of tag. Rather than people near me, the Peloton users wanted people like me.

That’s interesting moments help us understand how other people see the world. Instagram once had a tool that fought spam by looking for accounts that posted a lot and deleted a lot. During one glance through the data, Mike System noticed that in Indonesia a person was doing that – but in an interesting way. Way back in 2013 she was uploading photos of her store’s products and when they sold she would remove the post. Interesting right?

JTBD feels like a spirit of philosophy as much as it feels like a technique or tactic. It’s a way of regularly reflecting on the world. JTBD isn’t an equation, it’s a long process with a lot of inquiry. But it’s worth the work.

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Two cool Peloton stats: they film thirty hours of content a day and their 18 month churn is 14%.

Two health designs

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We highlight design because humans are conditional creatures. Certain circumstances make certain actions more or less likely. Living near a huge retirement community in Florida shows this contrast clearly. The involvement in new sports like pickleball, water volleyball, and sand tennis exemplify the design principle: If you build it, they will come.

In talking about his book,Drink?, David Nutt notes how much alcohol is a cultural act. Per Nutt, alcohol’s health impacts are terrible, the societal costs are large, and meaningful outcome changes wouldn’t require that much tweaking to the current system. But we don’t change.

Culture is design too. So to not drink a person needs to counter culture.

“In my book I suggest if people say to you, ‘Why aren’t you drinking?’ quite a good repost is to say, ‘Because I’ve got quite a busy day tomorrow.'” – David Nutt, London Real, February 2020

That’s good communication, it’s in the listener’s language.

The second design is around fasting, an area design helps.

“Right around the five hour mark of a fast you’ll probably get hungry (this being our ‘normal’ time between meals), and that’s the most difficult time. Sleeping through that is the best idea then. If you can start a fast at three p.m., then in the evening you have to stay away from the snacks, but when you wake up you’re in that cruise state of twelve plus hours.” – Matt Tullman, No Meat Athlete Radio, October 2021

In my experience this is true. Fasting pangs are non-linear. Depending on the time, circumstances, and maybe even hydration, a fast can be more or less difficult. Sleeping through those time-based hunger troughs can help.

You are a designer. I’m a designer. We are all designers.

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Nutt sounded quite certain in the podcast about the health effects, but a query for “cancer alcohol meta analysis” showed less convincing results. In an attempt to be more Bayesian I’ll update from ‘quite bad for you’ to ‘pretty bad for you’.

Sports bras and pickup trucks

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One of the hallmarks of a Job-To-Be-Done approach (our series) is not to ask the people what they want. A better approach is to understand what job people want to do. That means looking at how people hack your product or when there is zombie revenue. It means what people do, not what people say. Here are two additions to our collection.

Of the thousands of first Title Nine catalogs only a handful of orders came in. But…

“Many of the people put a sports bra on their order. So while I may not have been the quickest study, you don’t have to tell me twice, wow, sports bras are the most essential piece of sports equipment for the average American woman.” – Missy Park, How I Built This, October 2021

Park built her first catalog by choosing the clothes she wanted. Park grew her business by solving the JTBD.

Our second comes from another CEO:

“Being with F-150 customers is like having a barbecue with the next door neighbors, we know them that well. The Homer had a doughnut maker and beer dispenser. If you ask people what they want, you get a Homer, but if you know the customers really well you can surprise them with twelve kilowatts of power to power their home. They won’t tell you that. They won’t tell a focus group that. But if you know them well enough you know they’ll like it.” – Jim Farley, Decoder with Nilay Patel, April 2021

Whether the Ford Lightning succeeds remains to be seen, but the fact that they promote the frunk’s ability to hold two carry-ons and one full-size suitcase shows a JTBD focus.

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My first collection is 26 Jobs to be done, it’s $3 on Amazon.

TTID Restaurant

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Restaurants are an interesting case study. In part because of the accessibility, everyone has can cook something. So much like making movies or winning wines there’s a fair bit of “I’ve seen that done so I could do that”.

But restaurants are difficult businesses. The pricing power belongs to the landlord not the chef. Staffing is brutal. Inventory expired expediently. Examples like Chez Panisse, Five Guys, McDonald’s, and In-N-Out provide great history through industry, but are outliers in business.

I’m reminded of Sir David Spiegelhalter’s (OBE FRS) comments about health news. Basically the Brit wants us budding Bayesians to not update. Health news, David said, is only news because it is novel.

But.

Maybe.

This time is different.

One template for TTID is to ask if the technology has changed the system in an important way. For hobbies it was the internet. For air travel it was deregulation. For tickets it might be NFTs. For high jump it was the landing materials.

Another way to think of technology is: rules of the system.

For restaurants it might be robot. A restaurant rule of thumb is that food, labor, and real estate each tend to eat up 30% of the costs. Yet just with location is the issue of “wholesale transfer pricing“. It’s the same idea behind Netflix’s original content: Can my suppliers raise prices faster for me than I can for my customers? If the answer is yes then we don’t need Admiral Ackbar to note it’s a trap.

But the pasta robot changes that:

“The robot means Cala saves 60% on real estate costs, which it says it puts into spending more on the cost of food ingredients, allowing it, Richard says, to deliver higher quality meals at a better price. The company’s labour costs are similar to other restaurants — they still have staff serving the meals to customers.” – Freya Pratty, Sifted, October 2021

Cala, like ghost kitchens, has shifted the 30/30/30 economic equation. If the JTBD of food has changed then maybe the economics have changed too. Maybe future restauranteurs will feel a little more full.

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Even TTID is subject to Spiegelhalters’s scorn. It’s attention grabbing to say that things really are different this time. Hopefully our series helps us figure out when it truly is.

Always buy two new cars

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I’ve been driving my wife’s car a lot lately. Her car is nice. It’s smooth, it’s got more room, and it has bells and whistles. It’s always had these things but I’d never noticed.

It’s refreshing to notice instances of relative rather than absolute value. Her car is nice relative to mine but not so nice relative to the newest thing for sale. After driving her car I kinda wanted a new car.

Like made up start ups, the advice to ‘always buy two new cars’ is half a joke. Much of the personal finance advice around here is to choose from pretty good options. Emergency funds should be generally right, both 15 and 30 year mortgages are good choices, and personal finance expertise is from experience not eduction.

To buy two new cars then means that the relative value of the next new car will be largely hidden from me. Sure there will be neighbors and Ubers and advertisements but I make – we make – easy decisions. If it’s not easy to compare then it’s a comparison that won’t occur.

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Ironically I noticed this idea with iPhones a couple of years ago. It only mattered that the phone was newer, not that it was newest. All value is perceived value.

Favorites or the field? (part two)

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In the first post we jumped off with the idea that the S&P500 is an unbalanced collection of stocks. The top five companies, noted Carl Kawaja, generate 22% of the earnings, so why not only ‘bet on’ the best?

This led to thinking about predictability. Chess is easier to predict than my morning pickleball game. Michael Mauboussin wrote a wonderful book about predicability as framed by skill and luck.

Think about events, Mauboussin explained, as a continuum: from the least skill activities (roulette) to the more skill components (stock markets, hockey, football) to the mostly skill components (basketball, chess). An event’s predictability slides as the skill portion of an outcome increases.

Here is how sport predictions look.
Update two

On the Wharton Moneyball podcast the hosts often talk about betting the favorites or the field. Bettors in events toward the left of the graph are better off betting favorites and events toward the right taking the field.(1)

With two years of data (sorta) it seems that some sports are more predictable (more skill, less luck) than others. Here’s how Mauboussin ranked sports, starting with most skill based: NBA, EPL, MLB, NFL, NHL.

Contrast that with our (sorta) model: EPL, NBA, MLB, NHL, NFL. That’s a pretty good match!

How to use this: to consider if our predictions are more like the NBA or more like the NFL and it’s “any given Sunday” ethos. In NBA-like situations there are a few big issues (players) that drive the results. In NFL-like situations there are more chances for odd bounces (fumbles), subjective decisions (pass interference), or fortuitous circumstances (turnover-worthy pass attempts).

Nothing in real life will be like these sports, but to have a good analogy is a good decision making tool.

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(1) “Buying good things can’t be the secret to success in investing,” wrote Howard Marks), “It has to be the price you pay. It’s not what you buy, it’s what you pay. There’s no asset so good it can’t become overpriced.”

The capacity-efficiency tradeoff components

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One of the things brought to light by Covid has been the balance between capacity and efficiency. In general, things with high excess capacity aren’t efficient and things with high efficiency lack excess capacity. Another way to think about capacity is as a margin of safety.

Each link in the medical supply chain, wrote Scott Gottlieb, balances capacity and efficiency. Swabs, like those for Covid, are produced efficiently.

“One of the things I learned at the FDA, watching other critical medical products go into shortage, is that it’s often the lowest-margin constituent in a complex supply chain that’s most vulnerable to shortages. Often the only way to produce such a part profitably is to manufacture it at a very large scale, which means it’s likely to be made by a small number of big, consolidated suppliers.” – Scott Gottlieb, Uncontrolled Spread, published 2021

Sixty percent of the swab market was from an Italian supplied, an Italian supplier in the Lombardy region. Eventually the United States scaled up swab productions only to be limited by the machines that ran the tests, another part of the system where the incentives pointed more towards efficiency than capacity. A system is only as strong as the weakest-link-in-the-supply chain.

There is a tradeoff between efficiency and capacity and that tradeoff depends on time and consequences.

“Flatten the curve” demonstrates this. Our highly efficient (low capacity) healthcare system could have been overrun. The consequences of that were huge. One physician friend thought she might have to work in a field hospital – and she’s a dermatologist.

A smaller example is a student taking Algebra. If an Algebra exam is during a sport season, the student will have less time to study being highly efficient with their use of time. The lack of capacity might lead to a lower grade.

However, in the case of Algebra, the time and consequences are more muddled. Is the goal to ‘learn Algebra’ or ‘score 93% on the Algebra Chapter Two Test’? In the case of the former, the time pressure changes.

Personal finance encompasses this idea too. A large capacity is an emergency fund. Those dollars could be invested to earn the historic seven percent. Time here matters too. Typically a person has one source of income (their job) and these vary in pace of payment. A salesperson can work more – and get paid more faster – than a bookkeeper. Their time aspect is different and the consequences for each depends on the difference between income and expenses.

Like the tuning of stereo, these three aspects can be balanced to fit the conditions. There is a trade off between efficiency and capacity. There are consequences in that exchange. And there is cadence for reaction.

Big shocks, like Covid, are out of our control. But though exogenous to our life, there’s still at least three dials to get things that sound right.

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History through story

MIKE

In college I was a campus tour guide and even then had an inkling of the JTBD. While the training consisted of learning dates and statistics, the visitors wanted to know questions about ‘What it was like to be a student here?’.

Historical podcasts offer a similar idea. Rather than precision, it’s appreciation. I don’t remember much from high school or college history, but thanks to historical podcasts, I have some sense of the world. Much like the History through industry books, these are some recent favorites.

1760s The hero of two worlds, The Marquis de Lafayette. My daughters’s favorite character from Hamilton had quite the life experiences, truly a ‘skin in the game’ character. Washington considered him a son, and Lafayette named one of his sons Georges Washington. And it pairs well with…

1790s The Napoleonic Wars featuring Dr. Alexander Mikaberidze. Subtitle, you can’t do just one thing. Following France’s help in the American Revolution was France’s financial debts. This is a strong two hours about the life of Napoleon, his lucky breaks and the world that was kinda set up for someone like him.

1900s Sears Historian Jerry Hancock. Sears is nice to study because it spans a few retail periods: mail delivery catalogs and railroads, urbanization and city center, then stores and suburban malls. This interview is mostly about Sears’s connection to Atlanta.

1939 The start of WWII with Dan Snow. Like the French Revolution, a bunch of factors ‘lined up’. In both cases, one major factor was the economic conditions.

1940s The teenage communist hit squad of the Netherlands. A group of teenage girls are recruited to find (seduce?) inebriated German officers in local bars, convince them to go to the woods, and shoot them in the head. One member, was Hannie Schaft (Wikipedia).

1962 The Cuban Missile Crisis from Dan Carlin. This is one I listen to each October. Also, The Tunnel documentary on a handful of college students digging a tunnel from west to east Germany to help rescue their friends.

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Have a suggestions? Let me know!

How to board an airplane?

MIKE

Everyone knows how to board an airplane, back to front. It’s logical. Back to front means that if someone is taking their time in seat 21C the person in 20A can still seat themselves.

Physicist Jason Steffen built a computer model to see how much faster back to front was relative to front to back. He was shocked. The difference was minimal. Hmm, Steffen scowled at his code, is there a better way?

What if instead of 30 to 1 or 1 to 30, a plane boarded everyone in row 30, then rows 1, 2, 3…. That might work right? The folks in row 30 would have time to stow and seat without holding anyone up.

That kinda works. Steffen’s code continued and compared the 30, 1, 2…29 option to the 1, 2,…30 option. The code noted the faster sequence, and switched around two more numbers. Again it kept the faster option and computed another switcheroo. The fastest boarding process turned out to be boarding every other row.

“It turns the boarding process from a serial process, where one person gets to their place, puts their luggage away, and sits down into a parallel process where you send in fifteen people, say in all the even rows, and they all put their luggage away and sit down at the same time. And then you send in the next group of people.” – Jason Steffen, August 2021

Steffen’s code was a Markov Chain Monte Carlo, a way to solve problems through computer code and exploration. But like wet bias or wait times, ideal solutions may not be the best.

One problem with Steffen’s method is when people travel in groups, especially families. Another obstacle is the culture of air travel, there’s some established norms. Further confounding the case is an airline’s incentives. Faster turns do matter, but relative to upgrades how much does saving time save the company?

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