Tag: ambiguity

Drive Sober or Get Pulled Over

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One way to think of the world is not good or bad but underrated or overrated. Switch from binary to fluid. 

Reading like a Bayesian is underrated. Jobs theory is underrated. Books are underrated. 

Think of Over/Underrated as a normal distribution. Only learning from books or never learning from books are bad. Each could “move up the curve”. 

Education, like financial literacy, as a solution is overrated.

Education is a “tight” solution. When my daughter was in elementary school she went to a speech therapist to work on her *th* sound. She had a cast to fix a broken arm. Her volleyball coach helps her with footwork. These are “tight” solutions that fit the larger set. 

Drunk driving advertisements like “drive sober or get pulled over” is another tight solution. 

But it’s wrong. It doesn’t fit the larger set of circumstances. 

A “wider” approach is to think not about drinking but about driving. 

The public could subsidize a ride-share happy hour. If people leave home without their car they can’t return home with it. Or, use the marketing money to pay for random rides. 

Another option is to change where bars are built. If it’s easy to walk or hard to park, people won’t drive.

Gamblers can “self exclude” themselves from casinos. Can insurance companies offer a drink driving equivalent? Let people save 5% while committing their sense of self?

…

Education is overrated because it’s linear, rigid, and two-dimensional. If X is good, do more of it. If X is good, the opposite is bad. 

Not necessarily. 

Related: using ambiguity aversion to hint at punishments.

Addendum: Because overrated and underrated are fluid and because times change, the overrated can become underrated.

Copying the Inflation Buster

MIKE4 Comments

I don’t check my home equity every day, goes a joke among the Vanguard-Buffett-DCA crowd, why should I check my stock portfolio? It’s a riff on the availability heuristic: if I think it, it’s important.

‘Home’ is super available. Vacation rentals, of someone’s home. A chunk of net worth is home. Neighbors move. During Covid we were stuck in our homes. People began to work from home. After Covid the home market exploded. After that rates ran up. ‘Home’ is everywhere. 

Good copywriting, said Bob Bly, “enters the conversation people have in their mind.” Let’s look at a good Rocket Mortgage ad.

Transcript: “Buying a home? Rocket mortgage will cover one percent of your rate for the first year at no cost to you, saving you hundreds even thousands. With Inflation Buster that means more mini-vacations, a lot more lattes, and more date nights. Now imagine if rates drop within three years of your home purchase. You get exclusive savings when you refinance at that new lower rate. It’s more cash in your pocket. Save when you buy today and refinance tomorrow. Visit inflationbuster.com to get started.”

The good. Rapid fire: It’s not a house, it’s a home. One percent is a nice whole number, and worth more (psychologically) than 0.99999999%. First year… appeals to our myopia. More mini-vacations… highlight the opportunity cost. At no cost to you, and if rates drop… avoids our ambiguity aversion. Visit… as a call to action. 🧑‍🍳 😘

The bad. None!

The interesting. A picture is worth a thousand words, and this video is good. 

We’ve tracked ‘average’ monthly home payments (1971-2022). On a four-hundred-fifty-thousand dollar home, Inflation Buster saves about $200 a month. Put another way, it’s a year of payments on a four-hundred-thousand dollar house instead of the more expensive one. None of that factors into this ad. It’s not the customer’s language. 

Interest rates and home prices are not the important metrics. Only monthly payment matters. That’s the conversation in this ad.

Sweet words

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Successful copywriting uses the customer’s language. Find out what, how, when, and why the customer thinks – and the words they use.

One accent of customer language is certainty. We dislike not knowing. Not knowing feels risky. It’s why this bag of sugar is so sweet: 30 calories per serving. Diets are trends. Eat this or that? Now or later? Are health bars actually healthy? Is sugar bad for me? It’s too much! But this simple bag of sugar puts it in the customer language: calories, and not that many. 

Road construction is another example, only inverted. Fines doubled when workers present. I don’t know how much, but I certainly don’t want it to be doubled! In this case, the natural dislike of the unknown is magnified and aids in the messaging to slow down. 

This hook helped Jaws (1975) set the mold for summer blockbusters. It was a difficult movie to make, in part due to “that sonofabitchin’ bastard rig” (the shark) which kept breaking down. The footage was such a mess that during editing Steven Spielberg used barren shots of the water along with John Williams’ score. That was great because rather than seeing the shark, audiences imagined the shark, a worse fate. 

Organizations can remove or introduce anxiety in their customer communications. How much depends. On what? On what the customer thinks. 

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We talked about Jaws’ role in the evolution of the movie business model in this post, Batman BATNA. Contact too:

Gambling with CoVid19

MIKE9 Comments

Bias warning, My wife and I can work from home, my kids kinda like homeschool (but really miss their friends) and I wiped down the groceries in the garage. 

It’s always helpful to ask, has someone faced my situation before? The answer is often yes. Rory Sutherland thrives at this.

On recent podcasts from Deep Dive (#249) and Wharton Moneyball (April 1, 2020) there were two very good steps to understanding anything with uncertainty.

Wharton Moneyball takes its name from Michael Lewis’s Moneyball. That book shed light on using advanced statistics to find other ways to win baseball games, that walking to first after a full count was actually better than hitting a single to first on the first pitched ball. Moneyball thinking has extended to new areas like basketball, movies, and Jeopardy.

On Wharton Moneyball, Adi Wyner spoke with Alan Salzberg who mentioned that he’s starting looking at CoVid19 deaths rather than cases. The former takes longer to materialize in number form but is better than the former which is mostly a product of testing. It’s trading a sampling error for a time lag.

“It was what we would generally call ‘garbage data’. A confirmed case  might me it was confirmed because someone came to the hospital and was already sick.” Alan Salzberg

Ok, good so far.

We need good data (walks instead of hits) but then Alan goes too far. The virus is mostly airborne and mostly won’t bother someone if it lands on a surface someone might touch and then finds a path into their body. That’s a lot of ifs. “Is that enough,” Salzberg wonders, “It stays for a little while, but in my mind I don’t think that should be a worry. I think you should wash your hands, and I’ve been doing that and I try not to touch my face a lot. But I think being ridiculously uptight about it is kind of crazy.”

Ok, that’s fine if we had better data.

But we don’t. Instead of six feet we might heed caution and stand at least twenty-seven feet apart. What’s the R0? How long is someone infected and asymptotic?

Ok, those are good questions.

There’s a lot of unknowns here and on Deep Dive, Matt (@PlusEVAnalytics) talked through what we can do when there are so many unknowns.

Think of Tom Brady’s 2020 over-under passing line of 4,256 passing yards, or 266 yards per game. His last four years totaled; 4057, 4355, 4577, and 3554. But with Tampa Bay he’s got better receivers. And he wants to prove to everyone that he’s still got it! And he wants to do it without Belichick!! Yeah!!!

But how much do those things count for? Like how much we know about CoVid19, we don’t know. Matt gives us a guide though. Do the things we don’t know make one outcome more likely? With age, ambiguity, competition, injury and so on, the unknown makes the under much more likely.

Matt credits much of this thinking to Taleb but the concept of sports and gambling make it clear. It seems like the unknown parts of the CoVid19 pandemic tilt the outcomes in favor of what’s much worse. Good data is a necessary start but ambiguity must be considered too.

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