Snickers and Milky Way

Snickers and Milky Way

Reframing our perspective is a powerful thinking tool. ‘Sleeping on it’ is reframing. Reading books is reframing. Comparing novel things is reframing. 

For a business owner, thinking of time of day, place in life, and what happened prior is reframing.

Bob Moesta notes “context creates value”. Time and place create more or less value. Birthday gifts have one value on birthdays and another value when it’s not. 

But we miss this because of average lies. Average computes easily, is sometimes helpful, but is a crude tool. Sometimes we NEED this one thing RIGHT NOW! 

Contrast Snickers and Milky Way. Graphically: 

Commercially (2011):

Snickers is a chewy pick-me-up energy bar. Milky Way is a treat-yo-self deep breath of sweetness. The context creates value

According to Bob Moesta, the context for eating Snickers is that I’m hungry and I want something filling, tasty, cheap, and fast. Applying average thinking, there’s not a constant demand. Find when customers consume a product reveals that product’s JTBD.

“Context creates value” fits well with Alchemy too. Channeling Rory Sutherland, it wasn’t that Snickers needed to be tastier, rather reframed. Alchemy is about solving problems with psychology rather than physics. Instead of making travel faster, make it more enjoyable with wifi, charge ports, booking flexibility, a table for tea, someplace for the kids to burn off energy, and so on. Faster is only better when the process sucks. 

Consumers and customers have untapped wants. They’re hiding behind time, place, averages. They’re served by JTBD & Alchemy. 

$1 Toronto real estate

Average is like my reciprocating saw: never as useful as I expect. Part of the reason average sticks around is economics, It’s cheap to produce.. Average is a crude tool, like with student loan debt, and often hides the heterogeneity of a situation.

We’re entering an era of precision. One covid lesson has been the effect size of heterogeneity. At the macro level, the impact of covid depends on time and place. At the micro level, the impact depends on age, immunity, and social network. Covid was (is?) difficult to judge because there are a lot of factors that need fitted together.

If we need precision we should probably think about distributions at least as often as we think about averages. An example is the periodic one dollar real estate listing. Yes, this generates attention, spins up the market mechanism, and might be the marketing magic an owner needs. But it also changes the distribution of offers without changing the average offer.

“When you give people a listing price they ask if it’s worth more or less and by how much, so they anchor at the listing. If you don’t have an anchor people build a valuation from first principles. The average (offer) doesn’t change but the distribution does. For a one dollar listing you get some really high rates and some really low ones. In the listing price you get distributions around what the asking price was. This is a world where the seller doesn’t care about the average, they only care about the top end of the distribution.” – Dilip Soman, The Decision Corner, October 2021

Maybe this is being too hard. Average, like the saw, has its uses. The aim here is to combine numeracy with psychology to get by in the world. That means presenting the ‘best’ wait times or predicting rain more often. Being numerate is understanding that the average age is 78, but if you make it to 65 you’ll probably live well past eighty.


“The average looks like 10-12 years lost due to Covid – but that’s an average of a distribution with a very odd shape, a highly skewed distribution, some people have lost forty years of life. The peak of the distribution is people who lost less than a year of life.” – David Spiegelhalter, Risky Talk October 2021

Average Lies 2

Edit: see the comments about the original purpose of the VCR and why the clock was important. That said the overall idea still stands and applies to IOT, cameras, chips. 

When I was a kid there was something I didn’t get. Why did VCRs have a clock? The thing never seemed to work correctly, was slightly different from every other clock in the house, and wasn’t central to the functionality of the VCR unit. I was going to watch True Lies (again) and did not need to know what time it was.

giphy

As an adult I think I get it. The VCR had a clock because clocks were inexpensive to install. A few cents may not make sense from a JTBD approach, it does make sense from a sales approach. When people compare A to B at the same price but A has something B does not (a clock), consumers will choose A even if it’s a feature they don’t really need.

Like a VCR clock.

The same thing happened with pictures. Instagram changed not only how people took photos but how many. Pictures are cheap to take and share. As things become digitized they are cheaper.

Like numbers.

More counts, more code, more algorithms, more nodes. The network grows and the network shows everywhere that Mikey-boy goes. 

We are counting more and computing more which means we will be sharing more numbers. This Average Lies series (part 1) is a reminder to dig deeper into numbers and come up with a framework for when average is, and is not a good measurement.

  • Good: Biological (height). Mediocristan. Large samples. Homogeneous.
  • Bad: Social (media). Extremistan. Small samples. Heterogeneous.

Here are three more examples:

On The Long View, Moshe Milevsky said, “The number of times you’ve circled the sun, your chronological age, doesn’t really reflect the years you have remaining. You can be fifty-five years old chronologically, and I can be fifty-five years old chronologically but that doesn’t really tell us how long we have to spend in the lifecycle.”

Tuscan is cooler but south of Phoenix.

The most successful country in the NBA? Poland of course.

As numbers are cheaper to produce more numbers will be produced. Like VCR clocks and Instagram pictures, some will be good but some will not.

Want more? Check out this pay-what-you-want placebo prescription pdf.

Average Lies

“Often an average is such an oversimplification that it is worse than useless.” – Darrell Huff, How to Lie with Statistics.

We don’t really think about averages. The average hospital costs for hepatitis A was $16,000 in 2017. The average student loan debt for North Carolina residents is $36,000. The average American says they’ll spend $142 on Valentine’s gifts. Men, on average of course, say they’ll spend more than women.

For some things in life, average is fine. When my daughters were born, the hospital gave us a growth chart for their height and weight. It showed deciles and right in the middle was average. Growth charts are simple. Height. Weight. Plot. On chart meant on track, physically at least.

Now my daughters are twelve and ten and wow how things changed. New parents can track their child’s sleep, diet, movement—bowel or otherwise. And it’s not just parents. Everyone can track their taken steps, hours slept, and Spotify streams.

With technology, counting is easier.

With counts, averaging is easier.

Numbers are tools. Rather than bartering bananas for bread we have dollars and cents. With numbers, stores count their bananas bundles. With numbers, people have balanced budgets.

Numbers are tools. Like other tools, they take practice with feedback to build proficiency. I’m much more careful with the occasional use of power tools than the regular use of a chef’s knife. Numbers are like that. Well practiced and well used, numbers are a unique and powerful tool.

An example of numbers telling another story was the sabermetrics revolution in baseball. Smart teams realized that walks are better than hits, and that walks cost less to buy. Worth more, cost less. It’s like the successful Miller Lite advertising campaign: ‘tastes great, less filling’.

Decades later, sabermetrics happened in basketball with the insight that making one-third of three-point shots was the same as making one-half of two-point shots. Life, like sports, uses numbers more.

Numbers, though hidden in code, will become more prevalent in life and more important. 

Average, as numbers go, is often abused. This is due to many reasons, but just like technology has reduced the cost of tracking a baby’s bowel movements, average is used because the cost is low. It’s sixth-grade math. And it can hide important nuances.

For example, the average student loan borrower owed $28,000 in 2016. If we dig a bit deeper we find:

  • The median debt was $17,000.
  • The median for two-year degrees was $10,000.
  • The median for a four-year degree was $25,000.
  • One-in-four borrowers owed less than $7,000.
  • Only 7% of borrowers owed more than $100,000.

Those details are often omitted from the story. One poll showed that people viewed median debt of $17,000 as the “least bad figure about student loans”. Life is nuanced but numbers are not. Framed influences the way numbers are understood.

Thanks for reading.