61–70 of 144 posts
This seems like a great marketing technique for up starts trying to compete in the quality/price space. In the chicken soup example, for instance, cost to produce is a strong indicator that the soup has quality ingredients, and isn't just a big markup on some generic condensed soup. It's a similar thing with the backpack, but in the other direction - this price isn't low because we cheaped out on manufacturing and ma…
Most "premium" products costs are mostly Advertising, R&D, paying employees/shareholders, etc. They're valid things to spend money on, but don't reflect the "top quality ingredients" that consumers expect the money to be spent on. I suspect many consumers wouldn't like to know that the $1500 iPhone they just bought only has $400 worth of actual parts in.
Point taken, but perhaps a bad example. When I buy an iPhone, I'm paying a little for materials, but mostly I'm purchasing the culmination of tens of billions of dollars of R&D that possibly surpasses any other product on Earth. A better example might be a luxury brand wallet where they sell the illusion of better quality, but that may or may not be true.
I suspect that you'd find the iPhone purchase motivation is pretty much the same as the luxury wallet.
"People said they were 14.2% more likely to buy this chocolate bar when they were shown the version with a cost breakdown." What people say they would do and what they actually do are 2 very different things. The conversion rate of actual behavior needs to be tested for this to mean something useful.
Author of the summary here: You are right regarding the chocolate bar experiment. That's why the researchers ran multiple experiments, some of which measured actual behavior: - People were 16.1% more likely to bid for a gift card for an Everlane backpack (vs a J.Crew one) when they saw cost information about it - Sales of chicken noodle soup bowls ($4.95) in Harvard’s campus canteen increased 21.1% when costs were di…
Earlier quoted context omitted.
Thank you so much! I agree that the figures can vary for many reasons and we shouldn't expect them to be exactly the same (some things we don't end up controlling for). At the same time, if we take the experiment of the soup for example: They measured 9,227 sales of it so the 21.1% increase is quite robust and I'd expect the error margin to be much lower than 5% either way - so in some ways the precision is warranted…
You are confusing confidence intervals (used to say that you are confident the increase is positive at all) with error margin (the false precision in the 3rd significant figure).
Most "premium" products costs are mostly Advertising, R&D, paying employees/shareholders, etc. They're valid things to spend money on, but don't reflect the "top quality ingredients" that consumers expect the money to be spent on. I suspect many consumers wouldn't like to know that the $1500 iPhone they just bought only has $400 worth of actual parts in.
But that reflects an ignorance of how cost accounting works. Sure, it might include $400 of parts, but a hell of a lot of R&D, equipment, labor, and overhead are amortized into that phone. A very cursory look at Apple's latest income statements shows that the net profit margin on their revenue is about 20%, meaning that the phone's actual cost to produce is somewhere around $1200 (assuming a $1500 retail cost).
Earlier quoted context omitted.
But that reflects an ignorance of how cost accounting works. Sure, it might include $400 of parts, but a hell of a lot of R&D, equipment, labor, and overhead are amortized into that phone. A very cursory look at Apple's latest income statements shows that the net profit margin on their revenue is about 20%, meaning that the phone's actual cost to produce is somewhere around $1200 (assuming a $1500 retail cost).
Yes, but will you be the one to explain that cost accounting to each buyer? "Yes Maaam, we'll spend $200 of the money you're about to give us on a fancy campus with hammocks to try to attract good engineers. Oh, and the massive billboard outside that made you walk into the shop in the first place."
Earlier quoted context omitted.
Author of the summary here: You are right regarding the chocolate bar experiment. That's why the researchers ran multiple experiments, some of which measured actual behavior: - People were 16.1% more likely to bid for a gift card for an Everlane backpack (vs a J.Crew one) when they saw cost information about it - Sales of chicken noodle soup bowls ($4.95) in Harvard’s campus canteen increased 21.1% when costs were di…
Hi Thomas, thanks for your work. I am a fan! Just a minor comment: I find using decimals places (like 16.1% and 21.1%) in human experiments pretty irritating. It feels like false precision. After all, these experiments must have confidence intervals. If I had to guess, I’d assume at least a +/- 5 ppts variability in all these numbers. What’s your view on that?
Whether you say 21.1%, 21%, or 20%, you still have a single number. You could make an argument that decimal places like 21.147258% add clutter, but without an actual measure of uncertainty, all you're doing is reporting a summary of the data in the sample with different amounts of arbitrary rounding. That's not particularly helpful as a substitute for the full distribution.
Earlier quoted context omitted.
Thank you so much! I agree that the figures can vary for many reasons and we shouldn't expect them to be exactly the same (some things we don't end up controlling for). At the same time, if we take the experiment of the soup for example: They measured 9,227 sales of it so the 21.1% increase is quite robust and I'd expect the error margin to be much lower than 5% either way - so in some ways the precision is warranted…
You are confusing confidence intervals (used to say that you are confident the increase is positive at all) with error margin (the false precision in the 3rd significant figure).
Earlier quoted context omitted.
Good point! Sadly, I don't have access to the Harvard study ( https://pubsonline.informs.org/doi/10.1287/mksc.2019.1200 ), so maybe someone with access to it could check it, but the linked article might be misleading in some aspects (depending on the results of the study shown in the paper). From the study's abstract: > A preregistered field experiment indicated that diners were 21.1% more likely to buy a bowl of chi…
"21.1% more likely" is how articles phrase a 21.1% increase in observed frequency. "Sales increased 21.1%" is equivalent as long as the unit price remained the same.
> People said they were 14.2% more likely to buy this chocolate bar when they were shown the version with a cost breakdown
Surely that cannot be correct. Possibly 14.2% of the people who were asked said that they would be "somewhat" more likely to buy X with more data stuck to its label (does any data improve sales? Did they A/B the label by adding random info?). This is very different from them acting upon it though.