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.
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 disclosed
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?
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.
I also feel that if I were to round a 21.4% to 20% I'd be miscommunicating the findings of the research :)
//> we finally have an absolute number of sales measured, but no way of knowing - representing all sales within a period or just cherry picked?
//> for the rest of the population, did it reduced sales?
//> was there any randomised test or not, because in the latter case there could be other biases we're unaware of
//> the increase is compared to what exactly?
//> any WHY is purely speculative as what was measured was WHAT people did. Internal motivation is in this case unproven, there is just a potential correlation.
//> confuses me to hell people taking about error margins and confidence intervals for something measured directly.
Right, but you'd round 21.4% to 21%, not to 20%.
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.
You're also lacking cases where the markup is high, 500-2000% is common among a wide range of products, from Fashion to SaaS.
Edit: I'd also add, in the case of food products, if all vendors adopted the transparency strategy, once consumers see typical margins in that industry are in the 5-10% range, suddenly that not-unreasonably-priced organic chocolate bar looks like a high margin item...
and no one owes the author page views or their time either.
>This is a discussion board
Exactly, we're having a discussion on what the article is missing and why it matters.
It's the idea that this could work.
I want to encourage people to honestly communicate ideas to me and I want to discourage people who would discourage those first people.
I explicitly don't want to restrict only the highest-quality research. I want to permit some amount of scamming me.
The article does cover those cases explicitly:
> Extremely high profit margins (>55%) could trigger a negative reaction, although this was not tested.
Basically they didn't even bother because it's pretty clear that someone selling a commodity for a massive markup is not going to benefit from this approach. Of course, that doesn't mean this hypothesis isn't worth testing...
Disclosing your costs if you have a modest profit margin and also modest absolute costs is good signaling on multiple fronts (as long as you're credible):
- Higher parts/ingredients costs are a signal indicating good quality
- Low profit margins make customers feel like they're getting a good deal
- The appearance of transparency signals your own confidence in all aspects of your business
What sales decreased in relation to this? Would overall sales increase if all food items had costs listed? Or would people gravitate to the meals with the lowest margins, figuring them to be relative bargains?
It's really not simple to draw a conclusion from the limited data set here.
From the study's abstract:
> A preregistered field experiment indicated that diners were 21.1% more likely to buy a bowl of chicken noodle soup when a sign revealing its ingredients also included the cafeteria’s costs to make it.
From the linked article's sub-title:
> Sales of a chicken noodle soup increased 21.1% when people were shown the costs of making it.
The study's abstract mentions that they were more likely to buy a bowl of chicken; it's not mentioned that they actually bought it.
"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.
Rather I meant that it’s a bit odd that costs are clearly specified, but the consumer has to calculate the profit themselves. In my mind, that actually makes it less transparent as high profit margins are what drive the crazy capitalist market we have today, and associating small values to material and labor doesn’t make me feel better when the profit is the “artificial” portion of my purchase price.