All that machine learning and they still think I would like to re-buy every product I've ever purchased.
I'm sure Amazon engineering is well aware of the jokes about their recommendation engines. But their recommendation engines are not calibrated to reduce jokes, they are calibrated to increase sales.
If you only know ONE thing about a person, pinging them about that one thing might still be better than ignoring them, or offering completely random recommendations. A memorably annoying ad can work better than a boring and inoffensive one, sometimes.
In a perfect world for targeting sales, they will predict and show you exactly what it is you want to buy next. Repurchasing is just a very lazy way of predicting what you'll buy next.
(I find it super annoying, too; but that doesn't mean folks have figured out purchase reco's at scale)
Yet I bought a color laser printer and my first two pages of recommendations are for other variations on the same ("Hey, you bought the wired MFP! Perhaps you want the wireless too! And I know it's a HP printer, but maybe I can suggest some Brother toner for you!").
Still though, there could be a method to the madness. You bought a printer - are you setting up a home office? Perhaps you want a second printer for upstairs?
Also, you just bought an HP brand printer. Were you replacing a printer that just ran out of ink? Maybe THAT printer was a Brother printer. Perhaps this Brother ink cartridge can extend the life of the hypothetical printer you may be replacing...
My point is that I can contrive cases where the recommendations might make sense - not to you, specifically, but to a large enough cross-section of consumers.
Like mentioned in a sibling comment, I bet Amazon could very easily deduce that toner is sold a lot more often than actual printers.
I think maybe the real mistake is that someone who is buying a bunch of copies doesn't really need the item to be recommended to them. The recommendations are more helpful for things that you can't find in the history.
One is purely based off of pageviews. When you get recommendations for something you already bought, many times it is because you looked at it, but they don't know nor care if you already bought it. In their words, it works really well and accounting for sales brings in a lot of needless complexity.
Another is based off of sales. They also don't care if you already bought it because according to them, it works well. I remember trying to point out to them that for some types of products (specifically consumable products) this would work really well, but durables not so much. They claimed otherwise, that although they couldn't explain it, it was entirely common for people to rebuy things like vacuum cleaners and TVs and kitchen knives. I did a tiny bit of research to show them why they thought that, and proved with a small segment (vacuum cleaners, I believe), that after you filter for returns and replacements, that the probability of sequentially buying two of the same vacuum cleaner was effectively zero. They asked me to do it for the rest of their products, but I didn't have limitless time to spend on helping another team, especially one with a PM who was a complete dick to me for having the audacity to make a suggestion that he hadn't thought of.
In all, I believe there are a dozen or so recommender services, each with their own widget. There are tons of people that think all of the recommenders have merits in some areas and drawbacks in others, and the customer would be better off if they merged concepts into a single recommender system. But they all compete for sales lift, they all think their system is better than the other systems, and they refuse to merge concepts or incorporate outside ideas because they all believe they are fundamentally superior to the other recommenders. Just a small anecdotal glimpse at the hilariously counterproductive internal politics at Amazon.
"Dear Amazon, I bought a toilet seat because I needed one. Necessity, not desire. I do not collect them. I am not a toilet seat addict. No matter how temptingly you email me, I'm not going to think, oh go on then, just one more toilet seat, I'll treat myself."
And sure, I know that these are all pretty weak signals and that the toilet seat recommendations will probably go un-clicked. But Amazon has to do the best it can with not very much information, and if they have to hawk toilet seats to a hundred people who've already bought them to find the one guy who's remodeling several bathrooms, that's probably a really good gamble for them to take.
We're the product, willing or not, and the metric some chainsaw companies care about is simply 'searched "chainsaw"'. Google is happy to sell our eyes based on that, whether or not it converts into actual sales.
In fact, for Google to deduce further and use ML to say "this user actually isn't looking for a chainsaw despite that search," and the reverse of "this user is looking for a chainsaw despite not searching it," probably wouldn't go over well for an advertiser paying for views. It turns Google into more of a black box, and would appear less trustworthy or understandable.
Additionally, aren't there filters companies can choose, such as targeting users who have searched for certain keywords?
Whenever I travel to another country they show me ads for that country, in a language I don't speak.
This is on a ~10 year old account that I use daily, that has my language and country as settings, so 0 data analysis needed.
> "Naming your child Abigail can't make you more likely to get pregnant."
> Again, failure to do the math.
> (You get a 2X there, for free, from the observation "If you had a child named Abigail this year you are biologically capable of having children; this is not true of no less than half of humanity. Now apply same insight to childbearing age and you're already at 5X+.)
This is terrible math. Nursing a child severely inhibits getting pregnant; without use of formula (or a substitute like goat's milk or what have you), it is extremely rare for children to be spaced only one year apart. It's rare enough that there's a special term for such children -- "Irish twins".
If you want to swagger your conditional probability, you should know that you've got to account for that in the probability of giving birth one year given that you gave birth the year before. You can't just account for the positive adjustments, ignore the negative ones, and then say "the odds are at least as good as they would be if the existing anticorrelations didn't exist".
> What's a SWAG for how often a purchase immediately goes wrong? Not right color? Fridge DOA? Shoot I mismeasured my kitchen? Wife just hates it? Call that 2%. If I fix it within a week, then 2% / 7 = 2.9e-3 probability of purchasing a new fridge.
> That's a 10X relative risk.
But of those listed options for "immediately goes wrong", zero of them would result in re-buying the same fridge. Wrong color, I mismeasured my kitchen, and wife hates it would cause you to buy a different fridge. Fridge DOA would cause you to refund the fridge and get a replacement.
How many people, faced with the expense of a fridge that hasn't worked out, figure the best course is to just write it off and buy a second copy, hoping that it will work out better?
The timespan for two separate births in 2018 and 2019 isn't 12 months, it is anywhere between 12 and 24 months. While giving birth twice in 12 months is rare, I would assume that giving birth twice in 18 months or 24 months is not nearly as rare.
If you give birth in July of 2018, an 18 month spacing won't let you give birth in any month of 2019. That's still a very significant negative adjustment to "demographic factors get you to 5X+". Demographic factors aren't the only factors there are.
edit: a quick sanity check tells us the factor of 5 estimate is off by a huge amount. From https://www.cdc.gov/nchs/fastats/births.htm :
> Birth rate: 12.2 per 1,000 population
This is the birth rate (for 2016, in the US) reflecting the odds that a randomly-selected person, male, female, infant, or menopausal, will give birth in a calendar year.
> Fertility rate: 62.0 births per 1,000 women aged 15-44
And this is the birth rate demographically adjusted for the ability to give birth. It's about five times higher.
Since siblings born in consecutive calendar years are rare, we can only conclude that patio11's "true statement" is false by a pretty large margin. For it to be true, every birth in 2016 would have to have been to a woman who also gave birth in 2015.
I wish that, before asserting something is true, people would think about whether it's true.
So this has been bothering me, because if every birth in one year is to a woman who gave birth the previous year, and the years have equal birth rates, and we ignore aging in/out of the "fertile" demographic, then the odds of giving birth in one year given that you gave birth last year are 100%, well over 5x the population rate.
The claim is that the odds of giving birth this year given you gave birth last year are equal to the odds of giving birth this year given you're fertile, or in other words that, if you're fertile, whether you give birth this year is probabilistically independent of whether you gave birth last year. This is quite clearly false -- your odds of giving birth in one year are much lower, given you gave birth the year before, than your odds of giving birth in any randomly-selected year during which you're fertile. But my argument above is wrong.
For patio11's claim to be true, 6.1% of women who gave birth in one year would need to give birth again the next year. (Stated equivalently, 6.1% of people would need to have an older sibling born one calendar year before themselves.)
There's a certain truth to you being in that buyer's class now, because there is a certain population that buys laptops. Yet as he stated, I'm not in the buying cycle. I would probably be in the buying cycle again in 1-2-3-4-5 years from now. So immediately advertising for something you buy rarely (refrigerators more, laptops less, phones maybe more often) is still wasted ad space.
David Scotten's comment on that thread is a bit more on point: "There's the opportunity cost of not showing another ad in that slot, though. So they're showing you what they think is the ad with the highest expected value for them."
I think they don't calculate the opportunity cost right. They will advertise right away for something that is unlikely in their best slot in my emails.
It would be better for them to advertise other related electronics (headphones that work with bluetooth if you just bought a new phone with bluetooth, loudspeakers, computer screen, external keyboard, docking station, etc.) Those would actually be much more likely buys for me.
Funny actually how babies are mentioned. I never had a baby, nor do I have a girlfriend (something Amazon should probably know with some basic data science), yet they always advertise baby diapers to me, even to the point of sending me some as advertisement. I still can't figure out how that happened.
For me Amazon is a store where I always look for something specific. Only the book recommendations have really made me buy something I didn't think of before. I think there's a lot of lost potential here for Amazon.