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Comment by kevin_thibedeau | Hacker News Reader

They don't need apps to do this. I sat in a meeting with a data broker in 1998 where one of their managers was chuffed that they could determine menstrual cycles by analyzing purchasing records. And it wasn't hygiene products. Various foods and other spending patterns pop out after a 28-day correlation over groups of women that are artificially "synced" into cohort groups.

This invasiveness will continue so long as there are no consumer data protection laws.

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Can you elaborate?

Because even with hygiene products, people buy them before they need them and stock up. And with food, you're often shopping for a week and for the whole family. You put things on a shopping list and don't buy them the same day you're using them, or even the same week.

I suspect that even if the manager thought they could determine it, their actual "results" were entirely random. After all, how is he going to check? Call two hundred of the women and ask? Also, periods are irregular. They're not a perfect 28 days each time. They vary month to month.

I don't doubt the manager thought he was doing that. I doubt it actually worked at all, though.

While I expect most women buy in advance, I expect they also don't buy enough and so are out there mid-period at least some of the time. Their cycles also affect their buying patterns, so even women who are stocking up are more likely to buy differently in different parts of the cycle.

The real question is what does the manager do with the data. Across a city on any given day there are about the same number of women in every day of their cycle so it isn't like they are marking up pads on the 25th-28th day of the month to get women who didn't stock up. As such I don't think this data is useful, what is useful is when they discover a women missed her cycle and thus needs to get ads for pregnancy wear in a couple months. Since that is their need for data, the fact that it is noisy and not very accurate is still close enough.

That said, they probably are more accurate than you would expect despite all the noise. Not 100% accurate, but being greater than 50% accurate is a lot better than chance and should be obtainable.

Just knowing what is bought together with menstrual products can influence how they're marketed. It can influence where certain products are placed on shelves relative to menstrual products, where and how they're marketed, which brands in your conglomerate to co-market with, and so on. This is the most innocent use of data in aggregate. The real creepy shit follows now that it's individualized and easily deanonymized.

> The real question is what does the manager do with the data.

Direct-mail (or these days, in-app with notifications) coupons. If you know you'll pay full price at CVS, but it's 30% off at Rite-Aid, you'll go to Rite-Aid and buy other stuff as long as you're making the trip.

I mean, maybe there really are enough women buying last-minute to be able to predict, at least for those women -- and identify who those women are.

If that is your motivation you just blanket send those advertisements all month. Because you want women to think of you when they have need. Some women are stocking up before they have need, and so the coupon when they are having their period is when they won't be buying supplies. Even those who are buying as they have need, if they are out they are buying from the closest store at whatever price, while those who are not in an emergency know where the low prices are (wal-mart or such)

So... the risk to women's safety is that they might end up paying less for hygiene products?

No, the risk to women's safety is that someone the woman doesn't want to know, could find out that they're pregnant. (Or using birth control or whatever)

Someone being an advertiser buying the data?

Someone being the government criminalizing miscarriages and abortions.

Actually yes.

https://www.forbes.com/sites/kashmirhill/2012/02/16/how-targ...

You think the data brokers aren't aware of varying spending habits? You might be surprised to learn the number of people living day-to-day, cheque-to-cheque who don't have the ability to stock up on much of anything. These are the consumers who are "ripe for the picking" in marketer's eyes. Back in the late 90's this would have been much harder too, probably working with not much more than cash register receipts.

You're making a lot of assumptions there. The guy probably had a training dataset with real cycle data and shopping data and went off of that.

I am, but that's because this is 1998. Where would you ever get such a training dataset with real cycle data tied to shopping data? Menstrual tracking apps weren't a thing then. And any anonymized medical studies that actually did such tracking certainly couldn't have been correlated with identifiable shopping data, I would think.

I think the idea is that the cycle is inferred by analyzing purchasing data.

The person I responded to literally said "training dataset with real cycle data".

> Because even with hygiene products, people buy them before they need them and stock up.

One would think so, but in my experience this is not the case on average. Of the half-dozen long term relationships I've had, only 1 partner was ever prepared for the monthly inevitable. For everyone else it was always treated as a surprise. Suggesting to my current partner that she stock up on the products she just used was dismissed with an "oh I don't need that stuff for weeks".

The trope of guys not wanting to go get tampons, or uncomfortably navigating the feminine hygiene isle, did not just appear out of nowhere.

From an advertising POV, isn’t that even better? Then they’d know when to advertise to someone to increase sales.

That is, the marketers wouldn’t actually care about a woman’s cycle, but at which points they could monetize it.

(Good lord, I need a shower after just typing that.)

I think it's more about when to push than about what to push. Maybe there are specific types of products aside from what's obvious, or actions to take or not to take. Like avoiding discounts when customers are least likely to develop loyalty, maybe even how to rotate through choices of products like meats to vegs to dish soaps.

By the way, I've seen self proclaimed male on social media posting how they use these trackers to predict their irresistible sushi cravings. Apparently, and contrary to intuition, men also have the cycle, just less obvious. Pelvis opening up and such.

I don't tend to keep candy in the house. You could track my youngest daughter's menstrual cycle pretty accurately by how often I buy Hershey's bars with almonds. They are never consumed in our household other than during the first part of a particular week. My older daughter and wife don't have quite the same tells though.

They don't care about your menstrual cycle. Advertisers want to predict your purchase pattern to better target you. So, what you said just reinforces the idea that using menstrual cycle data for placing ads is useful. Essentially using your body against yourself.

> And with food, you're often shopping for a week

That’s not how people shop here in Europe

Assuming purchase history has that clear a pattern (I'm doubtful) doesn't that mean that purchase history alone would ... what? Not be allowed? Because it could be used to determine other things?

I'm not sure what consumer protections could really do much here if the pattern is obvious and the data exists.

read up on that case. it clearly did have a pattern. that was no random guess.

i never pay with card and i don't join any member programs to avoid creating a purchase history. pattern or not, a lot can be gleaned from what i buy.

This theme of anecdote has been trotted out for more than a half century. In the 80’s the yarn was that a supermarket could tell when a woman was pregnant before her doctor from her purchase patterns alone.

To this date, no supermarket has ever produced this result - or any thematically similar.

target

Target’s able to confidently estimate that a woman is pregnant from 20 weeks from their purchase history, if they’d purchased pregnancy tests. A doctor can tell you 70+ days earlier.

They’d probably have an equivalent outcome if they just looked at cctv footage and guessed based on weight gain.

Joke's on them, my wife likes ice cream every day.

does she eat more ice-cream during menstruation or pregnancy? does she change flavors? there may be patterns that you are not aware of. only a steady ice-cream subscription would be able to hide that.

Doesn't that assume Just In Time purchasing practices on the behalf of women? Granted it depends upon the type of food. You usually don't buy restaurant food to stock up on it, but you might buy say, three tubs of ice cream because of a buy two get one free.

This is interesting though. How would these sorts of correlation be protected against? We already know that anonymous health data can be traced back. Gather enough data in any domain, and you can pinpoint someone.

Given recurring purchase subscriptions from Amazon Prime, perhaps this data is different from 27 years ago

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