Updated 6.14.20: Added 4 instances
Updated 8.19.20: Added 1 instance
This post is nothing more than a list of snippets to show how companies are using our data against our best interests and will be updated periodically. No real commentary as I believe these all speak for themselves.
Amazon.com Inc. founder and CEO Jeff Bezos said today it was "a mistake" for the Seattle-based online retailer to experiment with charging different customers different prices for the same products.
After a doctor diagnosed her with major depression, she started receiving monthly sick-leave benefits from Canada's Manulife Financial Insurance.
But this fall, the checks stopped coming. When Blanchard called Manulife to find out why, she said she was told it was because the Facebook pictures indicated she was no longer depressed and ready to return to work.
He shows him an advertisement that was sent to his high school daughter, filled with maternity clothing and baby items. The perplexed manager apologizes and even calls the man at home the following week to further apologize for the advertising faux pas. However, when he does the man sheepishly admits he's found out that his daughter is, in fact, pregnant.
First, all Target customers are assigned a Guest ID. Associated with this ID is information on “your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you've moved recently, what credit cards you carry in your wallet and what Web sites you visit.” Pole found that by analyzing this data combined with purchasing history on 25 products – things like unscented lotion and certain types of vitamins — he could determine the likelihood that a woman was pregnant. He quantified this information by assigning women a “pregnancy prediction” score; his data analysis was so good that he could estimate, within a fairly small window of time, a woman's due date. This further allowed Target to send the women coupons targeted at different stages of pregnancy.
A Wall Street Journal investigation found that the Staples Inc. website displays different prices to people after estimating their locations. More than that, Staples appeared to consider the person's distance from a rival brick-and-mortar store, either OfficeMax Inc. or Office Depot Inc. If rival stores were within 20 miles or so, Staples.com usually showed a discounted price.
Orbitz Worldwide Inc. has found that people who use Apple Inc.'s Mac computers spend as much as 30% more a night on hotels, so the online travel agency is starting to show them different, and sometimes costlier, travel options than Windows visitors see.
For example, Travelocity reduced the prices on 5 percent of hotel rooms shown in search results by around $15 per night for smartphone users. Interestingly, Cheaptickets and Orbitz gave unadvertised “Members Only” discounts of about $12 per night on 5 percent of hotels rooms to users who were logged-in to their accounts on the site.
Expedia and Hotels.com conduct what marketers and engineers call A/B tests to steer a subset of their users toward more expensive hotels. [...] In this case, visitors to Expedia and Hotels.com were randomly assigned to groups A, B or C based on the cookies stored on their computers. Users in groups A and B were shown hotels with an average price of $187 a night, while users in group C were shown hotels with an average price of $170/night.
Home Depot served almost completely different products to users on desktops versus mobile devices. A desktop user searching Home Depot typically received 24 search results, with an average price per item of $120. In contrast, mobile users receive 48 search results, with an average price per item of $230. Bizarrely, products are also $0.41 more expensive on average for Android users.
Say a grocer realizes a consumer never shops during the fourth week of the month—likely, because money is tight right before payday. By sending him an offer for free bread that week, the supermarket can encourage an extra trip, during which the customer will likely pick up additional items. Every $1 given away generates $8 in extra sales, says Todd Morris, an executive vice president at Catalina Marketing, which provides personalized coupons for retailers and brands by tracking the behavior of more than 230 million U.S. shoppers monthly.
CHEN: We do though, you know, in the Uber data, see a lot of really, really interesting patterns. So, for example, a data scientist named Peter at Uber discovered somewhat accidentally this really, really kind of interesting fact. And that is one of the strongest predictors of whether or not you are going to be sensitive to surge - in other words, whether or not you are going to kind of say, oh, 2.2, 2.3, I'll give it a 10 to 15 minutes to see if surge goes away - is how much battery you have left on your cell phone.
In a 2016 study, McGee and his team conducted 372 searches on nine airline ticketing websites. The searches were simultaneous with the exact same itinerary and website but two different browsers — one with its cookies intact, another one that was scrubbed.
McGee found that 59% of the times when the searches differed, the fares were higher on the scrubbed browser — the browser with no search history — but those higher fares often came from online travel agencies such as Orbitz. The lower fares on scrubbed browsers tended to come from meta-search sites, such as Google Flights or Kayak.
Here CA writes that it segmented “persuadable and low-turnout voter populations to identify several key groups that could be influenced by Bolton Super PAC messaging”, targeting them with online and Direct TV ads — designed to “appeal directly to specific groups’ personality traits, priority issues and demographics”.
Psychographic profiling — derived from CA’s modelling of Facebook user data — was used to segment U.S. voters into targetable groups, including for serving microtargeted online ads. The company badged voters with personality-specific labels such as “highly neurotic” — targeting individuals with customized content designed to pray on their fears and/or hopes based on its analysis of voters’ personality traits.
A student who had been accepted into a Chinese university was denied his spot because of his father's bad social credit score.
[...] One of these blacklists is designed to punish debtors by preventing them from flying, using high-speed trains, booking fancy hotels, or enrolling their children at expensive schools. This appears to be the type of blacklist the student's father, identified by his surname Rao, ended up on after failing to pay 200,000 renminbi ($29,900/£22,600) back to a bank after two years.
Here's a sample of some of the social credit offenses that can land you on the blacklist: Bad driving, jaywalking, smoking, not cleaning up after your dog, not having your dog on a leash, playing too many video games, watching porn, making frivolous purchases, and consuming too much alcohol or junk food
"Four million people have been blocked from buying high-speed train tickets over low social credit," VICE News reported earlier this year, "and more than 11 million from buying flights."
When Avondale police found themselves without a lead one week later, they drafted a geofence warrant to Google, a type of search warrant that asks the tech giant to produce information on all devices in a given area during a certain time period. Police asked Google to provide information on any wireless communication devices that passed through the same geographical locations that the suspect vehicle did on the night of the murder.
"They threw him in a cell in one of the worst jails in the country even after they confirmed he had an alibi and let him rot for six days when they knew he didn't do this," Molina's attorney, Heather Hamel, told New Times.
The victim was a 97-year-old woman who told police she was missing several pieces of jewelry, including an engagement ring, worth more than $2,000. Four days after she reported the crime, Gainesville police, looking for leads, went to an Alachua County judge with the warrant for Google.
In it, they demanded records of all devices using Google services that had been near the woman’s home when the burglary was thought to have taken place. The first batch of data would not include any identifying information. Police would sift through it for devices that seemed suspicious and ask Google for the names of their users.
"When an individual applies for a loan, the lender examines the credit ratings of members of the individual’s social network who are connected to the individual […]. If the average credit rating of these members is at least a minimum credit score, the lender continues to process the loan application. Otherwise, the loan application is rejected."
In other words: The patent would let a bank analyze your Facebook friends when you applied for a loan. If too many of your friends have poor credit histories, the bank could reject your loan application—even if your own credit was fine.
The LenddoScore complements traditional underwriting tools, like credit scores, because it relies exclusively on non-traditional data derived from a customer’s social data and online behavior.
Things that can determine loan default rates and affect approval/denial: type of phone you use, how you're ordering a product, type of email account you have, what your email address is, your typing accuracy, and how you got to the webpage to begin with
The difference in default rates between iOS and Android users, for instance, was equivalent to the difference between a median FICO score and the 80th percentile of FICO scores. On one level, these types of insights are intuitive: The average iPhone is much more expensive than the average Android device, and previous research has shown whether someone owns an iOS device is one of the best predictors of whether they're in the top 25 percent of earners.
The study's other findings, though, are more subtle. For example, customers who placed orders through cell phones rather than desktop computers were also more likely to default. The use of a largely outdated email service—like Hotmail or Yahoo—was also an indicator of a higher default rate. Customers who incorrectly entered their email address defaulted 5.09 percent of the time; those who didn't were at .94 percent.
Even how you arrive at an e-commerce website can be used to predict whether you'll default. Those coming in from a price-comparison website were half as likely to default as those who clicked on a targeted ad. That makes sense; savvy, careful consumers browse different retailers' prices before making a purchase. But even seemingly irrelevant information can say more about your spending behavior than you might expect. For example, customers who have their first or last names in their email addresses were 30 percent less likely to default than those who used something like "cutie367."
Federal investigators trying to solve arson cases in Wisconsin have scooped up location history data for about 1,500 phones that happened to be in the area, enhancing concerns about privacy in the mobile Internet era.
The two warrants Forbes obtained together covered about nine hours' worth of activity within 29,400 square meters—an area a smidge larger than an average Milwaukee city block. Google found records for 1,494 devices matching the ATF's parameters and sent the data along.
The advertisers identify someone's location by grabbing what is known as "phone ID" from Wi-Fi, cell data or an app using GPS.
Once someone crosses the digital fence, Kakis says, the ads can show up for more than a month — and on multiple devices.
A Massachusetts advertising agency has agreed not to use location technology to target women entering clinics that offer abortion with smartphone ads with messages including “You Have Choices,” state officials said on Tuesday.
Samba TV declined to provide recent statistics, but one of its executives said at the end of 2016 that more than 90 percent of people opted in.
Once enabled, Samba TV can track nearly everything that appears on the TV on a second-by-second basis, essentially reading pixels to identify network shows and ads, as well as programs on HBO and even video games played on the TV. Samba TV has even offered advertisers the ability to base their targeting on whether people watch conservative or liberal media outlets and which party’s presidential debate they watched.
The investigation began when five watches, valued at about $3,800, were stolen from a Shinola luxury retail store in Detroit in October 2018. Investigators reviewed the security footage and identified a suspect: an apparent Black man wearing a baseball cap and a dark jacket. In March 2019, according to the complaint, Detroit police conducted a facial recognition search using an image from the surveillance footage; that search matched the image to Williams' driver's license photo.
Several months later, in July 2019, DPD investigators showed a lineup of six images to a Shinola security guard "who had not witnessed the incident in person and who had merely watched the same security camera footage," according to the complaint. The guard identified Williams' photo, and police issued an arrest warrant, which they then did not enforce until January 2020, when they showed up at Williams' home to arrest him on his return from work.
During an interrogation the next day, it became clear that Williams was not, in fact, the man from the security camera footage, according to the complaint, and a confused officer told him, "the computer must have gotten it wrong."