A good overview of the state of UDID by Kim-Mai Cutler:
What Does A Post-UDID World Look Like For iPhone And iPad Developers? | TechCrunch.
A good overview of the state of UDID by Kim-Mai Cutler:
What Does A Post-UDID World Look Like For iPhone And iPad Developers? | TechCrunch.
How Twitter is monetizing their archived data. Btw, I love Daily Mail’s summary bullet points at the beginning of each article:
via Twitter sells years of everyone’s old, vanished Tweets to online marketing companies | Mail Online.
This is some innovative stuff:
A friend-of-a-friend tipped us off over the weekend to a rather clever way that Facebook is taking it one step further: non-existent sound files.
via Facebook Knows When You Open Their Emails. How? Creepy Silent Sounds… | PandoDaily.
Interesting commentary on how Custora would implement the tracking discussed in the Target article from last week:
The pregnancy prediction problem can be further broken apart as follows:
via How We Would Do It: Predicting Customer Pregnancy At Target | Custora Blog.
This made me think back to the good old days when Google was not a big fan of cookies–
Google is caught using a known method to get around 3rd party cookie blocking on Safari:
To get around Safari’s default blocking, Google exploited a loophole in the browser’s privacy settings. While Safari does block most tracking, it makes an exception for websites with which a person interacts in some way—for instance, by filling out a form. So Google added coding to some of its ads that made Safari think that a person was submitting an invisible form to Google. Safari would then let Google install a cookie on the phone or computer.
The cookie that Google installed on the computer was temporary; it expired in 12 to 24 hours. But it could sometimes result in extensive tracking of Safari users. This is because of a technical quirk in Safari that allows companies to easily add more cookies to a user’s computer once the company has installed at least one cookie.
via Google Tracked iPhones, Bypassing Apple Browser Privacy Settings – WSJ.com.
How Companies Learn Your Secrets – NYTimes.com.
This is an article about Target’s complex profiling systems and processes used for marketing and targeting coupons and offers. It’s kind of on the longish side so I’ve pulled out some interesting points below.
The most interesting nugget for me is at the end, which describes how in order to ensure that customers wouldn’t be freaked out by the targeted offers, Target made them feel untargeted by adding their regular offers into the mix.
The article is written around the story of a statistician, Andrew Pole, who was recruited to Target to work in their “Guest Marketing Analytics” department and was tasked with finding a way to figure out whether a customer was pregnant. This event is valuable to a marketer because that’s when most shoppers will fall out of their old shopping habits.
Target has invested a boatload into collecting a lot of data on their shoppers:
For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code — known internally as the Guest ID number — that keeps tabs on everything they buy. “If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID
Target also augments this with data they buy from third party sources:
Target can buy data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own. (In a statement, Target declined to identify what demographic information it collects or purchases.)
The article takes a turn and dives deeply into the hot topic of habit formation and how a wide range of firms, from supermarkets to NFL teams, are trying to use this to improve their outcomes.
A case study on the creation of Febreze is thrown in to show how the initial product positioning was ineffective but by leveraging survey and real world data, they were able to re-position the product using what experts know about habit formation (in this case, house cleaning habits).
Getting back to how Target profiled expecting mothers, this is how Pole did it- by looking at purchases that are correlated:
Lots of people buy lotion, but one of Pole’s colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.
This is not that interesting to me because it’s not a new and fancy “big data” capability. Anyone can do this sort of thing as long as you have the data.
What I enjoyed reading more was how Target handled the potential backlash to this sort of targeting– they basically made their circulars and ads feel untargeted by mixing in random items along with baby stuff:
“With the pregnancy products, though, we learned that some women react badly,” the executive said. “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.
“And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”
Outside of this interesting fact, this is really the same old story about a big company (with access to lots of high quality data) using information technology to improve marketing.
Long read for the weekend:
In this excerpt from his new book, The Daily You, University of Pennsylvania professor Joseph Turow takes you on a tour of the industry that’s trafficking in the data you generate every day on the Internet. You don’t have to be a privacy stickler to be worried.
The key tradeoff in discussion seems to be:
On sites where there’s private information in the URLs + links to external sites, overriding the referrer is necessary in order to protect users’ privacy / identity.
How not to do URL redirects (… the way Quora does) | Hacker News.
It’s convenient that for this particular issues, preserving privacy works in favor of the web property. Often times, that’s not the case.
This is an amazing statistic and if you think about it, it does make sense. Taking the flip side of this statement below (which implies the privacy issues of such combined data), there are huge implications on the ability to do targeting with limited data:
It was found that 87% (216 million of 248 million) of the population in the United States had reported characteristics that likely made them unique based only on 5-digit ZIP, gender, date of birth. About half of the U.S. population (132 million of 248 million or 53%) are likely to be uniquely identified by only place, gender, date of birth, where place is basically the city, town, or municipality in which the person resides. And even at the county level, county, gender, date of birth are likely to uniquely identify 18% of the U.S. population. In general, few characteristics are needed to uniquely identify a person.
CiteULike: Uniqueness of Simple Demographics in the U.S. Population.
Via @timoreilly
Here’s why GRPs will be good for online ads (MediaPost)
Very compelling argument.
