Direct response marketers have been using various statistical models for decades to determine how to predict human behavior. They've built proven models that can help a marketer reach a highly targeted audience with a high degree of reliability and show that audience a message that has a higher probability of success than a random untargeted message. The easiest way to see this at work is to buy a house.
Two years ago, I bought a house (my timing was impeccable). Within weeks of my mortgage closing, I began to receive all sorts of interesting things in the mail. This was interesting because I explicitly opted out of having the data from my mortgage shared with anyone (or so I thought). As it turns out, this isn't really possible -- at least, I wasn't able to pull it off, and I am aware of how the DR industry works. The average consumer hasn't got a chance.
The kinds of mail I began receiving included lots of offers for things like mortgage refinance (despite that I had only bought my house weeks before), various types of insurance (most were flavors of home warranties), and then literally hundreds (possibly thousands) of offers from local businesses to try their services. This included some that were logical and tied to my physical relocation to a new neighborhood -- various dentists, hair salons, landscapers, accountants, hardware stores, and roofing companies.
The DR industry has statistical models that clearly show the series of marketing opportunities that are associated with major life events. So when you have a baby, there are many things you're likely to need to buy. When you buy a house, it's very similar (in fact, these events are highly correlated). For instance, having a baby frequently is followed by purchasing a new (and safer or more spacious) car, SUV, crossover, or minivan. Life insurance is another highly correlated purchase.
These models are built, and the "sensing" mechanisms flow out into the various sources of publicly available data, as well as numerous private sources of data like financial services companies. For decades, your every credit card purchase has been carefully scrutinized and analyzed and applied against highly refined statistical models to figure out what opportunities exist to sell you other products and services.
Many people have begun to realize this -- but it took decades to build the systems, and decades more to have the knowledge of its existence permeate the culture. So by the time you read this, many of you have simply accepted that this is standard practice. You've come to terms with your outrage at the fact that, without explicitly asking for your permission, data about your private life has been used to segment you into various buckets in order to more effectively market to you.
One of the major problems with this traditional direct response marketing is the massive expense behind it. Despite being a highly profitable, high-revenue business, it's extremely expensive to operate. Building the statistical models, mining the data across numerous sources, and then building personalized (not private in any way, mind you) profiles against which to sell the personal contact information you've amassed -- including phone numbers, physical home addresses, and names -- isn't cheap. And when it first started out, the costs were much higher because computing power was relatively much more expensive.
And that's been the problem with DR since it began: Building these mailing lists of highly personalized targeting opportunities is so expensive, the pools of individuals who match them are so small, and the amount of time that the data are fresh and relevant is so short that the opportunity for any single marketer to reach target audiences is pretty small. Maintaining the freshness of the data is a big part of the expense. From a marketer's perspective, the decision to use these mechanisms is quite simple -- the response rates are well known and the ROI decision is easy. But the number of customers any one company can create using these tools is low enough that other forms of marketing are needed.
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