We are approaching a political tipping point where the global public will demand new regulatory safeguards and responsible data practices from industry leaders. The mergering of Big Data techniques with Big Digital Ad Targeting of individuals online, inc. when on mobile devices, illustrates the utter failure of self-regulation (as if so-called icons and faux Do-Not-Track schemes were ever really designed to protect the public. They were created to enable the industry to continue its data collection and user targeting/tracking status quo). Here's some excerpts which illustrate what's wrong with how online marketers view their power to capture every move we make, whether online or off. Our bold.
1. Deep Dive is a web-based application that provides a window into the soul of your customers. With a few clicks of the mouse, you can understand what their passions are, and what they are doing above and beyond moving through the purchase funnel for product X. You will also know what interests are behind the highest performing audiences, and what combinations will deliver converters most efficiently. We launched Deep Dive internally about six months ago, and we have over 200 advertisers in the system from around the world. [Tribal Fusion]
2. Online interactions are your target's way of telling you where they are and what they're up to right now. Even the most incidental contact is a tile in a vastly larger mosaic that few advertisers ever see – because they lack the resources to gather and process the carloads of data that pour in from a multitude of sources every day. Media Innovation Group turns an avalanche of data into competitive advantage. One-of-a-kind data engines anonymously collect every click, filter out the noise, and produce what amounts to an MRI scan of an advertiser’s entire marketplace as it is right now. This makes targeting a science. Better targeting improves results in any media. In digital media, better data transforms results. Because our systems combine incoming partner data with client-side data, we can generate a data set that none of our clients' competitors have. [Media Innnovation Group]
3. We acknowledge from the start that a user may have five or five hundred behaviours in his profile. As a result, we don’t try to describe the ideal user. We try to find the attributes that are most indicative of performance. Ours is a lift-based model.
We have over 15,000 user attributes in our system, but our goal is not to use as many of them as possible. We filter on the variables that are contributing to performance at 90% confidence. Then it’s just a question of ranking them and de-duplicating. Behaviours that indicate a high propensity to convert are usually really specific...[Tribal Fusion]