Breathless, and often fact-free, stories about “data mining” and “microtargeting” soon became plentiful. But few journalists had access to any of the campaigns’ data, or even much understanding of the statistical techniques they used. We found ourselves at the mercy of self-promoting consultants who described how they were changing politics by ignoring stodgy old demographics and instead pinpointing voters according to their lifestyles. We played along, guilelessly imputing new mythic powers to microtargeting. In many retellings, data analysis became the reason George W. Bush was re-elected.
Microtargeting was at once less directly influential, and more fundamentally disruptive, than these analyses suggested. The most colorful commercial variables that appeared prominently in journalistic accounts of microtargeting — whether someone drank gin or drove a Subaru — were never of much value on their own. It was the combination of hundreds, sometimes thousands, of data points that offered value: algorithms could weigh previously imperceptible relationships among variables to predict political attitudes and behavior.
Now, instead of defining voters by a handful of self-evident attributes like rural Hispanic Democratic men or non-college-educated white seniors, campaigns could group individual citizens according to segments or scores that reflected gradations of predicted habits — of how likely they were to turn out to vote or to support a specific candidate. They could be aggregated into what campaigns call a universe — targets for the same persuasive media or get-out-the-vote drive — not by visible demographic commonalities but because they were projected to behave in similar ways.