It often seems that academics and practitioners reside in parallel universes, both interested in the same phenomena, yet seeing things differently.

But with the advent of new evidence-based practices and their intersection with practical politics, there is a bit more overlap than usual. It just may be that training in social science is directly applicable to campaign politics.

After a 2008 presidential campaign that was considered a watershed for data-driven politics, it is clear already that 2012 will again revolutionize the way campaigns are conducted. Microtargeting and experimentation, prominent techniques developed and refined over the past 15 years, are marking the intersection of social science and practical politics, rendering academics and operatives not-so-strange bedfellows.

Campaigns driven by data are nothing new. In fact, it’s difficult to imagine even a 19th century party machine without information stored, at the very least, in the heads of the party workers. But these days, evidence-based practices represent a cultural phenomenon of sorts—a seismic shift in decision making that extends widely from the world of business where it began to sports and politics. It even extends to the personal sphere, with people tracking their diets, fitness regimens, work habits and even doing a little experimentation—all as an approach to personal discovery.

In the world of politics, the Obama and Romney campaigns are leaders in the new campaign analytics, using data to guide decisions. The extent of microtargeting in Obama’s 2008 effort is legendary. Lest the Democrats think they have it wrapped up in predictive modeling or targeted voter appeals, candidate Romney brings a long history to the enterprise, microtargeting first in his 2002 run for Massachusetts governor. The candidate himself shows an affinity for evidence, revealing in 2007 to The Wall Street Journal editorial board, “I love data.”

Both campaigns are also known for their experimental methods, used to measure impact and refine appeals. In 2008, the Obama campaign and the Analyst Institute were doing randomized, controlled experiments in both the online and offline worlds. But two years earlier in Texas, Gov. Rick Perry had essentially run his first reelection campaign as an experiment, tailoring his 2010 effort to respond to the findings of a set of experiments from 2006.

Now in 2012, this mood of experimentation has extended even to broadcast appeals, having previously focused on direct voter contact and the digital world. Sasha Issenberg’s reporting for Slate documents much of this. His book on the new science of campaigns, “The Victory Lab,” is due out this fall.

It’s tempting to consider this approach to campaigns as a natural progression in politics—the newest set of techniques fueled by ever-sophisticated lists and technologies that permit real-time feedback and refinement. In fact, the trajectory of the approach is marked by a familiar pattern. First, the business world stakes out new ground, and then entrepreneurial political types follow suit.

However, this science of campaigning is a little different in the extent to which it is also rooted in social scientific practices and norms, even influenced by academicians. In many ways, a user guide for campaigns under this model would read like a political science methods text.

Hal Malchow’s “The New Political Targeting” has a clear social-scientific subtext. The Democratic direct mail and microtargeting guru touts the merits of individual-level data for understanding the voter. He emphasizes the development of good data and the application of appropriate statistical tools to identify targets, and he conveys the importance of returning to the data after the election to update them with new information gathered about the voter. Though Malchow’s message is primarily pragmatic, it is couched in social-scientific values and practices.

The microtargeter operates at the individual unit of analysis, sharing the bias of many scholars who try to understand political behavior. The voter lists, the canvassing and consumer data, the scores derived from predictive modeling—all of these reflect individuals or apply directly to them. Neither the practitioner nor the scholar dismisses the vote distribution at, say, the state level or the demographic breakdown of a census tract. However, for both the practitioner and the scholar, the attitudes and behaviors that mark individuals are key.