As a sector, online lead generation activities accounted for an estimated $2 billion last year. It's big business, and a difficult one to get right, given that a large number of those buying media to drive these form fills do so by taking principal risk.

Last year, you couldn't open a marketing industry email or attend an industry conference without hearing about social media, Twitter and Facebook. This year, data will become a huge focus: how to get it, what to collect, who to share it with and what to do with it once you have it. Recently, I attended two distinct events, an investment banker conference and a marketing conference, yet all of the talk was about data.

The use of predictive modeling to score online consumer sales leads has been embraced by interactive marketers across a wide range of product and service categories, from education, insurance and financial services to telecom and automotive. Real-time sales lead quality scoring has fundamentally changed the dynamics of lead pricing, budget allocations and conversion ROI for advertisers and lead providers alike. So what are the secrets behind these algorithms, and what makes them effective for lead scoring?

If the old saying (usually credited to Ralph Waldo Emerson) about inventors and mousetraps were updated for online advertising in 2010, it would read something like this: "Build the right mousetrap, and only the highest-quality customers will beat a path to your door." In the early days of the Internet, advertising was about eyeballs, and advertisers were imitating the mass-market tactics used by their broadcast and print forebears. But breakthroughs in analytics have shed light on the quality of online traffic, changing the focus from lead quantity to lead quality. After all, what good are impressions if they're seen by …