As consumer interaction with online resources (e.g., use of web services, e-commerce, browsing activity, etc.) has grown digital marketing too has becoming increasingly more common. Generally, digital marketers seek to deliver offers for products, services, and content to consumer audiences who will find the offers favorable and have high probability of responding to the offers. One challenge faced by digital marketers is finding “look-alike” groups that have traits comparable to known traits of existing target audiences so as to facilitate expansion of existing marketing campaigns to the look-alike groups.
Traditionally, demographic and behavioral data (e.g., audience data) may be collected and analyzed to model known target groups and identify potential new look-alike consumers. Due in part to the amount of audience data available for online consumers, though, the look-alike analysis may be complex and time consuming. As, such timely and effective manual analysis may be impractical. Moreover, digital marketers are traditionally provided little or no control over automated tools that purport to provide look-alike analysis. Rather, existing analysis tools are black-box solutions that output fixed audience segments without opportunity for digital marketers to adjust the analysis based on their intuition and experience. Accordingly, adequate mechanisms do not currently exist to identify and target offers to look-alike consumers that have characteristics similar to a known group.