Identifying the right target group has always been a challenge for a variety of real-world applications. For example, marketers or merchants constantly strive for finding the best way to reach their best customers from a big pool of “potential customers.” Similarly, social study scientists, pollsters, and other industries also wish to find the most likely target audience for them to perform next level of predictions or further studies. These users apply statistical analysis on the data and numbers they collected, and the key differentiator to identify the most relevant and accurate set has always been the data and the right modeling that applies to the data.
At a very basic-level, a look-alike modeling enables marketers, for example, to reach new prospects that “look-alike” their best customers, not just any customers. In this example, the process involves finding target audiences from the total audience pool that “look-alike” your seed audience, so you can target more audiences that fit the profile of your seed audience. Current practices require redefining of the desirable “look-alike” parameters, features or attributes before the computer system “re-runs” the data to output the desirable size or outcome. Moreover, many of the cluster parameters or features are predefined, which prohibit flexibility in a user-defined desirable “look-alike” amplification.