You Can Model Clickers and Converters, Too
The technique above is especially useful for finding ways to optimize campaigns that are focused on a click or online conversion metric – you simply track the campaign clickers or converters with a new audience in your DMP, and then index all audiences in your platform against their overlap in the clicking or converting audience. You could, for example, start running every performance based campaign in ROS to expose every audience to the campaign, and then after a short period of time figure out which audiences are responding more favorably and reliably to the campaign goal.
In an ideal world you have lots of audiences you can overlap against a target; hundreds or even thousands. You could then index all of them against your target, sort them by the index, and then optimize your campaign targeting into the top choices. Which segments you pick, the highest indexing or the largest scale (there will rarely ever be an option that is both large and high quality), depending on your goals for the campaign, budget, etc. You can also exclude the lowest indexing audiences as a technique, and reduce your distribution against lower performing audienciences.
The risk to this technique is that the amount of overlapping users is so small that you lack enough of a sample to reach a statistically significant index. In other words you don’t have enough data to trust the lookalike. To precisely calculate this, you’d need to employ a statistician, however my rule of thumb has been to rely on standard sample size tables that clearly define how many users you need to sample from a given population for the result to meet a particular confidence level. You can easily build this check into Excel to compare your overlapping users in the test segment (pet owners in our case) to the target segment (women).
As you can see though, in a population of almost any size, a mere 400 users is all you need for a representative sample to meet a 95% confidence level with a ± 5% margin of error. You can use this same check on creating general lookalike audiences, but it tends to be more relevant when working with very small target segments, like users who had to take a particular action. Of course, this isn’t the most sophisticated audience modeling method out there, far from it; but for Ad Ops teams who need to play fast and loose with campaign optimization, it’s a place to start, and a great way to get more out of your investment in a DMP.