Resonate Look-Alike Modeling Methodology

Resonate look-alike modeling is an instance-based approach, which calculates a distance-like measure that balances the frequency of visits to particular domains with their overall abundance. This measure is used to rank the similarity between a target set of records and a candidate set.

The method starts with a target set of records and computes the frequency of visit for each domain for each record in the target set. Then, for each candidate record, the frequency of visit for each domain is calculated, and we compare the total differences across these frequency-weight visit records. The distance-like measure is then computed by balancing the frequency of visits to particular domains with their overall abundance.

The distance measure can be used to determine the similarity between the target and candidate sets, and the target and candidate sets can be ranked based on the similarity score. This allows the user to identify the candidate records that are most similar to the target set.

LAM is a useful tool in targeting settings, where standard Key Performance Indicators (KPIs) apply, as well as in scenarios where sample sizes are small or where defining a null set is intractable. For example, it can be used to determine whether a person did not click on an advertisement due to the creative being ill-suited or because it was not even presented.

The performance of LAM can be assessed in multiple ways, including targeting, recall of the initial segments, and the ability to track performance metrics. In practice, LAM operates between a tension of theoretical expectations and small sample sizes, and recall of the training set is the only performance metric that can be tracked.

In conclusion, LAM is a useful and effective method for creating audience segments and ranking the similarity between target and candidate sets.