TraitWeight is a proprietary algorithm designed to discover new traits automatically. It compares trait data from your current traits and segments against all other first and third-party data that you have access to through Audience Manager. Refer to this section for a description of the TraitWeight algorithmic discovery process.
The following steps describe the TraitWeight evaluation process.
Step 1: Build a Baseline for Trait Comparison
To build a baseline, TraitWeight measures all the traits associated with an audience for a 30, 60, or 90-day interval. Next, it ranks traits according to their frequency. The frequency count measures commonality. Traits that appear often are said to exhibit high commonality, an important characteristic used to set a weighted score when combined with traits discovered in your selected data sources.
Step 2: Find the Same Traits in the Data Source
After it builds a baseline for comparison, the algorithm looks for identical traits in your selected data sources. In this step, TraitWeight performs a frequency count of all discovered traits and compares them to the baseline. However, unlike the baseline, uncommon traits are ranked higher than those that appear more often. Rare traits are said to exhibit a high degree of specificity. TraitWeight assesses combinations of common baseline traits and uncommon (highly specific) data source traits as more influential or desirable than traits common to both data sets. In fact, our model recognizes these large, common traits and does not assign excess priority to data sets with high correlations. Rare traits get higher priority because they are more likely to represent new, unique users than traits with high commonality across the board.
Step 3: Assign Weight
In this step, TraitWeight ranks newly discovered traits in order of influence or desirability. The weight scale is a percentage that runs from 0% to 100%. Traits ranked closer to 100% means they're more like the audience in your baseline population. Also, heavily weighted traits are valuable because they represent new, unique users who may behave similarly to your established, baseline audience. Remember, TraitWeight considers traits with high commonality in the baseline and high specificity in the compared data sources to be more valuable than traits common in each data set.
Step 4: Display and Work with Results
Audience Manager displays your weighted model results in Trait Builder. When you want to build an algorithmic trait, Trait Builder lets you create traits based on the weighted score generated by the algorithm during a data run. You can use these results to build accurate traits, or compromise accuracy for reach to help expand audience size.
Step 5: Re-evaluate the Significance of a Trait Across Processing Cycles
Periodically, TraitWeight re-evaluates the importance of a trait based on the size and change in the population of that trait. This happens as the number of users qualified for that trait increases or decreases over time. This behavior is most clearly seen in traits that become very large. For example, suppose the algorithm uses trait A for modeling. As the population of trait A increases, TraitWeight re-evaluates the importance of that trait and may assign a lower score or ignore it. In this case, trait A is too common or large to say anything significant about its population. After TraitWeight reduces the value of Trait A (or ignores it in the model), the population of the algorithmic trait decreases. The list of influential traits reflects the evolution of the baseline population. Use the list of the influential traits to understand why these changes are occurring.