Discover - Audiences
The Return of Audience Navigator & ARS
Last updated
The Return of Audience Navigator & ARS
Last updated
If you go through the philosophies of Discover - Topics and Cohorts you may have realized that one (Topics) covers the short term trending topics. On the other hand, Cohorts cover the behavioral cohorts of the consumers that change over 28 day rolling window. So we started from high volatility to mid and now we move to Audience, that is less of 3rd party component, but more of a long term client owned asset, that has the least volatility in Discover.
This section is designed to look at your owned audience that is uploaded on Meta to help you guide the most valuable audience that you can use as per the needs of your campaign or a consumer touch point.
If we go a few month back in time, the terms ARS (Audience Receptivity Score) and Audience Navigator were terms that we had all dealt with. Unfortunately, the way it was positioned in the system left us with a lot of questions and a weak narrative around it. Hence, we deprecated it and rethought the implementation of these two tools.
Audience is our attempt to bring them back with some changes in the way we position it. The logic of both the tools were almost similar except for the inputs that were used. Hence, we decided to join them both in a similar looking UI and let it be exploratory in nature.
Discover - Audience helps you look at all the audiences that a client owns from a perspective of either a 1st party audience or a 3rd party segment.
This was early known as the Audience Navigator. The purpose is almost the same even today. It asks you to select an Audience , as shown below.
Then it size up all the other audiences keeping the selected audience at the center of it all. In other words, all other audiences are placed relatively to the selected audience. From a usecase stand point, lets say that you want to understand how your audiences are placed in relation to the audience you have selected? Let's say your objective is to identify audiences that can help you sell the most. So you have to select your best performing audience that has given you the best conversions. These audiences will be plotted as per the affinity score and Overlap between the selected audience and the rest. Now as given in the graph below:
If an audience has high Overlap and high affinity to the audience selected. This means that it has similar potential as of the selected audience, but also has high overlap. Thus, it is logical that combining them together and doing a behavior report is the most logical action.
If an audience has low overlap but high affinity. It is best to run an overlap report or a behavior report with A-B and/or B-A to exploit the section that makes gets their affinity so high, but keeps the overlap low. It can help you get tremendous incremental reach with performance.
If an audience has high overlap but low affinity, then a contrast can help you identify the few nuances that, despites having very high overlap can repel the affinity score so far. You can stumble upon a high effective cohort that may create the magic for a campaign.
This is where you don't select 1st party but select a 3rd party audience - Interest to compare your 1st party audiences with. While one use case would be the one we discussed in the first party audience, where you compare all your audiences with a category you are interested in exploring via interest.
However, the other amazing way to explore this feature is to determine other communication channels as well. For example: eMailers. Lets say a brand is interested in communicating with their consumers who are NFL fans.
It will give you Reach, Affinity and Efficiency Scores. Just by sorting the right column, you can now understand your most viable audience to send emails to that has high affinity and/or efficiency towards NFL. There are many such use cases that we will be able to uncover and prove along the way. These are just a few examples.
