Filtering is often an input and is used as an initial step to define an audience, or as a follow-up selection step once an audience is created in Audiense. An example of this initial step would be in the audience manager, where you select the criteria to define an audience, such as followers of @AudienseCo and biography (bio-keywords) such as ‘marketer’, meaning that you only want Marketers that follow @AudienseCo.
However, filtering audience members after retrieving the insights and clustering is also possible, where you are creating a predetermined audience. For example, you might export members of a cluster and then filter them by those that follow you and your competitor brands. Another example, is filtering the Influencers & Brands tab to find accounts in a particular industry or with a particular follower count.
Audience segmentation is a data-driven method, provided as an output, applied to group people together from a broader audience into a number of manageable clusters based on specific common characteristics.
An audience can be grouped into communities based on unique characteristics and connections, and therefore, provide an unbiased understanding of what brings people together. Clustering allows us to uncover a range of insights, including what they care about and what trends hold them together.
Audiense segmentation methods are Interconnections and Affinities, which are explained in more detail here.
