Companion table to Personas - FAQ.

Term

Definition

Personas

Faraday Personas are quantitatively developed using your first-party customer data, our third-party consumer data, and unsupervised machine learning (ML). At a high level, the ML algorithm sorts your enriched customer data into distinct groups, which are ultimately used to define your personas.

Clustering

When clustering for personas, Faraday applies a version of the k-means clustering algorithm to sort your enriched customer data into groups based on a variety of consumer attributes from the Faraday Identity Graph (FIG).

Clusters/groups

The distinct personas/output of the clustering analysis. The personas are called groups or clusters.

Post-hoc analysis

After clustering, Faraday can apply first-party or FIG data to further analyze the already-determined clusters/groups/personas.

Distribution

A frequently-visual description of the relative numbers of times each possible outcome is observed or expected to occur.

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