Personas - Terminology

Common terms we use when discussing personas at Faraday

Companion table to Personas - FAQ.




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.


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).


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.


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