Segments can be thought of as a group of cross filters. These allow
us to save an arbitrary amount of filters on several columns that may
make semantical sense.For example, you could save demographic data in a more approachable way. In the
titanic dataset, we have age and gender in two different variables. Assuming we wanted
to have a coarse perspective on these two factors, like Young vs Old people, and
Men vs Women, we could create a “Demographics” segment.
By creating these four segments, we now have a very quick way to
reach for the “Young Men” category, which would involve selecting
all Male passengers under the age of 25.This is a simple example, but we could compose an arbitrarily complex
filter, which would make reaching for these specific rows much easier.
As we can see, this process just involves creating a new multivalued column
that assigns the name of the segment to the selected rows. If the row was present
in the initial filter, the row gets that category in the column. We can see that some
rows may be included in several filters, hence the multivalued column that can store
an array of different values.