Points to Remember
In this chapter, I present
five post hoc methods available in JMP for segmenting markets. The
JMP implementation of each offers you a lot of flexibility regarding
options for identifying the segments (I only mention a few for each
method but this doesn’t belie all that’s available)
and for profiling the segments once they’re identified. This
includes the dynamic graphs and tabulations, especially with the LDF,
presented in earlier chapters.
You should be familiar
with each of the five methods but yet you have to pick one. Hierarchical
clustering and k-means clustering are older, unsupervised learning approaches that don’t make efficient use of data. This is especially true for the former because you still have to do a lot ...
Get Market Data Analysis Using JMP now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
