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Segmentation Exercise: This exercise builds directly off the Product Optimization Exercise we just did. The exercise calls for follow up work to characterize the segments.
Segmentation Exercise:
This exercise builds directly off the Product Optimization Exercise we just did. The exercise
calls for follow up work to characterize the segments. For this exercise, apart from the files
from the Product Optimization Exercise, we need demographics data, which are provided in
this file: demographicsfull.xlsx The demographics variable are: income in thousands of
dollars per year age in years sports if the person is sports active, if not gradschl
if the person has a master's degree or beyond, if not
If you did the Product Optimization Exercise correctly you have a file similar to this
one: mugsanalysisfullincldemographics.xlsx You are given data on the preference parameters of consumers in this file: mugs
preferenceparametersfull.xlsx This is the same input file used in the Product Optimization
Exercise.
Your tasks for Part A:
Compute and report the characteristics of the affinitybased segment for Product
our candidate as given above Report the characteristics in terms of the following
descriptors: IPr, Iin, ICp, ICl, ICn, IBr, IpPr IpPr IpPr IpIn IpIn
IpIn IpCp IpCp IpCp IpClD IpClF IpClE IpCnSl IpCnSp
IpCnLk IpBrA IpBrB IpBrC and the demographics income, age, sports and
gradschl. For this you need to compute the weighted average of all the columns in the
"forclusteranalysis" worksheet, weighted by the probabilities given in column BF of
the "mugsfull" worksheet. You should not do this in Excel directly. You are to load
the data into python or R and compute the weighted averages in R or python; this is to
help you build familiarity with these languages and prepare you better for your job.
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