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Notice:

The dplyr package is not stable recently.See my Tweet about this:https://twitter.com/MKTJimmyxu/status/1253752342333153281. That said, when you get stuck, just try to run it again at a future date.

Keep in mind that no programmer can avoid errors. I strongly agree with this quote from “CodeAcademy” that “Errors in your code mean you’re trying to do something cool.”

https://news.codecademy.com/errors-in-code-think-differently/

Segmentation

Objective - Dividing the target market or customers on the basis of some significant features which could help a company sell more products in less marketing expenses.

Market segmentation

Market segmentation is a strategy that divides a broad target market of customers into smaller, more similar groups, and then designs a marketing strategy specifically for each group. Clustering is a common technique for market segmentation since it automatically finds similar groups given a data set.

Create a product which evokes the needs & wants in target market

For example: iPhone and iPad

Once the product is created, the ball shifts to the marketing team’s court. As mentioned above, they make use of market segmentation techniques. This ensures the product is positioned to the right segment of customers with high propensity to buy.

Examples of Objectives

1.Identify the type of customers who would respond to a particular offer

2.Identify high spenders among customers who will use the e-commerce channel for festive shopping

3.Identify customers who will default on their credit obligation for a loan or credit card

Example

The file segmetation.csv contains information on consumers’ perceptions toward a brand in the apparel industry. The purpose of the case analysis is to gain a better understanding of the consumer segments for the brand, in hopes that such understanding would allow the brand to develop effective segment- or product-specific advertising campaigns.

Questions

1.Can you perform a 5-cluster analysis? 2.How many observations do you have in each cluster? 3.List the cluster member IDs in each cluster.

#install.packages('dplyr')
library(dplyr)     # sane data manipulation
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)     # sane data munging
library(ggplot2)   # needs no introduction
library(ggfortify) # super-helpful for plotting non-"standard" stats objects

 #identifying your working directory
install.packages("readr")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library(readr)

mydata <-read_csv('Segmentation.csv')
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   .default = col_double()
## )
## ℹ Use `spec()` for the full column specifications.
# read csv file #This allows you to read the data from my Github site.

#Open the data. Note that some students will see an Excel option in "Import Dataset";
#those that do not will need to save the original data as a csv and import that as a text file.
#rm(list = ls())  #used to clean your working environment
fit <- kmeans(mydata[,-1], 3, iter.max=1000) 
 #exclude the first column since it is "id" instead of a factor #or variable. 
#3 means you want to have 3 clusters
table(fit$cluster)
## 
##  1  2  3 
## 66 90 65
barplot(table(fit$cluster), col="#336699")  #plot

pca <- prcomp(mydata[,-1])  #principle component analysis
pca_data <- mutate(fortify(pca), col=fit$cluster) 
#We want to examine the cluster memberships for each #observation - see last column

ggplot(pca_data) +  geom_point(aes(x=PC1, y=PC2, fill=factor(col)), 
                              size=3, col="#7f7f7f", shape=21) + theme_bw(base_family="Helvetica")

autoplot(fit, data=mydata[,-1], frame=TRUE, frame.type='norm')
## Warning: `select_()` is deprecated as of dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

write.csv(pca_data, "pca_data.csv") 
#save your cluster solutions in the working directory 
#We want to examine the cluster memberships for each observation - see last column of pca_data

References

Introduction to k-Means clustering in R https://www.r-bloggers.com/introduction-to-k-means-clustering-in-r/

Principal Component Methods in R: Practical Guide http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/118-principal-component-analysis-in-r-prcomp-vs-princomp/