Using Hierarchical Clustering for Market Segmentation
Nathaniel Martinez
04/22/2026
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Notice:
This is still an early draft. Let me know if there are any errors or typos.
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.
A potentially interesting question might be are some products (or customers) more alike than the others.
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
Imagine that you are the Director of Customer Relationships at Apple, and you might be interested in understanding consumers’ attitude towards iPhone 12 and Google’s Pixel 5. Once the product is created, the ball shifts to the marketing team’s court. As mentioned above, to understand which groups of customers will be interested in which kind of features, marketers will make use of market segmentation strategy. The cluster analysis algorithm is designed to address this problem. Doing this ensures the product is positioned to the right segment of customers with a 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 customer_segmetation.csv contains data collected by my students in spring 2020.
Importing data - No need to download R or R studio
Search for Rstudio Cloud, register (or set up a free user account), and log into the cloud environment with your Gmail credentials.
You will upload your dataset (.csv) from your own computer to R Studio Cloud first. Make sure the first column is id instead of a variable.
Once the dataset is uploaded, you will see the dataset available on the right pane of your cloud environment.
Now we will be using the package (readr) and the function read_csv to read the dataset.
library(readr)
mydata <-read_csv('customer_segmentation.csv')## Rows: 22 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (15): ID, CS_helpful, Recommend, Come_again, All_Products, Profesionalis...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Importing data
In the following step, you will standardize your data(i.e., data with a mean of 0 and a standard deviation of 1). You can use the scale function from the R environment which is a generic function whose default method centers and/or scales the columns of a numeric matrix.
Building distance function and ploting the trees (dendrograms)
Hierarchical clustering (using the function hclust) is an informative way to visualize the data.
We will see if we could discover subgroups among the variables or among the observations.
use = scale(mydata[,-c(1)], center = TRUE, scale = TRUE)
dist = dist(use)
d <- dist(as.matrix(dist)) # find distance matrix
seg.hclust <- hclust(d) # apply hirarchical clustering
library(ggplot2) # needs no introduction
plot(seg.hclust)
Identifying clustering memberships for each cluster
Imagine if your goal is to find some profitable customers to target. Now you will be able to see the number of customers using this algorithm.
groups.3 = cutree(seg.hclust,3)
table(groups.3) #A good first step is to use the table function to see how # many observations are in each cluster ## groups.3
## 1 2 3
## 17 2 3#In the following step, we will find the members in each cluster or group.
mydata$ID[groups.3 == 1]## [1] 1 2 3 6 7 8 9 10 11 12 13 14 15 16 17 18 21mydata$ID[groups.3 == 2]## [1] 4 22mydata$ID[groups.3 == 3]## [1] 5 19 20Identifying common features of each cluster using the aggregate function
#?aggregate
aggregate(mydata,list(groups.3),median)## Group.1 ID CS_helpful Recommend Come_again All_Products Profesionalism
## 1 1 11 1 1.0 1.0 2 1.0
## 2 2 13 3 2.5 1.5 3 1.5
## 3 3 19 2 1.0 3.0 3 2.0
## Limitation Online_grocery delivery Pick_up Find_items other_shops Gender Age
## 1 1 2 2 3.0 1 2.0 1 2.0
## 2 2 3 3 2.5 2 1.5 1 2.5
## 3 1 2 3 1.0 2 3.0 2 2.0
## Education
## 1 2
## 2 5
## 3 2aggregate(mydata,list(groups.3),mean)## Group.1 ID CS_helpful Recommend Come_again All_Products Profesionalism
## 1 1 10.76471 1.294118 1.117647 1.235294 1.823529 1.235294
## 2 2 13.00000 3.000000 2.500000 1.500000 3.000000 1.500000
## 3 3 14.66667 2.333333 1.666667 2.666667 3.000000 2.333333
## Limitation Online_grocery delivery Pick_up Find_items other_shops Gender
## 1 1.352941 2.235294 2.235294 2.705882 1.294118 2.647059 1.176471
## 2 2.000000 3.000000 3.000000 2.500000 2.000000 1.500000 1.000000
## 3 2.000000 2.000000 3.000000 1.000000 2.000000 3.000000 2.000000
## Age Education
## 1 2.411765 3.117647
## 2 2.500000 5.000000
## 3 2.666667 2.333333aggregate(mydata[,-1],list(groups.3),median)## Group.1 CS_helpful Recommend Come_again All_Products Profesionalism
## 1 1 1 1.0 1.0 2 1.0
## 2 2 3 2.5 1.5 3 1.5
## 3 3 2 1.0 3.0 3 2.0
## Limitation Online_grocery delivery Pick_up Find_items other_shops Gender Age
## 1 1 2 2 3.0 1 2.0 1 2.0
## 2 2 3 3 2.5 2 1.5 1 2.5
## 3 1 2 3 1.0 2 3.0 2 2.0
## Education
## 1 2
## 2 5
## 3 2aggregate(mydata[,-1],list(groups.3),mean)## Group.1 CS_helpful Recommend Come_again All_Products Profesionalism
## 1 1 1.294118 1.117647 1.235294 1.823529 1.235294
## 2 2 3.000000 2.500000 1.500000 3.000000 1.500000
## 3 3 2.333333 1.666667 2.666667 3.000000 2.333333
## Limitation Online_grocery delivery Pick_up Find_items other_shops Gender
## 1 1.352941 2.235294 2.235294 2.705882 1.294118 2.647059 1.176471
## 2 2.000000 3.000000 3.000000 2.500000 2.000000 1.500000 1.000000
## 3 2.000000 2.000000 3.000000 1.000000 2.000000 3.000000 2.000000
## Age Education
## 1 2.411765 3.117647
## 2 2.500000 5.000000
## 3 2.666667 2.333333cluster_means <- aggregate(mydata[,-1],list(groups.3),mean)Exporting cluster analysis results into excel from R Studio Cloud
write.csv(groups.3, "clusterID.csv")
write.csv(cluster_means, "cluster_means.csv")Downloading your solutions mannually
First, select the files (“clusterID.csv” & “cluster_means.csv”) and put a checkmark before each file.
Second, click the gear icon on the right side of your pane and export the data.
Finding means or medians of each variable (factor) for each cluster
Imagine if your goal is to find some profitable customers to target. Now using the mean function or the median function, you will be able to see the characteristics of each sub-group. Now it is time to use your domain expertise.
Discussion Questions for you
How many observations do we have in each cluster? Answer: Your answer here:
We can look at the medians (or means) for the variables in each cluster. Why is this important?
Answer: Your answer here:
- Do you think if mean or median should be used when it comes to analyzing the differences among different clusters? Why?
Answer: Your answer here:
- Now we need to understand the common characteristics of each cluster. Our goal is to build targeting strategy using the profiles of each cluster. What summary measures of each cluster are appropriate in a descriptive sense.
Answer: Your answer here:
- Any major differences between K-means clustering (https://rpubs.com/utjimmyx/kmeans) and Hierarchical clustering? Which one do you like better? Why? You may refer to the assigned readings.
Answer: Your answer here:
Q1: There are 3 clusters. Cluster 1 has 17 observations, cluster 2 has 2 observations, and cluster 3 has 3 observations.
Q2: Looking at the means or medians helps us understand the typical characteristics of each cluster. it allows us to compare cluster and identify what makes each group unique, which is important for market segmentation and targeting the right customers.
Q3: The median is usally better because it is less affected by outlines and skewed data. Since customer data can vary a lot, the median gives a more reliable representation of each cluster’s typical behavior. However,the mean can still be useful for overall trends.
Q4: Appropriate summary measures include the mean and median of variables like customer satisfaction (cs_helpful), recommendations, purchase behavior, and demographics (age, education). These measures help describe each cluster’s profile and allow businesses to build targeted marketing strategies based on customer behavior.
Q5: K-means clustering requires specifying the number of clusters beforehand and groups data based on distance from centroids, making it faster and more efficient for large data sets. Hierarchical clustering, like the method used here, builds a dendrogram and does not require choosing the numbers of clusters upfront, making it more flexible and easier to visualize relationships between observations. I prefer hierarchical clustering because it provides a clear visual representation (dendrogram)and help better understand how clusters and formed before deciding how many groups to use.
Advanced Questions (optional but highly recommended)
O. The aggregate function is well suited for this task. Should we use mydata or mydata[,-1] along with the aggregate function? Why? Hint: see the results on my tutorial.
References
Cluster analysis - reading (p.385-p.399) https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf
Comparison of similarity coefficients used for cluster analysis with dominant markers in maize (Zea mays L) https://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572004000100014&lng=en&nrm=iso
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/