Customer Segmentation Part 2: PCA for Segment Visualization
This post is the second part in the customer segmentation analysis. The first post focused on k-means clustering in
R to segment customers into distinct groups based on purchasing habits. This post takes a different approach, using Pricipal Component Analysis (PCA) in
R as a tool to view customer groups. Because PCA attacks the problem from a different angle than k-means, we can get different insights. We’ll compare both the k-means results with the PCA visualization. Let’s see what happens when we apply PCA.
3 Part Series
- CUSTOMER SEGMENTATION PART 1: K-MEANS CLUSTERING
- CUSTOMER SEGMENTATION PART 2: PCA FOR SEGMENT VISUALIZATION
- CUSTOMER SEGMENTATION PART 3: NETWORK VISUALIZATION
Customer Segmentation Cheat Sheet
We have a cheat sheet that walks the user through the workflow for segmenting customers. You can download it here.
10-Minute Overview of Segmentation and Clustering taught in our Business Analysis with R (DS4B 101-R) Course
Table of Contents
- Why PCA?
- Where We Left Off
- Getting Ready for PCA
- Applying PCA
- Visualizing the Results
- Further Reading
PCA is a dimensionality reduction algorithm: PCA takes your data and decomposes it using transformations into principal components (PC). Each PC is selected in the orthogonal direction that maximizes the linear variance of the data. In essence, PCA is nothing more than an algorithm that takes numeric data in x, y, z coordinates and changes the coordinates to x’, y’, and z’ that maximize the linear variance. A great article on the background behind PCA is Pricipal Component Analysis: Explained Visually. As the title suggests, the article has great visuals to explain the algorithm.
How does this help in customer segmentation / community detection? Unlike k-means, PCA is not a direct solution. What PCA helps with is visualizing the essence of a data set. Because PCA selects PC’s based on the maximum linear variance, we can use the first few PC’s to describe a vast majority of the data set without needing to compare and contrast every single feature. By using PC1 and PC2, we can then visualize in 2D and inspect for clusters. We can also combine the results with the k-means groups to see what k-means detected as compared to the clusters in the PCA visualization.
Before we jump into PCA, it’s a good idea to review where we left off in the previous customer segmentation post.
In the first post, we used k-means clustering to analyze the
bikes data set, a collection of excel files that contains data for bike shops (customers), bikes (products), and sales orders for the bike manufacturer, Cannondale. The bike shops and sales orders are fictional / simulated (see the orderSimulatoR post for more on this), but the bikes (products) are actual models from Cannondale’s website.
A hypothesis was formed that bike shops purchase bikes based on bike features such as unit price (high end vs affordable), primary category (Mountain vs Road), frame (aluminum vs carbon), etc. The sales orders were combined with the customer and product information and grouped to form a matrix of sales by model and customer. The
kmeans() function was run on a range of potential clusters, k, and the
silhouette() function from the
cluster package was used to determine the optimal number of clusters.
Rather than run through the previous post, the sections below can be used to get everthing ready for PCA.
You can access the data here if you would like to follow along. You’ll need to download the following files:
orders.xlsx: Contains the fictional sales orders for Cannondale.
customer.idin the orders.xlsx file relates to
bike shop.idin the bikeshops.xlsx file, and
product.idin the orders.xlsx file relates to
bike.idin the bikes.xlsx file.
bikes.xlsx: Contains information on products (e.g. bike model, primary category, secondary category, unit price, etc).
bike.idis the primary key.
bikeshops.xlsx: Contains information on customers (e.g. customer name and location).
bikeshop.idis the primary key.
The script to load and configure the data into a customer trends matrix is shown below.
This script will read the data. Make sure you have the excel files in a folder named “data” in your current working directory prior to running the script below.
The script below combines the
products data frames into
orders.extended, which is a data frame that simulates output we would get from an SQL query of a sales orders database / ERP system. The data is then manipulated to form
customerTrends, which has the data structured such that the rows contain products and the columns contain purchase quantity (as percentage of total) by customer. The output,
customerTrends, is then used for k-means clustering.
Here’s the first 6 rows of
|model||category1||category2||frame||price||Albuquerque Cycles||Ann Arbor Speed||Austin Cruisers||Cincinnati Speed||Columbus Race Equipment||Dallas Cycles||Denver Bike Shop||Detroit Cycles||Indianapolis Velocipedes||Ithaca Mountain Climbers||Kansas City 29ers||Las Vegas Cycles||Los Angeles Cycles||Louisville Race Equipment||Miami Race Equipment||Minneapolis Bike Shop||Nashville Cruisers||New Orleans Velocipedes||New York Cycles||Oklahoma City Race Equipment||Philadelphia Bike Shop||Phoenix Bi-peds||Pittsburgh Mountain Machines||Portland Bi-peds||Providence Bi-peds||San Antonio Bike Shop||San Francisco Cruisers||Seattle Race Equipment||Tampa 29ers||Wichita Speed|
|Bad Habit 1||Mountain||Trail||Aluminum||[ 415, 3500)||0.0174825||0.0066445||0.0081301||0.0051151||0.0101523||0.0128205||0.0117340||0.0099206||0.0062696||0.0181962||0.0181504||0.0016026||0.0062893||0.0075949||0.0042135||0.0182648||0.0086705||0.0184783||0.0074074||0.0129870||0.0244898||0.0112755||0.0159151||0.0108696||0.0092251||0.0215054||0.0026738||0.0156250||0.0194175||0.0059172|
|Bad Habit 2||Mountain||Trail||Aluminum||[ 415, 3500)||0.0069930||0.0099668||0.0040650||0.0000000||0.0000000||0.0170940||0.0139070||0.0158730||0.0031348||0.0110759||0.0158456||0.0000000||0.0094340||0.0000000||0.0112360||0.0167428||0.0173410||0.0021739||0.0074074||0.0095238||0.0040816||0.0190275||0.0026525||0.0108696||0.0239852||0.0000000||0.0026738||0.0078125||0.0000000||0.0000000|
|Beast of the East 1||Mountain||Trail||Aluminum||[ 415, 3500)||0.0104895||0.0149502||0.0081301||0.0000000||0.0000000||0.0042735||0.0182529||0.0119048||0.0094044||0.0213608||0.0181504||0.0016026||0.0251572||0.0000000||0.0140449||0.0167428||0.0086705||0.0086957||0.0172840||0.0242424||0.0000000||0.0126850||0.0053050||0.0108696||0.0092251||0.0053763||0.0000000||0.0156250||0.0097087||0.0000000|
|Beast of the East 2||Mountain||Trail||Aluminum||[ 415, 3500)||0.0104895||0.0099668||0.0081301||0.0000000||0.0050761||0.0042735||0.0152108||0.0059524||0.0094044||0.0181962||0.0138289||0.0000000||0.0220126||0.0050633||0.0084270||0.0076104||0.0086705||0.0097826||0.0172840||0.0086580||0.0000000||0.0232558||0.0106101||0.0155280||0.0147601||0.0107527||0.0026738||0.0234375||0.0291262||0.0019724|
|Beast of the East 3||Mountain||Trail||Aluminum||[ 415, 3500)||0.0034965||0.0033223||0.0000000||0.0000000||0.0025381||0.0042735||0.0169492||0.0119048||0.0000000||0.0102848||0.0181504||0.0032051||0.0000000||0.0050633||0.0042135||0.0152207||0.0202312||0.0043478||0.0049383||0.0051948||0.0204082||0.0162086||0.0026525||0.0201863||0.0073801||0.0322581||0.0000000||0.0078125||0.0097087||0.0000000|
|CAAD Disc Ultegra||Road||Elite Road||Aluminum||[ 415, 3500)||0.0139860||0.0265781||0.0203252||0.0153453||0.0101523||0.0000000||0.0108648||0.0079365||0.0094044||0.0000000||0.0106598||0.0112179||0.0157233||0.0278481||0.0210674||0.0182648||0.0375723||0.0152174||0.0172840||0.0103896||0.0163265||0.0126850||0.0026525||0.0139752||0.0073801||0.0053763||0.0026738||0.0078125||0.0000000||0.0098619|
We used the
kmeans() function to perform k-means clustering. The k-means post goes into great detail on how to select the right number of clusters, which I skipped for brevity. We’ll use the groups for \(k = 4\) and \(k = 5\) clusters with the PCA analysis. Five clusters was the theoretical best solution, and four clusters was the best solution upon inspection of the data.
Now, back to our main focus: PCA. Applying PCA is very simple once the data is formatted. We use the
prcomp() function available in base
R. It’s strongly recommended to scale and center the data (for some reason this is not the default). Further reading on PCA in
R can be found here.
Once PCA is performed, it’s a good idea to take a look at the variance explained. As stated before, the goal of PCA is to reduce the dimensions of the data. PCA does this by transforming the data into dimensions that are orthogonal to the variation. The greater the variance explained, the more information that is summarized by the PC. The
prcomp() function returns this variance by PC. You can use
summary(pca) to get the variance explained. Let’s take a look at the first nine PC’s visually.
PC1 and PC2 combined explain 44% of the variance of the data, and there’s a steep drop-off between PC2 and PC3. This means that plotting PC’s 1 and 2 will give us a reasonably good understanding of the data, and adding more PC’s beyond PC2 will result in minimal improvement. Note that it won’t always happen that there is a significant drop-off after PC2, and if more PC’s explain variance we would need to evaluate them as well.
We’ll plot PC1 and PC2, and we’ll color the clusters initially using the k-means groups previously created. To do this, we first need to get the
pca data into a data frame that combines the customers with the PC’s. Luckily, the
ggfortify package has a function called
fortify() to do just that. Once our data is fortified, we create two data frames with the k-means groups added to the end so we can group on them. We’ll use the k-means group to color the PCA results so we can compare to k-means and gain insights.
The script below provides sample
ggplot scripts to create a general plot for PC1 and PC2. The script uses my favorite interactive plotting library,
plotly, to turn the
ggplot results into interactive plots. The scripts used for the next section follow the same general procedure, so I won’t show the code for brevity.
The PCA segmentation shows some interesting results. It appears that there are five clusters as opposed to four. The \(k = 4\) k-means results would not pick this up, because we selected four clusters. Maybe your thinking that the silhouette analysis from the previous post indicated that we should pick \(k = 5\), so this is where we went wrong. Let’s see what happens with the five cluster k-means.
Well, that’s not what we thought was going to happen! It looks like Group 2 was incorrectly classified. From the k-means post, this actually makes sense: We inspected the clusters, and Group 2 and 4 were very similar in their preferences for bikes in the low-end price range. We decided to combine these clusters by switching to \(k = 4\) clusters. Next, we’ll look at clustering based on inspection of the PCA plot.
From the PCA visualization, we can see there are two bike shops that do not belong to Group 4. Based on our visual inspection, we can modify the groups (column 128) by switching Group 2 (San Antonio Bike Shop & Philadelphia Bike Shop) with the incorrectly classified Group 4 shops (Denver Bike Shop & Kansas City 29ers).
And, let’s visualize the results.
Everything looks good, but we need to inspect the newly created Group 2’s preferences to make sure they should truly be a standalone customer segment.
The script below modifies the
customerTrends data frame to select only customer columns that are in the group we want to inspect. The script then takes the average of the customer’s percent of quantity purchased vs total quantity purchased (the values in the customer columns). The data frame is sorted by most frequently purchased so we can see the group’s central tendency. For Group 2, the central tendency is a preference for low-end mountain bikes.
|model||category1||category2||frame||price||Denver Bike Shop||Kansas City 29ers||value|
|Catalyst 2||Mountain||Sport||Aluminum||[ 415, 3500)||0.0256410||0.0187266||0.0221838|
|Trail 5||Mountain||Sport||Aluminum||[ 415, 3500)||0.0204259||0.0216076||0.0210168|
|F-Si Carbon 4||Mountain||Cross Country Race||Carbon||[ 415, 3500)||0.0165146||0.0247767||0.0206456|
|Scalpel 29 4||Mountain||Cross Country Race||Aluminum||[ 415, 3500)||0.0186875||0.0210314||0.0198595|
|Catalyst 4||Mountain||Sport||Aluminum||[ 415, 3500)||0.0199913||0.0184385||0.0192149|
|F-Si 1||Mountain||Cross Country Race||Aluminum||[ 415, 3500)||0.0204259||0.0175742||0.0190000|
|Trail 4||Mountain||Sport||Aluminum||[ 415, 3500)||0.0152108||0.0218957||0.0185532|
|Trail 1||Mountain||Sport||Aluminum||[ 415, 3500)||0.0204259||0.0164218||0.0184238|
|Trail 2||Mountain||Sport||Aluminum||[ 415, 3500)||0.0208605||0.0158456||0.0183530|
|Beast of the East 1||Mountain||Trail||Aluminum||[ 415, 3500)||0.0182529||0.0181504||0.0182017|
Let’s compare to Group 4. Rerun the previous script changing
group.num from 2 to 4. We can see that Group 4’s preference is similar to Group 2 in that both groups prefer low-end/affordable bikes. However, Group 4’s top purchases contain a mixture of Mountain and Road, while Group 2’s top purchases are exclusively Mountain. It appears there is a difference!
|model||category1||category2||frame||price||Albuquerque Cycles||Dallas Cycles||Detroit Cycles||Los Angeles Cycles||Minneapolis Bike Shop||New York Cycles||Philadelphia Bike Shop||Phoenix Bi-peds||Portland Bi-peds||Providence Bi-peds||San Antonio Bike Shop||value|
|F-Si 2||Mountain||Cross Country Race||Aluminum||[ 415, 3500)||0.0174825||0.0256410||0.0119048||0.0471698||0.0258752||0.0246914||0.0040816||0.0183228||0.0186335||0.0129151||0.0215054||0.0207476|
|Slice Ultegra||Road||Triathalon||Carbon||[ 415, 3500)||0.0104895||0.0085470||0.0099206||0.0251572||0.0076104||0.0049383||0.0571429||0.0133897||0.0248447||0.0092251||0.0537634||0.0204572|
|CAAD12 Disc 105||Road||Elite Road||Aluminum||[ 415, 3500)||0.0139860||0.0085470||0.0297619||0.0157233||0.0136986||0.0222222||0.0204082||0.0176180||0.0310559||0.0092251||0.0215054||0.0185229|
|Catalyst 3||Mountain||Sport||Aluminum||[ 415, 3500)||0.0314685||0.0427350||0.0178571||0.0188679||0.0152207||0.0024691||0.0040816||0.0140944||0.0186335||0.0202952||0.0161290||0.0183502|
|F-Si Carbon 4||Mountain||Cross Country Race||Carbon||[ 415, 3500)||0.0104895||0.0128205||0.0238095||0.0188679||0.0152207||0.0271605||0.0448980||0.0183228||0.0139752||0.0129151||0.0000000||0.0180436|
|Synapse Carbon Disc 105||Road||Endurance Road||Carbon||[ 415, 3500)||0.0139860||0.0170940||0.0099206||0.0125786||0.0304414||0.0271605||0.0285714||0.0091614||0.0170807||0.0166052||0.0107527||0.0175775|
|CAAD8 Sora||Road||Elite Road||Aluminum||[ 415, 3500)||0.0069930||0.0213675||0.0099206||0.0188679||0.0273973||0.0098765||0.0040816||0.0204369||0.0217391||0.0202952||0.0215054||0.0165892|
|Synapse Disc 105||Road||Endurance Road||Aluminum||[ 415, 3500)||0.0279720||0.0213675||0.0019841||0.0094340||0.0121766||0.0271605||0.0204082||0.0155039||0.0093168||0.0129151||0.0215054||0.0163404|
|CAAD12 105||Road||Elite Road||Aluminum||[ 415, 3500)||0.0069930||0.0299145||0.0277778||0.0188679||0.0106545||0.0098765||0.0326531||0.0084567||0.0093168||0.0018450||0.0215054||0.0161692|
|Trigger Carbon 4||Mountain||Over Mountain||Carbon||[ 415, 3500)||0.0069930||0.0085470||0.0099206||0.0125786||0.0060883||0.0197531||0.0285714||0.0119803||0.0124224||0.0239852||0.0322581||0.0157362|
PCA can be a valuable cross-check to k-means for customer segmentation. While k-means got us close to the true customer segments, visually evaluating the groups using PCA helped identify a different customer segment, one that the \(k = 5\) k-means solution did not pick up.
This post expanded on our customer segmentation methodology by using PCA to visually examine the clusters. We manipulated our sales order data to obtain a format that relates products to customer purchases. We used the
prcomp() function to perform PCA on our formatted data frame. We fortified the PCA output using the
fortify() function from the
ggfortify package, which enabled us to plot the PC’s by customer. We added the k-means cluster groups to the fortified data frame for visual inspection of the k-means clusters. We saw that differences can arise because k-means programatically determines the clusters while PCA allows us to visually inspect the clusters. The end result was an improvement to the customer segmentation by attacking the community detection problem from two different angles and combining the results!
Pricipal Component Analysis: Explained Visually: This article is an excellent place to start for those that are new to PCA or those that would like to understand the details.
Computing and Visualizing PCA in R: This is a great article that takes PCA to the next level in
Rwith biplots, predictions, and the caret package.
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