Cluster Analysis and Marketing Segmentation - Discussion 2
Nicholas Courtney
2025-04-01
Part 1: Cluster Analysis Using White Wine Data
1. Introduction
This study segments white wines using clustering techniques based on their chemical properties. The analysis provides insights into wine differentiation and quality perception.
2. Data Overview
wine <- read.table("C:/Users/nicho/OneDrive/Desktop/MSDA/MK 6460 - Marketing Research & Analytics/Week 2 - Market Segmentation (Cluster Analysis) & Association Mining (Market Basket Analysis)/datasets/winequality-white(UCI).csv", sep=";", header=TRUE)
summary(wine)## fixed.acidity volatile.acidity citric.acid residual.sugar
## Min. : 3.800 Min. :0.0800 Min. :0.0000 Min. : 0.600
## 1st Qu.: 6.300 1st Qu.:0.2100 1st Qu.:0.2700 1st Qu.: 1.700
## Median : 6.800 Median :0.2600 Median :0.3200 Median : 5.200
## Mean : 6.855 Mean :0.2782 Mean :0.3342 Mean : 6.391
## 3rd Qu.: 7.300 3rd Qu.:0.3200 3rd Qu.:0.3900 3rd Qu.: 9.900
## Max. :14.200 Max. :1.1000 Max. :1.6600 Max. :65.800
## chlorides free.sulfur.dioxide total.sulfur.dioxide density
## Min. :0.00900 Min. : 2.00 Min. : 9.0 Min. :0.9871
## 1st Qu.:0.03600 1st Qu.: 23.00 1st Qu.:108.0 1st Qu.:0.9917
## Median :0.04300 Median : 34.00 Median :134.0 Median :0.9937
## Mean :0.04577 Mean : 35.31 Mean :138.4 Mean :0.9940
## 3rd Qu.:0.05000 3rd Qu.: 46.00 3rd Qu.:167.0 3rd Qu.:0.9961
## Max. :0.34600 Max. :289.00 Max. :440.0 Max. :1.0390
## pH sulphates alcohol quality
## Min. :2.720 Min. :0.2200 Min. : 8.00 Min. :3.000
## 1st Qu.:3.090 1st Qu.:0.4100 1st Qu.: 9.50 1st Qu.:5.000
## Median :3.180 Median :0.4700 Median :10.40 Median :6.000
## Mean :3.188 Mean :0.4898 Mean :10.51 Mean :5.878
## 3rd Qu.:3.280 3rd Qu.:0.5500 3rd Qu.:11.40 3rd Qu.:6.000
## Max. :3.820 Max. :1.0800 Max. :14.20 Max. :9.000str(wine)## 'data.frame': 4898 obs. of 12 variables:
## $ fixed.acidity : num 7 6.3 8.1 7.2 7.2 8.1 6.2 7 6.3 8.1 ...
## $ volatile.acidity : num 0.27 0.3 0.28 0.23 0.23 0.28 0.32 0.27 0.3 0.22 ...
## $ citric.acid : num 0.36 0.34 0.4 0.32 0.32 0.4 0.16 0.36 0.34 0.43 ...
## $ residual.sugar : num 20.7 1.6 6.9 8.5 8.5 6.9 7 20.7 1.6 1.5 ...
## $ chlorides : num 0.045 0.049 0.05 0.058 0.058 0.05 0.045 0.045 0.049 0.044 ...
## $ free.sulfur.dioxide : num 45 14 30 47 47 30 30 45 14 28 ...
## $ total.sulfur.dioxide: num 170 132 97 186 186 97 136 170 132 129 ...
## $ density : num 1.001 0.994 0.995 0.996 0.996 ...
## $ pH : num 3 3.3 3.26 3.19 3.19 3.26 3.18 3 3.3 3.22 ...
## $ sulphates : num 0.45 0.49 0.44 0.4 0.4 0.44 0.47 0.45 0.49 0.45 ...
## $ alcohol : num 8.8 9.5 10.1 9.9 9.9 10.1 9.6 8.8 9.5 11 ...
## $ quality : int 6 6 6 6 6 6 6 6 6 6 ...3. K-Means Clustering (with sampling)
wine1 <- wine[, 1:11]
wine2 <- as.data.frame(scale(wine1))
set.seed(123)
wine_sample <- wine2[sample(1:nrow(wine2), 1000), ]
library(NbClust)
nbc_kmeans <- NbClust(wine_sample, min.nc=2, max.nc=10, method="kmeans")
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
## ## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 9 proposed 2 as the best number of clusters
## * 4 proposed 3 as the best number of clusters
## * 1 proposed 4 as the best number of clusters
## * 3 proposed 5 as the best number of clusters
## * 4 proposed 6 as the best number of clusters
## * 1 proposed 7 as the best number of clusters
## * 1 proposed 9 as the best number of clusters
## * 1 proposed 10 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 2
##
##
## *******************************************************************km <- kmeans(wine_sample, 2, nstart=25)
table(km$cluster)##
## 1 2
## 624 376library(factoextra)## Warning: package 'factoextra' was built under R version 4.2.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.2.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
km.res <- eclust(wine_sample, "kmeans", k=2, nstart=25, graph=FALSE)
fviz_cluster(km.res, geom="point", ellipse.type="norm", palette="jco", ggtheme=theme_minimal())
4. Hierarchical Clustering
dist_mat <- dist(wine_sample, method="euclidean")
hc <- hclust(dist_mat, method="complete")
plot(hc, hang=-1, main="Complete-Linkage")
hcWard <- hclust(dist_mat, method="ward.D2")
plot(hcWard, main="Ward's-Linkage")
5. PAM with Mixed Data
wine2_mixed <- wine1
wine2_mixed$pH <- as.factor(ifelse(wine1$pH > 3.2, "High", "Low"))
library(cluster)
disMat <- daisy(wine2_mixed, metric="gower")
sil_width <- c(NA)
for(i in 2:10){
pam_fit <- pam(disMat, diss = TRUE, k = i)
sil_width[i] <- pam_fit$silinfo$avg.width
}
plot(2:10, sil_width[2:10], type="b", xlab="k", ylab="Silhouette Width")
pamFit <- pam(disMat, k=3)
table(pamFit$clustering)##
## 1 2 3
## 1424 2056 14186. Clusters vs. Quality
wine_quality_merge <- cbind(wine, cluster=pamFit$clustering)
table(wine_quality_merge$quality, wine_quality_merge$cluster)##
## 1 2 3
## 3 6 10 4
## 4 48 62 53
## 5 673 506 278
## 6 579 950 669
## 7 102 430 348
## 8 15 94 66
## 9 1 4 07. Marketing Insights
Clusters show different chemical profiles: - Cluster 1: Higher alcohol, lower sugar — may appeal to premium or bold-wine customers. - Cluster 2: Softer profile — could target casual drinkers or dessert wine enthusiasts.
Implications: - Tailor messaging to highlight these qualities (e.g., “crisp & bold” vs. “smooth & sweet”). - Distribute to relevant venues (e.g., restaurants, wine clubs). - Offer samples or bundle promotions by cluster type.
Part 2: RFM and Marketing Segmentation
1. Introduction
RFM (Recency, Frequency, Monetary) analysis segments customers based on purchasing behavior.
2. RFM Processing
rfm <- read.csv("C:/Users/nicho/OneDrive/Desktop/MSDA/MK 6460 - Marketing Research & Analytics/Week 2 - Market Segmentation (Cluster Analysis) & Association Mining (Market Basket Analysis)/datasets/RFM_data.csv", header=TRUE)
library(lubridate)## Warning: package 'lubridate' was built under R version 4.2.2## Loading required package: timechange## Warning: package 'timechange' was built under R version 4.2.2##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionrfm$trans_date <- dmy(rfm$trans_date)
ref_date <- max(rfm$trans_date)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrfm_summary <- rfm %>%
group_by(customer_id) %>%
summarise(
recency = as.numeric(ref_date - max(trans_date)),
frequency = n(),
monetary = sum(tran_amount)
)3. K-Means Clustering
rfm_norm <- scale(rfm_summary[,-1])
set.seed(123)
kmeans_rfm <- kmeans(rfm_norm, 4, nstart=25)
rfm_clustered <- cbind(rfm_summary, cluster=kmeans_rfm$cluster)
aggregate(rfm_summary[,-1], by=list(kmeans_rfm$cluster), mean)boxplot(rfm_summary$monetary ~ kmeans_rfm$cluster, col="skyblue", main="Monetary by Cluster")
4. Non-Cluster RFM Segmentation
rfm_quantiles <- rfm_summary
rfm_quantiles$R_score <- ntile(-rfm_quantiles$recency, 4)
rfm_quantiles$F_score <- ntile(rfm_quantiles$frequency, 4)
rfm_quantiles$M_score <- ntile(rfm_quantiles$monetary, 4)
rfm_quantiles$RFM_total <- rfm_quantiles$R_score + rfm_quantiles$F_score + rfm_quantiles$M_score
rfm_quantiles$segment <- cut(rfm_quantiles$RFM_total,
breaks=c(2, 6, 9, 12),
labels=c("Low Value", "Mid Value", "High Value"))
table(rfm_quantiles$segment)##
## Low Value Mid Value High Value
## 2611 2408 18705. Marketing Strategy Insights
- High Value: Recent, frequent buyers with high spend — target for loyalty programs, early access, premium offers, exclusive offers and benefits, etc.
- Mid Value: Show potential — send personalized promotions or upgrade nudges.
- Low Value: Use reactivation campaigns or surveys to understand disengagement.
More info on high value customers to lapsed customers and reengagement linked here -What is RFM analysis (recency, frequency, monetary)? | Definition from TechTarget