Hierarchical Cluster Analysis with Tidymodels on Retail Data

clustering

customer segmentation

exploratory data analysis

Hierarchical Cluster Analysis is conducted on UK-based online retail store data to segment customers based on recency, frequency and monetary metrics.

Author

Ceren Unal

Customer segmentation is one of the primary components of marketing strategy, informing promotional offers, personalized communications and audience signals on paid media platforms. In this study, we run a hierarchical cluster analysis using the Tidymodels package in R, to segment the customers of a real UK-based a online retail store.

1 Data Description

The data set is downloaded from UCI Machine Learning Repository and is sourced from a 2012 academic paper on data mining by Chen et al ¹. The company observed sells gifts for various occasions and the majority of its customers consist of wholesalers.

Variable Name	Role	Type	Description	Units
InvoiceNo	ID	Categorical	a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter ‘c’, it indicates a cancellation
StockCode	ID	Categorical	a 5-digit integral number uniquely assigned to each distinct product
Description	Feature	Categorical	product name
Quantity	Feature	Integer	the quantities of each product (item) per transaction
InvoiceDate	Feature	Date	the day and time when each transaction was generated
UnitPrice	Feature	Continuous	product price per unit	sterling
CustomerID	Feature	Categorical	a 5-digit integral number uniquely assigned to each customer
Country	Feature	Categorical	the name of the country where each customer resides

2 Load Packages & Data

We will use the Tidyverse package for data processing. The dataset is in a tidy format, with each variable represented as a column and each observation as a row.

InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	2010-12-01 08:26:00	2.55	17850	United Kingdom
536365	71053	WHITE METAL LANTERN	6	2010-12-01 08:26:00	3.39	17850	United Kingdom
536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	2010-12-01 08:26:00	2.75	17850	United Kingdom
536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	2010-12-01 08:26:00	3.39	17850	United Kingdom
536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	2010-12-01 08:26:00	3.39	17850	United Kingdom
536365	22752	SET 7 BABUSHKA NESTING BOXES	2	2010-12-01 08:26:00	7.65	17850	United Kingdom

For data exploration purposes, I will be adding a sales variable to show the amount collected from each transaction in pounds.

3 Data Exploration

We have 406829 observations stored in our data set, each representing the purchase or refund of a stock ID. There are 18287 unique customers across 37 countries. The data set covers a period from 01/12/2010 to 09/12/2011.

Data summary
Name	retail
Number of rows	541909
Number of columns	9
_______________________
Column type frequency:
character	4
numeric	4
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	n_unique
InvoiceNo	0	1	6	7	25900
StockCode	0	1	1	12	4070
Description	1454	1	1	35	4211
Country	0	1	3	20	38

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Quantity	0	1.00	9.55	218.08	-80995.00	1.00	3.00	10.00	80995.0	▁▁▇▁▁
UnitPrice	0	1.00	4.61	96.76	-11062.06	1.25	2.08	4.13	38970.0	▁▇▁▁▁
CustomerID	135080	0.75	15287.69	1713.60	12346.00	13953.00	15152.00	16791.00	18287.0	▇▇▇▇▇
Sales	0	1.00	17.99	378.81	-168469.60	3.40	9.75	17.40	168469.6	▁▁▇▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
InvoiceDate	0	1	2010-12-01 08:26:00	2011-12-09 12:50:00	2011-07-19 17:17:00	23260

There are some transactions with 0 sterling value, which may have been the result of an offer such as buy one, get one or a complementary addition to the order. The max and minimum quantity are respectively 80995 and -80995, suggesting there are substantial refunds among these transactions.

The dataset also has missing data in the Customer ID column.

  InvoiceNo          StockCode         Description           Quantity         
 Length:541909      Length:541909      Length:541909      Min.   :-80995.000  
 Class :character   Class :character   Class :character   1st Qu.:     1.000  
 Mode  :character   Mode  :character   Mode  :character   Median :     3.000  
                                                          Mean   :     9.552  
                                                          3rd Qu.:    10.000  
                                                          Max.   : 80995.000  
                                                                              
  InvoiceDate                    UnitPrice            CustomerID    
 Min.   :2010-12-01 08:26:00   Min.   :-11062.060   Min.   :12346   
 1st Qu.:2011-03-28 11:34:00   1st Qu.:     1.250   1st Qu.:13953   
 Median :2011-07-19 17:17:00   Median :     2.080   Median :15152   
 Mean   :2011-07-04 13:34:57   Mean   :     4.611   Mean   :15288   
 3rd Qu.:2011-10-19 11:27:00   3rd Qu.:     4.130   3rd Qu.:16791   
 Max.   :2011-12-09 12:50:00   Max.   : 38970.000   Max.   :18287   
                                                    NA's   :135080  
   Country              Sales           
 Length:541909      Min.   :-168469.60  
 Class :character   1st Qu.:      3.40  
 Mode  :character   Median :      9.75  
                    Mean   :     17.99  
                    3rd Qu.:     17.40  
                    Max.   : 168469.60

“DOTCOM POSTAGE” is the top product in terms of overall sales, followed by “REGENCY CAKESTAND”

Description	Sales
DOTCOM POSTAGE	206245.48
REGENCY CAKESTAND 3 TIER	164762.19
WHITE HANGING HEART T-LIGHT HOLDER	99668.47
PARTY BUNTING	98302.98
JUMBO BAG RED RETROSPOT	92356.03
RABBIT NIGHT LIGHT	66756.59
POSTAGE	66230.64
PAPER CHAIN KIT 50’S CHRISTMAS	63791.94
ASSORTED COLOUR BIRD ORNAMENT	58959.73
CHILLI LIGHTS	53768.06

UK is the biggest market, followed by Netherlands.

Country	Sales
United Kingdom	8187806.36
Netherlands	284661.54
EIRE	263276.82
Germany	221698.21
France	197403.90
Australia	137077.27
Switzerland	56385.35
Spain	54774.58
Belgium	40910.96
Sweden	36595.91

Global markets only account for 16% of total sales.

Likewise, the number of customers in the UK are 10 times the number of customers in Global. Expectedly, the mean and median are quite far from each other, indicating the presence of large outliers.

Market	Customers	mean_sales_per_customer	median_sales_per_customer
Global	423	3561.510	964.755
UK	3951	2072.338	627.130

5% of customers comprise over 50% of sales in both regions.

Market	top_5_share
Global	0.5711857
UK	0.5507178

According to this boxplot, these are indeed a group of customers that deviate greatly from the median in terms of sales volume.

4 Hierarchical Cluster Analysis

Data exploration showed that we have some very large wholesale customers that undoubtedly have great value to us. Still, there is room for further exploration via unsupervised learning, taking recency and frequency of of orders alongside order value to look for more granular segments in our customer base.

Data Preparation

Since we want to analyze customers, we will be removing the observations with missing customer ID from the cluster data set. Around 100,000 rows are missing a customer ID, which may lead to valuable data loss. However, seeing there is no way of connecting those transactions to an individual, they will have to be removed.

Each customer will be assigned RFM (recency, frequency, monetary) scores based on the recency of their last transaction, number of invoices generated and the volume of sales.

A quick look at histograms reveals all three of these RFM variables are right-skewed.

We normalize them by first reducing skewness with log transformation to compress large value and make data more symmetric. Then, we rescale them to ensure all variables are on a comparable scale, which is important for distance-based methods like hyerarchical clustering.

Creating Clusters

Silhouette Method confirms k = 2 is ideal for forming distinct segments.

k	silhouette
2	0.4013994
3	0.3331469
4	0.2464909
5	0.2494433
6	0.2393687

Using Tidymodels, we cut the dendrogram into 2 final clusters with Ward’s method to produce compact, roughly spherical clusters. Tidyclust will treat the result as a partitioning model, meaning every observation belongs to exactly one cluster.

Results

The clustering algorithm has bucketed customers into groups based on their RFM characteristics.

Their characteristics can be summarized as below:

Cluster 1 (positive F & M, negative R) → frequent, recent, high spenders → “High-Value” customers.
Cluster 2 (negative F & M, positive R) → infrequent, older purchases, low spend → “Low-Value” customers.

.cluster	mean_recency	mean_frequency	mean_monetary	n_customers	Segment
Cluster_1	-0.5658819	0.7170695	0.7075554	2212	High-Value
Cluster_2	0.5946464	-0.7535191	-0.7435214	2105	Low-Value

The radar chart visualizes the characteristics of these segments relative to the three variables and each other. We have cearly differentiated customer groups, the segmentation is strong.

Cluster 1’s polygon expands outward on Frequency and Monetary, and inward on Recency. These are customers with strong loyalty: Recent, frequent, high-spending.

Cluster 2’s polygon is the opposite, expanding towards Recency, indicating older purchases. Below average performance, less frequent, lower spending.

Analysis

By joining our cluster analysis table with the original customer dataset, we can obtain a list that can be used for marketing campaigns.

This also enables us to further examine our customer data, and see the sales metrics of the two segments.

A quick look shows that the segments are not only distinct, but give us a very meaningful look into the each segment.

High-Value Segment

This group likely includes wholesale or business buyers:

The average high-value customer spends almost 9× more than a low-value one.
High frequency (≈8.5 purchases per customer) suggests strong loyalty or recurring B2B activity.
Some very large spenders (wholesale clients) are pulling the average upward.

Low-Value Segment

These are likely individual or casual buyers.

Average sales per customer are very low (~$370).
Purchase frequency barely above 1. Mostly one-time or occasional buyers.
Mean and median are close, little skew. These are fairly consistent low-spenders.
Lifetime value will be highly limited.

Segment	Customers	mean_sales_per_customer	median_sales_per_customer	mean_frequency	median_frequency
High-Value	2212	3405.6107	1545.275	8.522604	5
Low-Value	2105	370.5863	300.920	1.542993	1
NA	56	25615.2318	-22.950	67.857143	1

NA Values

There are Customer ID rows there are either have missing Customer ID (leading to data loss) or have negative or zero Sales value (cannot be considered a customer). Both occurances were excluded from the cluster analysis.

CustomerID	n_rows	unique_customers	avg_sales	total_sales
NA	135080	1	10.717220	1447682.12
12607	202	1	0.000000	0.00
18072	36	1	0.000000	0.00
14557	32	1	0.000000	0.00
16546	31	1	-3.094516	-95.93
12454	30	1	0.000000	0.00

5 Conclusion

Hierarchical Clustering Analysis is a useful tool in segmenting customers based on shared characteristics, allowing marketers to tailor their strategy to the segments. Now that customers are assigned to clusters, the segment labels can be passed back to a CRM tool and an internal data warehouse for:

Segment-based A/B testing
Customer lifetime value prediction
Marketing automation targeting

Cluster 1 represents high-value customers who purchase more frequently, spend more, and buy more recently than average. Cluster 2 represents low-value or inactive customers with infrequent, lower-spend, and older purchase activity. This segmentation confirms a highly skewed customer base, where a smaller group of wholesale or loyal B2B buyers drive a large share of total revenue.

Therefore, each should receive communications tailored to their profile. High-Value segment in particular should be analyzed more deeply and the marketing strategy should focus on relationship building. Wholesale customers are difficult to acquire yet bring in the most revenue, so the bulk of the marketing budget should be allocated to retaining these accounts.

Low-Value customers don’t even make an average of 1 purchase per year and the purchase amount is significantly lower. Therefore, the communications should not be frequent and sales-focused. Some budget may be allocated to reactivation experiments for promising customers that may be small business or organizations.

Segment	Strategy	Recommended Action
High-Value	Retention & relationship growth	Account-based marketing, exclusive offers, volume discounts, early access to new inventory.
Low-Value	Light engagement / reactivation	Seasonal promotions, personalized email nurture flows, referral incentives.

Next steps should focus on deepening these insights by overlaying additional attributes such as product category preferences, size of the account, buying cycles etc. This will help validate behavioral patterns and reveal sub-segments within each group.

Footnotes

Chen, D. (2015). Online Retail [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5BW33.↩︎

--- title: "Hierarchical Cluster Analysis with Tidymodels on Retail Data" author: Ceren Unal categories: [clustering, customer segmentation, exploratory data analysis] image: segments.png description: Hierarchical Cluster Analysis is conducted on UK-based online retail store data to segment customers based on recency, frequency and monetary metrics. toc: true toc-title: Content toc-location: right number-sections: true number-depth: 2 smooth-scroll: true df-print: kable code-fold: true code-tools: true code-overflow: wrap code-block-bg: true code-block-border-left: "#31BAE9" highlight-style: pygments code-link: true execute: warning: false message: false --- Customer segmentation is one of the primary components of marketing strategy, informing promotional offers, personalized communications and audience signals on paid media platforms. In this study, we run a hierarchical cluster analysis using the Tidymodels package in R, to segment the customers of a real UK-based a online retail store. ## Data Description The data set is downloaded from UCI Machine Learning Repository and is sourced from a 2012 academic paper on data mining by Chen et al [^1]. The company observed sells gifts for various occasions and the majority of its customers consist of wholesalers. [^1]: Chen, D. (2015). Online Retail \[Dataset\]. UCI Machine Learning Repository. <https://doi.org/10.24432/C5BW33.> | **Variable Name** | **Role** | **Type** | **Description** | **Units** | |---------------|---------------|---------------|---------------|--------------| | InvoiceNo | ID | Categorical | a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation | | | StockCode | ID | Categorical | a 5-digit integral number uniquely assigned to each distinct product | | | Description | Feature | Categorical | product name | | | Quantity | Feature | Integer | the quantities of each product (item) per transaction | | | InvoiceDate | Feature | Date | the day and time when each transaction was generated | | | UnitPrice | Feature | Continuous | product price per unit | sterling | | CustomerID | Feature | Categorical | a 5-digit integral number uniquely assigned to each customer | | | Country | Feature | Categorical | the name of the country where each customer resides | | ## Load Packages & Data We will use the Tidyverse package for data processing. The dataset is in a tidy format, with each variable represented as a column and each observation as a row. ```{r} library(tidyverse) library(tidymodels) library(tidyclust) library(modeldata) library(factoextra) library(cluster) ``` ```{r} retail <- readxl::read_xlsx("retail.xlsx") head(retail) ``` For data exploration purposes, I will be adding a sales variable to show the amount collected from each transaction in pounds. ```{r} retail <- retail %>% mutate(Sales = UnitPrice * Quantity) ``` ## Data Exploration We have 406829 observations stored in our data set, each representing the purchase or refund of a stock ID. There are 18287 unique customers across 37 countries. The data set covers a period from 01/12/2010 to 09/12/2011. ```{r} skimr::skim(retail) ``` There are some transactions with 0 sterling value, which may have been the result of an offer such as buy one, get one or a complementary addition to the order. The max and minimum quantity are respectively 80995 and -80995, suggesting there are substantial refunds among these transactions. The dataset also has missing data in the Customer ID column. ```{r} summary(retail) ``` "DOTCOM POSTAGE" is the top product in terms of overall sales, followed by "REGENCY CAKESTAND" ```{r} retail %>% group_by(Description) %>% summarise(Sales = sum(Sales)) %>% arrange(desc(Sales)) %>% slice_head(n=10) ``` UK is the biggest market, followed by Netherlands. ```{r} retail %>% group_by(Country) %>% summarise(Sales = sum(Sales)) %>% arrange(desc(Sales)) %>% slice_head(n=10) ``` Global markets only account for 16% of total sales. ```{r} retail %>% mutate(Market = if_else(Country == "United Kingdom", "UK", "Global")) %>% group_by(Market) %>% summarise(Sales = sum(Sales)) %>% ungroup() %>% mutate(Perc_of_Sales = Sales / sum(Sales)) %>% ggplot(aes(x = Market, y = Sales, fill = Market)) + geom_col() + geom_text(aes(label = percent(Perc_of_Sales, accuracy = 0.1)), vjust = -0.5, size = 5) + scale_y_continuous(labels = comma) + labs( title = "Sales Distribution by Market", x = "Market", y = "Total Sales", fill = "Market" ) + theme_minimal(base_size = 14) ``` Likewise, the number of customers in the UK are 10 times the number of customers in Global. Expectedly, the mean and median are quite far from each other, indicating the presence of large outliers. ```{r} retail %>% group_by(CustomerID, Country) %>% summarise(Sales_per_Customer = sum(Sales, na.rm = TRUE), .groups = "drop") %>% mutate(Market = if_else(Country == "United Kingdom", "UK", "Global")) %>% group_by(Market) %>% summarise( Customers = n_distinct(CustomerID), mean_sales_per_customer = mean(Sales_per_Customer, na.rm = TRUE), median_sales_per_customer = median(Sales_per_Customer, na.rm = TRUE) ) ``` 5% of customers comprise over 50% of sales in both regions. ```{r} retail %>% group_by(CustomerID, Country) %>% summarise(Sales_per_Customer = sum(Sales, na.rm = TRUE), .groups = "drop") %>% mutate(Market = if_else(Country == "United Kingdom", "UK", "Global")) %>% group_by(Market) %>% mutate(percentile = ntile(Sales_per_Customer, 100)) %>% summarise(top_5_share = sum(Sales_per_Customer[percentile > 95]) / sum(Sales_per_Customer)) ``` According to this boxplot, these are indeed a group of customers that deviate greatly from the median in terms of sales volume. ```{r} retail %>% group_by(CustomerID, Country) %>% summarise(Sales_per_Customer = sum(Sales, na.rm = TRUE), .groups = "drop") %>% mutate(Market = if_else(Country == "United Kingdom", "UK", "Global")) %>% ggplot(aes(x = Market, y = Sales_per_Customer, fill = Market)) + geom_boxplot(alpha = 0.4) + scale_y_log10(labels = comma) + scale_size_continuous(range = c(1, 10), guide = "none") + labs( title = "Customer Sales Distribution by Market", x = "Market", y = "Sales per Customer (log scale)" ) + theme_minimal(base_size = 13) ``` ## Hierarchical Cluster Analysis Data exploration showed that we have some very large wholesale customers that undoubtedly have great value to us. Still, there is room for further exploration via unsupervised learning, taking recency and frequency of of orders alongside order value to look for more granular segments in our customer base. ### Data Preparation Since we want to analyze customers, we will be removing the observations with missing customer ID from the cluster data set. Around 100,000 rows are missing a customer ID, which may lead to valuable data loss. However, seeing there is no way of connecting those transactions to an individual, they will have to be removed. Each customer will be assigned RFM (recency, frequency, monetary) scores based on the recency of their last transaction, number of invoices generated and the volume of sales. ```{r} # define analysis date (last date in dataset) cutoff_date <- max(retail$InvoiceDate, na.rm = TRUE) rfm <- retail %>% filter(!is.na(CustomerID)) %>% # remove transactions missing customerID group_by(CustomerID) %>% summarise( recency = as.numeric(cutoff_date - max(InvoiceDate)), # number of days since last transaction frequency = n_distinct(InvoiceNo), # number of transactions monetary = sum(Sales) # total spending ) %>% ungroup() ``` A quick look at histograms reveals all three of these RFM variables are right-skewed. ```{r} hist(rfm$recency) hist(rfm$frequency) hist(rfm$monetary) ``` We normalize them by first reducing skewness with log transformation to compress large value and make data more symmetric. Then, we rescale them to ensure all variables are on a comparable scale, which is important for distance-based methods like hyerarchical clustering. ```{r} rfm <- rfm %>% filter(monetary > 0) %>% # remove customers with negative monetary value mutate( recency = log1p(recency), # reduce skewness frequency = log1p(frequency), monetary = log1p(monetary) ) %>% mutate(across(c(recency, frequency, monetary), scale)) #standardize mean=0, sd=1 ``` ### Creating Clusters Silhouette Method confirms k = 2 is ideal for forming distinct segments. ```{r} fviz_nbclust( rfm[, c("recency", "frequency", "monetary")], FUN = hcut, # hierarchical clustering method = "silhouette", hc_method = "ward.D2" ) + labs(title = "Silhouette Method for Optimal Number of Clusters") ``` ```{r} # Define the range of k values you want to test k_values <- 2:6 # Compute silhouette average width for each k sil_scores <- sapply(k_values, function(k) { model <- hcut(rfm[, c("recency", "frequency", "monetary")], k = k, hc_method = "ward.D2") model$silinfo$avg.width }) # Put results in a tidy data frame sil_df <- data.frame(k = k_values, silhouette = sil_scores) sil_df ``` Using Tidymodels, we cut the dendrogram into 2 final clusters with Ward’s method to produce compact, roughly spherical clusters. Tidyclust will treat the result as a partitioning model, meaning every observation belongs to exactly one cluster. ```{r} RecipeCustSeg <- recipe(~ recency + frequency + monetary, rfm) ModelDesignClust <- hier_clust(num_clusters = 2, linkage_method = "ward.D") %>% set_engine("stats") %>% set_mode("partition") WFModelClust <- workflow() %>% add_model(ModelDesignClust) %>% add_recipe(RecipeCustSeg) %>% fit(rfm) ModelTrainedClust <- extract_fit_engine(WFModelClust) DataCustSegWithAssignm <- extract_cluster_assignment(WFModelClust) %>% cbind(rfm) DataCentroids <- extract_centroids(WFModelClust) ``` ### Results The clustering algorithm has bucketed customers into groups based on their RFM characteristics. ```{r} fviz_cluster( list( data = as.matrix(rfm[, c("recency", "frequency", "monetary")]), cluster = as.factor(DataCustSegWithAssignm$.cluster) ), geom = "point", ellipse.type = "convex", palette = "jco", ggtheme = theme_minimal(), main = "Customer Segments (Hierarchical Clustering, k = 2)" ) ``` Their characteristics can be summarized as below: - **Cluster 1 (positive F & M, negative R)** → frequent, recent, high spenders → “High-Value” customers. - **Cluster 2 (negative F & M, positive R)** → infrequent, older purchases, low spend → “Low-Value” customers. ```{r} DataCustSegWithAssignm %>% group_by(.cluster) %>% summarise( mean_recency = mean(recency), mean_frequency = mean(frequency), mean_monetary = mean(monetary), n_customers = n() ) %>% mutate(Segment = if_else(.cluster == "Cluster_1", "High-Value", "Low-Value")) ``` ```{r} DataCentroids %>% pivot_longer(cols = c(recency, frequency, monetary), names_to = "metric", values_to = "mean_value") %>% ggplot(aes(x = metric, y = mean_value, fill = .cluster)) + geom_col(position = "dodge") + geom_text(aes(label = round(mean_value, 2)), position = position_dodge(width = 0.9), vjust = -0.5, size = 3) + scale_fill_manual( values = c("Cluster_1" = "blue", "Cluster_2" = "orange") ) + labs(title = "Average Scaled RFM Values by Cluster", x = "RFM Metric", y = "Scaled Mean Value", fill = "Cluster") + theme_minimal(base_size = 12) ``` The radar chart visualizes the characteristics of these segments relative to the three variables and each other. We have cearly differentiated customer groups, the segmentation is strong. Cluster 1’s polygon expands outward on Frequency and Monetary, and inward on Recency. These are customers with strong loyalty: Recent, frequent, high-spending. Cluster 2’s polygon is the opposite, expanding towards Recency, indicating older purchases. Below average performance, less frequent, lower spending. ```{r} library(fmsb) # Convert to matrix format rfm_radar <- as.data.frame(DataCentroids[, -1]) row.names(rfm_radar) <- DataCentroids$.cluster # Add upper/lower bounds for fmsb rfm_radar <- rbind(rep(2, 3), rep(-2, 3), rfm_radar) radarchart(rfm_radar, axistype = 1, pcol = c("blue", "orange"), plwd = 2, plty = 1, title = "RFM Cluster Profiles (Scaled Values)") legend("topright", legend = rownames(rfm_radar)[-c(1, 2)], # skip the first two scale rows col = c("blue", "orange"), lty = 1, # same line type as chart lwd = 2, # same line width as chart title = "Clusters") ``` ### Analysis By joining our cluster analysis table with the original customer dataset, we can obtain a list that can be used for marketing campaigns. ```{r} retail_with_clusters <- retail %>% left_join( DataCustSegWithAssignm %>% select(CustomerID, .cluster) %>% rename(Cluster = .cluster), by = "CustomerID" ) %>% mutate( Segment = recode(Cluster, "Cluster_1" = "High-Value", "Cluster_2" = "Low-Value") ) ``` This also enables us to further examine our customer data, and see the sales metrics of the two segments. A quick look shows that the segments are not only distinct, but give us a very meaningful look into the each segment. **High-Value Segment** This group likely includes **wholesale or business buyers**: - The average high-value customer spends almost **9× more** than a low-value one. - High frequency (**≈8.5 purchases per customer**) suggests strong loyalty or recurring B2B activity. - Some very large spenders (wholesale clients) are pulling the average upward. **Low-Value Segment** These are likely **individual or casual buyers**. - Average sales per customer are very low (\~\$370). - Purchase frequency barely above 1. Mostly one-time or occasional buyers. - Mean and median are close, little skew. These are fairly consistent low-spenders. - **Lifetime value will be highly limited.** ```{r} segment_summary <- retail_with_clusters %>% group_by(Segment, CustomerID) %>% summarise( Sales_per_Customer = sum(Sales, na.rm = TRUE), Frequency = n_distinct(InvoiceNo), .groups = "drop_last" ) %>% summarise( Customers = n_distinct(CustomerID), mean_sales_per_customer = mean(Sales_per_Customer, na.rm = TRUE), median_sales_per_customer = median(Sales_per_Customer, na.rm = TRUE), mean_frequency = mean(Frequency, na.rm = TRUE), median_frequency = median(Frequency, na.rm = TRUE), .groups = "drop" ) segment_summary ``` **NA Values** There are Customer ID rows there are either have missing Customer ID (leading to data loss) or have negative or zero Sales value (cannot be considered a customer). Both occurances were excluded from the cluster analysis. ```{r} na_segment <- retail_with_clusters %>% filter(is.na(Segment)) na_segment %>% group_by(CustomerID) %>% summarise( n_rows = n(), unique_customers = n_distinct(CustomerID), avg_sales = mean(Sales, na.rm = TRUE), total_sales = sum(Sales, na.rm = TRUE)) %>% arrange(desc(n_rows)) %>% head() ``` ## Conclusion Hierarchical Clustering Analysis is a useful tool in segmenting customers based on shared characteristics, allowing marketers to tailor their strategy to the segments. Now that customers are assigned to clusters, the segment labels can be passed back to a CRM tool and an internal data warehouse for: - Segment-based A/B testing - Customer lifetime value prediction - Marketing automation targeting **Cluster 1** represents *high-value customers* who purchase more frequently, spend more, and buy more recently than average. **Cluster 2** represents *low-value or inactive customers* with infrequent, lower-spend, and older purchase activity. This segmentation confirms a highly skewed customer base, where a smaller group of wholesale or loyal B2B buyers drive a large share of total revenue. Therefore, each should receive communications tailored to their profile. High-Value segment in particular should be analyzed more deeply and the marketing strategy should focus on relationship building. Wholesale customers are difficult to acquire yet bring in the most revenue, so the bulk of the marketing budget should be allocated to retaining these accounts. Low-Value customers don't even make an average of 1 purchase per year and the purchase amount is significantly lower. Therefore, the communications should not be frequent and sales-focused. Some budget may be allocated to reactivation experiments for promising customers that may be small business or organizations. | Segment | Strategy | Recommended Action | |---------------|-----------------------------|-----------------------------| | **High-Value** | **Retention & relationship growth** | Account-based marketing, exclusive offers, volume discounts, early access to new inventory. | | **Low-Value** | **Light engagement / reactivation** | Seasonal promotions, personalized email nurture flows, referral incentives. | Next steps should focus on deepening these insights by overlaying additional attributes such as product category preferences, size of the account, buying cycles etc. This will help validate behavioral patterns and reveal sub-segments within each group.