Executive Summary

This report is conducted to examine how H&M can more effectively and efficiently maximize

the profits from their current customer base. The stated objective of this research is to identify

highly profitable customers and frequent shoppers for H&M and to offer managerial implications

based on the key findings of this research. The research conducted is meant to segment H&M’s

customers based on their most relevant characteristics as shoppers, allowing for more effective

promotions and increasing sales for the company. H&M is in the fast-fashion industry that is a

highly competitive and defined as a monopolistic competition. In order to sustain their current

positioning within the market, H&M must understand the buying behaviors of their customers

for maximizing the profitability of each individual customer.

The data we received for this research is the transactional records for H&M over a period of

nearly eight years. From these records we are able to select important variables and aggregate the

data into a customer database that we can then use to establish characteristics about specific

customers. The variables that we use to segment H&M’s customers are total revenue, total

profits, months from last order, and the number of orders placed. Our variable selection is based

on the Recency Frequency Monetary Model (RFM Model). The RFM Model is a technique to

quantitatively measure who are the best customers (Rouse, 2005). The channel and payment

method in the data are also analyzed to find possible business opportunities for H&M.

Once the variables are selected and the transactional data is converted into customer data, we run

Hierarchical Cluster Analysis and K-means Cluster Analysis with Ward’s Method. Cluster

analysis is run on data in an attempt to accurately cluster the customers into segments H&M will

be able to target effectively. According to the meaningful customer characteristics we select,

H&M has seven segments of customers. Each segment represents a group of H&M’s customers

that can be targeted with similar strategies. H&M will then be able to allocate marketing

resources more efficiently, which leads the company to save money and increase their

customer’s lifetime value.

While conducting this research we found some very interesting characteristics of H&M’s

customer base. Over 88% of their customers who generated over ten million dollars of revenue

for H&M only shopped there once. This means that many of H&M’s customers are not repeat

customers. It is always less expensive for a company to retain current customers and increase

average revenue than to create new customers to buy products. We recommend H&M to develop

a membership program for its customers to not only increase the number of visits the customer

will make to the store, but also to create customer value and incentives so customers shop at

H&M more often. Another interesting finding of H&M’s customers was a group that shared

similar traits, such as number of orders and average revenue. These are semi-frequent shoppers

who might spend more at H&M if they felt there was more value for them. Initiating a promotion

where the percentage discounted from the order was correlated to the amount they spent on the

order might encourage more spending on their visits to the stores. The final meaningful segment

of customers for H&M contains a higher average revenue than the previously mentioned

segments, but is also smaller than the previous two sections as well, at only about thirteen

hundred customers. These customers generate a large revenue for H&M and the way to increase

this revenue is to increase the number of times these customers order from H&M. By sending

this group of customers the new promotion products H&M has received since their last visit, it

would create value for the customer in returning to H&M and possibly purchasing more products

than they otherwise would have.

With the findings from the research and the managerial implications determined from these

findings, H&M will have the tools to increase revenue and sales from their current customers.

Segmenting customers helps identify the groups to focus on for acquiring and retaining

consumers (Hinshaw, 2013). Once one can determine the customers that should be focused on,

one can take the next step, which is learning what the customers' wants and needs are in order to

create more customer value for the company. By understanding this research, H&M will be able

to address each group of customers in the way that most effectively maximizes the buying

potential of those customers (Optimove, 2015).

TABLE OF CONTENTS

INTRODUCTION ............................................................................................................................... 1

BACKGROUND ................................................................................................................................. 4

METHODOLOGY .............................................................................................................................. 5

VARIABLE SELECTION ........................................................................................................................ 5

METHOD ............................................................................................................................................... 7

ANALYSIS ...................................................................................................................................... 10

POST -HOC ANALYSIS .................................................................................................................. 27

CHANNEL ANALYSIS ......................................................................................................................... 27

PAYMENT METHOD ANALYSIS ......................................................................................................... 29

CONCLUSION ................................................................................................................................. 30

LIMITATIONS ................................................................................................................................. 33

FUTURE RESEARCH ....................................................................................................................... 34

REFERENCES ................................................................................................................................. 36

APPENDIX ...................................................................................................................................... 38

INFOGRAPHICS .............................................................................................................................. 42

1

Introduction

H&M, a Swedish multinational clothing company, is well known for its affordable, yet

fashionable clothing for men, women, teenagers, and children. With over 3,700 stores in

61 countries, H&M is considered to be the second largest global clothing retailer

(Wikipedia, n.d.). With this title comes responsibility as H&M faces a few challenges

when trying to acquire and retain consumers. For example, there is a large number of

customers who are one-time shoppers at H&M. Meaning, they make one or two

purchases and never come back. For the amount of customers that do this, wouldn’t it

make sense for H&M to try to get these customers to stay? Just think if H&M can keep

even just 25% of those one-time customers; they will already be more profitable. This is

just one problem that we seek to address in our study.

The environment for retailers has become increasingly more complex as consumers are

given the ability to shop online via website, phone, or mail. According to an annual

survey done by PwC on consumers' retail shopping habits, “only 27 percent of U.S.

consumers say they shop online weekly. Although people [reserved] the strength of the

traditional store, 68 percent of U.S. respondents say they have intentionally browsed

products at a store but decided to purchase them online” (PwC, 2015). The retail-clothing

market is filled with much variety and competition in the fast-fashion industry. H&M

prides itself on two main concepts: one is its cheap price and second is its high volume of

cute, stylish clothing (Rosenblum, 2015). H&M attracts a target market based on these

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principles. Why would a consumer spend hundreds of dollars on one item when he or she

can buy something similar at a quarter of the price? Secondly, retailers such as H&M

understand the hassle of looking through catalogs of large department stores for bargain

prices. As a result, H&M eliminates that problem by providing a store that only sells

bargains (Rosenblum, 2015).

Within this industry, however, there lies much competition for H&M and its rival catalog

companies. First, a catalog company competes with in-store retailers. According to the

Omnichannel Shopping Preferences Study, “90 percent of all U.S. retail sales still happen

in stores” (Lesonsky, 2014). Meaning, that profits for a catalog company can increase if

the company targets consumers who are more likely to purchase online versus in-store.

Secondly, H&M faces competition from other top catalog retailers that include Zara,

Uniqlo and Gap. In order to figure out how to keep consumers loyal to H&M’s catalog

branch, we analyzed a large data set comparing the company’s total revenue, total profits,

the number of orders, months from last order, the payment method, and the channel

through which the purchase was made.

In this study, we sought to identify different segments of H&M customers by conducting

a series of K-Means Cluster analyses. We examined the variables of total revenue, total

profits, months from last order, and the number of orders placed to identify the highly

profitable customers and frequent shoppers of H&M. This is important for the future

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catalog business of H&M for a number of reasons. Not only does this research allow for

H&M to discover which customers are most profitable, but it also allows the company to

come up with further marketing implications on how to keep the customers or even get

rid of those they do not feel are beneficial to the company. Throughout this study, the

reader will learn what managerial suggestions we have for H&M to retain valuable

customers and how to maximize profits based on customer segmentations. A few key

findings within this research include separating the segments into groups that are similar

to one another. One group of segments we found should be rewarded to come back and

make more purchases. By turning these new customers into frequent shoppers, H&M can

reward these customers through increased customer satisfaction or better service. For

another identified group of segments, we found that the best approach would be to reward

these consumers when they spend more money using the catalog. For example, H&M can

use the idea of cross promotion to allow customers to buy jeans and a sweatshirt together

for a cheaper price. It is also important for H&M to keep the most profitable customers.

In this case, establishing a loyalty program or offering them top-tier customer service

may allow for higher brand loyalty. The key findings will be discussed in detail in the

report.

In the next section, we will give background information on the report, followed by the

methods used, such as the data, analysis, and results of the study. We will conclude this

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report with managerial recommendations and a future focus for H&M based on the

results from the study.

Background

The goal for H&M in this study is to understand the segments of their customers that are

highly profitable and the most frequent shoppers. H&M’s general target consumers are

those who are fashionable and stylish, yet looking for affordable prices. Due to their

trendy, quality clothing and reasonable pricing, H&M competes with a number of

fast-fashion stores including Gap, Zara, and Uniqlo. Because of this monopolistic

competition market, there are multiple buyers and sellers in the market. In order to retain

and acquire new consumers, we recommend that H&M follow the suggested managerial

implications in the upcoming sections.

To understand this market, we conducted K-Means Cluster analyses and found seven

optimal clusters. Among them, there were a few key segments that were most profitable,

others that were not as profitable. Based on our findings, we offered suggestions on how

to best handle these segments. After determining the useful variables from our analyses,

which included total revenue, total profit, months from last order, the number of orders

placed, we were able to come up with a few recommendations for H&M. We identified

one segment group of consumers, the most profitable group, which would benefit from

customized service and loyalty programs. We identified another segment group of

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consumers who were not frequent shoppers. This calls for a different approach when

targeting this group. Maybe they are new customers that H&M needs to turn into repeat

customers. By offering different groups different levels of services, H&M can work to

increase their customer retention and increase profits in the long run.

Our study is meaningful because we help H&M identify highly profitable customers and

frequent shoppers. Why is this useful to H&M? Our results help identify segments of

consumers that would be most beneficial for increasing company profits. Also, we offer

managerial implications for H&M to effectively reach customers. Such managerial

implications can help the company decide to keep or get rid of segments of customers.

Without this study, H&M would have a hard time deciding what appropriate managerial

implications to make when targeting consumers. All consumers are different and should

be treated as such. After reading this study, the executives at H&M will have a better

understanding as to which customers are their most profitable and most frequent shoppers.

Thus, our research will result in efficient and effective marketing strategies for the future.

It is important to understand the usefulness of this research for future marketing

implications and without the identification of these segments, H&M would have a hard

time determining the best strategies to take.

In the following parts, we will explain more details about the variables we chose and

introduce the process of the cluster analyses. Based on the customer segments, we will

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summarize the key findings and make managerial implications accordingly. Finally,

suggestions of future research are offered after limitation analysis.

Methodology

Variable Selection

In order to identify highly profitable customers and frequent shoppers for H&M, we

collected lots of transactional data from H&M for further studies1. Within this data file,

there are over 226,000 transaction records that represent more than 137,000 orders from

100,000 customers of H&M. Moreover, all transactions recorded in this dataset were

made from Dec 16, 2004 to Sep 17, 2012 at H&M.

To achieve our goals of this project, we took advantage of Recency, Frequency &

Monetary (RFM) Model to segment our customers into clusters with similar

characteristics. Basically, RFM Model is a marketing technique and a segmentation tool

that helps companies identify their best customers (Rouse, 2005). Based on RFM Model,

we selected four main variables to segment customers. Following is a table of four main

variables (Figure 1) that we used in this project.

Figure 1: Descriptions of Four Main Selected Variables

1 Data used in this report is not from H&M. We assumed that we collected data from H&M to do cluster analysis.

7

According to this RFM Model, the best customers for H&M are those people who made

their purchase recently, who shop at H&M frequently or regularly, and who can generate

huge profits for the company.

Additionally, we included two more variables, Payment Method and Channel of Order

Placement so that we can better understand which payment method were widely used by

H&M customers and their preference of where to place order.

Method

Below is a flowchart, showing the process that we followed to conduct a series of cluster

analyses in this project. Generally, we segmented customers of H&M based on their

characteristics in profitability, frequency and recency, which allowed us to further

identify the most profitable customers and frequent shoppers of H&M. Brief introduction

of the cluster analysis procedures are given in the method part while more details of the

whole process are explained in the analysis part. We also interpreted the meaning of

numbers in each clusters and summarized key findings that we believe are important for

executives of H&M to understand. Based on key findings and different characteristics of

each cluster, we offered managerial suggestions so that H&M can effectively reach more

customers and retain valuable customers to maximize profits.

8

Flowchart of The Cluster Analyses

9

The data we collected from H&M are transactional records that are useless for customer

segmentation. Therefore, we firstly aggregated transactional data into customer data based on

selected variables so that we could continue cluster analysis. Then, we calculated Z-scores for

our four selected variables as a process of data standardization. After that, we randomly split the

data set into a Calibration (60% of entire dataset) and a Validation (40% of entire dataset) sample

so that we could compare results from two independent samples to reduce the chance of missing

important customer clusters and ensure the accuracy of cluster analysis.

To further analyze the Calibration sample, we randomly selected 10% of the Calibration data as a

new small subset to conduct Hierarchical Cluster Analysis. Both Ward’s Method and Furthest

Neighbor Method were used to test which method was more effective in customer segmentations.

We decided to use Ward’s Method because it gave us clusters with similar size while Furthest

Neighbor Method produced too many outliers. In order to detect differences more clearly, we

adopted distance measures of Squared Euclidean to make variances between each customer even

bigger. After conducting several times of Hierarchical Cluster Analysis, we successfully

identified the optimal number of clusters (seven clusters) and created the initial seeds for

K-means Cluster Analysis. We repeated the aforementioned steps of cluster analysis with the 10%

of the Validation sample.

10

To determine whether results of the Calibration and Validation sample were consistent, we

compared the managerial implications of the optimal clusters from those two samples. When

looking into the optimal clusters, we found both Calibration sample and Validation sample had

one group of outliers. Therefore, we just ignored the impact of outliers in comparison of

managerial implications. Since the results from Calibration and Validation samples were

consistent, we conducted K-means Cluster Analysis on the entire data set and the result was

consistent with those of Calibration and Validation samples. Finally, we offered managerial

implications for each cluster based on their characteristics of recency, frequency and

profitability.

Analysis

Variable Identification and Data Aggregation

From the initial transactional data, we were able to determine four main variables that play

influential roles in understanding H&M customers. These factors included the customer’s total

revenue, total profit, months since last order and the number of orders placed. These variables

were critical to segment customers in the cluster analysis. After identifying these variables, we

performed data aggregation. In detail, we put the variable, customer order number, as break

variable, and put those variables and two additional variables (payment method and channels

where customers placed order) as aggregated variables. We outputted the result on a new SPSS

11

chart as a platform where we then performed the cluster analysis process. From there we

transformed these four variables into Z-score variables.

Hierarchical Cluster Analysis of Random Cases of Calibration

We randomly divided the customers data into two sample groups: Calibration and Validation.

We put 60% of population in the Calibration sample and the remaining 40% into the Validation

sample. Next, we randomly selected a 10% sample of the whole population, and selected cases

both in Calibration and 10% population as a new Calibration subset. Then, using Ward’s Method,

we started Hierarchical Cluster Analysis on these selected cases. The reason why we chose

Ward’s Method instead of Furthest Neighbor Method is mainly because there were too many

outliers in the outputs with Furthest Neighbor Method that influence our further analysis. Also,

we hope to segment customers into groups with similar size to make sure that most clusters are

meaningful in our study. Figure 2, 3 & 4 are the three outputs of Hierarchical Cluster Analysis

on selected cases of Calibration. We believe seven clusters (Figure 3) are the optimal numbers as

our target output.

Why We Chose Seven Clusters?

Based on Figure 2, we find that Cluster 1 is divided into two clusters (Cluster 1 and Cluster 6 in

Figure 3). We believe seven clusters are better than six clusters because the Cluster 1 and Cluster

6 in Figure 2 are very different from each other. Customers in Cluster 1 generated relatively high

12

revenue and high profits while they did not purchase at H&M for a long time. However,

customers in Cluster 6 produced less revenue and profit while they shopped at H&M more

recently. These two clusters represent two different groups of customers. Thus, the managerial

implications for these two clusters are different. We advise that for Cluster 1, H&M needs to

increase service quality to bring these customers back to H&M. For Cluster 6, H&M should

increase advertising of latest style clothes to stimulate those customers’ purchase motivation.

This is why we prefer seven clusters, instead of six clusters for this study.

Figure 2: Output of Six Clusters of Calibration Subset by Using Ward’s Method

Figure 3: Output of Seven Clusters of Calibration Subset by Using Ward’s Method

Figure 4: Output of Eight Clusters of Calibration Subset by Using Ward’s Method

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We also find that Cluster 3 in Figure 3 is divided into two clusters (Cluster 3 and Cluster 6 in

Figure 4). We consider that seven cluster is still better than eight clusters as there is no

significant difference between Cluster 3 and Cluster 6 in Figure 4. Customers in these two

clusters all created relatively low revenue and profit. According to their low recency and

frequency, they might have already left H&M and shop at other fast-fashion companies, such as

Zara or Uniqlo. The managerial implications for these two clusters are same. H&M has to make

them frequent shoppers, such as sending out coupons, advertising emails or discounts. As a result,

we insist that seven clusters are optimal numbers of clusters.

K-means Cluster Analysis of Calibration

After using Z-score variables to do Hierarchical Cluster Analysis again, we chose seven clusters

to get our initial seeds centers of K-means Cluster Analysis of Calibration. Figure 5 below is the

initial seeds center for K-means Cluster Analysis of Calibration.

Figure 5: Initial Seeds Centers of K-means Cluster Analysis of Calibration Subset

After choosing initial seeds, we put them into a new SPSS file and then conducted K-means

Cluster Analysis. We got the final seeds centers and Figure 6 shows these centers. Besides,

Figure 7 shows the output of K-means Cluster Analysis of Calibration.

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Figure 6: Final Seeds Centers of K-means Cluster Analysis of Calibration Subset

Figure 7: Output of K-means Cluster Analysis of Calibration Subset

Hierarchical Cluster Analysis of Random Cases of Validation

We proceeded to then execute a K-means cluster analysis on the validation sample as well. We

randomly selected 10% of the validation sample to run Hierarchical Cluster Analysis on. Once

again we used Ward’s Method. Figure 8 shows the output of this Hierarchical Cluster Analysis

on the validation subset.

Figure 8: Output of Hierarchical Cluster Analysis on random cases of Validation

15

K-means Cluster Analysis of Validation

We used Z-score variables to do Hierarchical Cluster Analysis again and chose the same number

of initial seeds, seven, as we did in the earlier analysis. Once we determined the initial centers,

we were able to perform the K-means Cluster Analysis on the Validation sample. Figure 9 shows

the initial seeds centers of K-means Cluster Analysis of Validation.

Figure 9: Initial Seeds Centers of K-means Cluster Analysis of Validation

After we got this initial seeds, we put them into a new SPSS file and then conducted the

K-means Cluster Analysis. We found the final seeds centers (Figure 10) and output of K-means

Cluster Analysis of Validation (Figure 11).

Figure 10: Final Seeds Centers of K-means Cluster Analysis of Validation

Figure 11: Output of K-means Cluster Analysis of Validation

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Compare K-means Cluster Analysis Results of Calibration and Validation Samples

When we found the K-means Cluster Analysis results of Calibration and Validation sample, we

compared these two results and wanted to figure out whether or not the managerial implications

are similar. If managerial implications are similar, then we could continue to do K-means Cluster

Analysis for the whole data set. However, if managerial implications are completely different,

then we have to go back to check previous steps, find any mistakes, and repeat the process again.

Compare Outputs of Calibration and Validation Subsets

Fortunately, we found that these two outputs had satisfactory results. Figure 12 and Figure 13 are

our compared results. The clusters in the same color have the similar managerial implications.

Figure 12: Output of K-means Cluster Analysis of Calibration

Figure 13: Output of K-means Cluster Analysis of Validation

In detail, Cluster 1 from the Calibration sample and Cluster 5 from the Validation sample are the

same (marked in yellow). H&M can organize special events exclusively for those customers to

17

retain them and increase customer satisfaction because those customers have potentials to

generate more profits for H&M.

Cluster 2 from the Calibration sample and Cluster 6 from the Validation sample have similar

managerial implications (marked in purple). Customers in these two clusters can be considered

as the best customers of H&M who generate huge profits and shop at H&M frequently.

Therefore, H&M will invite them to join the loyalty program so that they can stick to H&M and

even influence other customers’ purchasing behaviors.

Cluster 3 from the Calibration sample and Cluster 2 from the Validation sample are the same

(marked in grey). Especially, these two clusters have large-scale population. Cluster 3 has mean

revenue of 126.70, mean profit of 66.81, mean months from last order of 63 and mean number of

order of 1. Also, there are 27938 customers in Cluster 3. Meanwhile, Cluster 2 has mean revenue

of 122.40, mean profit of 64.53, mean months from last order of 63 and mean number of order of

1. Even though the number of customers in Cluster 2 is less than Cluster 3, reaching to 18463,

the customer base is also huge enough for H&M. What H&M should do is to send emails and

catalogs to inform them of latest products or sales so that they are motivated to shop at H&M

again.

18

Cluster 4 from the Calibration sample and Cluster 3 from the Validation sample are the same

(marked in pink). Probably, they are new customers of H&M who shopped once but did not

make purchase recently at H&M. In order to attract those customers, H&M can promote the

membership cards to them and make them loyal customers.

H&M can use same market strategies to Cluster 5 from the Calibration sample and Cluster 1

from the Validation sample (marked in blue). For example, those customers should be rewarded

to spend more at H&M and they can get cash back on every dollar they spend at H&M.

For Cluster 6 from the Calibration sample and Cluster 4 from the Validation sample (marked in

green) who are all frequent shoppers of H&M, they will get coupons and special offers in their

birthday months.

Apparently, Cluster 7 from the Calibration sample and Cluster 7 from the Validation sample are

outliers in this project (marked in beige). Thus, we will ignore these two groups when comparing

the consistency of the Calibration and Validation samples since they have little impact on

H&M’s marketing strategies.

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Hierarchical Cluster Analysis of Population

After we got matched results from K-means Cluster Analysis of Calibration and Validation, we

continued the analysis process on the whole data set. We randomly selected 5% of the whole

population as sample to do Hierarchical Cluster Analysis by Ward’s method. We chose seven

clusters as our optimal output of Hierarchical Cluster Analysis on selected cases of Population

(Figure 14). Compared to six clusters, seven clusters gave us more specific customer

segmentation and each cluster has very different characteristics. Therefore, it is meaningful to

offer suggestions on each cluster so that H&M can better understand its customers and have

more effective marketing strategies.

Figure 14: Output of Hierarchical Cluster Analysis on Selected Cases of Population

K-means Cluster Analysis of Population

We used Z-score variables to do Hierarchical Cluster Analysis again and chose 7 as cluster

number to get initial seeds centers of our K-means Cluster Analysis of Population. Figure 15 is

the initial seeds centers of K-means Cluster Analysis of Population.

Figure 15: Initial Seeds of K-means Cluster Analysis of Population

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After we conducted K-means Cluster Analysis, we got the final seeds centers (Figure 16).

Besides, we had our final output of K-means Cluster Analysis of Population.

Figure 16: Final Seeds Centers of K-means Cluster Analysis of Population

Findings

When we finished our K-means Cluster Analysis on the whole data set, we concluded some key

findings of each clusters from our final result (Figure 17). Customers of H&M could be divided

into seven different clusters, and each cluster has its own unique characteristics and features in

terms of profitability, frequency and recency. In this part, we firstly interpreted the secrets of

numbers for each cluster and then concluded key findings for clusters with similar

characteristics.

Figure 17: Output of K-means Cluster Analysis of Population

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In detail, Cluster 1 has the largest scale of customers among all seven clusters. These 45760

customers create an average of 116.68 for revenue and 61.71 for profits. Besides, their mean

months from the last order are 63 months and placed only 1 order on average.

The size of customers is not very big in cluster 2 with the number of 5524 customers. These

customers create 422.57 revenue and 231.52 profits on average. Besides, their mean months

from the last order are 23 months and mean the number of orders is 4.

For customers in cluster 3, the number of these customers is 5806. These customers average

create 629.47 for revenue and 331.34 for profits. Besides, their mean months from last order are

40 months and mean the number of orders is 2.

Cluster 4 is the second largest group among seven clusters. It has 41382 customers who generate

the average amount of 124.76 in revenue and 68.97 in profits. Moreover, their mean months

from last order is 20 months and also placed only 1 order.

For customers in cluster 5, the number of these customers is 1321. These customers average

create 1498.04 for revenue and 789.45 for profit. Besides, their mean months from last order are

21 months and the mean number of orders is 6.

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For customers in cluster 6, the number of these customers is 102. These customers average create

5109.90 for revenue and 2535.65 for profit. Besides, their mean months from last order are 22

months and the mean number of orders is 9.

For Cluster 7, there are only 2 customers in this cluster who can be considered as outliers of this

project. These customers create the average amount of 38622.38 in revenue and 20278.65 in

profits. Besides, their mean months from last order are 37 months and the mean number of orders

is 4.

Based on the numerical features of each cluster, we can conclude key findings so that executives

of H&M can have a better understanding of what their customers like look. We believe that

customers in Cluster 1 and Cluster 4 are similar. These customers are most important customers

for H&M because they have huge potentials to purchase more products and generate more profits.

Also, these customers can be regarded as the largest customer base in this project. The only

difference between these two clusters is how long customers have not purchased at H&M since

their last order. These customers only placed 1 order, so perhaps these two clusters are new

customers of H&M. What H&M should do is to turn them into repeat customers. Another

explanation of this phenomenon is that these customers are not satisfied with H&M. So they

become one-time shoppers who never come back again. All in all, customers in Cluster 1 and

Cluster 4 are people with high potentials and these two clusters are most important clusters due

23

to its large scale (Figure 18). We find that Cluster 1 and Cluster 4 occupy about 87% of whole

customers and generate over 56% of total profit. Obviously, even H&M could get a small part of

these customers back, it would be a significant increase in its sales and profits.

Figure 18: The Proportion of The Number of Customers In Each Cluster

We also believe that Cluster 2 and Cluster 3 have features in common. Cluster 2 and Cluster 3

have the similar number of customers as well as have similar profit margin (53% for Cluster 2,

55% for Cluster 3). Since these customers placed order for several times, they actually are not

new customers of H&M. What H&M should do is to stimulate their purchase motivation so that

they can purchase more frequently and produce more profits.

Cluster146%

Cluster26%Cluster3

6%

Cluster441%

Cluster51%

Cluster60%

Cluster70%

24

Figure 19: The Proportion of Profits Generated By Each Cluster

Additionally, customers in Cluster 5 are also potential customers who have certain purchasing

power but they are not loyal customers of H&M. So, they tend to buy products from our

competitors and H&M. It is important for H&M to differentiate itself from competitors and

attract more potential customers to shop H&M more frequently.

We identify customers in Cluster 6 and Cluster 7 are highly profitable customers of H&M who

create huge profits and shop frequently at H&M. Apparently, those customers have huge

purchasing power and are highly satisfied customers of H&M. Also, they can be very loyal

customers as they purchase much more than customers in Cluster 1 to Cluster 4.

Cluster128%

Cluster213%

Cluster318%

Cluster428%

Cluster510%

Cluster63%

Cluster70%

25

Managerial Implications

Based on key findings from the final result of K-means Cluster Analysis of the whole data set,

we would like to offer some managerial implications for each cluster. As Cluster 1 and Cluster 4

have similar characteristics, we have the same managerial implication for these two clusters. In

detail, we suggest that H&M should reward these customers to come back and spend more since

they have high potentials in purchasing. For example, H&M can establish the membership

program and invite these customers to become a member of H&M. Also, H&M should provide

discounts, such as 10% off for the entire store, for their first purchase after joining the

membership program. H&M can send emails to their members to inform them of latest events in

H&M. Furthermore, H&M should focus on improving the quality of its customer service. It

should include some unique services, such as return period extension so as to increase customer

satisfaction.

For customers in Cluster 2 and Cluster 3, we believe that H&M should attract and motivate these

customers to become frequent shoppers. In other words, H&M could provide promotions for

these customers to purchase more products. There are three promotional ideas we designed for

H&M to reach these customers. Initially, H&M should take advantage of the cross-promotion

strategy to encourage customers to spend more. For example, if customers buy jeans and

sweatshirts together, they only have to pay $90 while the actual price for those two would be

$120. Secondly, H&M can offer a money-back policy for those customers. For example,

26

customers can get 5% of their purchase money back to their membership accounts. The money in

membership accounts could be used for next purchase in all H&M stores. Thirdly, H&M will

provide 10% off if customers purchase more than $200 per order.

For Cluster 5, H&M must transform those capricious customers into loyal customers who prefer

H&M to other fast-fashion companies. A good way to increase customer loyalty is to create

strong connections with those customers. For instance, H&M can form online communities on

different social media so that customers can share feedback and ideas on products and service.

Also, H&M can hire famous fashion bloggers to write reviews for its products, which create

more channels for these customers to follow H&M.

Customers in Cluster 6 and Cluster 7 are highly profitable customers of H&M. In order to retain

these best customers, H&M should firstly establish the loyalty program to keep these profitable

customers to stick to H&M. Additionally, we recommend that H&M should interact with these

customers frequently and notice them with the latest news, such as sending emails to inform

them of new arrivals, best selling products, so that these customers have more motivation to

continuously purchase at H&M. Most valuable customers deserve best services. Therefore,

H&M could provide personalized service to maintain high customer satisfaction and customer

engagement. Customers who have high customer engagement are willing to refer new customers

to H&M and influence others’ purchasing experience at H&M.

27

Post-Hoc Analysis

After we get the optimal clusters from the final K-means Cluster Analysis, we performed the

Crosstabs Analysis on payment method and channel through which customers placed orders. We

believe the Post-Hoc analysis can help H&M better understand customers’ preferences of

payment methods and channels for their purchase.

Channel Analysis

Based on the Figure 20, we find that the most popular channel through which customers tend to

place orders is web. If looking into the preferences of customers in different clusters, we notice

that group with different characteristics have different preferences for shopping channels. For

example, customers in Cluster 1 and Cluster 4 rely heavily on websites to make purchase. Since

we identify them as valuable customers with huge growth potentials, H&M can offer coupons on

their website to motivate these customers to spend more. Simultaneously, customers in Cluster 6

and 7 are highly profitable customers of H&M and they prefer to place orders on mobile Apps. It

is important for H&M to improve the design and functions of its mobile App so that customers

can have better shopping experiences via App. In addition, H&M can train its employees to

enhance service quality of live chat on website and mobile Apps to increase customer

satisfaction.

28

Figure 20: Shopping Channel Analysis By Using Crosstabs

Figure 21 shows that over 90% of total profits come from purchase on mobile Apps and websites.

Therefore, H&M should focus on the advertising and promotions on these two channels so as to

attract more customers to buy its products.

Figure 21: Total Profits Generated From Different Shopping Channels

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29

Payment Method Analysis

Based on Figure 22, we conclude that most customers use Visa, Master Card and American

Express when purchasing products at H&M. It is of great significance for H&M, a fast-fashion

company, to offer multiple payment methods to easier payment for its customers. Among these

three payment methods, almost half of profits come from Visa (Figure 23). So, we suggest that

H&M should cooperate with Visa Company to provide discounts for customers who use Visa to

pay. For example, customers can have $15 off to purchase on H&M’s website if they pay with

Visa.

Figure 22: Payment Method Analysis By Using Crosstabs

30

Figure 23: Total Profits Generated From Different Payment Methods

Conclusion

Through this research we were able to discover many interesting findings about H&M’s

customers. The first, and perhaps most important, finding about their customers is that just over

eighty seven thousand of them (about 88% of their total customers) have very similar buying

patterns. These customers have similar traits such as the average amount of revenue they brought

to H&M as well as the average months since last order is also over one and a half years. Perhaps

the most startling characteristic about this group is that the average number of orders for all of

these customers is one. This means that nearly 88% of H&M’s customers only order from H&M

once. This is a very telling sign of the average H&M customer. Even though these customers

shop at H&M so little, they still generate over ten million dollars of revenue for the company.

These customers have value and increasing the value of these customers will be a much more

cost effective way for H&M to increase sales rather than increasing their number of customers.

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31

H&M already has information on these current customers and now must use that information to

market their products at their target customers. The objective for these customers is clear and that

is to turn them into more frequent H&M shoppers. The first thing you notice about this group of

customers is the large size of it. Meaning that even if only a small percentage of customers

become repeat customers for H&M, for example 10%, this will still provide a huge sales increase

of nearly nine thousand more customers for H&M. To encourage these customers to become

repeat customers, H&M should develop a membership promotion for their customers. This

membership would be available to the customer for a set fee, then with their membership they

would be entitled to store discounts on the products they buy. A customer who joins the H&M

membership program will be much more likely to shop at their stores on a more frequent basis.

The second most influential group of customers for H&M is also the second largest group of

customers with over eleven thousand customers. These customers are responsible for almost six

million dollars in revenue and have had multiple orders from H&M before. H&M should reward

these customers for spending more than their other groups of customers. One way to reward

these customers for more spending is a deal where the percentage discounted from their order

correlates with the total amount of their order. For example, if a customer spends two hundred

and fifty dollars then they will receive twenty five percent off on their order or if they spend

three hundred dollars they receive thirty percent off. These deals would encourage more

32

spending at H&M every visit and could increase the average revenue for each of these

customers.

The final group of customers we determined to have potential for increased growth is defined as

follows. These customers bring an average revenue to H&M of almost fifteen hundred dollars

per customer, generating over four million dollars of revenue so far. This group has a high level

of disposable income and H&M must increase this group's number of orders and the total amount

of products in each order. This group appears to spend a lot of money on clothes shopping at

H&M. H&M must give these customers a reason to make more trips. By introducing these

customers to products H&M even before the customer gets to the store will accomplish two

things. First thing it will do is create customer value. So, the customer will be led to visit a store

or website because the customer will be searching for the specific item they liked. The second

thing it will do is that it will let the customer know that H&M has fresh merchandise that perhaps

the customer did not see on his or her last visit to H&M. Customers do not want to come back to

H&M if the merchandise is exactly the same as the last time they were there. Customers need

new reasons to motivate them to go to H&M again. By promoting H&M’s new products it will

encourage more store traffic and lead to increased sales as well.

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Limitations

Even though we reasonably segmented customers into groups through a series of cluster analyses

with different methods, there are still some limitations of this project. Initially, we clustered

customers of H&M based on the RFM Model. The four main variables, Total Revenue, Total

Profits, Months From Last Order and Number of Orders, do help us understand the past

purchasing behaviors of customers and the customer profitability. However, those variables are

unable to reflect the customers’ potentials in purchase and development growth in the future.

Obviously, past purchasing behavior is only a hint of possible trends but future customer

behavior will be influenced by many other elements, such as age, education, income, etc.

Moreover, the main objective of this project is to identify most valuable customers. However, it

is hard to come to the conclusion that we can find the best customers by simply looking into

customer profitability and their frequency of purchase. For example, Customer Engagement

Value (CEV) is also one important model to evaluate total customer value. Based on CEV,

valuable customers can be people who will refer new customers to H&M, who can influence

other existing customers on their purchasing behavior or customer satisfaction, and who are

willing to offer feedback for H&M to improve its services.

Additionally, variables in this data file are not enough for us to investigate customers at an

individual level but more at a collective level. We summarized key findings and offered

34

managerial implications for the optimal clusters we selected. But those findings and suggestions

may appear less accurate and effective due to lack of detailed demographic information of

customers. The implications are made for each group of customers who have shared

characteristics in profitability, frequency and recency. Nevertheless, customer in each group may

also have completely different backgrounds, preferences or needs. So, we should combine past

purchasing behavior and customers’ demographic information together to offer more precise and

practical suggestions to H&M.

Therefore, further research is needed to increase the accuracy of key findings and improve the

effectiveness of managerial implications.

Future Research

The limitations of this research present many opportunities for future research. Future research

should investigate more in depth buying habits of their customer base. If H&M was able to

determine what these customers shopped for, they could then tailor specific promotions to fall in

line with what that specific customer might purchase. For example, you could determine whether

your customer is a thirty four year old woman who shops for her two sons at H&M or if your

customer is a fifty two year old man who buys his own clothes from H&M. This research would

allow H&M to target current customers much more effectively which would enable them to

potentially increase the amount of sales per customer from their current customer base. From

35

H&M’s perspective, if you know that your customer is a thirty four year old mother of two then

you will be able to send her promotions and deals related to boys clothing as well as send her

reminders that H&M carries women's clothing too and perhaps she should shop there for herself

as well. There would be many applications of this future research that could be highly beneficial

for H&M to pursue.

Future research should also consider exploring ways to track how much time customers spend in

the stores. Depending on how customers shop they might spend only ten minutes inside the store

or perhaps even an hour. Knowing what type of shopper your customer is can be an effective tool

when trying to market your product to your target customers. Some customers go to stores for

long periods of time, however they do not purchase much and are using the trip to the store as

more of a social outing. These customers are described as wandering customers. Wandering

customers may be most of your in store traffic, however they may produce very little in total

sales for H&M. Another type of customer is described as a need-based customer. These

customers do not browse the store but instead have a detailed idea of what they want and do not

side track from this mission. Even though need-based customers may only spend a short amount

of time inside the store, they might contribute greatly to the overall sales (Hunter, 2010). Future

research in this area could reveal what type of shoppers H&M’s customers are and present new

opportunities for them to retain customers and increase total sales.

36

References

Hinshaw, M. (2013). “5 Segmentation Lessons From CVS.” CMO. Webcast on July 09, 2013.

Accessed on November 09, 2015 on

http://www.cmo.com/articles/2013/7/8/_5_segmentation_less.html

Hudson, Katura. (2015). "Physical Store Beats Online as Preferred Purchase Destination for U.S.

Shoppers, According to PwC." PwC. Webcast on February 09, 2015. Accessed on

November 8, 2015 on

http://www.pwc.com/us/en/press-releases/2015/2015-us-total-retail-press-release.html

Hunter, Mark. (2010). “The Five Types of Shoppers.” The Sales Hunter. Webcast on October 08,

2010. Accessed on November 05, 2015 on

http://thesaleshunter.com/resources/articles/retail-sales-trends/the-five-types-of-shoppers/

Lesonsky, Rieva. (2014). "Study Shows Consumers Prefer Shopping in a Store, Not

Online." Small Business Trends. Webcast on August 20, 2014. Accessed on November 08,

2015 on http://smallbiztrends.com/2014/08/consumers-prefer-shopping-in-a-store.html

Optimove. (2013). “Customer Segmentation.” Optimove Learning Center. Webcast on July 20,

2015. Accessed on November 09, 2015 on

http://www.optimove.com/learning-center/customer-segmentation

Rosenblum, Paula. (2015). "Fast Fashion Has Completely Disrupted Apparel Retail." Forbes.

Webcast on May 21, 2015. Accessed on November 8, 2015 on

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http://www.forbes.com/sites/paularosenblum/2015/05/21/fast-fashion-has-completely-disr

upted-apparel-retail/

Rouse, Margeret. (2005). “What Is RFM Analysis Definition.” Search Data Management.

Webcast on November 12, 2005. Accessed on November 02, 2015 on

http://searchdatamanagement.techtarget.com/definition/RFM-analysis.

Wikipedia. (2015). "H&M." Wikipedia. Webcast on November 08, 2015. Accessed on

November 08, 2015 on https://en.wikipedia.org/wiki/H%26M

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Appendix

Outputs For Hierarchical Cluster Analysis of Calibration Subset With Ward’s Method

Output With 6 Clusters

Output With 7 Clusters

Output With 8 Clusters

Outputs For Hierarchical Cluster Analysis of Calibration Subset With Furthest Neighbor

Output With 7 Clusters

39

Output With 8 Clusters

Output With 9 Clusters

Outputs For Hierarchical Cluster Analysis of Validation Subset With Ward’s Method

Output With 6 Clusters

Output With 7 Clusters

Output With 8 Clusters

40

Outputs For Hierarchical Cluster Analysis of Validation Subset With Furthest Neighbor

Output With 7 Clusters

Output With 8 Clusters

Output With 9 Clusters

Outputs For Hierarchical Cluster Analysis of Population With Ward’s Method

Output With 6 Clusters

Output With 7 Clusters

41

Output With 8 Clusters

Post-Hoc Analysis

Most Popular Payment Method In Each Cluster

Most Popular Shopping Channel In Each Cluster

01234

Cluster1

Cluster2

Cluster3

Cluster4Cluster5

Cluster6

Cluster71: American Express2: Paypal3: Master Card4: Visa

0

1

2

3Cluster1

Cluster2

Cluster3

Cluster4Cluster5

Cluster6

Cluster71: Mail2: Phone3: Web

42