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Saks Fifth Avenue Customer Behavior Report ——Based on Data Driven Analysis Group 8: Linhan zhang, Zhongyuan Lian, Huiruo Zhang, Yitian Chen

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Page 1: Saks Fifth Avenue

Saks Fifth Avenue Customer Behavior Report ——Based on Data Driven Analysis

Group 8: Linhan zhang, Zhongyuan Lian, Huiruo Zhang, Yitian Chen

Page 2: Saks Fifth Avenue

Executive Summary

Saks Fifth Avenue is a luxury department chain store which sells high-end brands both

online and offline. The objective of this research is to help Saks Fifth Avenue (hereafter Saks)

decrease customer’s return rate and cancel rate so as to improve customer’s profitability and

satisfaction. Also, we want to regain our old customers as well as increase their loyalty.

The original data in our research comes from the Customer Relationship Management

Database in Saks. This database records a wide range of historical sale information based on every

single order line, including over 137,000 orders from 100,000 customers. Each order line records

customer information in terms of customer ID number, and ZIP Code, and transaction-related

items such as order date, shipping date, revenue, cost etc.

Since we intend to segment our customers based on their return record, order cancel record,

total profits, and the time of their most recent order, we aggregate all records into a new data file

with individual level. Then, we choose four key factors as our variables which are profits, return

rate, cancel rate and time duration since last order date. We use K-means cluster analysis as our

major segmentation method. We divide whole data into calibration set and validation set, and

conduct K-means cluster analysis on each of them to make sure that we will not miss any

meaningful group of customers. Furthermore, different methods are conducted to explore our

research several times.

After outcomes of K-means cluster analysis match our expectation, we summarize and

interpret our key findings. There are 8 clusters which have meaningful features respectively.

Among them there are three groups that interest us most.

The first group makes up about 30% of all customers which generate high profits, and their

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return/cancel rate are extremely low. They have shortest time duration since last order date.

Obviously, they are the core customers for our company and we should take an action to retain

these customers in order to generate more profits. For example, we could offer them better services

and high quality products to increase customer loyalty and satisfaction.

The second one is a group of customers who generate relatively high profits to our

company, while their cancel rate are extremely high. These customers were able to generate a huge

profit for us. However, they are likely to cancel their orders due to some reasons. What makes

things worse is that they will cause additional costs for our company since we need to provide

special services when they return items. For these kinds of customers, we need to figure out their

true needs and the reasons of high cancel rate. They have huge financial potential if we can increase

their customer satisfaction. They could turn into the first group of customers and generate a huge

profit to the company.

The third group includes customers who have relatively lower profits, but their return rate

is very high. These customers are unsatisfied with our products or services so they keep returning

their items back. This group is a huge financial burden for Saks, so we have to decrease their return

rate by figuring out the reasons and taking any actions to increase their satisfaction.

Ultimately, we analyze the major reasons, which cause high rate/cancel rate. Based on our

previous analysis, we provide different managerial recommendations for each groups regarding

their significance and characteristics. These recommendations will serve to decrease customers’

return rate and cancel rate and eventually increase profits for Saks in the future.

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Table of Contents

1. Introduction ................................................................................................. 1

2. Background ................................................................................................. 2

3. Methodology and Analysis ............................................................................ 4

Definition of Clustering Analysis ................................................................... 5

Data Obtained and Used ................................................................................ 7

Variables selection and Explanation ............................................................... 7

Data Preparation ........................................................................................... 9

Calibration and Validation ........................................................................... 11

Clustering Settings ...................................................................................... 11

Measure Interval: Euclidean Distance.................................................... 12

Cluster Method: Ward’s Method ............................................................ 12

Standardization: Z scores ...................................................................... 13

Specific Operations ..................................................................................... 13

Findings from Clustering Results ................................................................. 18

4. Conclusion & Recommendations ................................................................. 20

Recommendations....................................................................................... 22

5. Limitations and Future Research ................................................................. 28

6. Appendix ................................................................................................... 30

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1. Introduction

Imagine you are a store owner selling limited-edition Prada’s purse which normally more

than $5000. Which kind of customer is more valuable for you? A customer who spends average

amount of money but never returns or cancels the order? Or a customer who spends huge amount

of money but returns or cancels most their orders at the end? This is a significant but tricky question

for every company, especially for Saks Fifth Avenue who has higher unit price.

It is said that customers are the most valuable equity for companies. As a luxury department

store, Saks sells products that are much more expensive, which means every single purchase means

a lot to the company in financial level. As a result, high return and cancel rate are more lethal for

Saks than regular department stores, for example, Macy’s. At the meantime, customer satisfaction

and loyalty that directly decide the company’s fate are also extremely significant for Saks. What

is more, it is also important for us to know how often a customer comes back and purchase.

According to our background research, the major managerial issue of Saks is to increase

profit by reducing return/cancel rate as well as regaining customers who have not purchased more

than one year. Through a series of analysis and comparison, we segment whole customers into 8

groups based on profit that they generate, the time duration since their last order date, return rate

and cancel rate. Each of group has their own meaningful features. Some of them generate the

highest profits while have not purchased for more than two years. Some of them generate high

profit while also have high return/cancel rate. We have discussed each cluster in detail in the

following report. We will elaborate each group’s features and provide managerial

recommendations.

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2. Background

Saks Fifth Avenue is a luxury department chain store that was founded in 1867. With such

a long history, Saks has established its own customer pool with large quantity of loyal customers.

Most customers go shopping in Saks for their nice service and latest fashion. There are a number

of world famous luxury brands in Saks including Gucci, Prada and FENDI. Staffs in Saks are very

professional and they usually offer customers thoughtful advices during the purchase process.

However, based on our research, Saks cannot generate as much revenue as it did a few

years ago. The competition between department stores is becoming more and more fierce. Main

competitors of Saks such as Bloomingdale’s and Neiman Marcus have made much pressure on

Saks by using price-off promotions. Even medium range department stores, saying Macy’s, and

online stores, like amazon.com, are competing with Saks. More competitions mean customers have

more choices. However, for Saks it leads to high return and cancel rate because once customers

find a lower price on amazon.com, the first action they will take is to cancel their orders on our

website. Moreover, high service costs make Saks more difficult to generate considerable profits.

As a result, Saks has faced much more challenges than it ever did and they need to find a way to

solve their own problems and keep growing.

In recent years, Saks has introduced their online stores and app to enlarge their market

share and attract more young customers. Online shopping is an easier and cheaper way to purchase

items for both customers and companies. However, it raises several issues as well. Since Saks sells

many apparels and makeups, it is impossible for customers to try them on before purchasing on

the website. Once customers find out that the product does not match their expectation, they will

return items back. Therefore, online shopping has increased return/cancel rate, which leads the

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company to spend additional costs. Our team will help Saks to figure out solutions to these issues

by reducing return/cancel rate as well as increasing customer satisfaction.

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3. Methodology and Analysis

The nature of retailing industry reflects the great importance of a deep understanding of

customers. Saks Fifth Avenue specializes in selling various high-end brands including Gucci,

Burberry, and Prada etc. In an effort to increase the company’s profit, we notice that working on

reducing return rate and cancel rate could play a crucial role in achieving this objective. Once a

customer returns a product or cancels an order, we actually lose not only the potential profit, but

also the previous effort we invested in acquiring this customer and in attracting her to visit our

locales. Therefore, we pay most of our attention in investigating returning and cancelling so as to

obtain actionable insights of which we can take advantage.

We intend to conduct cluster analysis to segment our historic customers in terms of their

return record, cancel order record, and total profit generated throughout their accumulated

consumptions in Saks Fifth Avenue, as well as the time of their most recent order. By conducting

cluster analysis, we discover separate groups that differ from each other in these aspects. Then we

compare them, identify their differences, and evaluate the possible reasons to these differences.

After understanding the characteristics and implications of these groups, we are able to come up

with corresponding recommendations that can improve their future performance.

The objective of this study is to identify different customer groups in terms of the above

four aspects and screen out specific contact information of the customers in each group for direct

marketing, eventually decreasing return rate and cancel rate, increasing customer satisfaction and

profit, and regaining old customers.

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Definition of Clustering Analysis

Originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology

by Zubin in 1938 and Robert Tryon in 1939, cluster analysis is the task of grouping a set of objects

in such a way that objects in the same group (called a cluster) are more similar (in some sense or

another) to each other than to those in other groups (clusters). [Reference] The outcome of cluster

analysis is to create a set of segments from a set of individual samples. Samples in the same

segment share more commonalities with each other than they do with samples from other

segments. In business, Cluster Analysis is a popular and frequently used method to realize market

segmentation, which is an important part of marketing planning.

Our research adopts two types of clustering methods: Hierarchical Clustering and K-means

Clustering. Hierarchical Clustering is useful when sample size is relatively small. Different

selections of clustering method and measure interval lead to different clustering results. Among

them we select the one that meets our expectation in terms of segment size, segment characters

and between-segment differences. Hierarchical clustering engenders an exploratory insight for

following K-means clustering analysis, which, together with hierarchical clustering, is capable of

big-size-sample segmentation. An effective and efficient cluster analysis on a big size data set

requires the combination of these two clustering methods. The Flowchart (see Fig. 3.1)

demonstrates the procedure of our cluster analysis.

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Figure 3.1 Analysis Flowchart

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Data Obtained and Used

The data analyzed in our research comes from the Customer Relationship Management

Database in Saks. This database records a wide range of historical sale information based on every

single order line. Each order line records customer information including customer ID number and

ZIP Code, and some key items recorded during a transaction such as order date, shipping date,

price, cost, etc. Different order lines may have the same order number, showing that these order

lines are from the same order. By the same token, different order numbers may have the same

customer number, meaning that the customer placed these orders in different times.

The size of the data we obtain is significant enough to produce representative insights.

We select the records start from 12/16/2004 to 09/17/2012, covering more than 226,000 order

line records. These records reflect over 137,000 orders from 100,000 customers.

Variables selection and Explanation

After understanding the descriptions of the variables in a record line, we determine four

clustering variables. They are: Total Profit, Return Rate, Cancel Rate, and Time Duration since

Last Order Date. These variables are not included in the current data set but can be calculated from

some of the existing variables. There are other variables we will use to describe the features and

attributes of our result segments, including Customer Number, Zip Code, etc. Each of the

clustering variables has its unique meaning and implication for us.

Total Profit

Profit is the most important indicator of a customer’s value. The higher the profit a

customer generates, the more imperative it is to maintain him/her. Generally, a company has finite

resources available for customer relationship management. If it invests equal resources in every

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customer in spite of their value, unavoidably, it will end up with high profitable customers not

served and maintained hospitably and with low profitable customers occupying much resources

but not creating enough profit in return. Therefore, while our final objective is to come up with

actionable recommendations to different customer groups, the Total Profit tells us which group

requires more attentions and hence, more resources.

Return Rate

Return Rate conveys important information about the consumption characteristics of a

customer. A high return rate has many implications. For example, an unclear or misleading product

description could result in customers’ complaints after receiving their packages, which always

leads to returning. A high return rate could also be attributed to customer’s particular taste. No

matter what leads to high return rate, the higher the return rate, the more profit we loss. While

increasing revenue is a pathway to greater profit, lowing unnecessary loss is also an effective one.

Saks dedicates in high-end niche market. The nature of high-end brands, generally speaking, have

smaller sale volume than lower tier brands, but they invest more to support their high-end brand

positioning and marketing. Saks’s well-qualified salesperson, high rental fee, and high advertising

budget imply a high operational cost. Once return or cancel happens, though most of products can

be resold, we waste a lot of costs. This is another reason we attach great importance to return rate

and cancel rate. With these considerations, we select return rate as one of our clustering

variables.

Cancel Rate

Cancel Rate refers to the ratio of one’s cancel order lines to total order lines. The same as

return rate, it has a strong relation with profit, but in a different way. While returning is a

customer’s decision after receiving the ordered products, cancelling means a customer changes her

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mind before that. We assume that return rate, relatively, comes down to the dissatisfaction of our

products and that it implies unsatisfying customer purchasing experiences such as chaotic

shopping guidance and poor customer services. By the same token as return, a decreased cancel

rate brings corresponding increased profit. Therefore, we put cancel rate in our variable list.

Time Duration since Last Order Date

Time duration since last order date is the time period between the date a customer placed

his last order and current date. We audit the data set and find there are a large number of customers

have been a long time not shopping in Saks again. The longer the duration, the higher the

possibility that the customer has already defected. This variable matters our decision making in

that marketing strategies and plans can be totally different towards new customers and old ones.

And so too is the resulting marketing effects. Relatively new customers are easier to contact and

attract because their contact information is up to date and because they have stronger connection

with our brand and products. On the other hand, customers who have more than three years not

coming back are of lesser value and priority due to the opposite reasons. Therefore, differentiating

new and old customers through clustering is meaningful.

Data Preparation

The records in the data set is ordered basing on every single order line. Since the objective

of our cluster analysis is the acquirement of information on an individual basis, we aggregate all

records into a format with customer number as key value. We then audit the aggregated date set

and determine the calculations to transform existing variables into the four clustering variables.

Table 3.1 offers a comprehensive explanation of the variables used and the ones we compute, in

the order of variables used throughout this analysis.

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Table 3.1 Overview of the Variables Used

Variables Explanation

Original variables

Customer Number A unique customer identification numeric string with 11 digit. Each customers has only one customer number.

ZIP Code 5 digit ZIP Code referring the location of a customer where he/she places an order.

Order Number A unique 9 digit numeric string referring to a specific order. One customer could have placed more than one orders with different order number.

Order Line Line number for each unique product in an order.

Order Date Date an order was placed

Quantity Quantity of a product in an order

Revenue The total price of an order line

Cost The total cost of an order line

Return Quantity The quantity of returned product

Computed Variables before aggregation

Profit

The profit of a single order line.

Calculation:

Profit = Revenue - Cost

Time Duration since Order Date (Month)

The time duration between today and the day the order was placed.

Calculation:

Time Duration Since Order Date = Date of Today – Order Date

Aggregated Variables (Aggregate by Customer Number)

Last ZIP Code The ZIP Code a customer places his/her last Order

Total Profit The summed Profit of a customer’s all order(s)

Time Duration since Last Order Date (Month)

The time duration between today and the day the customer’s last order was placed.

Total Quantity The total quantity of products of a customer has ever purchased including returned and cancelled quantity

Total Return Quantity The total quantity of products a customer has ever returned

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Total Cancel Quantity The total quantity of products a customer has ever cancelled

Computed Variables after aggregation

Return Rate

The return rate of a customer’s historical consumptions.

Calculation: Return Rate = Total Return Quantity / Total Quantity

Cancel Rate

The cancel rate of a customer’s historical consumptions.

Calculation: Cancel Rate = Total Cancel Quantity / Total Quantity

Calibration and Validation

After the four clustering variables are ready, we divide the data set into two subsets:

Calibration sample set (including 60% records of all) and Validation sample set (including 40%

records of all). The Calibration sample set is used to generate a promising division of

segmentations, while the validation sample set is used to verify whether that division is appropriate

and representative. Conducting clustering on both these sets ensures no meaningful segments are

missed. If true, then a clustering on the entire data set is conducted to further testify that division.

This verification mechanism is useful in guaranteeing the accuracy and the representativeness of

our analysis.

Clustering Settings

Randomly selecting 10% samples from calibration set, we formulate the approach to

hierarchical clustering. There are three crucial decisions: selection of cluster method, selection of

measure interval, and whether or not to standardize clustering variables.

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Measure Interval: Euclidean Distance

Measure Interval decides the calculation standard of the distance between two samples.

Two popular measure interval metrics are Squared Euclidean Distance and Euclidean Distance.

While the distance between two given samples is X according to Euclidean Distance algorithm, it

becomes X 2 in the case of Squared Euclidean Distance algorithm. Squared Euclidean Distance

amplifies the numeric value of a fixed distance, and thus the variance between samples is enlarged.

An enlarged variance alienate two samples. However, we prefer two similar samples to be

convergent rather than distant. Therefore, we select Euclidean Distance.

Cluster Method: Ward’s Method

Cluster Method decides the criterion that judges the distance between two clusters. Two

alternative methods are Furthest Neighbor and Ward’s method. Furthest Neighbor method

determine the longest distance between any two members of the two clusters as the distance

between the two clusters. This method is effective in identifying the small sample groups that are

conspicuously different from others, and correspondingly, the outcome clusters always happen to

have the majority of samples converge in a few large groups with the rest minority samples

assigned to much smaller groups. On the other hand, Ward’s method used sum of squared-errors

as the measure of distance and thus tends to produce groups of similar size.

Our analysis aims at identifying groups with different characters with respect to the four

clustering variables. The identified groups should be adequately sizable for actionable marketing

campaigns, which means that some of the segments identified by Furthest Neighbor method might

be too small to meet our expectations. On the contrary, Ward’s method provides groups with

relatively even sample distribution and is our choice.

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Standardization: Z scores

Standardization is required when clustering variables produce different weighted

influences on the result. Standardization transforms the variables into comparable forms so that

they have equal influences and significances. In our study, the four variables have obviously

different value ranges and variances. To guarantee an accurate analysis, we standardize them.

Specific Operations

Firstly, according to our purpose, we must create several new variables in order to complete

further analysis. Since customer’s profit is the key factor for our analysis, we use the following

equation to calculate a new variable named “Profit”.

Profit = Revenue – Cost1

Because we want to know how many months passed since each customer’s last order date, we use

“Date and Time Wizard” to create a new variable named “Time Duration”. Secondly, we use

“Recode into Same Variables” to replace the missing values in cancel quantity, return quantity and

quantity with zero. Thirdly, we aggregate the original data file into a new data file. The break

variable is “Customer Number”, and the aggregated variables are “zip code(last)”, “profit(sum)”,

“last order data(minimum)”, “cancel quantity(sum)”, “return quantity(sum)” and “quantity(sum)”.

Finally, because we want to know each customer’s return rate and cancel rate, we create two new

variables named ”Return Rate” and “Cancel Rate” by using following formulas:

Return rate = return quantity / quantity

1 * Actually the precise total profit of a customer should be calculated by the formula: Profit = (Revenue - Cost)*[1-(Return Quantity + Cancel Quantity)/Quantity] However, this calculation losses the ability to demonstrate a customer’s potential consumption power since his/her returned and cancelled profit are excluded. In our study, we want to investigate customer’s true consumption power and therefore, we use: Profit = Revenue – Cost.

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Cancel rate = cancel quantity / quantity

Standardize Decision Variables

We calculate Z-Scores for all decision variables including “Profit”, “Time Duration”,

“Return Rate” and “Cancel Rate”. Then we save them as new variables.

Split the Sample

We use “Select Cases” to split the whole data into a calibration sample which is about 60%

of all data and a validation sample which is about 40% of all data.

Hierarchical Clustering

Firstly, we choose 10% from the calibration sample as our small subset. Secondly, we run

Hierarchical Cluster Analysis to determine the number of clusters. We choose Ward’s method,

Euclidean distance and Z scores as our methods. According to the marked line, we choose 6 to 8

as the range of solutions. Based on the comparison of the Custom Tables, we choose 8 clusters as

the number of clusters because we can obtain most clear and meaningful managerial insights. The

detailed Custom Tables are attached on Appendixes. Thirdly, we conduct Hierarchical Cluster

Analysis to identify the cluster centers. Finally, we save the outcome in a new data file as initial

seeds which are attached on Appendixes (see Table 1 in the Appendix).

K-Means Cluster Analysis

We use the results of Hierarchical Cluster Analysis as initial seeds and conduct K-means

Cluster Analysis for the Calibration Sample. We choose 80 as maximum iterations and save cluster

membership. The valid cases are 60,025, and the missing cases are 54. The Initial Cluster Centers

(see Table 2 in the Appendix), Iteration History (see Table 3 in the Appendix), Final Cluster

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Centers (see Table 4 in the Appendix) and Number of Cases (see Table 5 in the Appendix) in each

Cluster are attached on Appendixes.

Exploring Results

In order to make sure we obtain optimal result, we use different random subsets and

different methods to conduct Hierarchical Cluster Analysis. When we use Furthest Neighbor and

Squared Euclidean distance as methods, the outcome is obviously inappropriate because most data

are concentrated on 2 clusters. Other 6 clusters have extremely small and meaningless counts.

More importantly, we cannot find the ideal group which has high return rate and high cancel rate.

Then we save these outcomes as initial seeds in order to run K-means Cluster Analysis. We

conduct K-means Cluster Analysis using different initial seeds. As expected, the results of

calibration sample, the results of validation sample, and all data results cannot match in major

clusters respectively.

Finalize Calibration Results

Based on our previous analysis, we finalize our decision by running K-means Cluster

Analysis on Calibration sample. The following is calibration results (Table 3.2).

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Validation Sample

Firstly, we conduct Hierarchical Cluster Analysis to identify the cluster centers. We still

use Ward’s method and Euclidean distance as our methods when we run Hierarchical

Cluster Analysis. Then we run K-means Cluster Analysis on Validation sample using new

initial seeds. The valid cases are 39,872, and the missing cases are 45. The Initial Cluster

Centers (see Table 6 in the Appendix), Iteration History (see Table 7 in the Appendix), Final

Cluster Centers (see Table 8 in the Appendix) and Number of Cases in each Cluster (see Table 9

in the Appendix) are attached on Appendixes. The following is validation results (see Table 3.3).

Cluster Number

Mean Count Mean Count Mean Count Mean Count

Profit 67.6 7057 916.98 1021 100.48 17777 168.81 2802

Return Rate 0.79 7057 3.41 1021 0.6 17777 24.94 2802

Cancel Rate 0.44 7057 5.43 1021 0.15 17777 16.47 2802

Time Duration 81 7057 26 1021 13 17777 35 2802

Cluster Number

Mean Count Mean Count Mean Count Mean Count

Profit 77.49 13597 67.79 2626 79.26 13894 107.68 1251

Return Rate 0.28 13597 98.04 2626 0.1 13894 0.2 1251

Cancel Rate 0.09 13597 0.02 2626 0.02 13894 97.09 1251

Time Duration 61 13597 41 2626 38 13894 46 1251

5 6 7 8

Table 3.2 Calibration Clustering Results

1 2 3 4

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Compare and Finalize

We compare the calibration results and validation results, and they are consistent.

Especially, the most managerial meaningful clusters which have high return rate and cancel rate

are consistent. Therefore, we conduct Hierarchical Cluster Analysis to identify the cluster centers.

We still use Ward’s method and Euclidean distance as our methods. Then we run K-means Cluster

Analysis on all data using new initial seeds. The valid cases are 99,897, and the missing

cases are 99. The Initial Cluster Centers (see Table 10 in the Appendix), Iteration History

(see Table 11 in the Appendix), Final Cluster Centers (see Table 12 in the Appendix) and

Number of Cases in each Cluster (see Table 13 in the Appendix) are attached on Appendixes.

The following is all data results (see Table 3.4).

Cluster Number

Mean Count Mean Count Mean Count Mean Count

Profit 86.26 11681 525.19 1604 1939 99 78.24 2395

Return Rate 0.89 11681 4.02 1604 2.91 99 85.14 2395

Cancel Rate 0.17 11681 2.96 1604 10.45 99 0.01 2395

Last Order Date 13 11681 25 1604 27 99 40 2395Cluster Number

Mean Count Mean Count Mean Count Mean Count

Profit 70.47 12078 73.82 10425 161.98 810 93.1 780

Return Rate 0.4 12078 0.6 10425 4.05 810 0 780

Cancel Rate 0.09 12078 0.07 10425 42.07 810 99.93 780

Time Duration 69 12078 41 10425 41 810 46 780

5 6 7 8

Table 3.3 Validation Clustering Results

1 2 3 4

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Findings from Clustering Results

The cluster 3 is one of the key customer clusters because this group of people contribute the

highest profit, which is $914.13, to us. Also, their return rate is 3.47% and their cancel rate is

5.28% which are relatively low. The average time duration since last order date is 26 months

which is the second shortest among all clusters.

The cluster 6 is also extremely important for us because the profit of this cluster is $100.16

which is relatively high. The last order time is the shortest among all clusters, and their return

rate and cancel rate are both under 1%. Moreover, the customer number of this cluster is the

largest and makes up nearly 30% of all data sample.

The cluster 4 is one of clusters which our team wants to highlight. The customers in this group

have second highest profit which is $172.52 and third shortest time duration since last order

date which is 35 months. However, their return rate and cancel rate are 24.78% and 16.64%

Cluster Number

Mean Count Mean Count Mean Count Mean Count

Profit 67.38 11778 78.05 23015 914.13 1717 172.52 4726

Return Rate 0.8 11778 0.11 23015 3.47 1717 24.78 4726

Cancel Rate 0.4 11778 0.02 23015 5.28 1717 16.64 4726

Last Order Date 81 11778 38 23015 26 1717 35 4726Cluster Number

Mean Count Mean Count Mean Count Mean Count

Profit 77.4 22793 100.16 29415 67.23 4371 103.31 2082

Return Rate 0.29 22793 0.59 29415 98.08 4371 0.17 2082

Cancel Rate 0.09 22793 0.14 29415 0.01 4371 97.41 2082

Time Duration 61 22793 13 29415 41 4371 46 2082

5 6 7 8

Table 3.4 All Data Clustering Results

1 2 3 4

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respectively. We can obtain huge financial return if we can lower their return rate and cancel

rate.

The cluster 7 is another group which we want to deeply analyze. The profit of this cluster is

$67.23, and their cancel rate is 0.01%, and last order period is 41. But, we are surprised that

their return rate is 98.08%. It means we have been spent large amount of money to serve this

group of customers and they have a huge negative influence on our company’s financial status.

We can largely cut down company’s cost by decreasing their cluster’s return rate.

The cluster 8 is surprising us as well. Their profit is $103.31 which is third highest among all

clusters. The return rate of this group is 0.17%, and the time duration since last order date is

46 months. However, the cancel rate of this cluster is 97.41%. From our perspective, this

group of customers has large profit potential if we can optimize our purchase process to lower

the cancel rate.

The cluster 2 and cluster 5 are also significant for our analysis because of several reasons.

These two groups have large customer number. Although the profit of these two groups is

both under $80, the return rate and cancel rate are extremely low which all under 0.3%.

Meanwhile we also need to notice that their time duration since last order date are more than

3 years, so we must figure out how to “arouse” those old customers.

Cluster 1 is relatively unimportant for this analysis. Although the profit is $67.38, the return

rate and cancel rate are low. These customers didn’t buy any product from our store more than

6 years. Thus, it is very hard to re-target this group of people.

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4. Conclusion & Recommendations

After all data analysis, we segment our customers in 8 groups. Our goal is to decrease return

rate and cancel rate so that we can improve our customers’ profitability and satisfaction. We also

want to regain our old customers and increase their loyalty. According to Table 3.4, we create the

pie chart (see Fig.4.1) that illustrates the percentages of different segments that make up total

profits.

Figure 4.1 Profit Distribution among Groups

From Fig. 4.1, we can clearly find that cluster 6, 2, 5, and 3 contribute the majority (79%) of

our total profits. These customers are our key customers in terms of total profits they generate.

According to Table 3.4, Customers in cluster 6 have the shortest time duration since the last

order date, which means these people now have the highest awareness of Saks among all customers

and have a stronger connection to us. We need to retain these customers for the long-term

development because they have higher probabilities to bring potential profits. In addition, the fact

that their return rate and cancel rate are both low shows that they currently are satisfied with our

Group 18%

Group 218%

Group 315%

Group48%

Group 517%

Group 629%

Group 73%

Group 82%

Profits Distribution

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products and services.

Cluster 2 has the lowest return rate and second lowest cancel rate. These customers are highly

satisfied with our products and services. These customers purchase products in Saks with fewer

hesitations. But they have not placed an order for more than 3 years, so it is important for us to

retarget them.

For the cluster 5, customers’ return rate and cancel rate are low, so their satisfaction is stable.

But they have not placed an order for more than 5 years. The mean profits of this group is relatively

low. Low return/cancel rates and low profits suggest that these customers are perhaps concerned

that the return/cancel process will bring them many troubles, so they are unwilling to buy the

product with a very high price. For these customers, we need to soothe their worries and convey

the information that Saks is the ideal store to buy high-end products. Meanwhile, we should update

their personal information and demands since they have not purchased products from us for more

than 5 years.

Since the mean of profits in cluster 3 is the highest, these customers are valuable for us. The

return rate and cancel rate are relatively low, but we still need to decrease return and cancel rate

indoor to increase their satisfaction as much as possible. This group’s time duration since last order

data is the second shortest, so we need to retain them and persuade them to set up a long-term

trustworthy relationship with us, helping us to generate more profits in the future.

Cluster 8 has the second-lowest return rate, so these people at least are satisfied with the

products that they have already bought. However, their cancel rate is extremely high which means

we lost most of our potential profits that they intended to purchase at the beginning. Meanwhile,

customers in cluster 8 have long time duration since their last order date, which means that they

are not willing to purchase products from our store since they had bad purchase experience before.

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For example, they may be disappointed with our website’s slow updating frequency or long

shipping time. Thus, we may need to regain these customers by setting up specific strategies to

target their needs more efficiently.

Although the profits that cluster 4 bring to us are very high, these customers’ net profits are

not as high as we see in the table because of their high return rate and cancel rate. This situation

indicates that customers are dissatisfied with our products or services. We should improve the

quality of our products and optimize our services to convince them to keep purchasing products

from Saks with a lower return rate and cancel rate. In this way, we can prevent the loss of potential

profits from these customers.

No matter in terms of total profits or the mean of profits, customers in cluster 7 generate low

profits for us. Their return rate is the highest, which means they almost return all the products that

they purchased before. Although our employees spend much time and effort serving them and

trying to meet their needs, these people return most of our products. So there must be something

wrong with our products or services. Since this group of customers has negative influences on our

financial situation right now, the spending on them will be more productive and efficient if we can

lower their return rate.

Recommendations

In order to provide appropriate recommendations for our customers based on their different

characteristics, we need to analyze some reasons for consumer’s return and cancelation behaviors.

The difference between these two behaviors is that returns happen when customers have already

purchased products and cancelations happen when people have not paid for the product yet.

As we all know, Saks Fifth Avenue is both a retailer and e-retailer. In our physical store,

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customers return items largely because of our staff who cannot provide the proper product

information or shopping advice for customers. On the other hand, an increasing amount of

customers are purchasing products on our official website or app. Thus some problems appear. For

example, when a customer purchases a pair of shoes on our website, he/she cannot look at or try

on these products in person. Many customers will be disappointed when they receive the packages

because the products do not match their expectations. Therefore, for these reasons, customers have

higher possibilities of returning their products.

In addition, more and more online retailers appear, which gives people multiple opportunities

to compare price. They can easily find a better price for the same product on other websites, and

once they find it, they will switch to other retailers. Our team has summarized several possible

reasons for return behaviors:

The product itself cannot satisfy our customers. For instance, if one customer bought a sweater

on our website and she was not satisfied with the material of the cloth, she might return this

sweater.

Another normal situation is that the product is damaged during the shipping process. Under

this situation, the customer definitely will return his/her product.

The description of the product is not consistent with the real product or the details of the

product are not provided very clearly. The higher the expectations customers have based on

the description on our website, the more disappointed they will be if the product doesn’t match

the description.

Shopping guides don’t offer clear explanations for our customers. When customers ask our

shopping guides for some advice or information in our physical stores, it is possible that our

shopping guides are unable to provide proper advice. Misleading information and advice will

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probably result in return behaviors.

Poor post-purchase service is another important factor that will cause people to return their

products. Saks is a high-end retailer that the prices of our products are relatively high. When

customers pay a premium for a product, they will have higher requirements for customer

services. If our post-purchase services cannot solve their problems in time and effectively, they

may return their products as well. For instance, when a customer calls our representative to

require an exchange, if we process this demand very slowly, the customer may run out of

patience and decide to return the product directly.

Our team has summarized several possible reasons for cancelation behaviors:

Customers make some mistakes when they place an order. For instance, they may find that

they chose the wrong size or wrong color when they checkout. Under this situation, they will

cancel the order and replace it with the right order, so this kind of cancelation will not

essentially influence our sales. However, we still need to provide a clearer website design and

better information to help customers place orders correctly. The other condition is that

customers fill in the wrong personal information when they checkout, so they need to cancel

the order and order the product again. This condition doesn’t have significant influences on

our profits because customers usually will place the order again.

Customers find a better offer on other websites. Since more and more online retailers appear,

many customers are used to comparing prices of the same products on different websites before

they checkout. Once they find a better offer on another on-line retailer, they will cancel the

previous order on our website.

Personal factors. It happens all the time that customers put items in their shopping carts when

they are stimulated by some external incentives, but they still hesitate to buy. Products from

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Saks usually have high prices, so a majority of customers need a longer time to consider. After

the impulse disappears, most customers will recover their rational thoughts and decide to

cancel the order.

Based on the previous analysis, Saks can prevent lots of customers’ return and cancelation

behaviors by taking practical actions. We hereby provide managerial recommendations based on

each group’s characteristics.

Regarding cluster 7, customers generate relatively low profits but their return rate is the

highest. Obviously, we need to decrease the return rate in order to encourage them spend

more on Saks. Firstly, we should improve the quality of the information on our website, such

as providing them more description about product’s details. In this way, customers could have

better understanding before they purchase products.

Secondly, Saks should use better shipping packaging in order to protect products from being

damaged by external forces. According to our research, we find that customers care more

about the packaging when they pay high prices for products. So, delicate packaging can not

only convey a good impression for our company but also match customers’ expectations.

Besides, due to their frequent return behaviors, this group’s profits may be relatively low, so

if we can decrease their return rate, their profits will increase somewhat.

Regarding cluster 8, this group generates relative high profits, but it also has the highest cancel

rate and has not purchased products from us for a long time. Saks should provide these

customers more straightforward information about products when they do shopping on our

website so as to reduce the probability of misleading them. In addition, Saks should highlight

“low stock” next to the quantity box in order to give customers a hint that this product may be

not available in a short time. In this way, we can largely reduce the time they hesitate and

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motivate them to pay for the order immediately.

Besides, we can remind customers the number of people who are watching this product at the

same time. Giving them an impression that this product is really popular can motivate them to

complete the transaction quickly. A lot of potential profits will be realized if this group’s

cancel rate can be decreased. Since we have the contact information of these customers, Saks

should send them greeting emails to show our care. By telling them the new changes about

our company and our new arrivals, we can trigger their interests again.

Regarding cluster 4, the mean profits of this group is the second highest, but their cancel rate

and return rate are relatively high among all groups. Firstly, we need to systematically train

our salespersons and shopping guides so that they have the ability to provide more appropriate

advice and information for our customers. Considering that this group has not placed orders

from us for more than two years, it is really helpful to retarget them by sending them

promotional emails seasonally, especially for holidays. In order to prevent the return behaviors,

we can also provide them discount coupons for their next purchases if they agree to keep their

products this time. If they insist to return, we can offer them a refund, like 5% of the original

price, to convince them not to return.

Regarding cluster 3, this group generates much higher profits than other groups. So these VIP

customer’s return and cancelation behaviors have more serious negative effects on our profits.

Saks should provide a personal shopping guide for each of them so that we can be aware of

and solve their problems in a timely manner and correctly. Saks will gain huge financial

returns if we can decrease these VIP customer’s return rate to below 1%.

Regarding cluster 2 and 5, the mean profits of these two groups are in the middle level, and

their cancel rate and return rate are extremely low. Based on our previous analysis, these

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customers may have some concerns that the returning and canceling process would bring them

inconvenience, so they are unwilling to purchase high-price items. For these customers, we

need to provide them a guarantee that if they are not satisfied our products, they have multiple

channels to contact us, and we will deal with their problems in 24 hours. We believe that they

will spend more money if Saks’ shopping process become more convenient.

Regarding cluster 6, the population in this group is the largest, which accounts for 30% of all

population. Their mean profits is relatively high. More importantly, the time duration since

their last order is the shortest. In this situation, we should send them promotional emails or

mailings more frequently to maintain their interests and to convince them to keep purchasing

from us. For example, we send them promotion coupons, like 10% discount. For these

customers, we also want them to generate more profits for our company because they have

potential profitability. Thus, we can try to offer them information about some high-end brands’

products through emails or mailings, in an effort to persuade these customers to buy higher

priced products.

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5. Limitations and Future Research

Though we successfully identify 8 groups with diverse characteristics, we understand our

analysis has its limitations.

We lack some supportive data to serve decision making and reinforce our

recommendations. Our study aims at identifying and investigating actionable customer groups

with unique features. For example, for a high return rate group, convincingly lowering its return

rate increases its profit. However, the current data is capable of identifying who are high

return/cancel rate customers, but does not enable us to investigate why they return and/or cancel

orders. As discussed in the previous sections, the reasons leading to high return/cancel rate are

diverse. Knowing the motivations and reasons of returning and cancelling enables us to improve

and optimize in avoidance of future similar situations. Unfortunately, we could not learn relevant

insights from the current data, or otherwise we would have been able to come up with more specific

recommendations for different segments.

For future research, we have to extend our data diversity, especially adding the data that

assists in learning returning and cancelling reasons. Saks has two major retail channels: online

stores and offline stores. To comprehensively analyze the entire customer pool anticipates an

improved data collection mechanism. For the online channel, one suggestion for future data

collection is to add a check box listing possible return/cancel reasons in the after-sale-service page.

The check box window appears when customers apply for a return or a cancel so that our database

could record and store what we need. By the same token, when customers return in offline stores,

our sale assistants should also learn their return reasons and record them into the sale system.

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The ultimate goal of analyzing customer information and consumption data is to obtain

financial returns, increased profit for instance. We note that there are various ways to improve

profit. While this study aims at investigating return rate and cancel rate, future research could focus

on improving profit through increasing revenue.

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6. Appendix

Cluster Number

M C M C M C M C M C M C

Last Order Date 32 627 76 1039 18 2302 43 254 53 1684 48 135

Profit 308 627 50 1039 85 2302 66 254 88 1684 75 135

Return Rate 18 627 0 1039 0 2302 100 254 0.05 1684 0 135

Cancel Rate 11 627 0 1039 0 2302 0 254 0.11 1684 100 135

Cluster Number

M C M C M C M C M C M C M C

Last Order Date 28 223 76 1039 18 2302 34 404 43 254 53 1684 48 135

Profit 611 223 50 1039 85 2302 141 404 66 254 88 1684 75 135

Return Rate 4 223 0 1039 0 2302 26 404 100 254 0.05 1684 0 135

Cancel Rate 6.6 223 0 1039 0 2302 14 404 0 254 0.11 1684 100 135

Cluster Number

M C M C M C M C M C M C M C M C

Last Order Date 28 223 76 1039 27 994 34 404 12 1308 43 254 53 1684 48 135

Profit 611 223 50 1039 58 994 141 404 106 1308 65.91 254 88 1684 75 135

Return Rate 4.06 223 0 1039 0 994 26 404 0.01 1308 100 254 0.05 1684 0 135

Cancel Rate 6.6 223 0 1039 0 994 14 404 0 1308 0 254 0.11 1684 100 135

7 8

1 2 3 4 5 6 7 8

1 2 3 4 5 6

Table 1. Hierarchical Cluster Analysis on 10% Calibration Sample

1 2 3 4 5 6 7 8

Cluster Number 1 2 3 4 5 6 7 8

Zscore (Profit) -0.49722 1.41462 -0.56187 -0.26828 -1.16908 0.07006 0.50463 0.29141

Zscore (Return Rate)

Zscore (Cancel Rate)

-0.27425 -0.27678

0.23681 -0.1999 -0.1999 0.70025 -0.1999 -0.1999 -0.19288 6.4128

-0.08598 -0.27678 -0.27678 0.94381 -0.27648 4.42492

Table 2. Initial Cluster Centers for Calibration Sample

2.86974 -0.29206 -0.24894 0.22321 0.02253 -0.20202 -0.07664 -0.15222Zscore (Time Durtion)

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1 2 3 4 5 6 7 81 1.299 0.88 0.303 0.49 1.333 0.338 0.324 0.4552 0.104 0.388 0.401 0.158 0.26 0.004 0.108 0.0313 0.029 0.412 0.151 0.065 0.064 0.003 0.141 04 0.037 0.322 0.083 0.041 0.009 0.001 0.131 0.025 0.029 0.254 0.08 0.033 0.018 0 0.105 06 0.011 0.207 0.029 0.031 0.03 0 0.055 0.0067 0.004 0.15 0.018 0.027 0.023 0 0.034 08 0.005 0.132 0.012 0.022 0.016 0 0.02 0.0029 0.004 0.116 0.006 0.018 0.011 0.001 0.009 0.00210 0.007 0.082 0.006 0.014 0.007 0.001 0.007 0.00611 0.006 0.057 0.003 0.012 0.006 0 0.005 012 0.006 0.055 0.003 0.005 0.004 0 0.003 013 0.007 0.046 0.002 0.006 0.004 0 0.001 014 0.009 0.033 0.001 0.002 0.006 0 0 015 0.016 0.026 0.001 0.003 0.01 0 0.001 016 0.014 0.026 0.001 0.005 0.011 0 0.003 017 0.006 0.031 0.001 0.005 0.007 0 0.004 018 0.004 0.021 0.001 0.005 0.003 0 0.002 019 0.001 0.019 0.001 0.003 0.001 0 0.001 020 0.001 0.015 0 0.003 0.001 0.001 0 021 0 0.015 0 0.001 0.001 0 4.68E-05 022 0.001 0.017 0.001 0.003 8.96E-05 0 4.97E-05 023 0 0.014 0 0.002 0 0 0 024 0 0.007 0 0.002 0 0 0 025 0 0.002 0 0.003 0 0 5.31E-05 026 0 0 0 0.001 0 0 0 027 0 0 3.02E-05 0 0 0 3.86E-05 028 0 0 0 0 0 0 0 0

Table 3. Iteration History for Calibration Sample

IterationChange in Cluster Centers

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Cluster Number 1 2 3 4 5 6 7 8

-0.1987 -0.1987 6.2202

Zscore (Time Duration)

Zscore (Profit)

Zscore (Return Rate)

Zscore (Cancel Rate) -0.1706 0.15895 -0.1903 0.88898 -0.1938

-0.1915 -0.1269 0.03311

-0.2396 -0.1166 -0.2484 0.89573 -0.2634 4.33294 -0.2722 -0.2672

-0.1925 4.58926 -0.0074 0.37726 -0.1368

Table 4. Table Final Cluster Centers for Calibration Sample

1.60161 -0.6019 -1.1245 -0.2261 0.8055 0.00026 -0.1061 0.2118

1 7057

2 1021

3 17777

4 2802

5 13597

6 2626

7 13894

8 1251

Valid

Missing

Table5. Number of Cases in each Cluster for Calibration Sample

Cluster

60025

54

Cluster Number 1 2 3 4 5 6 7 8

Zscore(Profit) 0.05353 0.9094 1.63409 0.00658 -0.50291 -1.07304 -0.23003 0.15992

2.46949 6.4128

-0.27678 0.00582 -0.27678

Zscore (Cancel Rate) -0.1999 -0.18996 -0.1999 -0.1999 -0.19252 -0.1999

Zscore (Return Rate) -0.27678 -0.27678 -0.27678 4.42075 0.56365

Table 6. Initial Cluster Centers for Validation Sample

Zscore (Time Duration) -0.21249 -0.09599 -0.24407 -0.20105 0.62656 -0.19915 0.30391 -0.02783

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Iteration 1 2 3 4 5 6 7 91 0.405 0.206 1.192 0.294 0.859 0.543 0.579 0.2582 0.433 0.091 0.721 0.17 0.122 0.387 0.085 0.0633 0.129 0.134 0.59 0.199 0.062 0.094 0.019 0.0034 0.024 0.212 0.577 0.136 0.016 0.025 0.007 0.0185 0.021 0.214 0.534 0.071 0.009 0.017 0.018 06 0.012 0.189 0.553 0.024 0.01 0.017 0.011 07 0.003 0.135 0.431 0.009 0.006 0.014 0.006 08 0.004 0.102 0.404 0.003 0.002 0.009 0.003 09 0.006 0.099 0.412 0.002 0.001 0.007 0.004 010 0.005 0.098 0.498 0.001 0.001 0.006 0.007 011 0.005 0.078 0.383 0.002 0.001 0.004 0.006 012 0.004 0.068 0.286 0.002 0 0.004 0.005 013 0.005 0.065 0.26 0.002 0.001 0.003 0.008 014 0.004 0.057 0.225 0 0 0.003 0.003 015 0.004 0.051 0.177 0.001 0.001 0.002 0.006 016 0.003 0.042 0.19 0 0 0.001 0.002 017 0.003 0.039 0.178 0 0 0.002 0 018 0.003 0.033 0.191 0 0 0.001 0 019 0.002 0.034 0.175 0.001 0 0.001 0.002 020 0.001 0.033 0.257 0 0.001 0.001 0.004 021 0.002 0.032 0.21 0 0.001 0 0.002 022 0.002 0.033 0.265 0.001 0 0.001 0.008 023 0.001 0.023 0.079 0.001 0 0.001 0.002 024 0.001 0.015 0.041 0 0 0.001 0.007 025 0.001 0.012 0.041 0 0 0.001 0 026 0.001 0.007 0 0 0 0 0 027 0 0.002 0 0 0 0 0 028 0 0.001 0 0 0 0 0 029 0 0 0 0 0 0 0 0

Table7. Iteration History for Validation Sample

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Cluster Number 1 2 3 4 5 6 7 9

6.40827

-0.08657 -0.27678

Zscore (CancelRate) -0.18893 -0.00429 0.49115 -0.19921 -0.19388 -0.19533 2.58208

-0.15752 0.33883 -0.04898

Zscore (ReturnRate) -0.23482 -0.08775 -0.13979 3.7263 -0.25802 -0.24841

Zscore (Profit_sum) -0.08745 2.38355 10.34737 -0.13262 -0.17638

Table8. Final Cluster Centers for Validation Sample

Zscore (Time Duration) -1.10393 -0.62541 -0.55918 -0.03315 1.15412 0.01119 0.00878 0.20639

1 116812 16043 994 23955 120786 104257 8108 09 780

ValidMissing

Table 9. Number of Cases in each Cluster for Validation

Cluster

3987245

Cluster Number 1 2 3 4 5 6 7 8

Zscore (Time Duration)

Zscore (Profit)

Zscore (Return Rate)

Zscore (Cancel Rate)

Table10. Initial Cluster Centers for All Data

3.39681 -0.066 -0.239 0.26632 0.67681 -0.2987 -0.2043 -0.1314

-0.6108 -1.0087 1.36108 -0.2214 0.48402 0.24312 0.04853 0.2496

-0.1109 -0.2767 -0.2768 0.99106 -0.2506 -0.2768 4.42492 -0.2768

0.19242 -0.1999 -0.1999 0.75803 -0.1211 -0.1999 -0.1999 6.4128

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1 2 3 4 5 6 7 81 1.463 0.594 1.082 0.447 0.729 0.614 0.325 0.4142 0.308 0.183 0.411 0.139 0.103 0.195 0.009 0.0353 0.082 0.109 0.349 0.068 0.075 0.077 0.001 0.0114 0.042 0.063 0.285 0.051 0.047 0.03 0.001 0.0085 0.021 0.03 0.233 0.04 0.023 0.017 0 0.0056 0.011 0.021 0.184 0.03 0.017 0.01 0.001 07 0.006 0.015 0.151 0.02 0.013 0.008 0.001 08 0.004 0.006 0.121 0.01 0.004 0.007 0 09 0.001 0.002 0.109 0.01 0.002 0.005 0 0.008

10 0 0.001 0.087 0.012 0.001 0.003 0 0.00311 0 0.001 0.051 0.009 0 0.002 0 0.00212 0 0 0.048 0.009 0 0.002 0.001 0.00113 0 0 0.036 0.006 0.001 0.001 0 014 0 0 0.028 0.006 0 0.001 0 0.00215 0 0 0.022 0.005 0 0.001 0 016 0 5.54E-05 0.019 0.006 0 0 0 017 0.001 0 0.017 0.005 0 0 0 018 0 9.84E-05 0.018 0.004 9.04E-05 0.001 0 019 0 0 0.014 0.002 7.18E-05 0.001 0 020 0 7.04E-05 0.011 0.002 0 0 0 021 0 0 0.005 0.001 0 0 0 022 0 3.14E-05 0.01 0.002 0 0 0 023 0 0 0.013 0.002 0 0 0 024 0 0 0.01 0.003 0 0 0 025 0 0 0.004 0.003 6.85E-05 0 0 026 0 0 0.003 0.001 6.85E-05 8.99E-05 0 027 0 0 0.004 0.001 6.85E-05 9.97E-05 0 028 0 0 0.004 0.001 9.62E-05 0 0 029 0 0 0.005 0.002 9.80E-05 0 0 030 0 4.69E-05 0.004 0.002 0 7.14E-05 0 031 0 4.68E-05 0.006 0.002 0 6.64E-05 0 032 0 0 0.004 0.001 0 9.27E-05 0 033 0 0 0.004 0 0 0 0 034 0 0 0.003 0 0 8.59E-05 0 035 0 0 0 0 0 0 0 0

Table 11. Iteration History for All DataIteration Change in Cluster Centers

Cluster Number 1 2 3 4 5 6 7 8

6.2413

4.33483 -0.2688

Zscore (Cancel Rate) -0.1736 -0.1987 0.14937 0.9007 -0.1939 -0.1904 -0.1992

-0.0092 -0.1946 0.00853

Zscore (Return Rate) -0.2394 -0.2717 -0.1138 0.88815 -0.2632 -0.2489

Zscore (Profit) -0.1938 -0.1337 4.57319 0.39816 -0.1373

Table 12. Final Cluster Centers for All Data

Zscore (Time Duration) 1.60055 -0.1037 -0.6051 -0.2386 0.80404 -1.1248 0.00654 0.20816

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1 117782 230153 17174 47265 227936 294157 43718 2082

ValidMissing

Table 13. Number of Cases in each Cluster for All Data

Cluster

9989799