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日期：2023-10-25 10:08

Assignment 2

Customer Segmentation

The Case

The young and thriving credit card company FISA has just entered the Australian market

approximately 2 years ago and is rapidly growing there. You are a marketing analyst at

FISA. In a meeting with your Head Analyst Alec, you wonder why FISA is using an

undifferentiated approach in managing their customer relationships in Australia. Surprisingly,

it turns out that no one at FISA has a good understanding of your current customer

segments. Since FISA hast just entered the market 2 years ago, no one has undertaken a

customer segmentation analysis yet and marketing efforts have not really been adapted to

different segments. Since competition is fierce, Alec and you decide that you should perform

a Cluster Analysis in order to derive implications for adapting your offerings better to

customer needs.

The Research Goal

Your research goal is to segment your current customers according to their credit card

usage patterns (i.e., what do they use their credit cards for?) while also considering further

potential to extend the relationship and avoiding increased risk of default. Ideally you want to

understand which prototypical usage patterns can be observed among your customers in

order to tailor credit card plans to the different segments’ needs and to provide suitable

information material for each customer segment.

Data Sample and Variables

You talk to the Director of Data Management and he makes sure that you receive a suitable

sample. The sample you receive contains the annual data of a randomly drawn sample of

Australian customers, which accounts for approximately 5% percent of FISA’s current

customer base in Australia.

The data set contains the following variables:

PURCHASES : Total amount of purchases made from account.

ONEOFFPURCHASES : Maximum purchase amount done in one-go; the whole amount of

purchases that are paid in one go.

INSTALLMENTSPURCHASES : Amount of purchase done in an installment plan; the whole

amount of purchases that are made in installments (i.e., you buy a fridge for $ 1,000 but pay

for it in ten installments of $100).

CASHADVANCE : Cash in advance given by the user; cash in advance is if you withdraw

cash from your credit card account.

PURCHASESFREQUENCY : How frequently the purchases are being made, score between

0 and 1 (1 = frequently purchased, 0 = not frequently purchased)

ONEOFFPURCHASESFREQUENCY : How frequently Purchases are happening in one-go,

score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)

PURCHASESINSTALLMENTSFREQUENCY : How frequently purchases in installments are

being done, score between 0 and 1 (1 = frequently purchased, 0 = not frequently

purchased)

CASHADVANCEFREQUENCY : How frequently the cash in advance being paid, score

between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)

CREDITLIMIT : Limit of Credit Card for user

PAYMENTS : Amount of Payment done by user; payments are the amounts the credit card

holder owed the credit card issuer that were actually paid back to the credit card issuer.

MINIMUM_PAYMENTS : Minimum amount of payments to be made by user; the cardholder

must pay a defined minimum portion of the amount owed by a due date, or may choose to

pay a higher amount. The credit card issuer charges interest on the unpaid balance if the

billed amount is not paid in full (typically at a much higher rate than most other forms of

debt). In addition, if the cardholder fails to make at least the minimum payment by the due

date, the issuer may impose a late fee or other penalties.

CREDIT_EXAUST: This variable was calculated by dividing PURCHASES by CREDITLIMIT.

It was suggested that this variable could be useful as it informs whether users’ credit card

purchases are capped. The rationale is that if users exhaust their credit card limit there might

be further potential to develop the relationship by enhancing their credit card limit.

PAYMENT_DIFFERENCE: This variable was calculated by subtracting

MINIMUM_PAYMENTS from PAYMENTS. The rationale is that customers who do not cover

their minimum payments pay much higher interests plus extra fees and therefore cumulate

debt, which might entail financial distress in the long-run. (Tipp: In your interpretation,

consider what low and negative values of this variable indicate!)

Data Preparation

You start mining the data. Please first load the data file into R. Then write a code to get an

overview over the data.

Since the data has been cleaned by a colleague, you decide not to delete any cases and

you do not replace any observations by NA.

Two-step Cluster Analysis

Next, you perform two-step cluster analysis. In the hierarchical cluster analysis, please use

the same methods to calculate distances between and within clusters you used in the tutorial

(i.e., euclidean, ward.D2). You use the following variables in your cluster analysis:

· ONEOFF_PURCHASES,

· INSTALLMENTS_PURCHASES,

· CASH_ADVANCE,

· ONEOFF_PURCHASES_FREQUENCY,

· PURCHASES_INSTALLMENTS_FREQUENCY,

· CASH_ADVANCE_FREQUENCY,

· CREDIT_EXAUST,

· PAYMENT_DIFFERENCE

As a general rule, please standardize all variables before you use them. Please produce the

clustering solution you find most appropriate. Once you have done that, please answer the

questions in the Moodle assignment.