Data Mining, Statistical Analysis, Clustering and segmentation, profiling, determining CLV (customer lifetime value), and validating the results and creating reports with executive summaries and provide recommendations for a given business scenario.

1. BigEast Bank: Case analysis
MKTG 5220
Team 3
Antonio Zuniga Cynthia
Jayarajan Palangat Reshma
Somraj Shilpa

2. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013
Case: BigEast Bank – Credit card Approval
Business Objective:
To help Ms. Garcia evaluate the impact of high credit card application denial rate on customer
retention for BigEast Bank. In addition to the importance of such an evaluation for BigEast
Bank, her performance on this assignment is also critical to the development of key relationships
between CYA Consulting (Ms. Garcia’s employer) and BigEast Bank’s CRM efforts.
Known Issues:
There are several issues with the case. Some of the key issues are:
There is no central warehouse that contains complete customer relationship data for
BigEast. Data is scattered across product groups. While this may be productive enough, it is
essential at times, such as while determining credit card approval, to have some overall key
indicators.
Data is available only for January 2001. This is not sufficient as customer profitability
might change for the better / worse towards the beginning of the year. January being the
beginning of the year might indicate employment loss / gain, college tuition fees, recent
vacation, and numerous other such possible factors, which might have an impact on account
balances and customer profitability. Drawing conclusions on only one month’s data is not
advisable.
Data is not available for customers who apply offline. Since this case is based in 2001
when not many customers were yet accustomed to online banking, analyzing data for customers
who applied through other options is also important.
Profitable customers might not need a credit card as much as those who are nonprofitable. To understand the risk posed it is essential to analyze both sets of customers.
However, data is not available for those who did not apply for a credit line.
Analysis:
Based on the contingency analysis of Declined by Defected customers, we observed two things:
1. Only 6.35% of the total bank customers defected after being declined a credit approval
(Fig.1).
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013

3. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013
2. However, among the 441 customers that defected, 93.65% were declined a credit card
approval (Fig. 2).
3. A t-test was conducted to confirm that the difference in means was statistically
significant (Fig. 3).
For defected customers, we ran a stepwise model to find that the first variable to enter the model
was not "Declined" but "ODFeeRev". Hence, the overdraft fee that BigEast is charging seems to
be one of the prime reasons for customers defecting.
Fig. A: Step History
The bank does not need to change its policies regarding credit denial since declining is not the
only cause for customer defection.
When we only look at the given Revenues for the declined customers, they seem to be profitable.
We understand that the given revenue is calculated based on Net interest revenue and Fee
revenue (includes over draft fees). Checking profitability including over draft fees does not seem
to be the appropriate, hence we excluded over draft fee from our profitability index. We also
recreated Fee revenue, Revenue and Profit without the Overdraft fees. We checked the mean
profit under each of these circumstances:
1. At first glance, the mean profit for non-declined customers was $28.44 as opposed to
declined customers who resulted in $49.52 (Fig. 4).
2. After eliminating the overdraft fees, there is a drastic change in trends. The mean profit
for non-declined customers now is $13.99 as opposed to declined customers who resulted
in $6.45 (Fig. 5).
3. A t-test was conducted to confirm that the difference in means was statistically
significant (Fig. 6).
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013

4. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013
Since overdraft fees occur as a result of bounced checks and it is not a positive indicator for
customer credibility, it might be risky for BigEast Bank to grant them a credit approval, despite
the fact that over draft fees would have a positive impact on the bank’s revenue.
Upon analysis of the distribution of profit less overdraft fee for declined customers, we notice
that the bank currently looks at existence of overdraft fee as a deciding factor to deny credit.
However, a deeper scrutiny of the data set led us to observe some outliers which make the bank’s
assumption questionable (Fig 7).
Fig. B: Extract from data set
For instance customer with ID 240 has a high balance, is extremely profitable even without the
overdraft fee, and costs relatively less for the bank when compared to other customers. However,
the bank seems to have declined his credit request primarily based on overdraft fee.
Additional data on the number of overdraft occurrence, how frequently and recently it occurred
needs to be acquired and analyzed before coming to a conclusion about a customer’s credit
worthiness.
Since the scope of the given data set only spans over the month of January, it is impossible to
know if the high balance was maintained at a steady level or if it was just a one-time occurrence.
In case a customer has consistently maintained a high account balance, the bank might be losing
out on a potentially profitable customer by denying credit.
To predict whether a customer will be with the bank in the future, we ran logistic regression.
Among the 5097 customers that have not defected, 3 of them are most likely to defect in the
future, which an almost negligible percentage.
Furthermore, an ROC curve was plotted and we observe that at probability of 8.45%, we can
achieve a balance of good sensitivity and specificity of 70.07% and 40.28% respectively. (Figs.
8-11)
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013

5. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013
Analysis Summary:
1. Declining a credit card application does not seem to customers defecting from BigEast
Bank.
2. The overdraft fee that BigEast is charging appears to be one of the primary reasons for
customers defecting.
3. Checking profitability including over draft fees does not lead to appropriate results.
4. Additional data from across the year and certain other metrics are required to calculate
credit card acceptance criteria for the outliers mentioned.
Recommendations:
Based on the known issues, and our analysis, we recommend the following to Ms. Garcia.
1. Only 6.35% customers who were denied a credit card defected. Also, “defected” seems to
be more dependent on “ODFeeRev” (the first to enter stepwise) than on “declined” (third
to enter stepwise). Hence the current credit card policy need not be changed.
2. We would suggest excluding overdraft fee from the fee revenue, revenue, and profit
while determining credit card acceptance criteria. From our analysis above, profits earned
differ significantly when over draft fee is included vs. excluded. Also, since a repeated
overdraft might indicate a risky customer, declining might be the right solution for such
cases. However, we currently do not have the data to check the number of overdrafts per
month.
3. It will be good for BigEast to have indicators across customer account data in different
branches to indicate what other accounts this customer has, if any. Though all details are
not required, it is advisable to have at least some key indicators.
4. Developing a secondary mechanism to check customer acceptance criteria for credit
cards might be beneficial. We suggest including steady balance checks and overall
profitability in this method as the key deciding factors. This way, BigEast Bank will not
lose those customers who might have overdraft fees but are valuable customers overall.
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013

6. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013
Course: MKTG 5220
Term: Fall 2013
Appendix:
Fig. 1
Fig. 2
Fig. 3
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)

7. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Fig. 4
Course: MKTG 5220
Term: Fall 2013
Course: MKTG 5220
Term: Fall 2013
Fig. 5
Fig. 6
Fig. 7
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)

8. Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Fig 8.
Fig 10.
Course: MKTG 5220
Term: Fall 2013
Fig 9.
Fig 11.
Team: 3 (Cynthia Antonio, Reshma Palangat, Shilpa Somraj)
Course: MKTG 5220
Term: Fall 2013