dadm-secb

8/2/2019 DADM-secB

1/9

Amit | Chanakya | Dipayan | Felix | Ravi | Satyam | Vijay (Group2-SecB)

2011

DATA AN ALYS I S &D E C I S I O N M A K I N G

PGP-I | 2010-2012Prof. Utpal Bhattacharya

PILGRIM BANK: CUSTOMERPROFITABILITY

ASSIGNMENT Submitted by:

Group 2 | Section B

Ravi Shankar Niranjan

Felix G

Chanakya Levaka

Satyam Gupta

Dipayan Roy

Amit Kumar Bhil

Raja Vijay S

SUBMITTED ON 11 T H MARCH, 2011INDIAN INSTITUTE OF MANAGEMENT INDORE

1 Data Analysis and Decision Making

8/2/2019 DADM-secB

2/9


2011

Executive Summary

Joe Greene, a new manager at Pilgrim Bank wants to better understand

profitability data for banks customers. He needs to answer the decision

problem of whether charging fees for online banking use is more profitable for

Pilgrim Bank than offering incentives to promote wider use of the online

channel. To begin solving the problem, Mr. Green first must address the

following research issues: how much more/less profit do online users generate;

is this difference significant, what are the measures of customer profitability,

what are the characteristic of the banks online users and profitable

customers, what are the costs of operating the online banking channel, and

finally what measures does the bank take to retain its most profitable

members.

Data Collection & Methodology

Greene collected the information about the research design in two-part. The first is an informal qualitative meeting with analyst Jane Raines. The

purpose of this research is to obtain any useful general knowledge on

measures of profitability, customer behavior, cost structure, profitability

management and their relations with each other. The second part is an in-

depth qualitative research based on statistical analyses on a database of

customer profits, online usage, demographics (age, income, geographic), and

tenure years.

The meeting with Jane Raines was an eye-opener for Greene as he

learned the customer profitability is given by:

(Balance in deposit Accounts) * (Net Interest Spread) + (Fees) +

(Interest from Loans) - (Cost to Serve)2 Data Analysis and Decision Making

8/2/2019 DADM-secB

3/9


2011

The total cost is further divided into variable costs and fixed costs.

Variable costs are lower for online transactions, but it has a higher fixed cost

structure. Mr. Green also finds that there is no clear correlation between

balance amounts and customer profitability. Lastly, Alan learns about the

initiatives Pilgrim Bank take to increase profitability and retain its most

profitable customers.

Data Analysis

Data analysis of the customer database begins with the testing for samplebias. Customers are sorted from descending profitability and they are charted

against percent cumulative profitability of the bank. Alan finds congruency

between his findings and the one results presented by Jane Raines, thus finds

reassurance that his sample is not biased. The results also confirm that

roughly 10% of the customers constitute 70% of Pilgrim Banks profits. He

then proceeds to summarize the statistics and finds that, on average, online

users are more profitable than non users ($116.36 versus $110.79.) Thesummary of the statistics also include standard deviation. The mean and

standard deviation is not calculated for geographic information since it is a

nominal scale.


8/2/2019 DADM-secB

4/9


2011

Plotting a frequency chart yields results where the majority of profits lie

in the -$100 to $200 range. By examining the samples, Green concludes that

only a little more than half of the customers are profitable, and more

importantly, a little over 20% of Pilgrims Bank customers contribute to 100%

of total profits. This shows the importance of retaining existing highly

profitable customers, but it also shows that there is plenty of room to make

non-profitable customers valuable.

Regression Analysis

Dorstamp provide Greene with 31,634 sample data to analyze. But some

of the attributes like age bucket and income bucket have incomplete

information. By cleaning up those samples, we get 22812 samples to workupon

1. Regression analysis with profit as the dependant variable and

online usage as the independent variable


8/2/2019 DADM-secB

5/9


2011

Regression StatisticsMultiple R 0.006R Square 0.000

Adjusted RSquare 0.000

Standard Error 282.857

Observations 22812.000

ANOVA

Df SS MS F Significance F Regression 1.000 64359.478 64359.478 0.804 0.370

Residual 22810.00

0

1824986620.

02080008.182

Total 22811.0001825050979.

498

Coefficients

StandardError t Stat P-value

Lower 95%

Intercept 126.522 2.007 63.033 0.000 122.5879Online 5.003 5.578 0.897 0.370 -5.930

Regression equation: Profit = 126.522 + 5.003(Online)

The adjusted r-squared value is very close to 0, meaning that the best

fit line does not accurately estimate the relationship between profit and online

usage. It also translates to a possibly poor regression model where important

variables are left out. The most significant information derived from the

regression is the p-value. The associated Ho is 2 = 0 and Ha is 2 0 in the

model y = 1 + 2x+ where y is profit, 1 is the intercept, 2 is the co-

efficient of online usage and is the standard error. The 0.370 p-value is

much greater than the significance level of 5%, thus we do not have enough

evidence to reject Ho. In other words, we do not know for sure if online usage

significantly affects profit.


8/2/2019 DADM-secB

6/9


2011

2. Regression analysis with profit as the dependant variable and

online usage, Age, Income, tenure, Location as the independent

variables

Regression StatisticsMultiple R 0.240R Square 0.057

Adjusted RSquare 0.057

Standard Error 274.644Observations 22812.000

ANOVA

df SS MS F Significance F

Regression 5.000 104804631.889 20960926.378277.8

87 0.000

Residual 22806.000 1720246347.609 75429.551

Total 22811.000 1825050979.498

Coefficients

StandardError t Stat

P-value

Lower 95%

Intercept -103.924 46.831 -2.219 0.026 -195.7179Online 18.242 5.509 3.311 0.001 7.4449Age 18.288 1.246 14.682 0.000 15.8479Inc 17.842 0.785 22.734 0.000 16.303

9Tenure 4.028 0.236 17.083 0.000 3.5669District 0.010 0.038 0.263 0.792 -0.065

y = 1 + 2(online usage)+ 3(Age)+ 4(Income)+ 5(Tenure)+

6(District)+ .

Y= -103.924+18.24(online usage) + 18.288 (Age) + 17.84

(Income) + 4.028 (Tenure) + 0.010 (District) + .

We accept Ha ( 2 0), and conclude online usage significantly affects

profit. Looking at the other p-values, it should also be noted that there is very

strong evidence that age and income and tenure are both related to profit.6 Data Analysis and Decision Making

8/2/2019 DADM-secB

7/9


2011

Not surprisingly, there does not seem to be any proof supporting geographic

region as a significant estimator of profit.

In trying to explain why the p-value becomes significant in the latter

regression, we need to first look at correlation values of online usage and the

demographic variable. The correlation matrix reveals that age is slightly

negatively correlated with online usage and income has an extremely small

positive correlation. This information seems reasonable since younger

generations are more computer savvy, and some income is required to have

computer and internet access, plus the education to be computer literate.

Thus, when these variables are factored in the regression, we obtain a truer

effect of online usage on profit, and consequently, enough evidence to reject

the null hypothesis.

To investigate if there is any systematic difference between consumers

with complete demographic records and those who dont, basic descriptive

statistics should first be reviewed. Profit and online usage averages are

127.18 and 0.1295 respectively for data with demographic records, and 72.95and 0.104 for incomplete records. There is a big difference of more than 50 in

the two means. Nonetheless, a hypothesis testing is required to see if the

difference is significant.

Sampl

es

Samples With Complete

Demographics

Samples With Incomplete

Information

Profit Online Usage Profit Online Usage

Mean 127.18 0.129 72.95 .104

Deviati

on282.87 0.335 243.01 0.304


8/2/2019 DADM-secB

8/9


2011

The results from a hypothetical testing conclude that the differences for both

profit and online usage are material.

This raises the question of external validity. The regression analysis

shows that there is a positive effect on profit from online users, but the

analysis omits the data without complete demographic records, thus we need

to excise caution when generalizing conclusions to the entire population.

]

Conclusion

To effectively implement the online banking promotion strategy, we

need to determine any significant characteristics of online users. The results

indicate that age and income are significant variables, while geographic region

is not. Since the age co-efficient is negative, Pilgrim Bank should focus more

efforts to younger customers to migrate them to the online channel. The

same should be done with customers in higher income brackets since theincome co-efficient is positive.

Although we have determined the statistical significance of online users,

the economical significance should also be reviewed. Offering incentives to do

online banking will increase the load of the online channel. The management

of Pilgrim Bank must carefully assess the estimated increase in online usage

after the promotions to see if existing infrastructure can support the extra

load. All costs associated with the increase of online bankers, including anynew infrastructure needed to be built, will need to be compared with the

expected increase in profit to determine the net value. Only then will the

economic value of this strategy be entirely addressed.

Retain the 20% profit-making customers


8/2/2019 DADM-secB

9/9


2011

Use incentives to improve the transaction costs of the other 80% and

make them profitable

Theres a small positive correlation between profit and online-usage, so

consider the economic benefits before employing online rebates

Rope in more young investors as age has a negative correlation to profit

due to the tech-savvy mind of the young users.

Rope in more income investors as more the income, more the profit (a

positive correlation) No correlation between profit and district-wise usage.

The more the customer retains the same bank (tenure), the more the

profitability, hence try to add incentives to retain customers


dadm-secb

Documents