pilgrim bank
Mel Lazo
ISDS 7540 – Marketing Analytics Module
Dr. Black
7 December 2012
Pilgrim Bank Case Analysis
Introduction and Defining the Relationships
The business analyst at Pilgrim Bank, Alan Green, was tasked with guiding the marketing team to a better understanding of customer profitability. With the help of his supervisor and the IT department, he has access to an extensive data set of over 30,000 customers, both old and new. Included are continuous variables for Profit, Tenure, and Satisfaction, categorical variables for Age buckets and
Income buckets, and indicator variables for Online customers and BillPay users. In particular, Pilgrim
Bank wants to understand the dynamics of profit between Online uses and non-users.
For both years, 1999 and 2000, the Online customers had the greatest average profit; also there was an increase in the number of Online customers:
_9Profit
_0Profit
Table 1 - Summary Statistics by Online Users
N
Sum
_9Online
0
23167
$3,317,624.00
1
3305
492,721.00
_0Online
0
21582
$3,364,815.77
1
4890
889,556.25
Mean
$143.20
149.08
$155.91
181.91
Though the difference was not significant in 1999, it did turn out to be significant in the year
2000, probably from the increase in users. In fact, the total Profit from Online customers almost doubled since 1999. On the surface, banking online does seem to drive profitability, or at least, should be reason to explore the relationship further.
Lazo 2
In order to explain the relationship between a customer and his/her profit, I have decided to view it in the frame of Regression Equations. If we find significant predictors of profitability, it could shed light on factors the marketing team should focus on to attempt and boost annual profit for the bank. For a very holistic, simplified view of this relationship, we can consider to following “regression” type relationship:
The idea of building this regression model is for the purpose of
ISDS 7540 – Marketing Analytics Module
Dr. Black
7 December 2012
Pilgrim Bank Case Analysis
Introduction and Defining the Relationships
The business analyst at Pilgrim Bank, Alan Green, was tasked with guiding the marketing team to a better understanding of customer profitability. With the help of his supervisor and the IT department, he has access to an extensive data set of over 30,000 customers, both old and new. Included are continuous variables for Profit, Tenure, and Satisfaction, categorical variables for Age buckets and
Income buckets, and indicator variables for Online customers and BillPay users. In particular, Pilgrim
Bank wants to understand the dynamics of profit between Online uses and non-users.
For both years, 1999 and 2000, the Online customers had the greatest average profit; also there was an increase in the number of Online customers:
_9Profit
_0Profit
Table 1 - Summary Statistics by Online Users
N
Sum
_9Online
0
23167
$3,317,624.00
1
3305
492,721.00
_0Online
0
21582
$3,364,815.77
1
4890
889,556.25
Mean
$143.20
149.08
$155.91
181.91
Though the difference was not significant in 1999, it did turn out to be significant in the year
2000, probably from the increase in users. In fact, the total Profit from Online customers almost doubled since 1999. On the surface, banking online does seem to drive profitability, or at least, should be reason to explore the relationship further.
Lazo 2
In order to explain the relationship between a customer and his/her profit, I have decided to view it in the frame of Regression Equations. If we find significant predictors of profitability, it could shed light on factors the marketing team should focus on to attempt and boost annual profit for the bank. For a very holistic, simplified view of this relationship, we can consider to following “regression” type relationship:
The idea of building this regression model is for the purpose of