question archive This data set consists of observations taken from account holders at a large financial services firm

This data set consists of observations taken from account holders at a large financial services firm

Subject:MathPrice: Bought3

This data set consists of observations taken from account holders at a large financial services firm. The accounts represent consumers of home equity lines of credit, automobile loans, and other short- to medium-term credit instruments. The BANK data set contains the original data in its raw form. The target variables relate to whether that account holder purchased a new product from the bank in the past year. The data sets contain more than one million rows and 24 columns. The “dataset” column further identifies whether the observation is to be used for training (60% of the observations), validation (20%), and testing (20%).

Client's Need:

· Your client would like to embark on a direct marketing campaign to increase bank revenues. To do that, they would like to understand what drives a customer to try new products/ services (B_TGT), understand what drives the total new sales (INT_TGT) as well as the total number of new products and services purchased by customers (CNT_TGT).  Thus, you have been given sample data from which you are to build, validate, and test your predictive models. Your client requires three models, one for each of the three variables they would like to predict to better help them target their direct marketing campaign. Your client would also like to see how your model performs against the test holdout dataset and will use your model’s performance on the test dataset for their long-term consulting relationships as well as to determine whose model will be deployed.

Data Dictionary:

Target Variables

B_TGT

New Product (Binary) (yes/no)

INT_TGT

New Sales (Interval)

CNT_TGT

Count Number New Products

 

Categorical Inputs

CAT_INPUT1

Account Activity Level

CAT_INPUT2

Customer Value Level

 

Interval Inputs

RFM1

Average Sales Past Three Years

RFM2

Average Sales Lifetime

RFM3

Average Sales Past Three Years Dir Promo Resp

RFM4

Last Product Purchase Amount

RFM5

Count Purchased Past 3 Years

RFM6

Count Purchased Lifetime

RFM7

Count Purchased Past 3 Years Dir Promo Resp

RFM8

Count Purchased Lifetime Dir Promo Resp

RFM9

Months Since Last Purchase

RFM10

Count Total Promos Past Year

RFM11

Count Direct Promos Past Year

RFM12

Customer Tenure

 

Demographic Inputs

DEMOG_AGE

Customer Age

DEMOG_GENF

Female Binary (yes/no)

DEMOG_GENM

Male Binary (yes/no)

DEMOG_HO

Homeowner Binary (yes/no)

DEMOG_HOMEVAL

Home Value

DEMOG_INC

Income

DEMOG_PR

Percentage retired in the area

 

Dataset  (NOTE:  you do not to use the test or validation dataset to train your models)

dataset

1 = training dataset

2 = validation dataset

3= test dataset

pur-new-sol

Purchase A New Answer

Custom new solution created by our subject matter experts

GET A QUOTE