MKTG3509 Temple University Logistic Regression Analysis Homework 5: Logistic Regression Analysis Homework 5: Logistic Regression Analysis Criteria Rating

MKTG3509 Temple University Logistic Regression Analysis Homework 5: Logistic Regression Analysis

Homework 5: Logistic Regression Analysis

Criteria Ratings Pts

This criterion is linked to a Learning OutcomeQuestion 1: Model Comparison 1

20.0 pts

Full Marks

0.0 pts

No Marks

20.0 pts

This criterion is linked to a Learning OutcomeQuestion 1: Model Comparison 2

20.0 pts

Full Marks

0.0 pts

No Marks

20.0 pts

This criterion is linked to a Learning OutcomeQuestion 2

10.0 pts

Full Marks

0.0 pts

No Marks

10.0 pts

This criterion is linked to a Learning OutcomeQuestion 3

10.0 pts

Full Marks

0.0 pts

No Marks

10.0 pts

This criterion is linked to a Learning OutcomeQuestion 4

30.0 pts

Full Marks

0.0 pts

No Marks

30.0 pts

This criterion is linked to a Learning OutcomeQuestion 5

10.0 pts

Full Marks

0.0 pts

No Marks

10.0 pts

Total Points: 100.0 Data
Lift Per Deci
Volume Number of Number of Cumulativ Cumulative
Decile
Cases
Responses e Cases
Responses
1
2
3
4
5
6
7
8
9
10
50000
4522
9.04%
Decile
Response
Rate
(a)
Lift Per Decile
Overall
Response
Rate (b)
Cumulative Lift
Lift
Index
(a/b)
Cum
Overall Cumulative
Respons
Response Lift Index
e Rate
Rate (e)
(d/e)
(d)
Gains
Cumulative Gain
Responses Index
Profitability Analysis
Volume
Decile
1
2
3
4
5
6
7
8
9
10
Contrib
ution
Margin
Fixed
Costs
Operating
Profit
lysis
Cum
Operating
Profit
Data
Predicted
Number of Number of Cumulativ Cumulative
Probability
Cases
Responses e Cases
Responses
Decile
1
2
3
4
5
6
7
8
9
10
50000
4522
9.04%
Lift Per Decile
Decile
Overall
Response Respons
Rate
e Rate
(a)
(b)
Lift
Index
(a/b)
Cumulative Lift
Cum
Overall
Cumulative
Respons Respons
Lift Index
e Rate
e Rate
(d/e)
(d)
(e)
Gains
Cumulative Responses
Profitability Analysis
Gain
Index
Predicted
Contributio
Probabilit
n Margin
y Decile
1
2
3
4
5
6
7
8
9
10
Fixed
Costs
ility Analysis
Cum
Operating
Operating
Profit
Profit
Course Number:
Semester:
MKTG 3509: Customer Data Analytics
Summer 1, 2019
Homework 5: Using Logistic Regression Models / Evaluation of Multiple Models
Objectives
The objectives of this final exercise are to: 1) practice building and evaluating different models to identify
which customers in our database should receive a future marketing communication from our company. You
will create a univariate segmentation, and a predictive model using logistic regression analysis, 2) evaluate the
lift and gains of each approach and 3) evaluate the profitability of each of the approaches.
Assignment
As a direct marketer of specialty books, the BookBinders Book Club has achieved steady growth in their
customer base. Yet while sales have grown steadily, profits began falling when the database got larger and
when the company diversified its book selection and increased the number of offers sent to customers. The
falling profits have led Dave Lawton, BookBinders’ marketing director, to experiment with different
database marketing approaches in order improve BookBinder’s mailing yields and profits.
Dave began a series of live market tests, each involving a random sample of customers from the database. An
offer for the current book selection is sent to the sample and then the sample customers’ responses, either
purchase or no purchase, are recorded and used to calibrate a response model for the current offering. The
response model’s results are then used to “score” the remaining customers in the database and select
customers from the full customer database for the ‘rollout’ mailing campaign.
Dave’s team continues to debate which of two methods will best identify potential targets for its new book,
‘The Art History of Florence’. The team is discussing two possible approaches to identify and target
customers for ‘The Art History of Florence’:
1. Volume Segmentation (univariate segmentation using Totdol as the segmentation variable), or
2. Predictive Modeling using Logistic Regression Analysis
Dave has a dataset containing the responses of a random sample of 50,000 customers to “The Art History of
Florence.” He is eager to assess the potential value of each of three approaches as methods for predicting
customer response and has asked you to complete the following analyses.
Note: In order to complete this analysis you will need the two attached files: 1) BBB.sav, SPSS data file
which contains all of the data needed to create volume deciles and to conduct the logistic regression
analysis, and 2) Homework 5 Lift Gains Profit Analysis.xlsx, which contains 2 pre-formatted worksheets to
assist you in your assessment of lift, gains and profitability. You will need to copy and paste data from SPSS
output into Excel to complete your assessment.
Due Date:
Thursday, June 20 at 7:00 p.m.
MKTG 3509, Summer 1, 2019
1
Dr. Christopher Monos
During class we will develop two segmentation models:
1) We will segment customers based on volume.
2) We will identify the best logistic regression model and then use the predicted probabilities from the
model to segment customers.
In order to complete the assignment, you will need to:
1) Provide answers to the questions on the following pages of this document, and
2) Conduct a lift/gains analysis in Excel.
You need to upload both this document and the Excel file which has been provided.
Attention: This assignment requires you to perform another lift/gains/profit analysis. If needed, review the
three videos in Fox Video Vault: 1) Evaluating Models: Lift Analysis, 2) Evaluating Models: Gains Analysis,
and 3) Evaluating Models: Profit Analysis.
MKTG 3509, Summer 1, 2019
2
Dr. Christopher Monos
Question 1: Evaluate the following two Model Comparisons
Comparison 1:
??
?????????? ??: ln (
) = ??0 + ??
1???
??
?????????? ??: ln (
) = ??0 + ??1 (????????????) + ??2 (????????) + ??
1???
-2LL(C)
PC
-2LL(A)
PA
Improvement Chi Square
Probability of
Improvement Chi Square (sig)
Interpretation of Findings
(Include Improvement Chi Square and
Probability of Improvement Chi Square )
Prediction Equation of
Log Odds for Model A
Using the Prediction Equation of Log
Odds for Model A, what is the predicted
probability of buying ‘The Art History of
Florence’ for a customer with:
Totdol = $419
Last = 3 Months since last purchase
MKTG 3509, Summer 1, 2019
3
Dr. Christopher Monos
Comparison 2:
??
?????????? ??: ln (
) = ??0 + ??1 (????????????) + ??2 (????????) + ??
1???
??
) = ??0 + ??1 (????????????) + ??2 (????????) + ??2 (??????) + ??2 (?????????) + ??2 (?????????) + ??2 (??????????) + ??2 (????_????) + ??2 (??????????????????) + ??2 (????????) + ??
?????????? ??: ln (
1???
-2LL(C)
PC
-2LL(A)
PA
Improvement Chi Square
Probability of
Improvement Chi Square
Interpretation of Findings
(Include Improvement Chi
Square and Probability of
Improvement Chi Square )
Prediction Equation of
Log Odds for Model A
MKTG 3509, Summer 1, 2019
4
Dr. Christopher Monos
Question 2:
Which set of variables gives
the best prediction of the
dependent variable?
What is your evidence?
Question 3:
Using the equation with all 9 independent variables, how are the odds of buying the ‘Art History of Florence’ affected
by:
a.
# purchases of Art books (Art) – How do the odds of buying change for an additional Art book
purchase?
b.
# purchases of Do it Yourself books (Do_it) – How do the odds of buying change for an additional
Do_It Yourself book purchase?
Question
Answer
3a
3b
MKTG 3509, Summer 1, 2019
5
Dr. Christopher Monos
Question 4:
Use the following cost information to assess the profitability of both modeling methods.
Cost to Mail Offer to Customer
Selling Price of ‘The Art History of Florence’
Contribution Margin
$.50
$18.00
$6.00
Using the data from the Lift/Gains/Profitability Analysis that you completed in Excel, complete the
following table.
Method to
Identify
Customers
Maximum
Cumulative
Operating Profit
Mail All Customers
in Database
% of Customers
Receiving Mailing
Acquisition Costs
per Customer
ROI = Operating
Profit/ Total Fixed
Costs of
Campaign
100%
Volume
Segmentation
Logistic Regression
Model
Question 5:
Which of the 3 approaches
would you recommend be
used in the future? Why?
MKTG 3509, Summer 1, 2019
6
Dr. Christopher Monos

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